SCODiA Report by MLBI Lab

Single-Cell Dissection of Adenocarcinoma and Squamous Cell Carcinoma Reveals Distinct Cellular and Molecular Landscapes in Lung Carcinoma

This report details a single-cell RNA sequencing analysis of Lung Adenocarcinoma and Squamous Cell Carcinoma, highlighting profound differences in their cellular composition, genomic alterations, cell-cell interaction networks, and pathway activities. We uncover subtype-specific tumor cell proliferation patterns, distinct immune cell polarizations, and unique stromal fibroblast phenotypes. These disparities suggest divergent disease biology and offer crucial insights into subtype-specific therapeutic vulnerabilities.

Contents

0. Dataset overview
1. UMAP Visualization of Single-Cell Data Annotations
2. UMAP Visualization of Major Cell Type Scores and Ploidy in Lung Carcinoma
3. Marker Expression Dot Plot for `celltype_subset`
4. Single-cell Copy Number Variation (CNV) Analysis in Lung Epithelial Cells
5. UMAP Visualization of CNV Patterns by Cell Type, Ploidy, Condition, and Sample
6. Minor Cell Type Population Bar Plot Across NSCLC Subtypes
7. T Cell and Innate Lymphoid Cell Subpopulation Analysis in Lung Carcinoma Subtypes
8. Differential T Cell and ILC Subset Proportions in Lung Carcinoma Subtypes
9. Macrophage Subset Population Analysis in Lung Carcinoma Subtypes
10. Differential Macrophage Subpopulation Proportions in Lung Carcinoma Subtypes
11. Ploidy Population Analysis in Lung Epithelial Cells by NSCLC Subtype
12. Cell-Cell Interaction Patterns in Lung Carcinoma Subtypes
13. Condition-Specific Cell-Cell Interaction Patterns in Lung Carcinoma Subtypes
14. Condition-Specific Cell-Cell Interaction Patterns in Lung Carcinoma Subtypes
15. Lung Epithelial Cell Condition-Specific Surfaceome Marker Expression
16. Macrophage Condition-Specific Surfaceome Markers in Lung Carcinoma
17. Condition-Specific Surfaceome Markers in Fibroblasts within Lung Carcinoma
18. T cell CD4+ Condition-Specific Marker Expression in Squamous Lung Carcinoma
19. Differential Cell Cycle Gene Expression Prevalence in Lung Epithelial Cells Across NSCLC Subtypes
20. Gene Ontology (GSA) Analysis for Lung Epithelial Cells: Condition and Ploidy-Specific Upregulated Pathways
21. Gene Set Enrichment Analysis Reveals Distinct Pathway Activities Across Lung Carcinoma Subtypes and Cell Types
22. Discussion
23. Query List

0. Dataset overview

Dataset Summary

• Dimensions: Contains 58,757 cells and 37,895 genes.
• Origin: Single-cell RNA-seq data from human lung tissue, likely related to Lung Carcinoma (Non-Small Cell Lung Carcinoma) given the conditions and diagnoses.
• Conditions: Includes 'Squamous' and 'Adeno' conditions, with 'Adeno' serving as the reference for comparative analyses.
• Cell Types: Features hierarchical cell type annotations: celltype_major, celltype_minor, and celltype_subset.
  • Major: Lung Epithelial cell, Myeloid cell, Mast cell, Stromal cell, T cell, unassigned, Endothelial cell, B cell.
  • Resolved Tumor-Origin: Lung Epithelial cell.
• Metadata (obs columns): Rich metadata includes clinical information (Tissue Diagnosis, Histology, Sex, Age, Stage, AJCC 8th ed, Type of Surgery, Smoking History, Tumor Mutation Burden, Survival), sample identifiers (pid, sample), and computed annotations (condition, copykat_pred, scevan_pred, ploidy_score, celltype annotations, cluster).
• Gene Information (var columns): Includes gene_symbol, variable_genes, chromosome, spot_no, and cytogenetic_band.
• CNV Estimates: Copy Number Variation (CNV) estimates are available, with observed ploidy labels: Diploid (94.6%), Aneuploid (5.3%), and Unclear (0.1%).
• Precomputed Results: The dataset contains precomputed results for Cell-Cell Interaction (CCI), Differential Expression Genes (DEG), Gene Set Enrichment Analysis (GSEA), and Gene Ontology (GSA_up) for various cell types and conditions.

1. UMAP Visualization of Single-Cell Data Annotations

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a series of UMAP (Uniform Manifold Approximation and Projection) plots, a dimensionality reduction technique used to visualize high-dimensional single-cell RNA-sequencing data in two dimensions. These plots are colored by different biological and technical annotations: disease condition (Adenocarcinoma vs. Squamous Cell Carcinoma), individual sample origin, major cell types, minor cell types, cellular ploidy status, and highly granular cell type subsets. The objective is to visually assess the clustering patterns, heterogeneity, and relationships between cells based on these annotations, providing an overview of the dataset's structure and annotation quality.

Visual Summary

The UMAP visualizations provide a comprehensive overview of cell populations and their relationships:

• condition UMAP: This plot shows a notable separation between cells derived from "Adeno" (dark red) and "Squamous" (purple) conditions. While some mixing occurs, distinct clusters and regions are predominantly occupied by one condition or the other. Adeno cells form the majority of the larger, interconnected clusters, whereas Squamous cells occupy several more peripheral and distinct clusters, particularly in the lower-left region of the UMAP. This indicates inherent transcriptional and cellular differences between these two lung carcinoma subtypes.
• sample UMAP: This visualization displays the distribution of cells from various individual samples. While some smaller, localized clusters may be dominated by cells from a single sample, the larger, central clusters exhibit a relatively good intermixing of cells from multiple samples. This suggests that computational efforts to integrate data from different samples and mitigate batch effects have been largely successful, allowing biological variation to drive the primary clustering.
• celltype_major UMAP: This plot reveals distinct clusters corresponding to major cell types. "Lung Epithelial cell" (light orange) forms the most extensive and interconnected network of clusters, consistent with its resolved tumor-origin status. "T cell" (light blue/cyan) forms a large, clearly delineated cluster on the left, while "Myeloid cell" (light green) occupies a prominent region in the upper-right. "Stromal cell" (teal) and "Endothelial cell" (orange) form smaller, distinct clusters often adjacent to the epithelial populations. "B cell" (dark red) and "Mast cell" (yellow) are also present as more isolated clusters. "unassigned" cells (dark purple) are sparsely scattered, indicating a small proportion of cells that could not be confidently classified into major types.
• celltype_minor UMAP: This plot provides a finer resolution of cell identities within the major cell type clusters. For example, the "T cell" major cluster is further resolved into distinct "T cell CD4+" (light blue) and "T cell CD8+" (dark blue) populations. The "Myeloid cell" region is predominantly composed of "Macrophage" (light green) cells. Within the "Lung Epithelial cell" area, "Airway Epithelial cell" and "Alveolar Epithelial cell" become discernible. Other minor cell types like "Fibroblast", "Dendritic cell (DC)", "Plasma cell", "ILC", and "NK cell" are also clearly localized within their respective broader cell type areas.
• ploidy_dec UMAP: This plot highlights the distribution of cells based on their ploidy status. "Diploid" cells (yellow) represent the vast majority of the dataset (94.6% per context) and are broadly distributed across most clusters. "Aneuploid" cells (dark red), representing 5.3% of the total, form distinct and coherent clusters that largely co-localize with the "Lung Epithelial cell" populations observed in the celltype_major plot, particularly in the central and upper-left regions. This strong association supports their identification as tumor cells. "Unclear" cells (dark purple) are very rare and scattered.
• celltype_subset UMAP: This plot offers the most granular view of cell types. It further refines the minor cell types into highly specific subsets, such as various macrophage polarizations (Mac_M1, Mac_M2A, M2B, M2C, M2D), diverse T cell populations (T cell (Cytotoxic), T cell (Naive), Treg, Th1, Th2, Th9, Th17, Th22, Tfh), and distinct epithelial cell types (Basal cell, AT1, AT2, Ciliated cell, Goblet cell, Secretory club). This level of detail confirms the complex cellular heterogeneity within the lung tissue and its associated tumor microenvironment.

Biological Interpretation

The UMAP visualizations collectively offer significant biological insights into the lung carcinoma dataset:

1. Tumor Cell Identification and Heterogeneity: The ploidy_dec UMAP strongly suggests that the "Aneuploid" cell population represents the malignant cells, as they form distinct clusters that largely overlap with the "Lung Epithelial cell" population (resolved as tumor-origin cells). The clear segregation of "Adeno" and "Squamous" conditions on the UMAP points to fundamental biological differences between these two major NSCLC subtypes, likely driven by distinct genetic alterations, cellular origins, or microenvironmental interactions.
2. Complex Tumor Microenvironment (TME): The detailed celltype_major, celltype_minor, and celltype_subset UMAPs reveal a rich and diverse tumor microenvironment. Immune cells (T cells, B cells, Myeloid cells, NK cells, ILCs, Mast cells) are abundant and display significant sub-differentiation (e.g., various T helper subsets, M1/M2 macrophages, specific DC types). Stromal cells (Fibroblasts) and Endothelial cells (including lymphatic and tip cells) also form distinct populations, highlighting the multifaceted cellular ecosystem supporting or interacting with the tumor.
3. Annotation Quality and Granularity: The hierarchical clustering observed across the celltype_major, celltype_minor, and celltype_subset plots indicates robust and well-resolved cell type annotations. As granularity increases, broader clusters consistently break down into biologically meaningful sub-clusters, confirming the quality of the cell type assignments. The relatively low number of "unassigned" cells further supports the comprehensiveness of the current annotation scheme.
4. Batch Effect Mitigation: The relatively uniform distribution of cells from different samples across the major UMAP clusters, especially within the dominant cell populations, suggests that the data integration process effectively minimized strong sample-specific technical variations. This allows the observed clustering to predominantly reflect true biological differences rather than experimental artifacts.

Annotation Notes

The current annotations provide a strong foundation for downstream analyses. Key observations for annotation quality include:

• Consistency: The hierarchical nature of the annotations (major -> minor -> subset) is well-reflected in the UMAPs, with increasing specificity at each level.
• Completeness: The low proportion of "Unassigned" cells across major, minor, and subset levels suggests that most cells have been confidently classified.
• Biological Plausibility: The co-localization of Aneuploid cells with Lung Epithelial cells strongly supports the tumor cell identification. The diverse immune cell populations identified are consistent with known components of the lung tumor microenvironment.

Further investigation could involve exploring the specific gene expression profiles of the "unassigned" populations to determine if they represent novel cell states or require refined classification. Additionally, the specific distribution of minor and subset cell types across "Adeno" and "Squamous" conditions could be quantitatively evaluated to identify condition-specific microenvironmental compositions.

2. UMAP Visualization of Major Cell Type Scores and Ploidy in Lung Carcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis presents UMAP (Uniform Manifold Approximation and Projection) visualizations to display the distribution and confidence scores for various major cell types, alongside the ploidy status and final major cell type assignments, across a single-cell RNA-seq dataset of lung tissue. The purpose is to assess the quality of cell type annotation and understand the spatial organization of cell populations within the reduced-dimensional space. The plots show the HiCAT_major_score for individual major cell types, the ploidy_dec (aneuploidy status), and the celltype_major (final major cell type annotation).

Visual Summary

The UMAP projections reveal a complex cellular landscape with several distinct clusters.

• Major Cell Type Scores: Each HiCAT_major_score plot highlights regions of high transcriptional similarity for a specific major cell type (indicated by brighter yellow/green colors).
  • T cell scores are highest in a prominent cluster on the left side of the UMAP.
  • B cell scores concentrate in distinct clusters at the top of the UMAP.
  • Myeloid cell scores are high in a large cluster occupying the right side.
  • Mast cell scores show a localized concentration in a small cluster towards the top-middle.
  • Endothelial cell and Stromal cell scores demonstrate distinct but somewhat overlapping populations in the bottom-middle region of the UMAP.
  • Lung Epithelial cell scores are markedly high in a well-defined cluster located in the bottom-right of the UMAP.
• Ploidy Status (ploidy_dec): The ploidy_dec plot shows that Aneuploid cells (maroon) are predominantly localized within the cluster at the bottom-right, with some minor scattered presence. Diploid cells (light yellow) constitute the vast majority of cells and are distributed across all other major clusters.
• Major Cell Type Annotation (celltype_major): The final celltype_major annotation plot largely confirms the patterns observed in the individual score plots. Each major cell type forms coherent clusters that align well with the regions of high scores in their respective HiCAT_major_score plots. Specifically, Lung.Epi (Lung Epithelial cell, yellow) forms the distinct cluster at the bottom-right, which overlaps significantly with the Aneuploid cell population. Other immune cells (e.g., T cell (cyan), B cell (maroon), Myeloid (light green)) and stromal cells (Endo (Endothelial, red), Stromal cell (lime green)) also occupy spatially segregated regions. A small proportion of cells remain unassigned (dark blue), scattered across the UMAP.

Biological Interpretation

The robust clustering and clear spatial segregation of major cell types based on their HiCAT_major_score values indicate high confidence in the cell type annotations.

• Tumor Cell Identification: The most striking finding is the strong co-localization of cells with high Lung Epithelial cell scores and Aneuploid cells within a distinct cluster at the bottom-right of the UMAP. Given that Lung Epithelial cell is identified as the Resolved tumor-origin cell type and aneuploidy is a hallmark of cancer, this cluster very confidently represents the malignant epithelial cells characteristic of Lung Carcinoma. This provides robust evidence for the successful identification and isolation of tumor cells within the dataset.
• Tumor Microenvironment Composition: The clear separation of various immune cell types (T cells, B cells, Myeloid cells, Mast cells) and stromal cells (Endothelial cells, Fibroblasts, implied by Stromal cell scores) into distinct clusters suggests a diverse and well-defined tumor microenvironment (TME). The spatial organization of these immune and stromal populations on the UMAP reflects their distinct transcriptional profiles and functional identities within the lung tissue.
• Annotation Quality: The concordance between the HiCAT_major_score for each cell type and the final celltype_major annotation demonstrates the reliability and accuracy of the cell type assignment process. Cells with high scores for a particular type are indeed assigned to that type, reinforcing the validity of the annotation.

Annotation Notes

• The UMAP plots demonstrate that the major cell type annotations are well-supported by the underlying gene expression profiles, as evidenced by the distinct and concentrated high-score regions for each cell type.
• The clear visual separation of cell types, coupled with the strong signal from the scoring system, suggests a high-quality single-cell dataset and robust annotation pipeline.
• The identification of a distinct Lung Epithelial cell cluster that is also predominantly Aneuploid provides strong validation for the tumor cell population.
• A small fraction of cells categorized as unassigned suggests either rare cell populations not fully captured by the major categories, or cells with ambiguous expression profiles. Further investigation into these cells could refine the annotation.

3. Marker Expression Dot Plot for `celltype_subset`

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a marker expression dot plot for the various celltype_subset populations identified in the single-cell RNA-seq dataset. The purpose of this plot is to visualize the expression patterns of genes identified as potential markers across these distinct cell subpopulations. This visualization aids in validating the assigned cell identities by confirming the specific and expected expression of known cell type-specific genes. The size of each dot represents the fraction of cells within a given celltype_subset that express the gene (non-zero expression), while the color intensity reflects the mean expression level of that gene in the same cell group. The red boxes highlight the expected diagonal pattern, where a gene is a prominent marker for its assigned cell type.

Visual Summary

The dot plot displays a clear and distinct diagonal pattern, indicating that the identified markers generally show high specificity to their assigned celltype_subset populations. For most cell types, there are prominent red and large dots along the diagonal, signifying high mean expression and high proportion of expressing cells within the target cell group.

Key observations include:

• Strong Specificity: Many cell types exhibit highly specific marker expression, with genes showing strong signals (large, dark red dots) almost exclusively within their designated celltype_subset row. This is particularly evident for:
  • Alveolar type 2 cells with markers like *SFTPB*, *SFTPC*, *SFTA2*, and *NAPSA*.
  • B cell subsets (Breg, MZ, Memory) with markers such as *MS4A1*, *CD79A*, *CD79B*, *CD37*, *JCHAIN*, *MZB1*, and *PRDM1*.
  • Basal cells strongly express *KRT5*, *KRT6A*, *KRT14*, and *KRT17*.
  • Ciliated cells show specific expression of *RSPH1*, *FOXJ1*, and *TMEM190*.
  • Dendritic cells (Classical, Inflammatory, Plasmacytoid) are characterized by markers like *CD1A*, *CD1B*, *CD1C*, *CD83*, *IRF7*, *IRF8*, *SPIB*, and *TCF4*.
  • Fibroblasts display high expression of extracellular matrix genes such as *COL1A1*, *COL1A2*, *COL3A1*, *DCN*, and *LUM*.
  • Mast cells are clearly identified by *KIT*, *TPSAB1*, and *SRGN*.
  • NK cells show specific expression for *KLRD1* and *FCGR3A*.
  • Plasma cells are marked by *JCHAIN* and *PRDM1*, overlapping with B cell memory/MZ markers but showing higher specificity for plasma cells for *PRDM1*.
  • T cell subsets (Cytotoxic, Tfh, Th17, Treg) are largely distinct, with *CD8A* for Cytotoxic T cells and *CTLA4* for Treg cells being prominent.
• Shared Markers and Subtleties: While most markers are specific, some genes show expression across related cell types, indicating shared biological functions or lineage relationships. For example, some B cell markers are broadly expressed across B cell subsets. Macrophage subtypes (M1, M2A, M2B, M2C) show some overlap in general macrophage markers like *MSR1* and *CD68* (though not all shown), but also exhibit distinct patterns, aiding their sub-classification. *CEACAM5* appears to be expressed in both Goblet cells and Secretory club cells, which is consistent with their epithelial lineage.
• Cell Type Abundance: The bar chart on the right indicates the number of cells belonging to each celltype_subset. Several populations, such as Macrophage (M2C) and T cell (Cytotoxic), represent large groups, while others like LTI, ILCreg, and Endothelial tip cell are relatively smaller.

Annotation Notes

The marker expression dot plot provides strong evidence supporting the accuracy and robustness of the celltype_subset annotations. The distinct, highly specific expression patterns observed for numerous known marker genes align well with the expected biological characteristics of each cell type.

• Lung Epithelial Cells: Alveolar type 1 cells are marked by *AGER* (not explicitly shown but inferred by cell type group), *PDPN*, and *CAV1* (not shown). Alveolar type 2 cells are clearly defined by surfactant proteins (*SFTPB*, *SFTPC*, *SFTA2*), which are critical for lung function. Basal cells (e.g., *KRT5*, *KRT14*) and Ciliated cells (*FOXJ1*, *RSPH1*) represent key airway epithelial populations. Goblet cells are marked by *MUC5AC*, and Secretory club cells by *SLPI*, both important in airway mucociliary clearance. The clear separation and specific markers for these epithelial subsets enhance confidence in their annotation.
• Immune Cells: The various B cell subsets, T cell subsets (including cytotoxic, helper, and regulatory T cells), Dendritic cells, Macrophages, Mast cells, and NK cells all display characteristic marker gene expression profiles. For instance, the distinction between T cell subtypes based on markers like *CD8A* and *CTLA4* (for cytotoxic and regulatory T cells, respectively) is evident. The macrophage subtypes, while complex, show differential expression patterns that support their individual classification, even if some markers are broadly expressed across macrophage populations. The presence of known DC markers like *CD1A/B/C* and transcription factors like *IRF7/8* further confirms the DC annotations.
• Stromal and Endothelial Cells: Fibroblasts show high expression of collagen genes (*COL1A1*, *COL1A2*, *COL3A1*) and proteoglycans (*DCN*, *LUM*), which are characteristic of their role in tissue structure and extracellular matrix production. Endothelial cells and Lymphatic Endothelial cells are also defined by specific sets of markers (e.g., *PROX1* for lymphatic endothelium).

Overall, the visualization strongly corroborates the current celltype_subset annotations, demonstrating good separation and specific marker expression for the vast majority of populations. This provides a solid foundation for downstream analyses that rely on accurate cell type identification.

4. Single-cell Copy Number Variation (CNV) Analysis in Lung Epithelial Cells

[Analysis Visualization Results]...

Analysis Overview

This analysis characterizes Copy Number Variations (CNVs) in Lung Epithelial cells, which are identified as cells of tumor origin, across various patient samples. The primary objective is to visualize the log2(Copy Number Ratio) values across the genome and summarize recurrently amplified chromosomal regions, providing insights into the genomic instability inherent in lung carcinoma.

Visual Summary

CNV Heatmap (log2(CNR))

The heatmap illustrates log2(CNR) values across genomic spots (x-axis, organized by chromosome) for individual Lung Epithelial cell samples (y-axis). A clear distinction is observed between samples explicitly labeled "Diploid" and those without this prefix.

• Diploid Samples: Samples prefixed with "Diploid" (e.g., Diploid NSC010.T1, Diploid NSC004.T1) largely exhibit log2(CNR) values near zero (green/yellow), indicating a stable, diploid genome with minimal or no detectable CNVs. These likely represent non-malignant epithelial cells or a subpopulation with a stable genome.
• Aneuploid Samples: Samples without the "Diploid" prefix (e.g., NSC010.T1, NSC018.T1, NSC019.T2, NSC004.T1) display extensive genomic alterations. Red regions denote amplifications (log2(CNR) > 0), while blue regions indicate deletions (log2(CNR) < 0).
• Recurrent Alterations: Several regions show recurrent amplifications across multiple aneuploid samples, notably on chromosomes 1, 3, 5, 8, 17, and 20. Significant deletions are also evident, particularly on chromosome 3.
• Heterogeneity: There is considerable heterogeneity in CNV patterns observed across different patient samples, reflecting the diverse genetic landscape of individual tumors.

CNV Amplification Summary

This visualization summarizes regions with significant copy number amplifications detected in Lung Epithelial cells. The heatmap-like plot (left) shows the average amplification level (color intensity and numerical value) for specific cytogenetic bands (y-axis) across individual samples (x-axis). The barplot on the right displays the overall "Frequency" of amplification for each cytogenetic band across all samples.

• Highly Amplified Regions: Several cytogenetic bands show high amplification frequencies and levels:
  • 1q21.2:1q21.3 (NFASC): This region is frequently amplified across numerous samples (e.g., NSC004.T1, NSC009.T1, NSC010.T2, NSC016.T1), with some samples exhibiting average amplification levels greater than 1.0 (indicating more than a doubling of copy number).
  • 3q25.1:3q26.1 (SOX2): Strong amplification of this region is notable in samples like NSC010.T1, NSC018.T1, and NSC019.T1. SOX2 is explicitly labeled in this region.
  • 8q22.1:8q23.1 (EIF3E): This region also shows recurrent amplification in multiple samples, including NSC004.T1, NSC009.T1, and NSC016.T1.
  • 17q12:17q21.2 (ERBB2): Amplification of this region, encompassing the ERBB2 gene, is clearly detected in samples such as NSC010.T1 and NSC010.T2.
  • Other recurrently amplified regions include 1p34.3:1p33, 1q23.3:1q26.1, 1q32.1:1q32.3, 5p13.3:5q13.3, 20q13.1:20q13.2, and 22q13.1:22q13.3.
• Sample-Specific Patterns: While some amplifications are recurrent, the overall pattern of amplified regions varies considerably between samples, indicating unique genomic alterations in different tumors or tumor subclones.

Biological Interpretation

The observed CNV patterns in Lung Epithelial cells are highly consistent with the inferred disease context of Lung Carcinoma.

• Tumor Cell Identification: The distinct separation of Lung Epithelial cell samples into "Diploid" (stable genome) and "Aneuploid" (extensive CNVs) groups strongly supports the successful identification of malignant cells. Aneuploidy and widespread genomic alterations are hallmark features of cancer, distinguishing tumor cells from normal epithelial cells.
• Genomic Instability in Lung Carcinoma: The extensive amplifications and deletions observed in the aneuploid samples reflect the genomic instability characteristic of Non-Small Cell Lung Carcinoma (NSCLC), which includes both Squamous and Adeno subtypes present in the dataset.

Key Oncogenic Amplifications:

• SOX2 (3q25.1:3q26.1): Amplification of SOX2 is a well-documented oncogenic event, particularly prevalent in Lung Squamous Cell Carcinoma. SOX2 is a transcription factor involved in cell self-renewal and pluripotency, and its overexpression can drive tumor growth. [Reference: PubMed search for "SOX2 amplification lung squamous cell carcinoma": PubMed Search]
• ERBB2 (HER2, 17q12:17q21.2): Amplification of ERBB2 is a known driver mutation in a subset of NSCLC and is a significant therapeutic target. ERBB2 encodes a receptor tyrosine kinase involved in cell growth and proliferation. [Reference: GeneCards ERBB2: GeneCards; PubMed search for "ERBB2 amplification lung cancer": PubMed Search]
• NFASC (1q21.2:1q21.3) and EIF3E (8q22.1:8q23.1): While less commonly recognized as primary drivers, recurrent amplifications of genes like NFASC (involved in cell adhesion and neural development) and EIF3E (a component of the eukaryotic translation initiation factor 3 complex) suggest their potential roles in promoting tumor survival or progression in specific contexts. [Reference: GeneCards NFASC: GeneCards; GeneCards EIF3E: GeneCards]

• Tumor Heterogeneity: The variable CNV landscape across different samples highlights the genetic heterogeneity of lung tumors. This intra-tumor and inter-patient variability can influence disease progression, treatment response, and the potential for drug resistance.

Clinical or Translational Implications

• Biomarker Potential: The identified recurrent CNVs, especially those involving well-known oncogenes like ERBB2 and SOX2, represent potential biomarkers for patient stratification, diagnosis, and prognosis in lung carcinoma.
• Therapeutic Targets: ERBB2 amplification is an established target for therapies in NSCLC, underscoring the clinical relevance of identifying such alterations at the single-cell level. The presence of SOX2 amplification, particularly in Squamous subtype tumors, may also inform treatment strategies.
• Understanding Tumor Evolution: The detailed CNV profiles at single-cell resolution provide a powerful tool for understanding clonal evolution within tumors and identifying subclones with distinct genomic alterations that might drive resistance to therapy.
• Validation of Tumor Origin: The clear distinction in CNV patterns between "Diploid" and aneuploid Lung Epithelial cells robustly validates the identification of tumor-origin cells, which is crucial for downstream analyses focused on cancer biology.

5. UMAP Visualization of CNV Patterns by Cell Type, Ploidy, Condition, and Sample

[Analysis Visualization Results]...

Analysis Overview

This analysis presents UMAP (Uniform Manifold Approximation and Projection) visualizations based on Copy Number Variation (CNV) patterns for single cells from lung tissue. The UMAP embedding itself is computed using CNV data, meaning cells with similar genomic CNV profiles are positioned closer together. The resulting UMAPs are then colored by different cellular and sample annotations: major cell type, minor cell type, ploidy status, disease condition (Adenocarcinoma vs. Squamous Cell Carcinoma), and individual sample identifiers. This approach allows for the visual assessment of how these annotations correlate with underlying genomic alterations.

Visual Summary

Cell Type Distribution (celltype_major, celltype_minor)

• celltype_major: The UMAP shows distinct separation of major cell types based on their CNV profiles. Specifically, 'Lung Epithelial cells' (orange/brown) appear to form a substantial cluster, particularly evident in the left-mid region of the embedding. Immune cells (e.g., 'T cell', 'B cell', 'Myeloid cell') and 'Stromal cells' form more scattered or distinct clusters in other regions, largely separate from the main epithelial cluster. This suggests that the major cell types exhibit generally distinct CNV landscapes.
• celltype_minor: At the minor cell type resolution, a similar pattern holds. 'Airway Epithelial cells' (dark red) and 'Alveolar Epithelial cells' (orange) are prominent within the large epithelial cluster. Immune cell subsets (e.g., 'T cell CD8+', 'Macrophage', 'B cell', 'Dendritic cell') and 'Fibroblast' and 'Endothelial cell' populations are broadly distributed, often forming their own sub-clusters consistent with distinct non-epithelial genomic profiles.

Ploidy Status (ploidy_dec)

• The ploidy_dec UMAP reveals a striking separation based on ploidy. A distinct and well-defined cluster of 'Aneuploid' cells (maroon) is clearly visible, predominantly on the left side of the UMAP. The vast majority of cells are classified as 'Diploid' (light yellow), forming the larger, more diffuse clusters across the rest of the embedding. 'Unclear' cells are sparsely distributed. This strong separation indicates that cells with significant chromosomal abnormalities (aneuploidy) possess distinct CNV profiles that drive their clustering in this CNV-based UMAP.

Condition and Sample Distribution (condition, sample)

• condition: Both 'Adeno' (maroon) and 'Squamous' (purple) conditions are broadly represented across the UMAP. While there isn't a complete separation of the two conditions, 'Adeno' cells appear to be more dominant in the main 'Aneuploid' cluster on the left, suggesting a potentially higher prevalence or distinct CNV patterns associated with Adenocarcinoma in that region. Some intermixing is observed, indicating shared CNV features or the presence of normal cells across both conditions.
• sample: The sample UMAP demonstrates substantial sample-specific clustering. Cells from individual samples (represented by unique colors) often form coherent clusters, particularly within the 'Aneuploid' region. This indicates that individual patients (samples) harbor unique CNV profiles, which is expected for tumor cells due to their distinct somatic genomic alterations. Even within the diploid regions, some sample-specific patterns are discernible, which could reflect patient-specific normal genomic variations or subtle technical batch effects, though the CNV embedding should primarily highlight biological differences.

Biological Interpretation

The UMAPs generated from CNV patterns provide critical insights into the genomic landscape of lung carcinoma at single-cell resolution.

1. Tumor Cell Identification: The strong overlap between 'Lung Epithelial cells' (specifically 'Airway Epithelial cell' and 'Alveolar Epithelial cell' minor types) and the 'Aneuploid' cluster confirms the identification of the tumor compartment. As specified in the data context, 'Lung Epithelial cell' is the "Resolved tumor-origin cell type," and aneuploidy is a hallmark of cancer. This CNV-based UMAP effectively delineates tumor cells from the surrounding diploid stromal and immune cells. The distinct clustering of aneuploid cells suggests highly aberrant genomic profiles characteristic of malignancy, clearly separating them from the largely diploid normal cells.
2. Genomic Heterogeneity: The sample-specific clustering, especially within the aneuploid population, highlights inter-patient genomic heterogeneity in lung carcinoma. Each tumor appears to possess a unique set of CNVs, contributing to the distinct clustering of cells from the same patient. This observation aligns with the known genetic complexity and variability of cancer.
3. Subtype-Specific CNV Patterns: While 'Adeno' and 'Squamous' cells are intermixed, the apparent dominance of 'Adeno' cells in the core aneuploid region might suggest a tendency for Adenocarcinoma to exhibit a particular spectrum of CNV patterns or a higher proportion of aneuploid cells within this dataset. However, a more detailed analysis would be needed to confirm distinct CNV signatures for each NSCLC subtype. The presence of both conditions within the aneuploid cluster indicates that both subtypes share the fundamental characteristic of genomic instability and aneuploidy.
4. Reference Cell Population: The large, diffuse clusters of 'Diploid' cells that mostly comprise immune and stromal cell types serve as a critical reference. Their separation from the aneuploid epithelial cells reinforces the robustness of CNV-based cell type identification and tumor cell segregation.

Annotation Notes / Limitations

• The CNV-based embedding effectively separates cells based on genomic integrity, offering a powerful validation of ploidy calls and tumor cell identification.
• While the UMAP shows broad distributions, it does not quantitatively assess the extent of aneuploidy or specific chromosomal alterations. Further analysis using raw CNV data would be needed to characterize the nature of these genomic changes.
• The mixing of Adeno and Squamous conditions within the aneuploid clusters suggests that while aneuploidy is a shared feature, more granular CNV analysis (e.g., specific chromosomal gains/losses) might reveal finer distinctions between these subtypes.
• The distinct clustering by sample confirms patient-specific CNV profiles, but it also necessitates careful consideration in downstream analyses to avoid confounding patient-specific tumor biology with potential batch effects, although the CNV basis here primarily reflects biological differences.
• The 'unassigned' category for cell types and 'Unclear' for ploidy are scattered, indicating that these cells do not form coherent, distinct CNV-driven clusters and may represent ambiguous profiles or low-quality data.

6. Minor Cell Type Population Bar Plot Across NSCLC Subtypes

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a population bar plot illustrating the relative abundance of different minor cell types across individual samples, stratified by Non-Small Cell Lung Carcinoma (NSCLC) subtypes: Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous). Each bar represents a single sample, with stacked segments showing the percentage contribution of each identified cell type to the total cell population within that sample. The goal is to visualize the cellular heterogeneity within and between these two major lung cancer subtypes.

Visual Summary

The plot displays stacked bar charts for 20 Adeno samples and 6 Squamous samples. Each bar sums to 100%, representing the proportional cellular composition of a given sample.

Epithelial Cell Content:

• Squamous samples consistently show a prominent proportion of "Airway Epithelial cell" (dark red), often making up 20-30% or more of the sample cellularity (e.g., NSC009.T1, NSC004.T1/T2). "Alveolar Epithelial cell" (red) appears to be less abundant in Squamous samples.
• Adeno samples exhibit more variability in epithelial cell content. Some samples, like NSC016.T1, NSC019.T1, NSC036.T1, NSC010.T1, and NSC037.T1, show high proportions of either "Airway Epithelial cell" or "Alveolar Epithelial cell" (up to 40-50%). Other Adeno samples (e.g., NSC018.T1/T2/T3) have lower epithelial content and are dominated by immune and stromal cells.

Immune Cell Infiltration:

• Macrophages (light yellow) are a substantial component in both Adeno and Squamous samples, often contributing 15-40% of the cells. Some Adeno samples (e.g., NSC018.T1/T2/T3, NSC020.T2, NSC010.T2) show particularly high macrophage proportions, exceeding 40-50%.
• T cells (CD4+ and CD8+; teal and light blue) are also consistently present, typically ranging from 10-30% of the cell population across both subtypes.
• Other immune cells like B cells, Dendritic cells, ILCs, Mast cells, NK cells, and Plasma cells are generally present in smaller, more variable proportions.

• Stromal Cell Presence: "Fibroblast" (light orange) and "Endothelial cell" (dark orange) populations are observed across most samples in both conditions, indicating a significant stromal component in the tumor microenvironment.
• Unassigned Cells: A small proportion of "unassigned" cells (dark blue) is present in most samples, generally less than 10%, indicating comprehensive cell type annotation.

Biological Interpretation

The observed cell type proportions reflect the distinct biological characteristics and tumor microenvironment (TME) composition of Adenocarcinoma and Squamous Cell Carcinoma of the lung.

1. Tumor Cell Lineage Consistency: The higher and more consistent presence of "Airway Epithelial cell" in Squamous samples aligns with the known origin of Squamous Cell Carcinoma from the bronchial (airway) epithelium. In contrast, Adenocarcinoma, which typically arises from alveolar or bronchiolar cells, shows more variable "Alveolar Epithelial cell" and "Airway Epithelial cell" content, possibly reflecting tumor heterogeneity or varying levels of tumor purity in the analyzed samples. Given "Lung Epithelial cell" is the resolved tumor-origin cell type, these epithelial populations likely represent the malignant cell fraction alongside normal epithelial cells.
2. Immune Landscape Differences: The significant presence of macrophages and T cells in both subtypes underscores the crucial role of the immune system in the NSCLC TME. The high macrophage content observed in several Adeno samples is notable, as macrophages (particularly M2-polarized tumor-associated macrophages) are often linked to immunosuppression, tumor progression, and resistance to therapy in NSCLC. The variability in T cell proportions suggests heterogeneous immune infiltration patterns across patients, which can influence anti-tumor immunity and response to immunotherapies.
3. Stromal Contributions: The consistent presence of fibroblasts and endothelial cells highlights the importance of the stromal compartment in both NSCLC subtypes. Fibroblasts contribute to extracellular matrix remodeling and can exert pro-tumoral effects, while endothelial cells are essential for angiogenesis, supporting tumor growth.

Clinical or Translational Implications

Understanding the minor cell type populations in NSCLC has several potential clinical and translational implications:

• Biomarker Discovery and Subtype Characterization: The distinct epithelial cell proportions serve as an internal validation of the biological differences between Adenocarcinoma and Squamous Cell Carcinoma, consistent with their pathological origins. Differences in non-malignant cell populations (e.g., immune and stromal cells) could potentially serve as prognostic or predictive biomarkers for patient outcomes or therapy response.
• Therapeutic Targeting: The observed heterogeneity in immune cell infiltration, particularly the high macrophage content in some Adeno samples, suggests that these patients might benefit from therapies targeting tumor-associated macrophages or combination therapies that modulate the immunosuppressive TME. Differences in T-cell infiltration patterns could also inform patient selection for immunotherapies, where T-cell "hot" tumors might respond better to checkpoint inhibitors.
• Patient Stratification: The substantial inter-sample variability within each subtype emphasizes the need for personalized approaches in NSCLC. Stratifying patients based on their specific tumor microenvironment composition, as revealed by such cellular population analyses, could lead to more effective and tailored treatment strategies.

7. T Cell and Innate Lymphoid Cell Subpopulation Analysis in Lung Carcinoma Subtypes

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a population bar plot illustrating the relative proportions of T cell subsets and innate lymphoid cell (ILC) populations within individual samples, categorized by two primary lung carcinoma conditions: Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous). The 'T cell' major cell type was selected, and the breakdown into 'subset' level provides a detailed view of immune cell infiltration, which is critical for understanding the tumor microenvironment (TME) in non-small cell lung carcinoma (NSCLC). Each bar represents a unique sample, showing the percentage contribution of each T cell or ILC subset to the total 'T cell' major population.

Visual Summary

The stacked bar plots display the relative proportions of 19 distinct T cell and ILC subsets across multiple samples from Adeno (19 samples) and Squamous (5 samples) lung carcinoma.

• Dominant Subsets: In both Adeno and Squamous conditions, T cell (Cytotoxic) (light yellow), T cell (Naive) (pale yellow), and various ILC subsets (ILC3 (NCR-), ILCreg, ILC1, ILC2, ILC3 (NCR+)) (represented by the red to orange spectrum) constitute a substantial portion of the overall T cell and ILC compartment. NK cells (light orange/beige) are also consistently present, though generally in smaller proportions.
• Treg Cells: T cell (Treg) (darker blue) are observed across most samples in both conditions, typically forming a smaller but consistent fraction of the population.
• Th Cell Subsets: Other T helper cell subsets, including T cell (Th1), T cell (Th17), T cell (Th2), T cell (Th22), T cell (Th9), and T cell (Tfh), are present but generally contribute minor proportions to the total population.

Condition-Specific Patterns:

• Adeno samples show notable heterogeneity in their immune cell composition. Some samples (e.g., NSC040.T1, NSC019.T1, NSC035.T1, NSC036.T1) exhibit a higher proportion of T cell (Cytotoxic) and T cell (Naive) cells, while others (e.g., NSC018.T3, NSC018.T1, NSC018.T2, NSC010.T1, NSC019.T2, NSC021.T1, NSC021.T2, NSC016.T1, NSC016.T2, NSC020.T2, NSC020.T1) are characterized by a substantially higher representation of ILCs (red to orange spectrum, particularly ILC3 (NCR-) and ILCreg), sometimes making up over 40-50% of the population.
• Squamous samples (NSC009.T2, NSC004.T3, NSC004.T1, NSC004.T2, NSC009.T1) appear to display a more consistent distribution of T cell subsets and ILCs compared to the Adeno group, generally showing a noticeable proportion of T cell (Cytotoxic) cells (around 30-40%) and ILCs (around 20-30%), with less extreme variability in ILC proportion across samples.

• Unassigned Cells: A very small fraction of cells is labeled 'unassigned' (dark blue at the very top of the stacks).

Biological Interpretation

The detailed breakdown of T cell and ILC subsets reveals complex and distinct immune landscapes within the tumor microenvironment (TME) of lung adenocarcinoma and squamous cell carcinoma.

• Heterogeneous Immune Infiltration: The significant sample-to-sample variability in the proportions of cytotoxic T cells, naive T cells, and ILCs, especially within the adenocarcinoma group, underscores the highly heterogeneous nature of immune responses in NSCLC. This heterogeneity can be influenced by tumor genetics, microenvironmental factors, and host immune status.
• Role of Cytotoxic T cells: The presence of T cell (Cytotoxic) cells is crucial as these are primary mediators of anti-tumor immunity. Their variable proportions across samples suggest differential anti-tumor immune responses or varying degrees of immune evasion by tumor cells.
• Significance of Innate Lymphoid Cells (ILCs): The prominent presence of various ILC subsets (ILC1, ILC2, ILC3, ILCreg, LTI) in the lung TME is noteworthy. ILCs are key regulators of immunity and inflammation, and their specific roles in cancer are context-dependent.
  • ILC1s are often associated with anti-tumor immunity and Th1-type responses, while ILC2s can promote tumor growth in some contexts by fostering a Th2-like immune suppressive environment [PubMed search: ILC2 lung cancer].
  • ILC3s, particularly ILC3 (NCR-), are diverse and can contribute to both pro- and anti-tumor responses depending on their activation state and the specific cytokines they produce [PubMed search: ILC3 tumor microenvironment]. Their high abundance in some Adeno samples could indicate a significant role in shaping the inflammatory and immune suppressive milieu.
  • ILCreg (Regulatory ILCs) are a recently identified subset that may play a role similar to regulatory T cells in suppressing immune responses [PubMed search: Regulatory innate lymphoid cells]. Their presence suggests a potential mechanism for promoting immune tolerance within the TME.
• Immune Suppression: The consistent presence of T cell (Treg) cells in both conditions is a critical indicator of potential immune suppression within the TME, as Tregs actively inhibit anti-tumor immune responses, facilitating tumor progression and resistance to immunotherapy [GeneCards: FOXP3].
• Subtype-Specific Immune Landscapes: The observed differences in immune cell composition between Adeno and Squamous subtypes align with known biological distinctions. These distinct immune profiles could reflect differences in oncogenic drivers, mutational burden, and inflammatory pathways characteristic of each NSCLC subtype.

Clinical or Translational Implications

The findings have several implications for the clinical management and translational research of lung carcinoma:

• Personalized Immunotherapy: The pronounced heterogeneity in T cell and ILC populations within the tumor microenvironment, particularly in adenocarcinoma, underscores the need for personalized approaches to immunotherapy. Comprehensive immune profiling of individual patient tumors could help predict response to immune checkpoint inhibitors or guide the selection of appropriate therapeutic strategies.
• Biomarkers for Prognosis and Response: The specific proportions and functional states of cytotoxic T cells, regulatory T cells, and various ILC subsets could serve as predictive or prognostic biomarkers. For instance, a higher ratio of cytotoxic T cells to regulatory T cells might correlate with better outcomes, while an abundance of certain ILC subsets (e.g., ILC2s or ILCreg) might indicate a less favorable prognosis or resistance to specific treatments.
• Targeting ILCs for Therapeutic Intervention: Given their significant presence and diverse roles, ILCs represent potential novel therapeutic targets in lung cancer. Modulating the activity or inhibiting the pro-tumorigenic functions of specific ILC subsets could enhance anti-tumor immunity, especially in tumors where these cells are abundant and contribute to immune evasion.
• Subtype-Specific Treatment Strategies: The distinct immune landscapes observed between lung adenocarcinoma and squamous cell carcinoma suggest that immunotherapeutic strategies might need to be tailored to each subtype. Understanding these differences can inform the development of more effective and specific immune-modulating agents for NSCLC patients.

8. Differential T Cell and ILC Subset Proportions in Lung Carcinoma Subtypes

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportional differences of specific T cell and Innate Lymphoid Cell (ILC) subsets between two major subtypes of Non-Small Cell Lung Carcinoma (NSCLC), Squamous Cell Carcinoma and Adenocarcinoma (Adeno). The box plots visualize the cell type proportion for each identified subset across these two conditions, with statistical significance indicated by p-values. The goal is to identify immune cell populations that are distinctly enriched or depleted in one condition versus the other, providing insights into the differential immune microenvironments.

Visual Summary

The box plots display the celltype proportion on the y-axis against the condition (Squamous vs. Adeno) on the x-axis for five distinct cell subsets. Black dots represent individual sample data points.

• Th2 cells: The proportion of Th2 cells is significantly higher in the Adeno condition compared to the Squamous condition (p=0.00156). The median proportion is approximately 6.5% in Squamous and about 9.5% in Adeno.
• Th17 cells: Conversely, the proportion of Th17 cells is significantly lower in the Adeno condition compared to the Squamous condition (p=0.00779). The median proportion is approximately 5% in Squamous and about 3% in Adeno.
• unassigned cells: An "unassigned" cell population shows a significantly higher proportion in the Adeno condition (p=0.00932). The median proportion in Squamous is close to 0.2%, while in Adeno it is around 0.25%, with a wider spread and several higher outliers.
• ILC3(-): The proportion of ILC3(-) cells is significantly elevated in the Adeno condition compared to the Squamous condition (p=0.00969). The median is around 1.5% in Squamous and approximately 3.8% in Adeno.
• ILC1: There is a trend towards a higher proportion of ILC1 cells in the Adeno condition compared to Squamous, with a p-value of 0.0519. This difference is at the boundary of conventional statistical significance (p < 0.05) but meets the set cutoff of 0.1. The median is around 9% in Squamous and approximately 10.5% in Adeno.

In summary, Adenocarcinoma samples tend to have higher proportions of Th2, ILC3(-), ILC1 (marginally), and an unassigned cell population, while Squamous Carcinoma samples show a higher proportion of Th17 cells.

Biological Interpretation

The observed differences in T cell and ILC subset proportions between Squamous Cell Carcinoma and Adenocarcinoma reflect distinct immune microenvironments that may contribute to their varied pathological characteristics, clinical behaviors, and responses to therapy.

• Th2 Cell Dominance in Adenocarcinoma: Th2 cells are key orchestrators of type 2 immune responses, often associated with allergic reactions and parasitic infections. In cancer, Th2 responses can be complex; while sometimes contributing to anti-tumor immunity, they are frequently linked to pro-tumorigenic effects, including immunosuppression (e.g., via IL-4, IL-13 production which can activate M2 macrophages), promotion of angiogenesis, and tissue remodeling, leading to fibrosis PMID: 32677933. Their increased presence in Adenocarcinoma suggests a potentially more immunosuppressive or protumorigenic type 2-polarized immune environment compared to Squamous Cell Carcinoma.
• Th17 Cell Depletion in Adenocarcinoma: Th17 cells, characterized by IL-17 production, have a dual role in cancer, acting as either pro- or anti-tumorigenic depending on the context. In many cancers, Th17 cells can promote tumor growth, angiogenesis, and metastasis. The *lower* proportion of Th17 cells in Adenocarcinoma compared to Squamous suggests a potential difference in the inflammatory response or distinct pathways regulating inflammation in these two subtypes. A higher Th17 presence in Squamous may indicate a more inflammatory or neutrophil-rich tumor microenvironment, as IL-17 is a potent recruiter of neutrophils.
• ILC3(-) Enrichment in Adenocarcinoma: Innate Lymphoid Cells (ILCs) are crucial components of innate immunity. ILC3s produce IL-17 and IL-22, which are important for maintaining barrier integrity and host defense against extracellular bacteria and fungi. In cancer, ILC3s can also exhibit dual roles, contributing to both anti-tumor immunity and tumor progression depending on their specific subset and the tumor microenvironment PMID: 36712398. The increase in ILC3(-) in Adenocarcinoma, alongside Th2 cells, further points to a distinct innate immune landscape in this subtype, potentially driving specific inflammatory and immunosuppressive axes.
• ILC1 Increase in Adenocarcinoma: ILC1s are characterized by the production of IFN-$\gamma$, a cytokine typically associated with anti-tumor immunity and Th1 responses. While the p-value is marginal (0.0519), a tendency for increased ILC1s in Adeno could suggest a compensatory anti-tumor immune component or a complex interplay where both pro- and anti-tumorigenic innate immune cells are modulated.
• Unassigned Cell Population: The significant increase in an "unassigned" cell population in Adenocarcinoma highlights a group of cells whose identity is not yet fully resolved at the celltype_subset level. Further investigation into these cells would be crucial to understand their biological significance in Adenocarcinoma, as they represent a substantial difference between the two conditions.

These findings collectively suggest that Adenocarcinoma and Squamous Cell Carcinoma of the lung harbor distinct compositions of specific T cell and ILC subsets, indicative of different immune-mediated pathologies and microenvironmental characteristics.

Clinical or Translational Implications

Understanding these differential immune cell proportions can have several clinical and translational implications for NSCLC:

• Prognostic Biomarkers: The identified cell subset proportions could serve as prognostic biomarkers, potentially predicting disease aggressiveness or patient outcomes for Adenocarcinoma versus Squamous Cell Carcinoma. For instance, a Th2-dominant environment in Adenocarcinoma might correlate with certain clinical courses.
• Therapeutic Targeting: The distinct immune landscapes could inform the selection and development of subtype-specific immunotherapies. For Adenocarcinoma, strategies targeting Th2-related pathways or ILC3-mediated inflammation might be considered. For Squamous Cell Carcinoma, understanding the implications of a higher Th17 presence could be crucial.
• Immunotherapy Response Prediction: The observed immune cell profiles may influence a patient's response to current immunotherapies, such as checkpoint inhibitors. Patients with a more immunosuppressive Th2/ILC3-skewed tumor microenvironment (Adeno) might respond differently than those with a Th17-dominant environment (Squamous).
• Further Research into "unassigned" cells: Characterizing the "unassigned" cell population that is significantly enriched in Adenocarcinoma is critical. These cells could represent novel immune modulators or tumor-associated stromal cells that play an unrecognized role in tumor biology and may offer new therapeutic targets.

9. Macrophage Subset Population Analysis in Lung Carcinoma Subtypes

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a population bar plot illustrating the relative proportions of various macrophage subsets (M1, M2A, M2B, M2C, M2D) across individual samples from two major subtypes of Non-Small Cell Lung Carcinoma (NSCLC): Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous). Each bar represents a single patient sample, and the stacked segments show the percentage contribution of each macrophage subset to the total macrophage population within that sample.

Visual Summary

The visualization displays the distribution of five macrophage subsets: Macrophage (M1), Macrophage (M2A), Macrophage (M2B), Macrophage (M2C), and Macrophage (M2D) for multiple samples within Adeno and Squamous lung cancer conditions.

• Dominant Subsets: Macrophage (M1), Macrophage (M2A), and Macrophage (M2B) collectively represent the largest fractions of the macrophage population in most samples across both conditions. Macrophage (M2C) and Macrophage (M2D) appear as comparatively minor components.
• Adenocarcinoma (Adeno) Samples: In Adeno samples, Macrophage (M1) (dark red) consistently contributes a substantial portion, often ranging from approximately 20% to 40% of the total macrophages. Macrophage (M2A) (orange) is also highly prevalent, often at similar or slightly higher levels than M1. Macrophage (M2B) (light yellow) is present, typically contributing around 15-25%.
• Squamous Cell Carcinoma (Squamous) Samples: In Squamous samples, Macrophage (M1) (dark red) is present but appears to be generally at a slightly lower relative proportion compared to Adeno, frequently in the 20-30% range. Conversely, the combined proportion of Macrophage (M2A) (orange) and Macrophage (M2B) (light yellow) often appears to be larger than in Adeno, sometimes exceeding 50% of the total macrophage population in individual samples.
• Variability: There is some inter-sample variability in macrophage subset composition within both Adeno and Squamous conditions, suggesting heterogeneity in the tumor immune microenvironment among patients. However, the general trends for M1 and M2 proportions are observable between the two conditions.

Biological Interpretation

Macrophages play a critical and dual role in the tumor microenvironment, often polarizing into M1 (pro-inflammatory, anti-tumor) or M2 (anti-inflammatory, pro-tumor) phenotypes. The M2 category itself is heterogeneous, with subsets like M2A, M2B, M2C, and M2D associated with distinct functions related to tissue repair, angiogenesis, immune suppression, and tumor promotion PubMed: 22002715.

The observed differences in macrophage subset distribution between Adenocarcinoma and Squamous Cell Carcinoma suggest distinct immune microenvironments in these NSCLC subtypes:

• M2 Dominance in Squamous: The tendency for Macrophage (M2A) and Macrophage (M2B) to collectively represent a larger proportion in Squamous cell carcinoma samples, relative to M1, might indicate a more pronounced immunosuppressive and pro-tumorigenic environment in this subtype. M2 macrophages contribute to tumor growth by promoting angiogenesis, tissue remodeling, and suppressing anti-tumor T cell responses GeneCards: M2_MACROPHAGE_MARKERS.
• M1 Presence in Adeno: While M2 subsets are also present and substantial in Adenocarcinoma, the relatively higher or more consistent proportion of Macrophage (M1) might suggest a comparatively more active anti-tumor immune component in some Adeno samples. M1 macrophages are typically characterized by their ability to produce pro-inflammatory cytokines, present antigens, and directly kill tumor cells or pathogens GeneCards: M1_MACROPHAGE_MARKERS.
• Heterogeneity: The sample-to-sample variability within each condition highlights that not all tumors of the same histological subtype will have an identical immune landscape. This underscores the complexity of the tumor microenvironment and the need for personalized approaches.

These findings are consistent with the general understanding of tumor-associated macrophages (TAMs) often adopting an M2-like phenotype that supports tumor progression, and potentially suggest differences in the immune-contexture contributing to the distinct clinical behaviors of Adeno and Squamous lung cancers.

Clinical or Translational Implications

Understanding the distinct macrophage polarization patterns in Adenocarcinoma and Squamous Cell Carcinoma has several clinical implications:

• Prognosis and Therapeutic Response: A higher M2:M1 ratio is often correlated with poorer prognosis and reduced response to immunotherapy in various cancers, including NSCLC PubMed: 33719003. The potentially more M2-dominant landscape in Squamous samples might suggest different therapeutic sensitivities or outcomes compared to Adeno.
• Targeting Macrophages: The prevalence of M2-like macrophages in both subtypes, and particularly its potentially higher proportion in Squamous, suggests that therapies aimed at reprogramming TAMs from M2 to M1 or depleting M2 macrophages could be beneficial. Such strategies are under investigation in cancer therapy PubMed: 32677840.
• Biomarker Potential: The distinct macrophage profiles could serve as potential biomarkers to stratify patients for specific immunotherapeutic interventions or to predict response. For instance, patients with a more M1-rich environment might respond differently to checkpoint inhibitors than those with a predominantly M2-polarized environment.

10. Differential Macrophage Subpopulation Proportions in Lung Carcinoma Subtypes

[Analysis Visualization Results]...

Analysis Overview

This analysis presents box plots illustrating the proportions of specific macrophage subsets (M2A, M2B, M2C, M2D) in the tumor microenvironment of two major Non-Small Cell Lung Carcinoma (NSCLC) subtypes: Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous). The purpose is to identify macrophage populations that exhibit statistically significant differences in their representation between these two conditions, thereby shedding light on their distinct immunological landscapes.

Visual Summary

The box plots reveal statistically significant or borderline significant differences in the proportions of all four investigated macrophage subsets between Adenocarcinoma and Squamous Cell Carcinoma.

• Macrophage (M2B): This subset shows a significantly higher proportion in Squamous Cell Carcinoma compared to Adenocarcinoma (p=0.000806). The median M2B proportion in Squamous samples is notably higher, with less overlap in distribution compared to Adeno samples.
• Macrophage (M2C): Conversely, Mac (M2C) cells are found in significantly higher proportions in Adenocarcinoma compared to Squamous Cell Carcinoma (p=0.0367). The median M2C proportion is higher in Adeno samples, and the distribution in Squamous samples is tighter and at a lower range.
• Macrophage (M2D): The proportion of Mac (M2D) cells is higher in Squamous Cell Carcinoma compared to Adenocarcinoma (p=0.0624). While the p-value is above the conventional 0.05 threshold, it meets the specified pval_cutoff of 0.1, indicating a borderline significant difference.
• Macrophage (M2A): Similar to Mac (M2C), Mac (M2A) cells tend to be more abundant in Adenocarcinoma compared to Squamous Cell Carcinoma (p=0.0636). This difference also meets the pval_cutoff of 0.1, suggesting a borderline significant enrichment in Adeno.

Overall, M2B and M2D macrophage subsets appear to be enriched in Squamous Cell Carcinoma, while M2A and M2C subsets tend to be more prevalent in Adenocarcinoma within the statistically significant range.

Biological Interpretation

Macrophages are critical components of the tumor microenvironment (TME) and are often broadly classified into M1-like (pro-inflammatory, anti-tumor) and M2-like (pro-tumorigenic, immune suppressive) phenotypes. The subsets analyzed here (M2A, M2B, M2C, M2D) are all considered M2-like macrophages, which generally contribute to tumor growth, angiogenesis, metastasis, and immune evasion. The observed differential enrichment of these specific M2-like macrophage subsets between Adenocarcinoma and Squamous Cell Carcinoma suggests distinct immunomodulatory environments in these two major NSCLC subtypes.

• Enrichment of Mac (M2B) and Mac (M2D) in Squamous Carcinoma: M2B macrophages are often associated with wound healing and tissue repair, while M2D macrophages are linked to immune suppression and angiogenesis. Their increased presence in Squamous Carcinoma could indicate a more pronounced pro-tumorigenic and immunosuppressive microenvironment characterized by extensive tissue remodeling and suppression of anti-tumor immunity compared to Adenocarcinoma. This might contribute to the aggressive nature of certain squamous cell carcinomas.
• Enrichment of Mac (M2A) and Mac (M2C) in Adenocarcinoma: M2A macrophages are typically involved in allergic responses and parasitic infections, and M2C macrophages are associated with immune regulation, tissue remodeling, and fibrosis. Their higher proportions in Adenocarcinoma might suggest a different spectrum of immune modulation, potentially involving fibrotic processes or alternative pathways of immune evasion distinct from those dominant in squamous tumors. This could contribute to the known differences in immune checkpoint inhibitor efficacy observed between these two NSCLC subtypes.

These findings highlight that while both Adenocarcinoma and Squamous Cell Carcinoma are NSCLC, their underlying immune cell compositions, specifically regarding specific M2-like macrophage polarizations, are distinct. These differences could be driven by unique genetic alterations, cellular origins, or exposure to different environmental factors. Macrophages are highly plastic, and their polarization can be significantly influenced by cytokines and chemokines within the TME PubMed: 30046649.

Clinical or Translational Implications

The differential distribution of specific macrophage subsets between Adenocarcinoma and Squamous Cell Carcinoma has significant clinical implications:

1. Biomarker Potential: The specific macrophage subset proportions could serve as prognostic biomarkers or predictive markers for treatment response. For instance, a higher proportion of M2B or M2D macrophages in Squamous Cell Carcinoma might correlate with a poorer prognosis or resistance to certain immunotherapies.
2. Targeted Therapies: Macrophages are attractive targets for cancer therapy due to their critical role in supporting tumor progression GeneCards: Macrophage. The distinct macrophage landscapes suggest that macrophage-targeting therapies (e.g., CSF1R inhibitors to deplete macrophages or repolarize M2 to M1) might have differential efficacy between Adenocarcinoma and Squamous Cell Carcinoma. Therapeutic strategies might need to be tailored to the specific macrophage phenotypes prevalent in each NSCLC subtype.
3. Understanding Treatment Heterogeneity: The observed differences in TME composition, especially macrophage polarization, could help explain the heterogeneous responses to standard treatments, including chemotherapy and immune checkpoint inhibitors, often seen between Adenocarcinoma and Squamous Cell Carcinoma patients. Further research into the functional implications of these specific M2 subtypes in each context is warranted.

11. Ploidy Population Analysis in Lung Epithelial Cells by NSCLC Subtype

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the distribution of ploidy populations (Aneuploid, Diploid, and Unclear) within Lung Epithelial cells across individual samples. Lung Epithelial cells have been identified as the tumor-origin cell type in this dataset. The results are stratified by the two primary Non-Small Cell Lung Carcinoma (NSCLC) conditions: Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous). This allows for an understanding of the genomic stability characteristics of tumor cells within different NSCLC subtypes and across individual patients.

Visual Summary

The stacked bar plots illustrate the percentage of Aneuploid (dark red), Diploid (orange), and Unclear (light green) cells among Lung Epithelial cells for each sample.

Adeno Condition:

• The Adeno samples exhibit considerable heterogeneity in their ploidy profiles.
• Several samples (e.g., NSC021.T1, NSC021.T2, NSC035.T1, NSC018.T2, NSC018.T3) show a high proportion of aneuploid cells, ranging from approximately 30% to over 75% of the Lung Epithelial cell population.
• In contrast, other Adeno samples (e.g., NSC010.T1, NSC010.T2, NSC020.T1, NSC037.T1) are predominantly composed of diploid Lung Epithelial cells, with aneuploid cells making up a very small fraction (often less than 10%).
• The 'Unclear' population is consistently minimal across all Adeno samples, usually representing less than 5% of the cells.

Squamous Condition:

• The Squamous samples generally display a higher and more consistent proportion of aneuploid cells compared to the Adeno group.
• Most Squamous samples show a substantial aneuploid population, frequently exceeding 60-80% (e.g., NSC004.T1, NSC004.T2, NSC004.T3, NSC004.T4).
• Even samples with relatively lower aneuploidy (e.g., NSC009.T1, NSC009.T2) still show over 50% aneuploid cells.
• Similar to the Adeno group, the 'Unclear' population is very small in Squamous samples.

• Comparison Between Conditions: Overall, Squamous Cell Carcinoma samples appear to have a generally higher and less variable proportion of aneuploid Lung Epithelial cells compared to Adenocarcinoma samples, which show greater inter-sample heterogeneity in aneuploidy levels.

Biological Interpretation

• Confirmation of Tumor Origin: The presence of a significant aneuploid population within the 'Lung Epithelial cell' compartment strongly supports their designation as tumor cells. Aneuploidy, defined by an abnormal number of chromosomes, is a well-recognized hallmark of cancer and reflects genomic instability characteristic of malignant transformation. This finding aligns with the Resolved tumor-origin cell type: Lung Epithelial cell as indicated in the data context.
• Heterogeneity in Adenocarcinoma: The striking variability in aneuploidy percentages among Adeno samples suggests significant genomic heterogeneity within this NSCLC subtype. This could reflect different stages of tumor progression, diverse underlying genetic drivers, or varying degrees of chromosomal instability among individual adenocarcinomas. Tumors with low apparent aneuploidy might possess other oncogenic drivers or have maintained a near-diploid state, while highly aneuploid tumors have undergone extensive genomic rearrangement.
• Consistent Aneuploidy in Squamous Cell Carcinoma: The generally higher and more consistent prevalence of aneuploid Lung Epithelial cells in Squamous samples suggests that aneuploidy might be a more pervasive or characteristic feature of this subtype, at least in this cohort. Squamous cell carcinomas are often associated with complex genomic alterations and a high burden of chromosomal structural variations.
• Implications for Diploid "Tumor" Cells: The presence of diploid cells within the "tumor-origin" Lung Epithelial compartment could represent several possibilities: (1) normal lung epithelial cells that were inadvertently co-captured or are part of the tumor microenvironment but are morphologically similar, (2) a subset of tumor cells that maintain a near-diploid karyotype, or (3) limitations in the sensitivity of CNV detection for some tumor clones. Given the overall context, the latter two are more plausible for cells explicitly labeled as "tumor-origin."

Clinical or Translational Implications

• Prognostic and Predictive Value: The degree of aneuploidy and genomic instability can have prognostic implications in NSCLC. Tumors with higher aneuploidy are sometimes associated with more aggressive disease behavior or poorer outcomes, although this relationship can be complex and context-dependent. This observed difference between Adeno and Squamous subtypes, and the heterogeneity within Adeno, could serve as a potential biomarker for disease stratification.
• Therapeutic Resistance and Vulnerabilities: High aneuploidy often correlates with increased genetic complexity, which can contribute to treatment resistance and tumor evolution. Conversely, the stress induced by aneuploidy (e.g., proteotoxic stress, replication stress) can create specific vulnerabilities that might be therapeutically exploited.
• Personalized Medicine: The distinct ploidy profiles between and within NSCLC subtypes emphasize the need for personalized approaches. For instance, Adeno tumors with high aneuploidy might respond differently to therapies compared to those that are predominantly diploid, warranting further investigation into their specific vulnerabilities.
• Refining Tumor Cell Identification: While aneuploidy is a strong indicator of malignancy, the presence of a diploid population within the resolved tumor-origin cell type suggests that combining ploidy calls with other tumor-specific markers or mutation data could further refine tumor cell identification and characterization, especially in cases where aneuploidy is low.

12. Cell-Cell Interaction Patterns in Lung Carcinoma Subtypes

[Analysis Visualization Results]...

Analysis Overview

This analysis utilizes CellPhoneDB to map significant cell-cell interaction (CCI) patterns within the tumor microenvironment of lung carcinoma, specifically comparing Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous) subtypes. The investigation focuses on key cell populations, including tumor-origin Lung Epithelial cells (identified as Aneuploid Lung Epithelial cells), fibroblasts, macrophages, T cells (CD4+ and CD8+), endothelial cells, B cells, and plasma cells. The provided dot plots visualize up to 80 statistically significant ligand-receptor interactions per group (p-value < 0.05, mean expression > 0.01). Dot size correlates with the statistical significance (-log10(p-value)), while the color intensity reflects the interaction strength (log2(mean expression)).

Visual Summary

The dot plots reveal a complex and dynamic network of cell-cell communication for both Adeno and Squamous lung carcinoma, highlighting crucial interactions within their respective tumor microenvironments.

• Dominant Interaction Hubs: A high density of significant interactions is observed across both conditions, with Macrophages (Mac) and Aneuploid Lung Epithelial cells (Aneuploid Lung Epi) acting as central players in numerous highly significant interactions. T cells (T cell CD4+, T cell CD8+) and Plasma cells also exhibit notable engagement.

Highly Active Pathways Identified:

• SPP1 (Osteopontin) Signaling: The interactions involving SPP1 with its receptors (e.g., CD44, integrin_avb1_complex, integrin_avb3_complex, integrin_avb5_complex, integrin_avb6_complex) are remarkably prominent. These interactions are consistently strong in autocrine signaling within Aneuploid Lung Epi and Mac populations, as well as in paracrine interactions between Aneuploid Lung Epi and Mac across both Adeno and Squamous conditions.
• MIF (Macrophage Migration Inhibitory Factor) Axis: The MIF-CD74 and MIF-CXCR4 interactions are frequently observed and significant, particularly between Aneuploid Lung Epi and Mac in both conditions.
• CD99 Homotypic Interactions: CD99-CD99 interactions are widespread and highly significant across various cell-cell pairs, including Aneuploid Lung Epi with immune cells (T cells, Plasma cells), Mac (autocrine and with T cells), and Plasma cell (autocrine).

Condition-Specific Interaction Patterns:

• In Adenocarcinoma (Adeno), distinct interactions include HLA-E-KLRC1 (observed in Mac|Aneuploid Lung Epi and T cell CD4+|Aneuploid Lung Epi pairs), suggesting potential immune evasion mechanisms involving tumor cells and macrophages/T cells. Additionally, VEGFD-VEGFR3 interactions (e.g., Aneuploid Lung Epi|Mac) indicate active lymphangiogenesis or pro-tumorigenic signaling.
• In Squamous Cell Carcinoma (Squamous), CDH1-CDH1 (E-cadherin) homotypic interactions are particularly prominent within both Aneuploid Lung Epi and Diploid Lung Epi populations, possibly indicating differences in cell adhesion and tissue architecture compared to Adeno.

Biological Interpretation

The detailed cell-cell interaction profiles provide significant biological insights into the pathogenesis and microenvironment dynamics of lung carcinoma subtypes:

1. SPP1 Signaling as a Key Driver of Tumor Progression: The robust and consistent activity of SPP1 (Osteopontin) through its integrin and CD44 receptors underscores its critical role in both Adeno and Squamous lung cancers. SPP1 is a well-known modulator of tumor cell survival, migration, invasion, and angiogenesis. Its strong autocrine signaling within Aneuploid Lung Epi suggests direct promotion of tumor growth, while paracrine interactions with Mac likely contribute to shaping a pro-tumorigenic and immunosuppressive macrophage phenotype in the tumor microenvironment PubMed search: SPP1 lung cancer progression.
2. MIF-CD74/CXCR4 Axis in Inflammation and Immune Modulation: The significant interactions of MIF with its receptors, CD74 and CXCR4, between Aneuploid Lung Epi and Mac highlight MIF as a central mediator of inflammation, immune suppression, and angiogenesis. This axis can facilitate macrophage recruitment, polarization, and enhance tumor cell survival, contributing to immune evasion and tumor progression in both lung cancer subtypes GeneCards: MIF.
3. Ubiquitous Role of CD99 in Cell Adhesion and Communication: The widespread and highly significant CD99-CD99 homotypic interactions across various cell types suggest a fundamental role in cell-cell adhesion, migration, and regulation of cellular processes, including immune cell trafficking and activation within the lung tumor microenvironment UniProt: CD99.
4. Subtype-Specific Immune Evasion and Angiogenesis in Adenocarcinoma:
   • The HLA-E-KLRC1 interaction detected in Adeno is highly indicative of an immune evasion mechanism where tumor cells (presenting HLA-E) engage inhibitory receptors (KLRC1/NKG2A) on NK and T cells, thereby suppressing anti-tumor immune responses PubMed search: HLA-E NKG2A immune evasion cancer. This suggests a distinct immune evasion strategy potentially more prominent in Adenocarcinoma.
   • The VEGFD-VEGFR3 interaction in Adeno points to active processes of lymphatic angiogenesis or direct modulation of macrophage functions by tumor cells, both of which are critical for tumor metastasis and growth GeneCards: VEGFC/D.
5. E-cadherin (CDH1) in Squamous Cell Carcinoma Cohesion: The strong CDH1-CDH1 homotypic interactions observed in Squamous Aneuploid Lung Epi are consistent with the known pathology of squamous cell carcinoma, which often maintains more robust intercellular junctions compared to adenocarcinoma. This suggests potential differences in cell adhesion properties that could influence tumor architecture, metastasis patterns, or response to therapies UniProt: CDH1.

Clinical or Translational Implications

The elucidation of these cell-cell interaction patterns offers valuable insights for developing targeted therapies and identifying potential biomarkers for lung carcinoma.

1. Broad-Spectrum Therapeutic Targets: Given their pervasive roles, SPP1 and MIF pathways present compelling targets for therapeutic intervention across both Adeno and Squamous lung cancer. Strategies could involve blocking SPP1, its integrin receptors, CD44, MIF, CD74, or CXCR4 to disrupt tumor growth, metastasis, and immunosuppression. These molecules are actively being investigated in cancer therapeutics PubMed search: SPP1 inhibitor cancer clinical trial, PubMed search: MIF inhibitor cancer clinical trial.
2. Subtype-Specific Immunotherapy for Adenocarcinoma: The HLA-E-KLRC1 interaction in Adeno suggests a potential for enhancing anti-tumor immunity by targeting this axis. Investigating the therapeutic utility of NKG2A (KLRC1) inhibitors or strategies to interfere with HLA-E presentation could benefit Adenocarcinoma patients, particularly those resistant to other immunotherapies PubMed search: NKG2A inhibitor cancer clinical trial.
3. Targeting Angiogenesis/Lymphangiogenesis in Adenocarcinoma: The VEGFD-VEGFR3 interaction highlights the potential for anti-lymphangiogenic therapies in Adeno. Disrupting this pathway could impede metastasis and tumor progression.
4. Biomarker Development: The distinct interaction profiles, such as the differential prominence of CDH1-CDH1 in Squamous versus the unique immune evasion mechanisms in Adeno, could serve as prognostic or predictive biomarkers. These patterns might aid in stratifying patients for specific therapies or predicting disease aggressiveness.

These findings lay the groundwork for further experimental validation of these critical ligand-receptor interactions, with the ultimate goal of translating this knowledge into novel diagnostic tools and more effective, subtype-specific therapeutic strategies for lung carcinoma patients.

13. Condition-Specific Cell-Cell Interaction Patterns in Lung Carcinoma Subtypes

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interaction (CCI) patterns that significantly differ between Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous) conditions in single-cell RNA-seq data from human lung tissue. The focus is on interactions involving Lung Epithelial cells (identified as tumor-origin cells) and major immune and stromal cell populations. The dot plot visualizes the strength (color intensity) and statistical significance (dot size) of these interactions for individual samples, clustered by their overall CCI profile, to highlight condition-specific differences.

Visual Summary

The dot plot, titled "Condition-specific CCI pattern," displays numerous cell-cell interactions (CCI index) on the x-axis, separated into two main panels for "Adeno" and "Squamous" conditions. Samples are listed on the y-axis and are hierarchically clustered based on their overall interaction profiles.

Two distinct clusters of samples are evident:

• Top Sample Cluster (e.g., NSC010.T1 to NSC040.T1): These samples exhibit a high frequency of strong and significant cell-cell interactions primarily within the "Adeno" panel (left side, highlighted by the upper blue box). Interactions for these samples in the "Squamous" panel are generally sparse and weaker. This suggests these samples predominantly display an Adenocarcinoma-like interactome.
• Bottom Sample Cluster (e.g., NSC004.T1 to NSC009.T2): Conversely, these samples show very few or weak interactions in the "Adeno" panel (highlighted by the lower blue box in the Adeno panel, showing mostly white space). However, they exhibit many strong and significant interactions in the "Squamous" panel (also highlighted by the lower blue box, extending across the Squamous panel). This indicates these samples largely present a Squamous Cell Carcinoma-like interactome.

The strength of interaction is represented by the intensity of the red color (darker red indicates higher scaled log strength), and the statistical significance is indicated by the dot size (larger dots correspond to smaller p-values, i.e., higher -log10(p)). Most prominent interactions involve aneuploid Lung Epithelial cells (denoted as 'Lung.Epi (Aneupl)') interacting with various immune cells (e.g., Macrophage, T cells, Mast cells, B cells) and stromal cells (e.g., Fibroblast, Endothelial cell).

Biological Interpretation

The distinct clustering of samples and their preferential activation of specific CCIs in either the Adeno or Squamous condition provides strong evidence for subtype-specific tumor microenvironment (TME) dynamics in lung cancer. The resolution of aneuploid (likely malignant) vs. diploid (likely non-malignant) cell states within Lung Epithelial cells and other cell types (e.g., Macrophage, Fibroblast) adds critical nuance, confirming the tumor-centric nature of these interactions.

Interactions Enriched in Adenocarcinoma Samples

The top cluster of samples, characterized by a dominant "Adeno" interaction profile, shows strong engagement in pathways potentially supporting tumor growth and immune evasion in adenocarcinomas:

• APLP2_PIGR -- Lung.Epi (Aneupl) | Lung.Epi (Dip): This interaction between APLP2 (Amyloid beta A4 precursor protein-like protein 2) and PIGR (Polymeric Immunoglobulin Receptor) represents an intra-epithelial crosstalk, possibly involving both malignant (aneuploid) and non-malignant (diploid) epithelial cells. APLP2 is involved in cell adhesion and growth, while PIGR has roles beyond immune transport, suggesting potential altered epithelial homeostasis or signaling supporting tumor proliferation and survival within the complex glandular architecture often seen in adenocarcinomas.
• SEMA4D_PLXNB2 -- Lung.Epi (Aneupl) | T CD8+: Semaphorin 4D (SEMA4D) on aneuploid lung epithelial cells interacting with Plexin B2 (PLXNB2) on CD8+ T cells is a significant immune evasion mechanism. SEMA4D is known to suppress anti-tumor T cell responses, promoting an immunosuppressive TME. This interaction could directly impair the function of cytotoxic CD8+ T cells, allowing tumor progression. GeneCards: SEMA4D
• VEGFA_FLT1 -- Lung.Epi (Aneupl) | Mac (Aneupl): The interaction between Vascular Endothelial Growth Factor A (VEGFA) from aneuploid lung epithelial cells and FLT1 (VEGFR1) on aneuploid macrophages highlights a critical pro-angiogenic and pro-tumorigenic pathway. VEGFA-FLT1 signaling on tumor-associated macrophages (TAMs) can promote their recruitment, M2-like polarization, and subsequent support of tumor angiogenesis and immunosuppression.

Interactions Enriched in Squamous Cell Carcinoma Samples

The bottom cluster of samples, demonstrating a predominant "Squamous" interaction profile, is characterized by distinct pathways often associated with inflammation, extensive extracellular matrix (ECM) remodeling, and altered immune responses typical of squamous cell carcinomas:

• LeukotrieneC4_byLTC4S_CYSLTR1 -- Mast cell | Lung.Epi (Aneupl): This interaction involves Leukotriene C4 (produced by LTC4S) from mast cells activating the CysLT1 receptor on aneuploid lung epithelial cells. Mast cells are key players in inflammatory processes and are often abundant in the TME of SCCs. Leukotrienes promote inflammation, cell proliferation, and angiogenesis, contributing to the aggressive and inflammatory nature of SCC. PubMed search: LeukotrieneC4 CYSLTR1 lung cancer
• COL3A1_ADGRG1 -- Lung.Epi (Aneupl) | Fib (Aneupl): The interaction between Collagen type III alpha 1 chain (COL3A1) from aneuploid lung epithelial cells and ADGRG1 (GPR56) on aneuploid fibroblasts signifies crucial tumor-stroma crosstalk. Collagen III is a major ECM component, and its interaction with ADGRG1 can drive fibroblast activation into cancer-associated fibroblasts (CAFs) and promote ECM remodeling, leading to increased tumor stiffness and invasiveness, characteristic features of SCC.
• IL1B_IL1_receptor -- Mac (Aneupl) | Lung.Epi (Aneupl): Interleukin-1 beta (IL-1β) secreted by aneuploid macrophages signaling through the IL-1 receptor (IL1R) on aneuploid lung epithelial cells represents a potent pro-inflammatory axis. IL-1β drives chronic inflammation, which is a hallmark of SCC, promoting tumor cell proliferation, survival, angiogenesis, and immunosuppression. GeneCards: IL1B
• JAG1_NOTCH3 -- Lung.Epi (Aneupl) | T CD4+: Jagged1 (JAG1) from aneuploid lung epithelial cells interacting with NOTCH3 on CD4+ T cells suggests a role for Notch signaling in modulating the immune response within the SCC TME. Aberrant Notch signaling can promote tumor growth and immune evasion, potentially influencing T cell differentiation towards a suppressive phenotype or promoting T cell exhaustion.

Clinical or Translational Implications

The identification of condition-specific cell-cell interaction patterns offers significant clinical and translational insights for lung carcinoma:

1. Subtype-Specific Therapeutic Targets: The distinct interactomes in Adeno and Squamous subtypes suggest that therapeutic strategies should be tailored to the specific histological diagnosis. Targeting broadly active pathways might be less effective than focusing on interactions that are uniquely or predominantly active in a given subtype.
2. Targeting Angiogenesis and Immune Evasion in Adenocarcinoma:
   • SEMA4D-PLXNB2 axis: Blocking SEMA4D could enhance anti-tumor immunity by restoring CD8+ T cell function. Anti-SEMA4D antibodies (e.g., VX15/2503) are currently under investigation for various solid tumors.
   • VEGFA-FLT1 axis: Targeting the VEGFA-VEGFR pathway is a well-established strategy. Agents like Bevacizumab (anti-VEGFA antibody) or multi-targeted tyrosine kinase inhibitors (e.g., Nintedanib, approved for NSCLC) could be re-evaluated for their specific impact on tumor-macrophage interactions, particularly in Adenocarcinoma.
3. Targeting Inflammation and Stromal Remodeling in Squamous Cell Carcinoma:
   • LeukotrieneC4-CYSLTR1 axis: Inhibiting leukotriene synthesis or blocking the CysLT1 receptor could mitigate mast cell-driven inflammation and tumor progression. Existing CysLT1R antagonists (e.g., Montelukast) used in asthma could be repurposed or investigated for their efficacy in SCC.
   • IL1B-IL1R axis: Given the strong inflammatory component of SCC, blocking IL-1β signaling represents a promising strategy. Anakinra (IL-1R antagonist) or Canakinumab (anti-IL-1β antibody), approved for inflammatory diseases, could be explored in clinical trials for SCC patients.
   • COL3A1-ADGRG1 axis: Strategies aimed at disrupting ECM remodeling and inhibiting CAF activation, such as targeting collagen synthesis or cross-linking enzymes (e.g., LOXL2 inhibitors), could disarm the pro-tumorigenic stromal support in SCC.
4. Prognostic and Predictive Biomarkers: These distinct CCI signatures could serve as prognostic markers to stratify patients or predictive markers to identify patients most likely to respond to specific targeted therapies, particularly immunomodulatory or anti-inflammatory agents.
5. Future Research Directions: Further investigation into the precise mechanisms and downstream signaling of these identified interactions, especially considering the aneuploid status of the interacting cells, will be crucial for developing novel and effective subtype-specific therapeutic interventions.

14. Condition-Specific Cell-Cell Interaction Patterns in Lung Carcinoma Subtypes

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies and visualizes cell-cell interaction (CCI) patterns that significantly differ between Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous) subtypes of Lung Carcinoma. The focus is on interactions involving Lung Epithelial cells (tumor-origin), major immune cells (Myeloid, Mast, T, B), Endothelial cells, and Stromal cells (Fibroblasts), specifically incorporating genes related to immune checkpoints and cell cycle pathways. The dot plot displays the scaled log strength of interactions (color intensity) and their statistical significance (-log10(p) value, represented by dot size) for individual samples across the two conditions.

Visual Summary

The dot plot clearly segregates cell-cell interaction patterns by condition: Adenocarcinoma samples exhibit a distinct set of prominent interactions compared to Squamous Cell Carcinoma samples.

• Adenocarcinoma (Adeno) Pattern: Samples belonging to the Adeno group (e.g., NSC010.T1, NSC016.T1-3, NSC018.T1-3, NSC019.T1-2, NSC020.T1-2, NSC021.T1-2) show strong and highly significant interactions. These are predominantly characterized by various EGFR ligand-receptor pairs (AREG_EGFR, EREG_EGFR, HBEGF_EGFR) involving Lung Epithelial cells (both aneuploid and diploid), Macrophages, Endothelial cells, and Dendritic cells. Additionally, CD93_IFNGR1 interactions are notably active, primarily involving Endothelial cells, Macrophages, and Dendritic cells.
• Squamous Cell Carcinoma (Squamous) Pattern: Samples classified as Squamous (e.g., NSC004.T1-4, NSC009.T1-2) display a different set of highly active CCIs. These interactions include EREG_EGFR and HBEGF_EGFR mainly between aneuploid Lung Epithelial cells, Macrophages, and Fibroblasts. A key distinguishing feature is the strong presence of LCK_CD8_receptor interactions, indicating robust communication between T cells (CD4+, CD8+) and Innate Lymphoid Cells (ILC).
• Interaction Strength and Significance: In both conditions, the most prominent dots are dark red (high scaled log strength) and large (high -log10(p) value), indicating both strong mean interaction potential and high statistical significance, suggesting these interactions are consistently active and important within their respective conditions.
• Ploidy Status: The analysis differentiates between aneuploid (Aneup) and diploid (Dip) Lung Epithelial cells. In Adeno, EGFR ligand interactions involve both aneuploid and diploid Lung Epithelial cells. In Squamous, EGFR ligand interactions primarily involve aneuploid Lung Epithelial cells interacting with stromal cells, suggesting a more direct tumor-stromal axis.

Biological Interpretation

Adenocarcinoma (Adeno) Specific Cell-Cell Interactions

• EGFR Ligand-Receptor Interactions (AREG-EGFR, HBEGF-EGFR, EREG-EGFR)
  • Interactions Observed: AREG_EGFR -- Lung.Epi (Aneup) | Lung.Epi (Aneup), AREG_EGFR -- Lung.Epi (Dip) | Lung.Epi (Aneup), AREG_EGFR -- Mac | Lung.Epi (Dip), EREG_EGFR -- Lung.Epi (Dip) | Lung.Epi (Aneup), HBEGF_EGFR -- Lung.Epi (Aneup) | Lung.Epi (Aneup), HBEGF_EGFR -- Mac | DC.
  • Signaling Pathways & Mechanisms: The Epidermal Growth Factor Receptor (EGFR) is a receptor tyrosine kinase that, upon binding with its ligands such as Amphiregulin (AREG), Heparin-binding EGF-like growth factor (HBEGF), and Epiregulin (EREG), initiates critical downstream signaling pathways. These include the RAS/MAPK, PI3K/AKT, and STAT pathways, which collectively regulate fundamental cellular processes like proliferation, survival, migration, and angiogenesis. The prominent presence of these interactions involving both aneuploid (tumor cells) and diploid Lung Epithelial cells, as well as Macrophages (Mac) and Dendritic cells (DC), suggests an extensive autocrine and paracrine EGFR activation loop. This signaling network actively promotes tumor growth and survival by direct communication between tumor cells, and by modulating the tumor microenvironment (TME) through crosstalk with immune cells Source: PubMed search for "EGFR signaling in lung cancer": [https://pubmed.ncbi.nlm.nih.gov/?term=EGFR+signaling+in+lung+cancer].
  • Potential as Therapeutic Target & Agents: EGFR is a validated and highly important therapeutic target in NSCLC, particularly adenocarcinoma. EGFR tyrosine kinase inhibitors (TKIs) such as gefitinib, erlotinib, afatinib, and osimertinib are standard-of-care treatments for patients with activating EGFR mutations. For cases driven by overexpression of EGFR ligands, anti-EGFR monoclonal antibodies like cetuximab or necitumumab could be considered. Targeting EGFR signaling aims to directly suppress tumor cell proliferation and survival, and indirectly impact angiogenesis and immune evasion by disrupting the complex TME crosstalk.

CD93-IFNGR1 Interactions

• Interactions Observed: CD93_IFNGR1 -- Endo | Endo, CD93_IFNGR1 -- Endo | DC, CD93_IFNGR1 -- Endo | Mac, CD93_IFNGR1 -- Lung.Epi (Aneup) | Mac.
• Signaling Pathways & Mechanisms: CD93 (C-type lectin domain family 1 member B) is expressed on endothelial cells, myeloid cells (macrophages, dendritic cells), and certain tumor cells. It plays roles in angiogenesis, inflammation, and phagocytosis. IFNGR1 (Interferon Gamma Receptor 1) is the receptor for Interferon-gamma (IFN-γ), a cytokine crucial for anti-tumor immunity. These interactions suggest a role in modulating immune responses and vascular dynamics within the TME of adenocarcinoma. For instance, CD93 on endothelial cells might interact with IFNGR1 on immune cells to influence immune cell recruitment, activation, or the inflammatory state, thereby impacting local angiogenesis and anti-tumor immunity Source: GeneCards for CD93: [https://www.genecards.org/cgi-bin/carddisp.pl?gene=CD93; GeneCards for IFNGR1: https://www.genecards.org/cgi-bin/carddisp.pl?gene=IFNGR1].
• Potential as Therapeutic Target & Agents: While IFN-γ signaling is a target for immune modulation, CD93 itself is not a primary therapeutic target in current NSCLC clinical practice. However, understanding the CD93-IFNGR1 axis could open avenues for future therapeutic development, potentially by targeting CD93 to inhibit tumor angiogenesis or to modulate the immunosuppressive environment.

Squamous Cell Carcinoma (Squamous) Specific Cell-Cell Interactions

• EGFR Ligand-Receptor Interactions (EREG-EGFR, HBEGF-EGFR) with Stromal Cells
  • Interactions Observed: EREG_EGFR -- Mac | Fib, EREG_EGFR -- Lung.Epi (Aneup) | Fib, HBEGF_EGFR -- Mac | Fib.
  • Signaling Pathways & Mechanisms: Similar to adenocarcinoma, EGFR signaling is a critical pathway in squamous cell carcinoma. However, in this subtype, the interactions are characterized by a prominent involvement of Fibroblasts (Fib) communicating with Macrophages (Mac) and aneuploid Lung Epithelial cells. This highlights a robust tumor-stromal interaction, where fibroblasts, often activated as cancer-associated fibroblasts (CAFs), become key players in shaping the TME. CAFs contribute significantly to tumor progression by secreting growth factors (including EGFR ligands), remodeling the extracellular matrix, and fostering an immunosuppressive environment. These interactions sustain tumor cell growth and survival by activating EGFR pathways in tumor cells and potentially stromal cells, creating a highly pro-tumorigenic niche Source: PubMed search for "cancer-associated fibroblasts squamous cell carcinoma": [https://pubmed.ncbi.nlm.nih.gov/?term=cancer-associated+fibroblasts+squamous+cell+carcinoma].
  • Potential as Therapeutic Target & Agents: EGFR remains a crucial therapeutic target in squamous NSCLC, even though EGFR mutations are less prevalent than in adenocarcinoma. Overexpression of EGFR and its ligands is common. Anti-EGFR monoclonal antibodies (e.g., necitumumab, cetuximab) are approved for use in combination with chemotherapy for advanced squamous NSCLC. Beyond direct EGFR inhibition, strategies targeting CAFs or their crosstalk with tumor and immune cells are emerging. This could involve therapies that disrupt growth factor signaling from CAFs or modulate stromal remodeling to inhibit tumor progression.

LCK-CD8_receptor Interactions (T Cell-T Cell and T Cell-ILC)

• Interactions Observed: LCK_CD8_receptor -- T CD4+ | T CD8+, LCK_CD8_receptor -- T CD8+ | T CD8+, LCK_CD8_receptor -- ILC | T CD8+.
• Signaling Pathways & Mechanisms: LCK (Lymphocyte-specific protein tyrosine kinase) is a pivotal intracellular tyrosine kinase that associates with the cytoplasmic tails of CD4 and CD8 co-receptors, playing an indispensable role in T cell receptor (TCR) signaling and T cell activation. These observed interactions signify active communication between different T cell subsets (helper CD4+, cytotoxic CD8+) and Innate Lymphoid Cells (ILC), which are critical components of both adaptive and innate immunity. Such interactions are fundamental for coordinated immune responses; for instance, CD4+ T cells are crucial for activating and sustaining CD8+ cytotoxic T cell responses, which are essential for anti-tumor immunity. ILCs can also significantly influence the T cell landscape and overall immune microenvironment. This pattern suggests dynamic T cell activation, suppression, or regulation within the squamous TME, possibly reflecting ongoing immune surveillance or attempts at immune evasion Source: GeneCards for LCK: [https://www.genecards.org/cgi-bin/carddisp.pl?gene=LCK; PubMed search for "T cell interactions tumor microenvironment": https://pubmed.ncbi.nlm.nih.gov/?term=T+cell+interactions+tumor+microenvironment].
• Potential as Therapeutic Target & Agents: While LCK is an intracellular signaling molecule and not a direct ligand-receptor target, these interactions underscore the importance of T cell-mediated immunity in squamous NSCLC. Understanding these T cell communication patterns is highly relevant for immune checkpoint blockade (ICB) therapies, which aim to restore or enhance anti-tumor T cell activity. Immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 (e.g., pembrolizumab, nivolumab) or CTLA-4 (e.g., ipilimumab) are cornerstones of therapy for NSCLC, including squamous cell carcinoma. The observed LCK-related interactions provide insights into the specific T cell dynamics that could be modulated or exploited to improve the efficacy of ICIs and other immunotherapies.

Clinical or Translational Implications

The distinct cell-cell interaction patterns identified between Adenocarcinoma and Squamous Cell Carcinoma subtypes of Lung Carcinoma carry significant clinical and translational implications:

• EGFR Signaling as a Central Therapeutic Target: The widespread and strong EGFR ligand-receptor interactions in both Adeno and Squamous subtypes, involving tumor cells (especially aneuploid), macrophages, endothelial cells, and fibroblasts, solidify EGFR as a central therapeutic axis. The specific ligands (AREG, HBEGF, EREG) and interacting cell types (tumor, immune, stromal) provide a more nuanced understanding of EGFR activation in the TME. Existing EGFR inhibitors (TKIs for mutated EGFR, monoclonal antibodies for wild-type EGFR) could benefit from strategies that consider the specific TME context and broader ligand-receptor networks, potentially leading to combination therapies that block both tumor-intrinsic and TME-driven EGFR activation. Source: PubMed search for "EGFR inhibitors NSCLC": [https://pubmed.ncbi.nlm.nih.gov/?term=EGFR+inhibitors+NSCLC].

Differential Immune and Stromal Engagement:

• In Adenocarcinoma, the prominent CD93-IFNGR1 interactions involving endothelial and immune cells (DC, Mac) suggest a role for angiogenesis and immune modulation distinct from squamous. This indicates potential for therapies that combine anti-angiogenic agents with immune modulators, or for novel approaches targeting CD93.
• In Squamous Cell Carcinoma, the strong involvement of fibroblasts in EGFR-mediated interactions and the active LCK-CD8_receptor interactions highlight a highly active stromal compartment and potentially a more "inflamed" or immune-active TME compared to adenocarcinoma. This reinforces the importance of cancer-associated fibroblasts (CAFs) as therapeutic targets in squamous NSCLC and suggests that immunotherapies could be particularly relevant due to the active T cell communication, provided the T cells are not functionally exhausted or suppressed.

• Targeting Tumor Microenvironment (TME) Crosstalk: The analysis underscores that effective therapies for NSCLC must move beyond solely targeting tumor cells to comprehensively address the complex network of cell-cell interactions within the TME. The involvement of different stromal and immune cell types in condition-specific ways suggests that personalized therapeutic approaches might require tailored strategies to disrupt specific tumor-immune or tumor-stromal axes. For instance, combination therapies targeting both EGFR and TME components (e.g., CAFs, specific immune cell subsets) could yield better outcomes by simultaneously inhibiting tumor growth and reprogramming the immunosuppressive microenvironment.

15. Lung Epithelial Cell Condition-Specific Surfaceome Marker Expression

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a dot plot illustrating the expression of surfaceome markers in Lung Epithelial cells across different conditions: Adenocarcinoma (Adeno), Diploid (likely non-malignant or stromal cells within tumor samples, or normal lung epithelial cells), and Squamous Cell Carcinoma (Squamous). The data is derived from single-cell RNA-seq of lung tissue, focusing on tumor-origin lung epithelial cells. The goal is to identify condition-specific surfaceome markers that could serve as therapeutic targets, distinguishing between established targets and those with potential. Marker selection was based on differential expression criteria (nz_pct_score and nz_pct FC) comparing each condition against others, focusing on surfaceome genes.

Visual Summary

The dot plot effectively visualizes condition-specific marker expression patterns among Lung Epithelial cells.

• Condition Clustering: The samples clearly cluster by their diagnosed condition. The top group comprises Adenocarcinoma samples, followed by a distinct group of Diploid samples, and then Squamous Cell Carcinoma samples at the bottom.

Marker Expression Patterns:

• Adenocarcinoma (Adeno) Markers: A prominent cluster of genes shows high expression (darker red dots) and high prevalence (larger dot size) specifically in the Adeno samples (e.g., *APLP2, MUC1, CEACAM6, TACSTD2, EPCAM, ERBB3*). These markers are largely absent or expressed at very low levels in Diploid and Squamous samples.
• Diploid Samples: The Diploid samples (middle section) generally exhibit much lower expression and prevalence for most of the identified markers, appearing as small, light-colored dots or empty spaces. This suggests these samples represent a distinct population (likely normal or non-malignant cells) lacking the tumor-associated surfaceome expression profiles seen in the carcinoma types.
• Squamous Cell Carcinoma (Squamous) Markers: Another distinct cluster of markers displays high expression and prevalence specifically in Squamous samples (e.g., *CD44, SLC2A1, NOTCH3, FGFR2, MMP14, ITGB4*). These markers are typically expressed at low levels in Adeno and Diploid samples.

• Sample Heterogeneity: Within each condition, there is some variability in marker expression across individual samples, although distinct condition-specific clusters are evident.
• Overall Differentiation: The plot clearly demonstrates robust surfaceome marker differences between Adenocarcinoma and Squamous Cell Carcinoma, and both differ significantly from the Diploid cell population. This differential expression highlights potential targets for subtype-specific therapies.

Biological Interpretation

The distinct surfaceome expression profiles in Adenocarcinoma and Squamous Cell Carcinoma reflect their different pathological origins, molecular landscapes, and potential therapeutic vulnerabilities within Lung Epithelial cells.

Markers Enriched in Lung Adenocarcinoma Epithelial Cells

Markers highly expressed in Adenocarcinoma cells often play roles in proliferation, immune evasion, and altered cell adhesion.

• TACSTD2 (TROP2): This transmembrane glycoprotein is highly expressed in many epithelial cancers, including NSCLC adenocarcinoma. It is involved in cell proliferation, invasion, and metastasis.
  • Signaling/Mechanism: While its precise signaling is complex, TROP2 interacts with intracellular proteins involved in calcium signaling and is a component of multiprotein complexes at the cell surface that regulate cell growth and adhesion.
  • Therapeutic Agents: Established Therapeutic Target. Antibody-drug conjugates (ADCs) like Sacituzumab govitecan (Trodelvy) specifically target TROP2, delivering a cytotoxic payload to cancer cells. These ADCs are approved for other solid tumors and are undergoing clinical evaluation for NSCLC, showing promising results.
  • Evidence: High TROP2 expression is associated with poor prognosis in NSCLC. PubMed Search: TROP2 adenocarcinoma lung cancer ADC
• EPCAM (Epithelial Cell Adhesion Molecule): EPCAM is a pan-carcinoma marker overexpressed in many epithelial cancers, including NSCLC. It promotes cell proliferation and migration.
  • Signaling/Mechanism: EPCAM is involved in homophilic cell-cell adhesion and can influence Wnt signaling pathways, contributing to tumor growth and progression.
  • Therapeutic Agents: Established Therapeutic Target. EPCAM has been a target for immunotherapy, including bispecific antibodies. While some agents have faced development challenges, EPCAM remains a viable target for ADCs and CAR-T cell therapies in various cancers.
  • Evidence: EPCAM overexpression is correlated with advanced stages and poor prognosis in NSCLC. GeneCards: EPCAM
• MUC1 (Mucin 1): MUC1 is a transmembrane glycoprotein overexpressed and aberrantly glycosylated in many adenocarcinomas, including lung. It plays a role in cell signaling, anti-adhesion, and immune evasion.
  • Signaling/Mechanism: MUC1 can interact with various receptor tyrosine kinases and transcription factors (e.g., EGFR, β-catenin), promoting proliferation, survival, and metastasis. Its extracellular domain can also mask tumor antigens.
  • Therapeutic Agents: Established Therapeutic Target (potential). MUC1 has been extensively explored as a target for immunotherapies, including vaccines, antibodies, and CAR-T cell therapies. While no MUC1-targeting drug is widely approved for lung cancer, it remains a high-interest target due to its widespread expression.
  • Evidence: MUC1 overexpression is a common feature of NSCLC adenocarcinoma and is associated with aggressive disease. UniProt: MUC1
• ERBB3 (HER3): A member of the epidermal growth factor receptor (EGFR) family, often overexpressed in NSCLC adenocarcinoma. Unlike other EGFR family members, HER3 has impaired kinase activity but is a crucial heterodimerization partner for other HER receptors (like EGFR and HER2), mediating their signaling.
  • Signaling/Mechanism: HER3 acts as a co-receptor, primarily signaling through PI3K/AKT and MAPK pathways upon ligand binding (e.g., heregulin/NRG1) and heterodimerization, driving cell growth, survival, and drug resistance.
  • Therapeutic Agents: Established Therapeutic Target. HER3-targeting ADCs (e.g., Patritumab Deruxtecan) have shown significant clinical activity in NSCLC patients, particularly those with EGFR-mutant, TKI-resistant disease.
  • Evidence: HER3 is implicated in resistance to EGFR TKIs and is a prognostic marker in NSCLC. PubMed Search: ERBB3 NSCLC therapy
• CEACAM5 (Carcinoembryonic Antigen Related Cell Adhesion Molecule 5): A cell surface glycoprotein, often overexpressed in adenocarcinomas and used as a serum tumor marker (CEA).
  • Signaling/Mechanism: CEACAM5 is involved in cell adhesion, signal transduction, and has anti-apoptotic and pro-proliferative roles in cancer. It can promote immune evasion by interacting with immune cells.
  • Therapeutic Agents: Less well-characterized marker with potential as therapeutic target. ADCs targeting CEACAM5 (e.g., tusamitamab ravtansine) are currently in clinical development for NSCLC, demonstrating promising activity.
  • Evidence: High CEACAM5 expression is associated with poorer prognosis in NSCLC. GeneCards: CEACAM5

Markers Enriched in Lung Squamous Cell Carcinoma Epithelial Cells

Markers prevalent in Squamous Cell Carcinoma cells are often associated with unique differentiation programs, metabolism, and invasive properties.

• CD44: A cell surface glycoprotein involved in cell-cell interactions, cell adhesion, and migration. It is a well-known marker for cancer stem cells in many tumor types.
  • Signaling/Mechanism: CD44 binds to hyaluronic acid and other extracellular matrix components, activating downstream signaling pathways (e.g., Rho GTPases, PI3K/AKT) that regulate cell motility, survival, and stemness. Different CD44 splice variants exist with distinct roles.
  • Therapeutic Agents: Less well-characterized marker with potential as therapeutic target. Targeting CD44, especially specific isoforms, is an active area of research for inhibiting cancer stem cell properties and metastasis. Antibodies and small molecules are being explored.
  • Evidence: CD44 overexpression is linked to squamous differentiation, tumor initiation, and resistance to therapy in lung cancer. PubMed Search: CD44 squamous lung cancer stem cell
• SLC2A1 (GLUT1): A glucose transporter protein responsible for facilitated glucose uptake into cells. Overexpressed in many cancers, including Squamous Cell Carcinoma.
  • Signaling/Mechanism: High GLUT1 expression fuels the Warburg effect (aerobic glycolysis), providing energy and building blocks for rapid cancer cell proliferation, a hallmark of many aggressive tumors.
  • Therapeutic Agents: Established Therapeutic Target (indirect). While not a direct receptor for targeted drugs like ADCs, inhibiting GLUT1 (e.g., with glycolysis inhibitors) or exploiting the metabolic vulnerability of cancer cells is an active area of anti-cancer therapy research.
  • Evidence: GLUT1 overexpression is a common feature of squamous cell carcinoma and is associated with aggressive behavior and poor prognosis. GeneCards: SLC2A1
• NOTCH3 (Notch Receptor 3): One of the four Notch receptors, involved in cell fate determination, proliferation, and differentiation. Aberrant Notch signaling contributes to various cancers.
  • Signaling/Mechanism: Upon ligand binding, Notch receptors undergo proteolytic cleavage, releasing an intracellular domain (NICD) that translocates to the nucleus to regulate target gene transcription (e.g., HES family genes), influencing cell growth and survival. NOTCH3 signaling is often dysregulated in lung SCC.
  • Therapeutic Agents: Less well-characterized marker with potential as therapeutic target. Gamma-secretase inhibitors (GSIs) and monoclonal antibodies targeting Notch receptors are in clinical trials for various cancers, including lung cancer.
  • Evidence: NOTCH3 activation has been observed in squamous cell carcinoma, driving proliferation and resistance. PubMed Search: NOTCH3 squamous lung cancer therapy
• FGFR2 (Fibroblast Growth Factor Receptor 2): A receptor tyrosine kinase that plays a crucial role in cell proliferation, differentiation, and survival upon binding to FGF ligands.
  • Signaling/Mechanism: Ligand binding leads to receptor dimerization, autophosphorylation, and activation of downstream signaling pathways such as RAS/MAPK, PI3K/AKT, and PLCγ, promoting oncogenic processes.
  • Therapeutic Agents: Established Therapeutic Target. FGFR inhibitors (e.g., pemigatinib, futibatinib) are approved for cancers with specific FGFR fusions or alterations. While less common in lung SCC, targeting FGFR2 alterations, if present, is a validated strategy.
  • Evidence: FGFR2 gene amplifications or fusions are driver mutations in a subset of cancers, making it a target for selective inhibitors. GeneCards: FGFR2
• MMP14 (Matrix Metallopeptidase 14, MT1-MMP): A membrane-anchored matrix metalloproteinase that degrades extracellular matrix components.
  • Signaling/Mechanism: MMP14 facilitates tumor invasion and metastasis by breaking down the basement membrane and promoting angiogenesis. It also activates other MMPs and growth factors.
  • Therapeutic Agents: Less well-characterized marker with potential as therapeutic target. While broad-spectrum MMP inhibitors have largely failed in clinical trials due to toxicity, targeting specific MMPs like MMP14 with more selective inhibitors or novel approaches (e.g., ADCs delivering payloads specifically to MMP14-expressing cells) remains an area of investigation in cancer.
  • Evidence: High MMP14 expression is correlated with aggressive behavior and poor prognosis in NSCLC, particularly in squamous cell carcinoma. UniProt: MMP14

Clinical or Translational Implications

The identification of distinct surfaceome markers for Lung Adenocarcinoma and Squamous Cell Carcinoma epithelial cells has significant clinical and translational implications:

1. Subtype-Specific Targeted Therapies: The marked differences in surface marker expression between Adeno and Squamous subtypes suggest opportunities for developing highly specific targeted therapies that minimize off-target effects. For example, ADCs targeting TROP2 or HER3 could be particularly effective in Adenocarcinoma.
2. Biomarker Discovery: These markers can serve as diagnostic or prognostic biomarkers to differentiate between NSCLC subtypes and potentially predict response to specific treatments. Elevated levels of certain markers could guide patient stratification for clinical trials.
3. Combination Therapies: Understanding the underlying pathways driven by these surface markers can inform rational combination therapies. For instance, combining agents targeting metabolic pathways (e.g., GLUT1 inhibitors) with other anti-cancer drugs in Squamous Cell Carcinoma.
4. Immuno-Oncology Targets: Surface markers like CEACAM5 and EPCAM, which are prevalent on cancer cells, can be exploited for immunotherapeutic approaches, including CAR-T cell therapy or bispecific antibodies that direct immune cells to tumor cells.
5. Understanding Tumor Biology: The observed differences provide insights into the distinct biological mechanisms driving each NSCLC subtype, which can inform the development of novel therapeutic strategies beyond existing targets. For instance, the role of NOTCH3 in Squamous Cell Carcinoma suggests pathway inhibitors could be explored.

In summary, this analysis provides a valuable resource for prioritizing and investigating novel surfaceome markers as potential therapeutic targets for subtype-specific treatment strategies in Lung Carcinoma.

16. Macrophage Condition-Specific Surfaceome Markers in Lung Carcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies and visualizes surfaceome markers specifically expressed by Macrophage cells in two distinct conditions of Non-Small Cell Lung Carcinoma (NSCLC): Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous). The dot plot displays mean expression levels and the fraction of cells expressing each marker across various patient samples. This provides insights into the distinct functional states and roles of macrophages within the tumor microenvironment (TME) of these different NSCLC subtypes. The markers were selected based on a one-vs-rest comparison using a non-zero percentage score and fold change cutoffs.

Visual Summary

The dot plot, titled "Macrophage condition-specific markers," effectively illustrates differential surfaceome marker expression.

• Sample Grouping: The samples (e.g., NSC037.T1, NSC004.T3) are implicitly clustered by condition. The top approximately 15 samples predominantly show high expression of markers associated with the "Adeno" condition, while the bottom approximately 5 samples exhibit strong expression of markers for the "Squamous" condition. This clear separation indicates distinct macrophage phenotypes between the two NSCLC subtypes.

Marker Expression Patterns:

• Adeno-associated markers: Genes such as *HLA-DQB1, HLA-DQA1, HLA-DMA, HLA-DMB* (MHC Class II molecules), *CD83, MRC1 (CD206), SPP1, IFNGR1*, and *TREM2* show high mean expression (dark red dots) and a large fraction of expressing cells (large dot size) in the Adeno samples. Their expression is notably lower or absent in Squamous samples.
• Squamous-associated markers: Genes like *SLC2A3 (GLUT3), CXCR4, C5AR1 (CD88), TNFRSF1B, SLC2A14, EREG, ANPEP (CD13)*, and *TREM1* display robust expression in Squamous samples, with significantly reduced expression in Adeno samples.

• Quantitative Metrics: The color intensity of the dots represents the mean expression in the group (ranging from white/low to dark red/high), and the dot size indicates the fraction of cells expressing the marker in that group (larger dots mean higher fraction). The bars on the right indicate the number of Macrophage cells contributing to each sample's data, generally in the hundreds to thousands, suggesting robust cell numbers for analysis per sample.

Biological Interpretation

Macrophages are key components of the tumor microenvironment (TME), exhibiting remarkable plasticity and adopting diverse phenotypes that can either promote or inhibit tumor progression. The observed condition-specific surfaceome markers highlight distinct macrophage states in Adenocarcinoma and Squamous Cell Carcinoma, likely reflecting different underlying pathological mechanisms and immune responses.

Macrophage Phenotypes in Adenocarcinoma (Adeno)

Macrophages in Adenocarcinoma samples show high expression of markers generally associated with antigen presentation, immune regulation, and certain pro-tumorigenic M2-like functions.

MHC Class II Genes (HLA-DQA1, HLA-DQB1, HLA-DMA, HLA-DMB):

• Signaling Pathways: These molecules are central to the adaptive immune response, presenting processed antigens to CD4+ T cells, which initiates T cell activation. Their expression is typically upregulated by Interferon-gamma (IFN-$\gamma$).
• Role in TME: While often associated with an anti-tumor M1-like phenotype capable of initiating immune responses, high MHC-II expression on tumor-associated macrophages (TAMs) can be complex. TAMs can also present self-antigens in a tolerogenic manner or be dysfunctional, contributing to immune evasion despite maintaining antigen-presenting machinery. The co-expression with M2 markers like MRC1 suggests a heterogeneous or transitional population.
• Therapeutic Target: Directly targeting MHC-II is not a primary strategy. However, therapies aiming to enhance antigen presentation or overcome MHC-II-mediated immune tolerance could improve anti-tumor immunity.
• Relevant Therapeutic Agents: Immunomodulators that boost T-cell priming or checkpoint inhibitors (e.g., anti-PD-1/PD-L1) could indirectly leverage or modify these pathways.
• Pertinent Information: The presence of these markers suggests that macrophages in Adeno may retain some capacity for immune engagement, possibly indicating an "immune-hot" but potentially suppressed TME. GeneCards: HLA-DQB1

MRC1 (CD206):

• Signaling Pathways: Mannose Receptor 1 is a C-type lectin involved in endocytosis and phagocytosis of glycosylated ligands. It is a canonical marker for M2-polarized macrophages.
• Role in TME: M2 macrophages are generally associated with promoting tumor growth, angiogenesis, immune suppression (e.g., by contributing to T cell anergy), and tissue remodeling. High CD206+ macrophage infiltration in Adenocarcinoma suggests a pro-tumorigenic microenvironment that supports tumor progression.
• Therapeutic Target: Inhibiting M2 polarization or depleting M2-like TAMs is a significant therapeutic strategy in cancer. Targeting CD206 directly could block key pro-tumor functions.
• Relevant Therapeutic Agents: Colony-stimulating factor 1 receptor (CSF1R) inhibitors (e.g., Pexidartinib, Cabiralizumab) are designed to deplete or reprogram TAMs, many of which are CD206+.
• Pertinent Information: High expression of CD206 is often linked to poor prognosis in various cancers, including NSCLC. GeneCards: MRC1

TREM2:

• Signaling Pathways: Triggering Receptor Expressed on Myeloid cells 2 signals through DAP12 and is involved in phagocytosis, lipid metabolism, and cell survival. It plays a role in sensing lipid debris.
• Role in TME: TREM2+ TAMs are frequently found in the TME and are associated with an immunosuppressive and pro-tumorigenic phenotype. They contribute to tumor growth, metastasis, and resistance to therapy, partly by clearing apoptotic cells and modulating lipid metabolism.
• Therapeutic Target: Modulating TREM2 signaling or targeting TREM2+ TAMs could reverse immune suppression and inhibit tumor progression.
• Relevant Therapeutic Agents: TREM2-blocking antibodies or small molecule inhibitors are currently under investigation in preclinical and early clinical stages for cancer.
• Pertinent Information: TREM2 expression often identifies a distinct subpopulation of TAMs, sometimes referred to as lipid-associated macrophages (LAMs), which are highly plastic and adapt to the tumor's metabolic landscape. GeneCards: TREM2

SPP1 (Osteopontin):

• Signaling Pathways: SPP1 is a secreted phosphoprotein that binds to integrins (e.g., $\alpha_v\beta_3, \alpha_v\beta_5, \alpha_4\beta_1$) and CD44, activating various intracellular signaling pathways (e.g., FAK, MAPK, PI3K/Akt) that promote cell adhesion, migration, and survival.
• Role in TME: Macrophage-derived SPP1 contributes to an immunosuppressive and pro-metastatic TME. It promotes TAM recruitment, M2 polarization, angiogenesis, and directly enhances tumor cell survival and invasion in various cancers, including NSCLC.
• Therapeutic Target: Inhibiting SPP1 or its receptors (integrins, CD44) could disrupt multiple pro-tumorigenic processes mediated by TAMs and tumor cells.
• Relevant Therapeutic Agents: Integrin inhibitors have been explored in cancer, and targeting SPP1 itself is an emerging area.
• Pertinent Information: SPP1 is often highly expressed in advanced cancers and correlates with poor prognosis. It can act as a bridge between inflammatory and malignant cells. GeneCards: SPP1

• Other notable Adeno-associated markers: *CD83* (costimulatory molecule), *IFNGR1* (IFN-gamma receptor), *SORT1* (protein trafficking).

Macrophage Phenotypes in Squamous Cell Carcinoma (Squamous)

Macrophages in Squamous Cell Carcinoma samples exhibit markers indicative of recruitment, metabolic reprogramming, and strong pro-inflammatory and pro-tumorigenic communication with tumor cells.

CXCR4:

• Signaling Pathways: CXCR4 is a G-protein coupled receptor that binds to its ligand CXCL12 (SDF-1). This interaction activates downstream pathways (e.g., PI3K/Akt, MAPK, Rho GTPases) involved in cell migration, adhesion, and survival.
• Role in TME: The CXCL12-CXCR4 axis is crucial for cell trafficking and is highly active in the TME. CXCR4 expression on TAMs mediates their recruitment to the tumor site in response to CXCL12 gradients produced by tumor or stromal cells. CXCR4+ TAMs promote angiogenesis, metastasis, and immune evasion, making them a significant contributor to tumor progression.
• Therapeutic Target: The CXCL12-CXCR4 axis is a well-established and druggable target in cancer.
• Relevant Therapeutic Agents: Plerixafor (a CXCR4 antagonist) is FDA-approved for hematopoietic stem cell mobilization and is under investigation in combination therapies for solid tumors. Several other CXCR4 inhibitors are in development.
• Pertinent Information: High CXCR4 expression is often correlated with increased metastatic potential and poor prognosis in many cancers, including NSCLC. GeneCards: CXCR4

SLC2A3 (GLUT3):

• Signaling Pathways: GLUT3 is a high-affinity glucose transporter responsible for glucose uptake. Its activity is regulated by metabolic demands and signaling pathways like PI3K/Akt.
• Role in TME: Macrophages in the TME, particularly those adopting an M2-like phenotype or engaging in immunosuppression, often undergo metabolic reprogramming, relying heavily on glycolysis. High GLUT3 expression indicates increased glucose uptake, fueling their energy demands and supporting pro-tumorigenic functions. This metabolic shift is crucial for TAM survival and activity in the nutrient-poor, acidic TME.
• Therapeutic Target: Targeting glucose metabolism in TAMs could selectively inhibit their pro-tumor functions without broadly affecting other immune cells.
• Relevant Therapeutic Agents: Glucose transport inhibitors or glycolysis inhibitors are being investigated as potential anti-cancer agents, either alone or in combination with other therapies.
• Pertinent Information: The metabolic state of TAMs significantly influences their polarization and function, with glycolytic phenotypes often associated with pro-tumor roles. GeneCards: SLC2A3

EREG (Epiregulin):

• Signaling Pathways: EREG is a ligand for the Epidermal Growth Factor Receptor (EGFR). Binding of EREG to EGFR activates downstream signaling cascades (e.g., Ras/MAPK, PI3K/Akt) that promote cell proliferation, survival, migration, and angiogenesis.
• Role in TME: Macrophage-derived EREG acts as a critical paracrine factor, stimulating EGFR on tumor cells or other stromal cells, thereby promoting tumor growth, invasiveness, and resistance to therapy. This highlights a direct pro-tumorigenic communication axis between TAMs and tumor cells in Squamous NSCLC.
• Therapeutic Target: Inhibiting EGFR signaling is a cornerstone of cancer therapy. Targeting the macrophage-derived EREG-EGFR axis could be a strategy to overcome resistance or enhance efficacy of existing EGFR-targeted therapies.
• Relevant Therapeutic Agents: EGFR tyrosine kinase inhibitors (e.g., Gefitinib, Erlotinib, Osimertinib) and anti-EGFR monoclonal antibodies (e.g., Cetuximab) are widely used in NSCLC.
• Pertinent Information: Macrophage-derived EREG has been implicated in mediating resistance to EGFR inhibitors in various cancers, including NSCLC. GeneCards: EREG

C5AR1 (CD88):

• Signaling Pathways: C5AR1 is a G-protein coupled receptor that binds to C5a, a potent anaphylatoxin derived from the complement system. Activation of C5AR1 triggers intracellular signaling pathways (e.g., MAPK, PI3K) leading to inflammation, immune cell recruitment, and activation.
• Role in TME: The complement system is often activated in the TME, releasing C5a. C5a-C5AR1 signaling on TAMs promotes their recruitment, M2-like polarization, and subsequent pro-tumorigenic functions, including immune suppression, angiogenesis, and tumor growth. Its high expression in Squamous macrophages suggests a strong complement-driven inflammatory and pro-tumor microenvironment.
• Therapeutic Target: Inhibiting the C5a-C5AR1 axis is a promising strategy to modulate inflammation and immune suppression in cancer.
• Relevant Therapeutic Agents: C5aR inhibitors (e.g., Avacopan) are in clinical use for autoimmune diseases and are being explored in cancer.
• Pertinent Information: Dysregulated complement activation is a key feature of chronic inflammation, which is closely linked to the development and progression of squamous cell carcinoma. GeneCards: C5AR1

TNFRSF1B (CD120b):

• Signaling Pathways: TNFRSF1B is a receptor for TNF-alpha. Signaling through TNFRSF1B can activate NF-$\kappa$B and other pathways, leading to cell survival, proliferation, inflammation, and immune modulation.
• Role in TME: TNF-alpha can exert both pro- and anti-tumor effects, but in contexts of chronic inflammation, it often promotes tumor progression. Macrophages expressing TNFRSF1B are responsive to TNF-alpha, contributing to the inflammatory and pro-tumorigenic milieu characteristic of squamous cell carcinoma, potentially driving tumor cell survival and growth.
• Therapeutic Target: Targeting TNF-alpha signaling is complex due to its context-dependent roles, but antagonists are widely used in inflammatory diseases. Its role in cancer can be inhibitory or activating depending on the specific cancer and TME.
• Relevant Therapeutic Agents: Anti-TNF-alpha biologicals (e.g., Infliximab, Etanercept) are used for inflammatory diseases. In cancer, their application is still being investigated and is context-dependent.
• Pertinent Information: Chronic inflammation, a hallmark of squamous cell carcinoma, often involves persistent TNF-alpha signaling. GeneCards: TNFRSF1B

• Other notable Squamous-associated markers: *TREM1* (pro-inflammatory myeloid cell activation), *ANPEP (CD13)* (angiogenesis, invasion), *SLC2A14* (glucose transporter), *ADGRE2* (adhesion GPCR).

Clinical or Translational Implications

The distinct surfaceome marker profiles of macrophages in Adenocarcinoma versus Squamous Cell Carcinoma have significant clinical and translational implications:

1. Biomarker Potential: These condition-specific markers could serve as diagnostic or prognostic biomarkers for NSCLC subtypes. For instance, high CD206 or TREM2 in Adeno or high CXCR4 or EREG in Squamous TAMs might correlate with disease progression or response to specific therapies.
2. Therapeutic Targeting:
   • Adenocarcinoma: The presence of MRC1 and TREM2 suggests that targeting M2-like polarization or immunosuppressive TAM functions (e.g., with CSF1R or TREM2 inhibitors) could be particularly effective. While MHC-II is present, strategies to overcome potential tolerogenic antigen presentation might be required.
   • Squamous Cell Carcinoma: The prominent expression of CXCR4, SLC2A3, EREG, and C5AR1 points to several druggable pathways. Targeting the CXCL12-CXCR4 axis (e.g., Plerixafor), disrupting macrophage glucose metabolism (e.g., GLUT inhibitors), blocking macrophage-derived EGFR ligands (e.g., EREG-targeting strategies), or inhibiting complement signaling (e.g., C5aR inhibitors) could selectively impact pro-tumorigenic macrophage functions in this subtype.
3. Combination Therapies: Understanding these distinct macrophage phenotypes can guide the development of rational combination therapies. For example, combining immune checkpoint inhibitors with agents that reprogram or deplete specific TAM subsets could enhance anti-tumor responses by overcoming macrophage-mediated immunosuppression.
4. Stratification of Patients: The identification of these distinct macrophage populations might allow for better patient stratification for targeted macrophage-modulating therapies based on the NSCLC subtype and the dominant macrophage phenotype present.
5. Understanding Disease Pathogenesis: These findings underscore the importance of macrophage heterogeneity in cancer. The differences observed suggest distinct mechanisms of immune evasion, metabolic adaptation, and tumor-stroma interaction in Adenocarcinoma versus Squamous Cell Carcinoma, contributing to our understanding of their unique pathogenesis.

17. Condition-Specific Surfaceome Markers in Fibroblasts within Lung Carcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies and visualizes surfaceome marker expression in Fibroblast cells, specifically comparing samples from Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous) subtypes of Non-Small Cell Lung Carcinoma (NSCLC). The goal is to highlight condition-specific fibroblast markers that could reveal differential biological roles in the tumor microenvironment (TME) and potentially serve as therapeutic targets. Markers were identified using a one-vs-rest comparison with criteria including a nz_pct_score >= 0.183 and nz_pct FC > 1.31 for both Adeno and Squamous conditions, focusing exclusively on surfaceome genes.

Visual Summary

The dot plot, titled "Fibroblast condition-specific markers," effectively illustrates the differential expression of surfaceome markers in Fibroblast cells across various patient samples, grouped by their respective lung cancer histology (Adeno vs. Squamous).

• Distinct Condition-Specific Profiles: The plot clearly shows two dominant clusters of marker expression, corresponding to the Adeno and Squamous conditions. Samples originating from Adeno (NSC016.T2, NSC016.T1, NSC010.T1, NSC016.T3, NSC019.T2, NSC010.T2) exhibit a common set of highly expressed markers (highlighted by the left red box), while Squamous samples (NSC004.T3, NSC004.T4, NSC004.T2, NSC004.T1) show a distinct and generally larger set of highly expressed markers (highlighted by the right red box).

Expression Levels and Cell Fraction:

• Adeno Markers: Genes such as *IL6ST*, *APLP2*, *PLXDC2*, *TFPI*, and *ENG* are predominantly expressed in Fibroblasts from Adeno samples. These markers show a high mean expression (darker red dots) and are prevalent in a large fraction of Fibroblast cells within these samples (larger dot sizes).
• Squamous Markers: A more extensive panel of markers, including *MMP14*, *CYSLTR1*, *FAP*, *DDR2*, *PTK7*, *ITGB5*, *LRRC15*, and *OSMR*, are highly expressed and widely detected in Fibroblasts from Squamous samples. These markers display strong expression and high cell fraction, indicating their widespread presence and activity in the Squamous-associated fibroblast population.

• Sample Heterogeneity: While clear condition-specific patterns emerge, there is also some heterogeneity within samples of the same condition. For instance, some Adeno samples show slightly lower expression or cell fraction for certain markers compared to others in the same group, and similarly for Squamous samples.
• Fraction of Fibroblast Cells: The bar on the right side indicates the overall fraction of Fibroblast cells in each sample, ranging from 33% to 84%. This context is important as high marker expression in samples with a lower total fibroblast fraction still signifies strong cell-type-specific activity.

Biological Interpretation

Fibroblasts in the tumor microenvironment (TME), often termed Cancer-Associated Fibroblasts (CAFs), are critical players in lung carcinoma progression. They are heterogeneous, with distinct subpopulations capable of influencing tumor growth, metastasis, angiogenesis, and immune evasion. The identification of condition-specific surfaceome markers suggests that CAFs adopt different functional states or originate from different lineages depending on the lung cancer subtype (Adeno vs. Squamous).

General Role of Fibroblasts (CAFs) in Lung Carcinoma TME

CAFs contribute to lung carcinoma by:

• Extracellular Matrix (ECM) Remodeling: Secreting and modifying ECM components, leading to desmoplasia (tissue stiffening), which can hinder drug delivery and promote tumor cell invasion.
• Growth Factor and Cytokine Production: Releasing factors like TGF-β, IL-6, and chemokines that stimulate tumor cell proliferation, survival, angiogenesis, and immunosuppression.
• Immune Modulation: Interacting with immune cells to suppress anti-tumor immune responses, e.g., by recruiting regulatory T cells or expressing immune checkpoint ligands.
• Angiogenesis Support: Promoting the formation of new blood vessels that supply the tumor.
• Therapy Resistance: Protecting tumor cells from chemotherapy and radiation.

Condition-Specific Marker Interpretation

A. Markers for Fibroblasts in Lung Adenocarcinoma (Adeno)

Fibroblasts in lung adenocarcinoma samples prominently express markers indicative of inflammatory and growth factor-driven activation, contributing to a pro-tumorigenic environment.

1. IL6ST (Interleukin 6 Signal Transducer / gp130)
   • Signaling Pathway: IL6ST is the common signal-transducing receptor subunit for various IL-6 family cytokines (e.g., IL-6, Oncostatin M). Its activation leads to downstream signaling via JAK-STAT (particularly STAT3), MAPK, and PI3K pathways.
   • Role in TME: In Adeno-CAFs, high IL6ST expression suggests active engagement in cytokine signaling, promoting CAF activation, ECM remodeling, and secretion of factors that support tumor growth and immune evasion, including angiogenesis and metastasis [PMID: 32626578].
   • Therapeutic Target Potential: Targeting IL6ST or its associated pathways (IL-6/JAK/STAT3) could disrupt key pro-inflammatory and pro-tumorigenic signaling in CAFs.
   • Relevant Therapeutic Agents: Monoclonal antibodies against IL-6 or IL-6R (e.g., Tocilizumab, Siltuximab) or JAK inhibitors (e.g., Ruxolitinib) are in clinical use or trials for various inflammatory and oncological indications.
2. ENG (Endoglin / CD105)
   • Signaling Pathway: Endoglin is a co-receptor for TGF-β receptors, modulating TGF-β signaling, which is central to cell proliferation, differentiation, and ECM production.
   • Role in TME: Elevated Endoglin on Adeno-CAFs (and endothelial cells) suggests their active role in TGF-β-mediated processes, contributing to CAF activation, ECM remodeling, immunosuppression, and promoting angiogenesis, all driving tumor progression [PMID: 26369527].
   • Therapeutic Target Potential: Given its involvement in TGF-β signaling and its expression on activated CAFs and endothelium, Endoglin is a promising target to inhibit CAF functions and angiogenesis.
   • Relevant Therapeutic Agents: Anti-Endoglin antibodies (e.g., TRC105 / MTRX105) are undergoing clinical investigation for various solid tumors.

B. Markers for Fibroblasts in Lung Squamous Cell Carcinoma (Squamous)

Fibroblasts in lung squamous cell carcinoma samples display a broader array of markers, indicative of extensive ECM remodeling, strong pro-invasive properties, and potentially unique immune suppressive functions.

1. FAP (Fibroblast Activation Protein alpha)
   • Signaling Pathway: FAP is a serine protease expressed by activated CAFs. It cleaves specific ECM proteins and peptides, contributing to ECM degradation and remodeling.
   • Role in TME: FAP is a canonical marker of CAFs and plays a significant pro-tumorigenic role by facilitating ECM degradation, promoting tumor growth, invasion, and immunosuppression. Its high expression is consistently associated with poor prognosis in various cancers, including NSCLC [PMID: 32265007].
   • Therapeutic Target Potential: FAP is considered one of the most promising and specific targets for CAF-directed therapies due to its restricted expression in healthy adult tissues and high expression in the tumor stroma.
   • Relevant Therapeutic Agents: FAP-targeted therapies, including FAP-targeted antibodies, immunotoxins, CAR-T cells, and radioligands (e.g., FAPI-46 for diagnostics and theranostics), are actively being developed and evaluated in clinical trials [PubMed Search: FAP inhibitor lung cancer].
2. MMP14 (Matrix Metalloproteinase 14 / MT1-MMP)
   • Signaling Pathway: MMP14 is a membrane-bound matrix metalloproteinase that degrades ECM components and activates other MMPs. Its activity is crucial for cell migration, invasion, and angiogenesis.
   • Role in TME: CAFs in Squamous tumors highly express MMP14, significantly contributing to ECM degradation and remodeling, which directly facilitates tumor cell invasion and metastasis. It also promotes angiogenesis and creates a pro-tumorigenic niche [PMID: 28499256].
   • Therapeutic Target Potential: Targeting MMP14 in CAFs could inhibit ECM remodeling, tumor invasion, and metastasis.
   • Relevant Therapeutic Agents: While broad-spectrum MMP inhibitors have faced challenges, specific MMP14 inhibitors and antibody-based strategies are being explored.
3. DDR2 (Discoidin Domain Receptor Tyrosine Kinase 2)
   • Signaling Pathway: DDR2 is a receptor tyrosine kinase activated by collagen, initiating downstream pathways like MAPK, PI3K/Akt, and JNK. It is critical for ECM remodeling, cell adhesion, migration, and proliferation.
   • Role in TME: In Squamous-CAFs, DDR2 senses and responds to the collagen-rich desmoplastic TME, promoting CAF survival, proliferation, and further ECM production and cross-linking, which increases tumor stiffness and can hinder therapy penetration. DDR2 plays a role in lung fibrosis and cancer progression [PMID: 29511195].
   • Therapeutic Target Potential: Inhibiting DDR2 in CAFs could disrupt their interaction with the collagen-rich ECM, reducing desmoplasia, inhibiting CAF activation, and potentially improving drug delivery to tumors.
   • Relevant Therapeutic Agents: Small molecule kinase inhibitors targeting DDR2 are in preclinical development.
4. LRRC15 (Leucine Rich Repeat Containing 15)
   • Signaling Pathway: LRRC15 is a transmembrane protein that has emerged as a specific marker for a subset of CAFs, implicated in collagen binding and potentially modulating receptor-ligand interactions. Its precise signaling pathways are still under investigation.
   • Role in TME: LRRC15+ CAFs are abundant in the desmoplastic stroma of many solid tumors, including lung cancer. They contribute to ECM remodeling and have been linked to immunosuppression and resistance to immunotherapy, possibly by forming a physical barrier to immune cells [PMID: 35027814].
   • Therapeutic Target Potential: Due to its high specificity for CAFs in the tumor stroma and its role in immune exclusion, LRRC15 is a highly promising target for CAF-directed therapies, particularly to enhance the efficacy of immunotherapies.
   • Relevant Therapeutic Agents: Anti-LRRC15 antibodies, such as the antibody-drug conjugate ABBV-085, are currently in clinical trials for various solid tumors [PubMed Search: ABBV-085 LRRC15].
5. PTK7 (Protein Tyrosine Kinase 7)
   • Signaling Pathway: PTK7 is an atypical receptor tyrosine kinase that modulates Wnt/PCP signaling and Rho GTPase pathways, regulating cell migration, polarity, and invasion.
   • Role in TME: In Squamous-CAFs, PTK7 expression could enhance their migratory and invasive properties, contributing to ECM remodeling and creating paths for tumor cell dissemination. It is often upregulated in various cancers [PMID: 25164478].
   • Therapeutic Target Potential: As a cell surface protein overexpressed in cancer, PTK7 is an attractive target for antibody-drug conjugates (ADCs).
   • Relevant Therapeutic Agents: ADCs targeting PTK7 (e.g., vorsetuzumab mafodotin) have been explored in clinical trials for certain malignancies.
6. LY6E (Lymphocyte Antigen 6 Family Member E)
   • Signaling Pathway: LY6E is a GPI-anchored protein implicated in cell proliferation, survival, and adhesion, potentially through lipid raft association or interactions with other membrane proteins.
   • Role in TME: In CAFs, LY6E may contribute to their activated state, promoting tumor cell survival, migration, and drug resistance. It has been reported as a prognostic marker in various cancers [PMID: 30739938].
   • Therapeutic Target Potential: As a cell surface protein, LY6E could be a target for antibody-based therapies or ADCs to selectively inhibit or eliminate pro-tumorigenic CAFs.
   • Relevant Therapeutic Agents: No specific LY6E-targeting agents are in clinical use for cancer.
7. ITGB5 (Integrin Beta 5)
   • Signaling Pathway: ITGB5 forms the alphaVbeta5 integrin, mediating cell-ECM interactions, activating FAK, Src, and MAPK pathways, and influencing cell adhesion, migration, proliferation, and survival.
   • Role in TME: AlphaVbeta5 integrin in Squamous-CAFs can facilitate their migration and invasion, contribute to ECM remodeling, and promote growth factor signaling (e.g., TGF-β activation), supporting angiogenesis and tumor metastasis [PMID: 29969966].
   • Therapeutic Target Potential: Targeting alphaVbeta5 integrin could disrupt critical CAF-ECM interactions and reduce their pro-tumorigenic activities.
   • Relevant Therapeutic Agents: Integrin inhibitors (e.g., cilengitide, a pan-alphaV integrin inhibitor) have been investigated in clinical trials.

Clinical or Translational Implications

The identification of distinct surfaceome markers on Fibroblasts in Adenocarcinoma versus Squamous Cell Carcinoma of the lung has several important clinical and translational implications:

• Subtype-Specific CAF Targeting: The observed differences suggest that therapeutic strategies targeting CAFs may need to be tailored to the specific NSCLC subtype. Agents effective against Adeno-associated CAFs might not be equally effective against Squamous-associated CAFs, and vice versa.
• Biomarker Development: These subtype-specific surface markers could serve as diagnostic or prognostic biomarkers. For instance, high expression of FAP or LRRC15 might indicate a more aggressive Squamous tumor with a highly desmoplastic and immunosuppressive TME.
• Novel Therapeutic Targets: Markers like FAP, MMP14, DDR2, LRRC15 (for Squamous) and IL6ST, ENG (for Adeno) are attractive candidates for developing CAF-directed therapies. Targeting these surface proteins could inhibit CAF activation, ECM remodeling, angiogenesis, and immune suppression, thereby reducing tumor growth and metastasis.
  • Precision Medicine: This approach aligns with precision oncology, allowing for the development of highly specific agents (e.g., ADCs, CAR-T cells, small molecule inhibitors) that target unique CAF populations while sparing healthy cells.
  • Combination Therapies: CAF-targeting therapies could be combined with standard chemotherapy, radiation, or immunotherapy to overcome resistance mechanisms driven by the TME. For example, blocking LRRC15 could enhance T-cell infiltration and improve responses to checkpoint inhibitors in Squamous NSCLC.
• Understanding Disease Heterogeneity: The distinct marker profiles underscore the biological heterogeneity of NSCLC subtypes and the nuanced roles played by stromal cells in each. Further investigation into the upstream regulators and downstream effects of these markers could reveal critical differences in disease pathology and progression.

18. T cell CD4+ Condition-Specific Marker Expression in Squamous Lung Carcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies and visualizes surfaceome marker expression in CD4+ T cells specific to the Squamous lung carcinoma condition, as compared to other conditions (likely Adeno, given the dataset context). The dot plot displays the mean expression level (color intensity) and the fraction of cells expressing the marker (dot size) for selected markers across various patient samples. This provides insight into the phenotypic characteristics and potential functional roles of CD4+ T cells within the Squamous tumor microenvironment (TME).

Visual Summary

The dot plot visualizes the expression of 13 surfaceome markers in CD4+ T cells from 26 individual samples identified as belonging to the Squamous condition.

• Expression Patterns: There is considerable heterogeneity in marker expression across samples. Some samples, notably NSC037.T1, NSC004.T3, NSC004.T4, NSC004.T1, NSC004.T2, NSC009.T1, and NSC009.T2, show high mean expression (darker red) and a high fraction of expressing cells (larger dot size) for multiple markers, indicating a robust presence of these T cell phenotypes. Other samples exhibit more sparse or lower expression.
• Marker Prominence: Markers such as SLC2A3, CD44, IL7R, CYSLTR1, CD3D, CD2, TNFRSF1B, ITGB1, and ITGB2 generally show higher prevalence and expression across more samples compared to OR4D9, ATP1A1, and SLC2A14, which appear more restricted or lower in some samples.
• Sample-Specific Signals: The distinct clustering of high expression for many markers within specific samples (e.g., NSC004 series and NSC009 series) suggests sample-specific immunological profiles or different stages of immune infiltration and activation within the Squamous TME.
• Cell Counts: The bar chart on the right indicates the total number of CD4+ T cells sampled per group, ranging from 36 to 854 cells, providing context for the observed expression levels.

Biological Interpretation

CD4+ T cells play diverse roles in the lung tumor microenvironment, including helper functions (e.g., Th1, Th2, Th17) and regulatory functions (Tregs). The markers identified here as enriched in the Squamous subtype highlight specific biological processes and potential phenotypes within this population.

T Cell Identity and Co-stimulation

• CD3D (CD3 delta chain): An integral component of the T cell receptor (TCR) complex, essential for signal transduction upon antigen recognition. Its consistent expression confirms the T cell identity of these cells and suggests an intact TCR machinery capable of antigen-specific responses.
  • Role in TME: Fundamental for T cell activation and effector function. Maintained expression indicates functional T cells that can respond to antigens presented by MHC class II.
  • Therapeutic Target Potential: CD3 is a common target for T-cell redirecting therapies (e.g., bispecific antibodies) that aim to link T cells to tumor cells. However, direct modulation of CD3D itself is not typically sought, as it's critical for T cell function.
  • Relevant Therapeutic Agents: Blinatumomab (CD19/CD3 bispecific antibody).
  • References: UniProt CD3D
• CD2: A T cell surface glycoprotein involved in adhesion and co-stimulation, interacting with CD58 (LFA-3) on antigen-presenting cells. It enhances T cell activation, proliferation, and cytokine production.
  • Role in TME: Facilitates stable immune synapse formation and provides crucial co-stimulatory signals that augment T cell responses. Its presence supports active T cell engagement within the TME.
  • Therapeutic Target Potential: While CD2 is a pan-T cell marker, modulating its activity could impact T cell function. Some antibodies against CD2 have been investigated for immunosuppressive purposes in autoimmune diseases or to target T cell malignancies.
  • References: GeneCards CD2

Adhesion and Migration

• CD44: A cell adhesion molecule and a receptor for hyaluronic acid, involved in cell adhesion, migration, lymphocyte activation, survival, and proliferation.
  • Role in TME: Crucial for T cell trafficking to and retention within the TME, as well as interactions with stromal cells and antigen-presenting cells. High CD44 expression is often associated with memory or activated T cells and can promote T cell infiltration.
  • Therapeutic Target Potential: CD44 is explored as a therapeutic target in cancer, including for cancer stem cells. Modulating CD44 on T cells could influence their homing and persistence in the TME, potentially enhancing anti-tumor immunity.
  • References: GeneCards CD44
• GPR183 (EBI2): A G protein-coupled receptor that binds oxysterols, playing a key role in guiding immune cell migration, particularly for B cells and T cells (e.g., T follicular helper cells) to specific lymphoid compartments.
  • Role in TME: Its expression on CD4+ T cells suggests a role in their migration and spatial positioning within the tumor or draining lymph nodes, influencing the structure and function of immune infiltrates.
  • Therapeutic Target Potential: Modulating GPR183 activity could control immune cell trafficking, potentially guiding T cells more effectively into tumor sites or away from immunosuppressive niches.
  • References: PubMed search for GPR183 T cell migration cancer
• ITGB1 (Integrin Beta-1, CD29): A subunit of various integrin heterodimers, mediating cell adhesion to extracellular matrix components (e.g., fibronectin, collagen) and other cells.
  • Role in TME: Essential for T cell adhesion, migration, and tissue retention. High expression suggests active interaction with the tumor stroma, influencing T cell infiltration and survival within the TME.
  • Therapeutic Target Potential: Integrins are targets in various diseases. Modulating ITGB1 could impact T cell trafficking and retention, which might be exploited to enhance or control immune responses.
  • References: UniProt ITGB1
• ITGB2 (Integrin Beta-2, CD18): A leukocyte-specific integrin beta chain, forming heterodimers (e.g., LFA-1 with CD11a) crucial for leukocyte adhesion to endothelial cells and for immune synapse formation.
  • Role in TME: Vital for T cell extravasation into tissues, interactions with antigen-presenting cells, and target cell killing. High expression signifies active T cell migration and functional engagement with other cells in the TME.
  • Therapeutic Target Potential: ITGB2 is a pan-leukocyte marker. Clinically, LFA-1 inhibitors (containing ITGB2) have been used to reduce inflammation. In cancer, strategies to enhance T cell infiltration might consider modulating ITGB2, though systemic effects require careful consideration.
  • References: GeneCards ITGB2

Metabolic Regulation

• SLC2A3 (GLUT3) & SLC2A14 (GLUT14): Glucose transporters. GLUT3 is a high-affinity transporter, often associated with highly metabolically active cells. GLUT14 is another, less characterized, glucose transporter.
  • Role in TME: Activated T cells heavily rely on glycolysis. High expression of these glucose transporters indicates significant metabolic demand, characteristic of active or proliferating T cells, possibly adapting to nutrient competition with tumor cells in the glucose-poor TME.
  • Therapeutic Target Potential: Targeting glucose metabolism is a strategy in cancer. Understanding T cell-specific glucose transporter expression could inform combination therapies, where modulating tumor glucose uptake might indirectly benefit T cell function.
  • References: PubMed search for SLC2A3 T cell metabolism
• ATP1A1 (Na+/K+-ATPase Subunit Alpha-1): A ubiquitously expressed ion pump crucial for maintaining cell membrane potential and homeostasis. It also serves as a receptor for cardiotonic steroids.
  • Role in TME: Involved in cellular energy balance and signal transduction in immune cells, influencing activation and proliferation. Its specific upregulation on CD4+ T cells in the TME could suggest altered metabolic demands or unique signaling engagement in response to environmental cues.
  • Therapeutic Target Potential: Cardiotonic steroids, which target ATP1A1, have shown immunomodulatory and anti-cancer properties. Careful targeting could influence T cell function in the TME, but its ubiquitous expression necessitates strategies for cell-specific delivery.
  • References: GeneCards ATP1A1

Cytokine and Chemoattractant Receptors

• IL7R (CD127): The receptor for Interleukin-7, critical for the survival and homeostatic proliferation of naive and memory T cells, signaling through the JAK-STAT pathway.
  • Role in TME: Its expression on CD4+ T cells typically correlates with less exhausted, potentially more functional T cells, or central memory T cells, essential for sustained anti-tumor immunity. However, it can also be expressed on Tregs, making its precise role complex without additional markers.
  • Therapeutic Target Potential: IL-7 agonists have been explored to boost T cell immunity and enhance CAR-T cell persistence, particularly in lymphopenic states or to maintain T cell populations.
  • References: UniProt IL7R
• TNFRSF1B (TNF-R2): Tumor Necrosis Factor Receptor Superfamily Member 1B, also known as TNF-R2, mediates cell survival, proliferation, and inflammation via TNF signaling.
  • Role in TME: Often expressed on activated T cells, including effector T cells and regulatory T cells (Tregs). On Tregs, TNF-R2 can enhance their survival and suppressive function, contributing to immunosuppression. On effector T cells, it might promote their survival in an inflammatory context. Its presence suggests active TNF-alpha signaling in these CD4+ T cells in Squamous NSCLC.
  • Therapeutic Target Potential: TNF-R2 is an attractive target in cancer due to its pro-tumorigenic inflammatory role and its contribution to Treg survival. Blocking TNF-R2 could potentially reduce Treg-mediated immunosuppression and enhance anti-tumor immunity, especially in combination with other immunotherapies.
  • References: GeneCards TNFRSF1B
• CYSLTR1 (CysLT1 receptor): A G protein-coupled receptor that binds cysteinyl leukotrienes (LTC4, LTD4, LTE4), potent lipid mediators involved in inflammation.
  • Role in TME: Its expression on CD4+ T cells suggests they are responsive to cysteinyl leukotrienes, which are often elevated in inflammatory conditions and cancer. This could modulate T cell migration, activation, or differentiation in response to TME inflammatory signals, potentially contributing to either pro- or anti-tumor immunity.
  • Therapeutic Target Potential: CYSLTR1 antagonists are clinically available for asthma and could be repurposed or investigated in cancer to modulate TME inflammation and immune responses by targeting T cells.
  • References: PubMed search for CYSLTR1 T cell cancer

Novel/Ectopic Markers

• OR4D9 (Olfactory Receptor Family 4 Subfamily D Member 9): An olfactory receptor typically involved in olfaction, but ectopically expressed in non-olfactory tissues, including immune cells.
  • Role in TME: The specific function of OR4D9 on T cells in the TME is largely unknown. Ectopic olfactory receptors can influence cell proliferation, migration, and differentiation by sensing endogenous ligands. Its presence on CD4+ T cells suggests a potential novel sensing mechanism or an uncharacterized immunomodulatory role in the Squamous TME.
  • Therapeutic Target Potential: Currently, there are no established therapeutic implications for OR4D9 on T cells in cancer. Further research is needed to elucidate its functional relevance before considering it as a target.
  • References: PubMed search for OR4D9 immune cells cancer

Clinical or Translational Implications

The identified condition-specific markers for CD4+ T cells in Squamous lung carcinoma offer several potential clinical and translational implications:

• Biomarker Discovery: The specific expression profiles of these markers could serve as diagnostic or prognostic biomarkers for Squamous NSCLC, indicating specific immune cell states or activity within the TME. For instance, a high prevalence of T cells expressing particular metabolic or migratory markers might correlate with patient response to therapy or disease progression.
• Personalized Immunotherapy: The observed heterogeneity in marker expression across patient samples highlights the need for personalized approaches. Characterizing the dominant CD4+ T cell phenotypes in a patient's tumor could guide the selection of appropriate immunotherapies, potentially enhancing treatment efficacy.

Therapeutic Target Identification:

• Modulating T cell Metabolism: Targeting glucose transporters like SLC2A3 or SLC2A14 could be explored in combination with tumor-centric metabolic inhibitors. By alleviating glucose competition, T cell function might be preserved or enhanced, leading to better anti-tumor responses.
• Enhancing T cell Trafficking: Modulating integrins (ITGB1, ITGB2) or GPR183 could be a strategy to improve T cell infiltration into the tumor core or to prevent their sequestration in immunosuppressive niches, thereby boosting anti-tumor immunity.
• Targeting Inflammatory/Immunosuppressive Pathways: CYSLTR1 antagonists (e.g., montelukast) are existing drugs that could be repurposed to modulate TME inflammation, potentially reducing pro-tumorigenic effects or immunosuppression. Similarly, targeting TNFRSF1B could specifically alleviate Treg-mediated suppression, especially if highly expressed on intratumoral Tregs.
• Novel Therapeutic Avenues: While the role of OR4D9 and ATP1A1 in T cells is less understood in cancer, their specific enrichment warrants further investigation. If functional roles are confirmed, they could represent novel and highly specific targets for immunomodulation.

• Phenotypic Characterization of TME: These markers contribute to a more comprehensive understanding of the CD4+ T cell landscape in Squamous NSCLC, which is critical for deciphering disease mechanisms and for developing effective immunotherapeutic strategies. Further studies integrating these surface markers with intracellular markers and functional assays would be beneficial to precisely delineate the helper vs. regulatory phenotypes.

19. Differential Cell Cycle Gene Expression Prevalence in Lung Epithelial Cells Across NSCLC Subtypes

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the prevalence of cell cycle pathway gene expression in Lung Epithelial cells, comparing two major subtypes of Non-Small Cell Lung Carcinoma (NSCLC): Squamous Cell Carcinoma (Squamous) and Adenocarcinoma (Adeno). The visualization displays the "expressing cell fraction (sample)" for a selection of cell cycle-related genes, indicating the proportion of Lung Epithelial cells within each patient sample that express a given gene. This provides insight into the heterogeneity and active biological processes within the tumor-originating cell compartment of these lung cancer subtypes.

Visual Summary

The box plots illustrate significant differences in the expressing cell fraction for several cell cycle-related genes between Squamous and Adeno conditions in Lung Epithelial cells. Key patterns observed include:

• Genes with Higher Expressing Cell Fraction in Squamous Carcinoma: A prominent group of genes, including core DNA replication initiation factors (MCM2, MCM4, MCM6, MCM7, ORC6), the oncogene MYC, the proliferation marker PCNA, E2F dimerization partners (TFDP1, TFDP2), and components of the Anaphase-Promoting Complex (ANAPC7, CDC27), show significantly higher expressing cell fractions in Squamous samples compared to Adeno samples. Other genes in this group include the cell cycle driver CDK6, cyclin CCND2, histone deacetylases HDAC1 and HDAC2, GSK3B, and YWHAQ.
• Genes with Higher Expressing Cell Fraction in Adenocarcinoma: Conversely, genes like GADD45B (involved in DNA damage response and cell cycle arrest), cyclins CCND1 and CCND3, the cell cycle kinase CDK4, and PTTG1 (securin, involved in mitosis) demonstrate significantly higher expressing cell fractions in Adeno samples. YWHAB also falls into this category.

The p-values for these comparisons are highly significant (e.g., MCM7 p=1.17e-07, GADD45B p=1.43e-06, MYC p=0.000189), indicating robust statistical differences between the two conditions.

Biological Interpretation

The observed patterns reveal distinct proliferative characteristics and cell cycle regulatory landscapes in the Lung Epithelial cells of Squamous and Adenocarcinoma:

1. Enhanced Proliferative Signature in Squamous Carcinoma: The significantly higher prevalence of expression for MCM proteins (MCM2, MCM4, MCM6, MCM7), MYC, PCNA, ORC6, and E2F partners (TFDP1, TFDP2) in Squamous samples suggests a greater proportion of Lung Epithelial cells in these tumors are actively engaged in DNA replication and overall cell cycle progression. MCM proteins are integral to DNA replication licensing and initiation, and their increased expression is a hallmark of proliferation. MYC is a powerful oncogene driving cell growth and division 1, while PCNA is a widely recognized marker for cell proliferation 2. This collectively points towards a higher fraction of highly proliferative cells within Squamous Lung Epithelial tumors.
2. Differential G1 Phase Regulation: Both subtypes show upregulation of different G1-phase cyclins and CDKs. Squamous cells exhibit a higher fraction expressing CCND2 and CDK6, while Adeno cells show a higher fraction expressing CCND1 and CDK4. This indicates that while both subtypes are proliferative, they may leverage distinct cyclin-CDK partnerships to drive progression through the G1 phase of the cell cycle. Dysregulation of these pathways is common in cancer, and their differential patterns here could reflect specific oncogenic drivers or vulnerabilities in each NSCLC subtype 3.
3. Elevated DNA Damage/Stress Response in Adenocarcinoma: The significantly higher expressing cell fraction of GADD45B in Adeno Lung Epithelial cells is notable. GADD45B is a stress-inducible protein involved in DNA repair, cell cycle arrest, and apoptosis in response to DNA damage 4. Its higher prevalence in Adeno cells could suggest that these cells are experiencing more pronounced DNA damage or cellular stress, leading to a greater activation of cell cycle checkpoints or DNA repair mechanisms compared to Squamous cells.
4. Role of Cell Cycle Checkpoint and Mitotic Regulators: The higher prevalence of ANAPC7 and CDC27 (components of the Anaphase-Promoting Complex/Cyclosome, crucial for M-phase progression) in Squamous cells further supports heightened mitotic activity. Conversely, PTTG1 (securin), involved in sister chromatid separation during anaphase, shows higher prevalence in Adeno cells, suggesting potentially different mechanisms controlling mitotic progression or checkpoint evasion in this subtype 5.
5. Differential Epigenetic and Regulatory Protein Involvement: HDAC1 and HDAC2, which regulate gene expression through histone deacetylation and are often involved in cancer progression, show higher prevalence in Squamous cells. This suggests distinct epigenetic regulatory programs contributing to the Squamous phenotype. Additionally, the differential expression of YWHAB (higher in Adeno) and YWHAQ (higher in Squamous) from the 14-3-3 protein family, known regulators of numerous cellular processes including cell cycle and signaling, points to broader shifts in regulatory networks across the two subtypes.

Clinical or Translational Implications

These findings highlight fundamental differences in the proliferative dynamics and cell cycle control mechanisms between Squamous Cell Carcinoma and Adenocarcinoma at the single-cell level within the tumor compartment.

• Subtype-Specific Therapeutic Vulnerabilities: The distinct patterns of cell cycle gene prevalence suggest that therapeutic strategies targeting cell cycle components might need to be tailored to NSCLC subtypes. For instance, drugs targeting specific CDK/cyclin axes (e.g., CDK4/6 inhibitors) or DNA replication machinery (e.g., MCM inhibitors) could have differential efficacy or be more relevant for one subtype over another. The robust proliferation signature in Squamous cells suggests they might be more sensitive to therapies directly impacting DNA synthesis or mitotic progression.
• Biomarker Potential: Genes like MCM2, MCM7, MYC, PCNA for Squamous, and GADD45B, CCND1, CDK4 for Adeno, could serve as subtype-specific biomarkers of tumor activity or response to therapy, especially in the context of single-cell analysis which can capture intratumoral heterogeneity.
• Understanding Pathogenesis: The observed differences in DNA damage response elements (GADD45B in Adeno) and core proliferative machinery (MCMs, MYC, PCNA in Squamous) could reflect distinct etiological factors, oncogenic drivers, and mechanisms of tumor evolution for each NSCLC subtype. This can inform future research into subtype-specific cancer vulnerabilities.

20. Gene Ontology (GSA) Analysis for Lung Epithelial Cells: Condition and Ploidy-Specific Upregulated Pathways

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates differentially enriched Gene Ontology (GO) terms in Lung Epithelial cells across different conditions and ploidy states. Specifically, Gene Set Analysis (GSA) was performed using upregulated differentially expressed genes (DEGs) for Lung Epithelial cells, comparing:

1. Adenocarcinoma (Adeno) cells against all other cells (non-Adeno, including Squamous and non-epithelial cells).
2. Diploid cells against all other cells (including Aneuploid and non-epithelial cells).
3. Squamous cell carcinoma (Squamous) cells against all other cells (non-Squamous, including Adeno and non-epithelial cells).

The results are presented as bar plots, illustrating the top enriched GO terms ranked by statistical significance (-log(p-val) and -log(q-val)). The "Lung Epithelial cell" type is identified as the tumor-origin cell type within this dataset, making these findings particularly relevant for understanding lung carcinoma biology.

Visual Summary

The provided bar plots show the top enriched GO terms, ordered by their statistical significance (-log(p-val) and -log(q-val)), for upregulated genes in Lung Epithelial cells under three distinct comparisons: Adeno_vs_others, Diploid_vs_others, and Squamous_vs_others.

• GSA for Lung Epithelial cell: Adeno_vs_others: This plot displays a robust enrichment of numerous pathways. The most significant terms (highest -log(p-val) and -log(q-val)) include those related to neurodegeneration (e.g., "Huntington disease," "Parkinson disease," "Alzheimer disease," "Amyotrophic lateral sclerosis"), fundamental cellular processes (e.g., "Protein processing in endoplasmic reticulum," "Endocytosis," "Spliceosome," "Ubiquitin mediated proteolysis," "Oxidative phosphorylation," "RNA transport," "Regulation of actin cytoskeleton"), various infectious diseases, and directly cancer-associated pathways ("Pathways in cancer," "MAPK signaling pathway," "Apoptosis," "Tight junction," "Focal adhesion").
• GSA for Lung Epithelial cell: Diploid_vs_others: This plot shows a distinctly different and generally less significant enrichment profile compared to the Adeno and Squamous plots (lower -log(p-val) and -log(q-val) scores). The enriched terms are predominantly associated with immune responses, infection, and autoimmune conditions (e.g., "Phagosome," "Viral myocarditis," "Staphylococcus aureus infection," "Intestinal immune network for IgA production," "Antigen processing and presentation," "Th1 and Th2 cell differentiation," "Complement and coagulation cascades," "Epstein-Barr virus infection"). "Cell adhesion molecules" and "Lysosome" are also present.
• GSA for Lung Epithelial cell: Squamous_vs_others: Similar to the Adeno plot, this plot indicates highly significant enrichment for a wide array of pathways. Prominent terms include core cellular machinery components (e.g., "Ribosome," "Spliceosome," "RNA transport," "Protein processing in endoplasmic reticulum"), neurodegenerative pathways, general cancer pathways ("Pathways in cancer," "Cell cycle," "Proteoglycans in cancer," "HIF-1 signaling pathway," "mTOR signaling pathway," "PI3K-Akt signaling pathway," "FoxO signaling pathway"), and several infectious disease-related terms. A notable number of specific cancer types are listed within the enriched terms (e.g., "Hepatocellular carcinoma," "Colorectal cancer," "Renal cell carcinoma," "Small cell lung cancer," "Pancreatic cancer," "Breast cancer," "Chronic myeloid leukemia").

Biological Interpretation

The GSA results provide crucial insights into the distinct biological programs active in Lung Epithelial cells based on their histological subtype (Adeno vs. Squamous) and ploidy status.

Common Oncogenic Mechanisms in Lung Adenocarcinoma and Squamous Cell Carcinoma

Both Adeno and Squamous Lung Epithelial cells, when compared to other cell types, exhibit a strong upregulation of pathways critical for tumor growth, proliferation, and survival. This includes:

• Core Cancer Pathways: Direct hits like "Pathways in cancer," "Cell cycle," "Apoptosis" (often dysregulated in cancer), and key signaling cascades such as "MAPK signaling pathway," "HIF-1 signaling pathway," "PI3K-Akt signaling pathway," "mTOR signaling pathway," and "FoxO signaling pathway" are highly enriched. These pathways are fundamental drivers of tumorigenesis and progression in various cancers, including NSCLC. PubMed search: NSCLC MAPK PI3K mTOR HIF1 pathway
• Metabolic and Cellular Stress Responses: "Oxidative phosphorylation," "Protein processing in endoplasmic reticulum," "Ubiquitin mediated proteolysis," and "RNA transport" are essential cellular processes often reprogrammed in cancer cells to support increased metabolic demands and protein synthesis necessary for rapid growth and adaptation to stress.
• Cell Adhesion and Motility: "Tight junction" and "Focal adhesion" (particularly prominent in Adeno) are crucial for cell-cell contact, cell-extracellular matrix interactions, and cell migration, which are intimately involved in tumor invasion and metastasis. PubMed search: tight junction focal adhesion cancer metastasis
• Shared Disease Associations: The recurring enrichment of neurodegenerative diseases (e.g., "Huntington disease," "Parkinson disease") and various infectious disease pathways suggests shared underlying cellular stress responses, protein homeostasis disruptions, or inflammatory states that are common across diverse pathologies, rather than direct neurological involvement in lung cancer itself. Viral carcinogenesis (e.g., HPV, Epstein-Barr) is directly implicated in some cancer types and may represent relevant mechanisms or shared host responses.

Distinguishing Features between Adenocarcinoma and Squamous Cell Carcinoma

While sharing many common cancer pathways, there are subtle differences in their top enriched terms:

• Adeno-specific inclinations: The prominence of "Tight junction" and "Focal adhesion" might point to distinct cell-cell communication and migratory properties in adenocarcinoma, which often presents with different invasive patterns compared to squamous cell carcinoma.
• Squamous-specific inclinations: The very top enrichment for "Ribosome" and "Spliceosome" in Squamous cells could suggest a particularly heightened emphasis on protein synthesis machinery and RNA processing, which are critical for the aggressive proliferation characteristic of squamous cell carcinomas. The high ranking of "Proteoglycans in cancer" is also a notable finding for Squamous. PubMed search: proteoglycans squamous cell carcinoma

Diploid Lung Epithelial Cells Exhibit an Immune-Centric Profile

In stark contrast to the tumor subtypes, diploid Lung Epithelial cells show a highly significant enrichment for pathways related to immune response, host defense, and inflammation. This suggests:

• Normal-like or Reactive Phenotype: These diploid cells are likely representative of non-malignant or normal-like epithelial cells, which retain active immune surveillance or inflammatory responses. The immune pathways, such as "Phagosome," "Antigen processing and presentation," and various T cell differentiation pathways (Th1, Th2, Th17), are characteristic of immune cells and/or epithelial cells actively interacting with the immune system or responding to pathogens.
• Immune Microenvironment Interaction: The enrichment could also reflect diploid epithelial cells in proximity to immune infiltrates, actively participating in or responding to local immune stimuli, distinguishing them from transformed, immune-evading tumor cells.
• Lower Oncogenic Activity: The significantly lower -log(p-val) and -log(q-val) values compared to the tumor subtypes further underscore that the core oncogenic programs are less pronounced or absent in the diploid population, which is expected for non-malignant cells.

Clinical or Translational Implications

These findings have several potential clinical and translational implications for Lung Carcinoma:

• Targeted Therapies: The identification of highly enriched specific signaling pathways (e.g., PI3K-Akt, mTOR, MAPK, HIF-1) in both Adeno and Squamous Lung Epithelial cells reinforces the rationale for targeting these pathways in NSCLC. Differentiating between the two subtypes based on these specific pathway activations could inform more precise therapeutic strategies.
• Biomarker Discovery: Pathways like "Protein processing in endoplasmic reticulum," "Oxidative phosphorylation," "Tight junction," and "Focal adhesion" represent cellular vulnerabilities that could be explored for novel therapeutic targets or as biomarkers for disease progression and response to therapy. The distinct emphasis on ribosome/spliceosome in Squamous and cell adhesion in Adeno could lead to subtype-specific biomarkers.
• Immune Microenvironment: The strong immune signature in diploid epithelial cells highlights the importance of distinguishing between tumor and non-tumor epithelial components when studying the tumor immune microenvironment. It also suggests that modulating the immune responses within the non-malignant epithelial compartment could be relevant for cancer prevention or immunotherapy efficacy.
• Repurposing Drugs: The unexpected enrichment of neurodegenerative disease pathways might indirectly suggest shared underlying cellular stress mechanisms. While not directly implying neurological disease, investigating these common stress responses could potentially reveal novel targets or opportunities for repurposing drugs that modulate protein homeostasis or mitochondrial function.
• Understanding Tumor Heterogeneity: The distinct GSA profiles between Adeno, Squamous, and Diploid Lung Epithelial cells underscore the considerable biological heterogeneity within NSCLC. A comprehensive understanding of these subtype- and ploidy-specific pathway activations is crucial for developing personalized medicine approaches.

21. Gene Set Enrichment Analysis Reveals Distinct Pathway Activities Across Lung Carcinoma Subtypes and Cell Types

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results visualized as a dot plot. The GSEA was performed for various minor cell types, comparing gene expression profiles in the "Adeno" condition versus other conditions ("Adeno_vs_others") and "Squamous" condition versus other conditions ("Squamous_vs_others"). The plot illustrates the normalized enrichment score (NES) for 120 selected pathways, with dot size representing statistical significance (-log(p-value)) and color indicating the direction and strength of enrichment (red for positive NES, blue for negative NES). This provides insights into the differential biological processes active within specific cell populations in the context of Lung Adenocarcinoma and Squamous Cell Carcinoma.

Visual Summary

The dot plot reveals widespread differential pathway enrichment across the analyzed cell types and conditions. Several key observations include:

• Distinct Patterns for Adeno vs. Squamous: There are noticeable differences in enriched pathways between the "Adeno_vs_others" and "Squamous_vs_others" comparisons, even within the same cell type, suggesting subtype-specific biological alterations.
• Cell Type-Specific Enrichment: Certain pathways show enrichment predominantly in specific cell types. For example, immune-related pathways are prominent in T cells, Macrophages, and Dendritic cells, while structural and metabolic pathways are enriched in Lung Epithelial, Fibroblast, and Endothelial cells.
• Highly Enriched Pathways: Many large, intensely colored dots (indicating high significance and strong enrichment/depletion) are observed, particularly for pathways related to metabolism, cell proliferation, signaling cascades, and immune responses.
• General Downregulation of Ribosomal Activity: "Ribosome" pathway consistently shows negative enrichment (blue dots) across many cell types in both Adeno and Squamous conditions, suggesting a relative decrease in ribosomal biogenesis or general protein synthesis compared to the "others" group.
• Prominent Cancer-Associated Pathways: Pathways such as "Pathways in cancer", "PI3K-Akt signaling pathway", "HIF-1 signaling pathway", "VEGF signaling pathway", and "Cell cycle" are frequently enriched, highlighting their crucial roles in lung carcinoma.

Biological Interpretation

The GSEA results provide a detailed molecular landscape differentiating lung adenocarcinoma (Adeno) and squamous cell carcinoma (Squamous) across the tumor microenvironment.

Lung Epithelial Cells (Tumor Cells)

As the primary tumor cells, Lung Epithelial cells exhibit distinct pathway activities between subtypes:

• Adeno-associated Epithelial Cells: Show significant positive enrichment for "Oxidative phosphorylation", "HIF-1 signaling pathway", "PI3K-Akt signaling pathway", and "Proteoglycans in cancer". This suggests that Adenocarcinoma epithelial cells are characterized by altered cellular metabolism (aerobic glycolysis and mitochondrial respiration for rapid growth), increased cellular survival, proliferation, and invasion via receptor tyrosine kinase and HIF-1 activation, and significant extracellular matrix remodeling.
  • *Reference for HIF-1 in cancer metabolism:* PubMed Search: HIF-1 cancer metabolism
  • *Reference for PI3K-Akt signaling in NSCLC:* GeneCards: PI3K/AKT Signaling Pathway
• Squamous-associated Epithelial Cells: Display strong positive enrichment for "Cell cycle", "DNA replication", "Base excision repair", "Pyrimidine metabolism", and "Purine metabolism". This indicates a high proliferative capacity and active DNA damage response mechanisms, essential for coping with replication stress and genomic instability characteristic of squamous cell carcinoma.
  • *Reference for DNA repair pathways in cancer:* PubMed Search: DNA repair pathways cancer

Immune Cells (T cells, Macrophages, Dendritic cells)

The immune microenvironment also shows subtype-specific adaptations:

• T cells (CD4+ and CD8+): In Adeno, both CD4+ and CD8+ T cells show enrichment for immune activation and migratory pathways, including "Th1 and Th2 cell differentiation", "Th17 cell differentiation", "Cytosolic DNA-sensing pathway", and "Leukocyte transendothelial migration". This suggests an active but potentially dysfunctional adaptive immune response or immune cell trafficking within the adenocarcinoma microenvironment. In contrast, Squamous-associated T cells (CD8+) show negative enrichment for "T cell receptor signaling pathway", which could imply T cell exhaustion or an anergic state.

Macrophages

• Adeno-associated Macrophages: Show enrichment for "Phagosome", "Antigen processing and presentation", and "Fc gamma R-mediated phagocytosis", indicating an active role in antigen uptake and presentation, potentially contributing to either anti-tumor immunity or immune suppression depending on their polarization.
• Squamous-associated Macrophages: Are enriched for "HIF-1 signaling pathway" and "Glycolysis / Gluconeogenesis", suggesting metabolic reprogramming (Warburg effect) often associated with pro-tumorigenic M2-like macrophage polarization in the hypoxic tumor microenvironment. Negative enrichment for "Phagosome" and "Antigen processing and presentation" further supports a less immune-active phenotype in Squamous.

Stromal and Endothelial Cells

These components of the tumor microenvironment exhibit distinct roles:

Fibroblasts

• Adeno-associated Fibroblasts: Are enriched for "Focal adhesion", "ECM-receptor interaction", and "Proteoglycans in cancer", highlighting their active role in extracellular matrix (ECM) remodeling, which supports tumor invasion and metastasis in adenocarcinoma.
• Squamous-associated Fibroblasts: Show enrichment for "Cell cycle" and "DNA replication", indicating a proliferative stromal compartment, similar to the epithelial cells in Squamous, contributing to tumor bulk.

Endothelial Cells

• Adeno-associated Endothelial Cells: Strongly enriched for "VEGF signaling pathway", "HIF-1 signaling pathway", and "PI3K-Akt signaling pathway", underscoring active angiogenesis crucial for adenocarcinoma growth and metastasis.
• Squamous-associated Endothelial Cells: Also show enrichment for "Cell cycle" and "DNA replication", reflecting endothelial proliferation to support tumor growth.

General Observations

The widespread negative enrichment for the "Ribosome" pathway across multiple cell types in both Adeno and Squamous comparisons (relative to 'others') is intriguing. While cancer cells are generally characterized by high protein synthesis, this could imply that the 'others' group (potentially a mix of healthy and other disease states) has a higher baseline ribosomal activity, or it might point to a specific mechanism of ribosomal stress adaptation or a shift towards cap-dependent translation in cancer cells.

Clinical or Translational Implications

The differential pathway enrichment profiles provide valuable insights into the distinct biological underpinnings of lung adenocarcinoma and squamous cell carcinoma, which can have significant clinical implications:

• Targeted Therapies: The identification of active signaling pathways like PI3K-Akt, HIF-1, and VEGF in Adenocarcinoma, and the emphasis on cell cycle and DNA repair in Squamous cell carcinoma, suggests potential subtype-specific vulnerabilities. For instance, drugs targeting angiogenesis (VEGF inhibitors) or metabolic pathways might be more effective in adenocarcinoma, while inhibitors of DNA repair or cell cycle progression might show greater efficacy in squamous cell carcinoma.
  • *Reference for NSCLC targeted therapies:* PubMed Search: NSCLC targeted therapy subtypes
• Immunotherapy Response: The distinct immune cell pathway enrichments (e.g., active immune cell trafficking and antigen presentation in Adeno-associated macrophages/T cells versus potential T cell exhaustion or metabolic shift in Squamous-associated macrophages/T cells) could explain differential responses to immune checkpoint inhibitors between NSCLC subtypes. Understanding these pathways may help predict responsiveness or develop strategies to overcome resistance.
• Biomarker Discovery: Pathways and their constituent genes identified in this analysis could serve as potential diagnostic or prognostic biomarkers. For example, specific metabolic enzyme signatures or ECM remodeling factors in fibroblasts might distinguish subtypes or predict disease progression.
• Understanding Tumor Heterogeneity: This analysis highlights the heterogeneity not only between tumor subtypes but also within the tumor microenvironment across various stromal and immune cell populations, emphasizing the need for comprehensive approaches that consider the entire ecosystem for effective treatment strategies.

22. Discussion

The comprehensive single-cell analysis of Lung Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous) reveals fundamental disparities across cellular, genomic, and molecular levels, providing a granular understanding of Non-Small Cell Lung Carcinoma (NSCLC) heterogeneity. Initial UMAP visualizations clearly demarcate cell populations based on disease condition, major/minor cell types, and ploidy status, confirming the robust identification of aneuploid Lung Epithelial cells as tumor-originating and highlighting the distinct transcriptional landscapes of Adeno and Squamous subtypes.

Tumor Cell Characteristics (Lung Epithelial Cells):

Genomically, Squamous Lung Epithelial cells exhibit a higher and more consistent proportion of aneuploidy compared to Adenocarcinoma, which shows greater inter-sample variability in genomic instability. Recurrent amplifications of oncogenes like *SOX2* (3q25.1:3q26.1) are notably found in Squamous samples, while *ERBB2* (17q12:17q21.2) amplifications are present across both. Transcriptomic analysis of cell cycle genes indicates a robustly higher proliferative signature in Squamous cells, marked by increased prevalence of DNA replication initiation factors (MCM2, MCM4, MCM6, MCM7, ORC6), the oncogene *MYC*, and proliferation marker *PCNA*. Conversely, Adenocarcinoma epithelial cells show a higher expressing cell fraction for DNA damage response genes like *GADD45B* and different G1-phase regulators (CCND1, CDK4), suggesting potentially higher intrinsic stress or distinct cell cycle control. Gene Set Enrichment Analysis (GSEA) further corroborates these findings, showing Adeno epithelial cells enriched for "Oxidative phosphorylation," "HIF-1 signaling pathway," and "PI3K-Akt signaling pathway," indicative of metabolic reprogramming and survival. Squamous epithelial cells, in contrast, are strongly enriched for "Cell cycle," "DNA replication," and DNA repair pathways, reflecting their highly proliferative nature and genomic instability.

The surfaceome of Lung Epithelial cells also presents subtype-specific markers. Adenocarcinoma cells are characterized by high expression of established therapeutic targets and promising markers such as *TACSTD2 (TROP2)*, *EPCAM*, *MUC1*, *ERBB3 (HER3)*, and *CEACAM5*. In contrast, Squamous cells prominently express *CD44*, glucose transporter *SLC2A1 (GLUT1)*, *NOTCH3*, *FGFR2*, *MMP14*, and *ITGB4*, pointing to distinct differentiation programs, metabolic reliance, and invasive properties.

Tumor Microenvironment (TME) Disparities:

The immune and stromal compartments of the TME display significant subtype-specific characteristics. Macrophage population analysis reveals distinct polarization patterns. Adenocarcinoma macrophages are enriched for markers of antigen presentation (*HLA-DQA1, HLA-DQB1, HLA-DMA, HLA-DMB*) and M2-like immunosuppressive functions (*MRC1 (CD206), TREM2, SPP1*). GSEA confirms Adeno macrophages' involvement in "Phagosome" and "Antigen processing and presentation." Squamous macrophages, however, exhibit markers of active recruitment (*CXCR4*), metabolic reprogramming (*SLC2A3 (GLUT3)*), inflammation (*C5AR1 (CD88), TNFRSF1B, TREM1*), and pro-tumorigenic crosstalk (*EREG*), with GSEA showing enrichment for "HIF-1 signaling pathway" and "Glycolysis / Gluconeogenesis." These findings suggest a more immunosuppressive and metabolically adapted macrophage phenotype in Squamous TME.

T cell subsets also differ: Adenocarcinoma features higher proportions of Th2 and ILC3(-) cells, while Squamous has a higher proportion of Th17 cells. CD4+ T cells in Squamous demonstrate expression of metabolic transporters (*SLC2A3, ATP1A1*), adhesion molecules (*CD44, ITGB1, ITGB2*), and inflammatory receptors (*CYSLTR1, TNFRSF1B*), suggesting distinct functional adaptations within the Squamous TME. GSEA for T cells in Adeno shows enrichment for various "T cell differentiation" pathways and "Leukocyte transendothelial migration," indicating active but potentially dysregulated immune responses. Interestingly, Squamous CD8+ T cells show negative enrichment for "T cell receptor signaling pathway," which could imply T cell exhaustion or an anergic state.

Fibroblasts, key stromal components, also exhibit subtype-specific surfaceome profiles. Adenocarcinoma fibroblasts express *IL6ST* (gp130) and *ENG* (CD105), indicative of active inflammatory and TGF-β-mediated signaling. In contrast, Squamous fibroblasts display a broader set of markers associated with extensive ECM remodeling, invasion, and immunosuppression, including *FAP*, *MMP14*, *DDR2*, *LRRC15*, and *PTK7*. GSEA highlights Adeno fibroblasts' role in "Focal adhesion" and "ECM-receptor interaction," while Squamous fibroblasts show "Cell cycle" and "DNA replication" enrichment, suggesting a more proliferative stromal compartment.

Cell-Cell Interaction (CCI) Networks:

Condition-specific CCI analysis reveals distinct communication hubs. Both subtypes utilize *SPP1* and *MIF* signaling broadly, but Adenocarcinoma features prominent HLA-E-KLRC1 and SEMA4D-PLXNB2 interactions, pointing to specific immune evasion mechanisms involving tumor cells and T cells. VEGFA-FLT1 and various EGFR ligand-receptor interactions (AREG, EREG, HBEGF) are also highly active in Adeno, driven by tumor and immune cells. Squamous Carcinoma exhibits strong CDH1-CDH1 homotypic interactions, LeukotrieneC4-CYSLTR1 (Mast cell-epithelial), COL3A1-ADGRG1 (tumor-fibroblast), and IL1B-IL1R (macrophage-epithelial) axes, indicative of an inflammatory, ECM-rich, and highly stromal-interactive TME. *LCK-CD8_receptor* interactions in Squamous suggest active T cell communication, although this could also involve suppressive pathways.

In summary, this single-cell analysis paints a detailed picture of lung carcinoma, emphasizing that Adenocarcinoma and Squamous Cell Carcinoma are not merely histologically distinct but represent fundamentally different biological entities with unique genomic landscapes, proliferative programs, TME compositions, and intercellular communication networks. These disparities are critical for understanding disease progression and, more importantly, for identifying subtype-specific therapeutic vulnerabilities and guiding precision oncology strategies.

Hypotheses:

1. Squamous Lung Epithelial cells exhibit intrinsically higher proliferative rates and dependence on specific cell cycle regulators (e.g., MCMs, MYC) compared to Adenocarcinoma cells, contributing to their distinct histological features and potentially therapeutic vulnerabilities.
2. The Adenocarcinoma tumor microenvironment is characterized by a Th2/ILC3(-)/M2A/M2C macrophage polarization with prominent HLA-E-KLRC1 and SEMA4D-PLXNB2 immune evasion mechanisms, while Squamous TME features a more pronounced M2B/M2D macrophage polarization and an inflammatory, ECM-remodeling fibroblast phenotype (FAP, LRRC15, MMP14).
3. Macrophages and tumor cells in Squamous NSCLC display a stronger reliance on specific glucose transporters (SLC2A1/3) and glycolytic metabolism, representing a unique metabolic vulnerability compared to Adenocarcinoma.
4. Therapeutic agents targeting TROP2/HER3 (e.g., ADCs) will show greater efficacy in Adenocarcinoma, while those targeting FAP/LRRC15 (e.g., specific ADCs) or CXCR4 (e.g., antagonists) will be more effective in Squamous Cell Carcinoma, reflecting their subtype-specific expression and roles in the TME.

Potential therapeutic targets:

1. ERBB3 (HER3): Overexpressed on Adenocarcinoma Lung Epithelial cells. ERBB3 is a critical heterodimerization partner for other HER receptors, mediating PI3K/AKT and MAPK signaling pathways that drive cell growth, survival, and drug resistance. It is implicated in resistance to EGFR TKIs. Evidence: High expression observed in Adenocarcinoma Lung Epithelial cells (Section 15, dot plot). HER3-targeting ADCs (e.g., Patritumab Deruxtecan) have shown significant clinical activity in NSCLC, particularly in EGFR-mutant, TKI-resistant disease (Section 15 explanation). Validation: Evaluate the efficacy of HER3-targeting ADCs in Adenocarcinoma patient-derived organoids or PDX models. Assess changes in tumor cell proliferation, survival, and downstream signaling pathways (e.g., pAKT, pERK) upon treatment. Use IHC to confirm HER3 expression in clinical samples.
2. TACSTD2 (TROP2): Highly expressed on Adenocarcinoma Lung Epithelial cells. TROP2 is a transmembrane glycoprotein involved in cell proliferation, invasion, and metastasis, making it an attractive target for cytotoxic payload delivery. Evidence: High expression observed in Adenocarcinoma Lung Epithelial cells (Section 15, dot plot). Antibody-drug conjugates (ADCs) like Sacituzumab govitecan specifically target TROP2 and are under clinical evaluation for NSCLC, showing promising results (Section 15 explanation). Validation: Test the anti-tumor activity of TROP2-targeting ADCs in Adenocarcinoma cell lines and patient-derived models. Measure tumor growth inhibition and evaluate on-target specificity. Conduct companion diagnostic studies to identify patients with high TROP2 expression.
3. FAP (Fibroblast Activation Protein alpha): High expression on Cancer-Associated Fibroblasts (CAFs) in Squamous Cell Carcinoma. FAP is a serine protease that remodels the extracellular matrix, promotes tumor growth, invasion, and immunosuppression, and is a canonical CAF marker. Evidence: High expression observed in Fibroblasts from Squamous samples (Section 17, dot plot). FAP is considered a highly promising and specific target for CAF-directed therapies, with various FAP-targeted agents (antibodies, immunotoxins, radioligands) in active development (Section 17 explanation). Validation: Evaluate the effect of FAP-targeted therapies (e.g., FAP-targeted ADCs or immunotoxins) on tumor growth and metastasis in Squamous PDX models. Assess changes in ECM remodeling, immune cell infiltration, and CAF activation status. Use spatial transcriptomics to confirm FAP+ CAF reduction and TME changes.
4. LRRC15 (Leucine Rich Repeat Containing 15): Specific marker for a subset of CAFs in Squamous Cell Carcinoma. LRRC15+ CAFs contribute to ECM remodeling, immunosuppression, and immune exclusion by potentially forming a physical barrier to immune cells. Evidence: High expression observed in Fibroblasts from Squamous samples (Section 17, dot plot). Anti-LRRC15 antibodies, such as the ADC ABBV-085, are currently in clinical trials for various solid tumors (Section 17 explanation). Validation: Assess the anti-tumor effects of LRRC15-targeting agents (e.g., ABBV-085) in Squamous patient-derived models. Evaluate changes in immune cell infiltration (e.g., T cells), TME composition, and sensitivity to immune checkpoint inhibitors when combined with LRRC15 blockade.
5. CXCR4: Highly expressed on Macrophages in Squamous Cell Carcinoma. The CXCL12-CXCR4 axis is crucial for macrophage recruitment, M2-like polarization, angiogenesis, and immune evasion, contributing significantly to tumor progression in the Squamous TME. Evidence: High expression observed in Macrophages from Squamous samples (Section 16, dot plot). The CXCL12-CXCR4 axis is a well-established and druggable target in cancer, with antagonists like Plerixafor in clinical investigation for solid tumors (Section 16 explanation). Validation: Investigate the impact of CXCR4 antagonists (e.g., Plerixafor) on macrophage recruitment, polarization, and overall tumor growth in Squamous patient-derived models. Combine CXCR4 blockade with existing therapies (e.g., chemotherapy, immunotherapy) to assess synergistic effects on anti-tumor responses and TME modulation.
6. EGFR and its ligands (AREG, EREG, HBEGF): Highly active ligand-receptor interactions involving Lung Epithelial cells, Macrophages, and Fibroblasts in both Adeno and Squamous subtypes. This axis drives proliferation, survival, migration, and angiogenesis, and contributes to immune evasion. Macrophage-derived EREG acts as a critical paracrine factor in Squamous. Evidence: Prominent interactions observed in Adeno (AREG-EGFR, HBEGF-EGFR, EREG-EGFR between tumor cells, macrophages, endothelial cells) and Squamous (EREG-EGFR, HBEGF-EGFR between tumor cells, macrophages, fibroblasts) (Sections 13, 14). Macrophage-derived EREG is a Squamous-specific surfaceome marker (Section 16). EGFR inhibitors are standard-of-care in NSCLC (Section 14 explanation). Validation: Beyond existing EGFR TKIs and antibodies, explore strategies targeting macrophage-derived EGFR ligands (e.g., EREG-neutralizing antibodies) in combination with current EGFR-targeted therapies, particularly in Squamous models. Assess efficacy in overcoming resistance or enhancing responses by disrupting tumor-TME EGFR activation.

Follow-up validation ideas:

1. Perform in vitro 2D/3D culture experiments using patient-derived organoids or cell lines from Adeno vs. Squamous NSCLC. Assess proliferation rates (EdU incorporation, cell counts), cell cycle protein expression (Western blot, IHC), and sensitivity to cell cycle inhibitors (e.g., CDK4/6 inhibitors). Use FISH or RNA-ISH to validate differential expression of key proliferative genes (MCMs, MYC).
2. Utilize spatial transcriptomics or multiplexed immunostaining (e.g., IMC, CyTOF) on primary tumor sections to visualize spatial co-localization and distribution of Th2/ILC3/M2 cells in Adeno and M2B/M2D/FAP/LRRC15 fibroblasts in Squamous. Employ flow cytometry on dissociated tumor samples to confirm T cell/macrophage subset frequencies and their surface marker expression (CD206, TREM2 vs. CXCR4, FAP).
3. Conduct metabolic assays (e.g., Seahorse Analyzer) on isolated macrophages and tumor cells from Adeno vs. Squamous samples. Perform glucose uptake assays (e.g., 2-NBDG) and targeted proteomics for metabolic enzymes (e.g., GLUTs). Validate findings in vivo using metabolic inhibitors in patient-derived xenograft (PDX) models.
4. Perform efficacy studies in patient-derived organoids or PDX models of Adeno vs. Squamous using selected targeted agents (e.g., TROP2/HER3 ADCs for Adeno, FAP/LRRC15 ADCs or CXCR4 antagonists for Squamous). Evaluate tumor growth inhibition, changes in immune cell infiltration, and target engagement. Analyze cellular responses post-treatment using single-cell RNA-seq or multiplexed imaging.

Limitations:

This report is based on a single-cell RNA-seq dataset, providing a snapshot of gene expression and cellular states rather than dynamic processes in vivo. The sample size for Squamous Cell Carcinoma is smaller than for Adenocarcinoma, which may limit the statistical power and generalizability of some comparative findings. Ploidy calls are estimates based on gene expression and not direct karyotyping. The inferred disease context and cell type annotations are derived computationally and require independent histological and clinical validation. While detailed molecular insights are provided, functional validation through in vitro or in vivo experimentation is necessary to confirm the observed biological mechanisms and therapeutic potentials. The generalizability of these findings to the broader NSCLC patient population beyond this specific cohort requires further investigation.

23. Query List

1. Show and save UMAPs for condition, sample, major cell type, minor cell type, ploidy_dec, and celltype_subset in two columns.
2. Show major cell type scores on UMAP and save the result.
3. Show and save a marker expression dot plot for celltype_subset. SET target_cell = None and var_group_rotation = 45. Leave all other arguments at their default values.
4. Select Lung Epithelial cells (tumor-origin cells), show a CNV heatmap grouped by sample together with a summary of regions with significantly amplified copy numbers, and save the results.
5. Show and save UMAPs of CNV patterns colored by major cell type, minor cell type, ploidy results, condition, and sample in two columns.
6. Show and save a population bar plot of minor cell types.
7. Show and save a population bar plot of T cell subsets.
8. Show and save box plots of T cell subset populations with statistically significant differences between conditions, if any. Choose ncols based on the total number of panels.
9. Show and save a population bar plot of macrophage subsets.
10. Show and save box plots of macrophage subset populations with statistically significant differences between conditions, if any. Choose ncols based on the total number of panels.
11. Select Lung Epithelial cells (tumor-origin cells), show their ploidy populations as a bar plot, and save the result.
12. Show and save cell-cell interaction patterns involving Lung Epithelial cells (tumor-origin cells), fibroblasts, macrophages, T cells, and other relevant cell types. Select at most 80 cell-cell interactions per group.
13. Find cell-cell interactions involving Lung Epithelial cells (tumor-origin cells), major immune and stromal cells that differ significantly between conditions, show them as a dot plot, and save the result. Set max_n_items_per_group to 60. For each interaction identified under each condition, using the provided dataset context, describe the associated signaling pathways and mechanisms, its potential as a therapeutic target, and relevant therapeutic agents.
14. Find cell-cell interactions containing immune checkpoint and cell cycle pathway genes and involving Lung Epithelial cells (tumor-origin cells), major immune and stromal cells that differ significantly between conditions, show them as a dot plot, and save the result. Set max_n_items_per_group to 60. For each interaction identified under each condition, using the provided dataset context, describe the associated signaling pathways and mechanisms, its potential as a therapeutic target, and relevant therapeutic agents.
15. Show marker expressions for Lung Epithelial cells (tumor-origin cells) in a dot plot, and save the result. Use only surfaceome markers, with at most 60 markers per condition. For the markers identified under each condition, distinguish between established therapeutic targets and less well-characterized markers with potential as therapeutic targets. For each marker, using the provided dataset context, describe the associated signaling pathways and mechanisms, relevant therapeutic agents, and supporting evidence.
16. Show marker expressions for Macrophage in a dot plot, and save the result. Use only surfaceome markers, with at most 40 markers per condition. For each marker identified under each condition, using the provided dataset context, describe the associated signaling pathways, the role of the corresponding cell type in the tumor microenvironment, the marker’s potential as a therapeutic target, relevant therapeutic agents, and other pertinent information.
17. Show marker expressions for Fibroblast in a dot plot, and save the result. Use only surfaceome markers, with at most 40 markers per condition. For each marker identified under each condition, using the provided dataset context, describe the associated signaling pathways, the role of the corresponding cell type in the tumor microenvironment, the marker’s potential as a therapeutic target, relevant therapeutic agents, and other pertinent information.
18. Show marker expressions for T cell CD4+ in a dot plot, and save the result. Use only surfaceome markers, with at most 40 markers per condition. For each marker identified under each condition, using the provided dataset context, describe the associated signaling pathways, the role of the corresponding cell type in the tumor microenvironment, the marker’s potential as a therapeutic target, relevant therapeutic agents, and other pertinent information.
19. Among cell cycle pathway genes, select those with statistically significant expression differences between conditions in Lung Epithelial cells (major disease-related cells), show box plots, and save the result. Set max_n_items_to_plot to 24 and choose ncols based on the total number of panels for an overall width-to-height ratio of approximately 2:3.
20. Show and save Gene Ontology (GSA) analysis results for Lung Epithelial cells (tumor-origin cells) as a bar plot.
21. Show Gene Set Enrichment Analysis results as a dot plot, and save it. Set the color map to RdBu_r, n_pws_to_show to 120, and target_cells to None.