SCODiA Report by MLBI Lab

Comprehensive Single-Cell Analysis of Pancreatic Ductal Adenocarcinoma Reveals Cellular Heterogeneity, Immunosuppressive Networks, and Novel Therapeutic Opportunities

This single-cell RNA sequencing analysis of Pancreatic Ductal Adenocarcinoma (PDAC) elucidates profound changes in cellular composition, genomic stability, and intercellular communication compared to adjacent normal tissue. We identify Ductal cells as the tumor origin, characterized by widespread aneuploidy and deregulated cell cycle pathways. The tumor microenvironment exhibits significant remodeling, including altered T cell subsets, a diverse macrophage landscape, and an extensive desmoplastic stroma.

Contents

0. Dataset overview
1. UMAP Visualization of Pancreatic Single-Cell RNA-seq Data by Key Metadata
2. Major Cell Type and Ploidy Distribution on UMAP for Pancreatic Ductal Adenocarcinoma
3. Marker Expression Profile across Pancreatic Cell Subsets
4. Genomic Copy Number Variation Analysis in Pancreatic Ductal Cells
5. CNV-Based UMAP Analysis of Pancreatic Single-Cell Landscape
6. Pancreatic Minor Cell Type Population Analysis in PDAC vs. Adjacent Normal Tissue
7. T Cell and Innate Lymphoid Cell Subpopulation Analysis in Pancreatic Ductal Adenocarcinoma
8. Differentially Abundant T Cell Subsets in Pancreatic Ductal Adenocarcinoma
9. Population Analysis of Macrophage Subsets in Pancreatic Ductal Adenocarcinoma
10. Ploidy Analysis of Tumor-Origin Ductal Cells in Pancreatic Ductal Adenocarcinoma
11. Cell-Cell Interaction Patterns in Pancreatic Ductal Adenocarcinoma (PDAC) Tumor Microenvironment
12. Condition-Specific Cell-Cell Interaction Patterns in Pancreatic Ductal Adenocarcinoma
13. Condition-Specific Cell-Cell Interaction Patterns in Pancreatic Ductal Adenocarcinoma
14. Ductal Cell Condition-Specific Surfaceome Marker Analysis in Pancreatic Ductal Adenocarcinoma (PDAC)
15. Macrophage Condition-Specific Surfaceome Markers in Pancreatic Ductal Adenocarcinoma
16. T cell CD4+ Condition-Specific Surfaceome Markers in Pancreatic Tissue
17. Deregulated Cell Cycle Gene Expression in Pancreatic Ductal Adenocarcinoma Ductal Cells
18. Gene Ontology (GSA) Analysis of Ductal Cells in Pancreatic Ductal Adenocarcinoma (PDAC)
19. Gene Set Enrichment Analysis (GSEA) in Pancreatic Ductal Adenocarcinoma (PDAC) Cell Types
20. Discussion
21. Query List

0. Dataset overview

Dataset Summary:

• The AnnData object contains 49221 cells and 23910 genes from human Pancreas tissue.
• Experimental conditions include 'Adj_normal' and 'PDAC'.
• Cell type annotations are available at major, minor, and subset levels, covering various cell types such as Stromal cell, Endothelial cell, Acinar cell, T cell, Myeloid cell, Mast cell, Ductal cell, B cell, and unassigned populations.
• Ploidy labels indicate 84.9% Diploid, 15.0% Aneuploid, and 0.1% Unclear cells.
• The resolved tumor-origin cell type is 'Ductal cell'.
• Copy Number Variation (CNV) estimates are available.
• 'Adj_normal' serves as the reference condition for comparative analyses (DEG, GSEA, GSA_up).

Precomputed Results:

• Cell-cell interaction (CCI) results from CellPhoneDB are available per condition and per sample.
• Differential Expression Gene (DEG) results are precomputed for each celltype_minor, comparing one condition versus the rest.
• Gene Set Enrichment Analysis (GSEA) results are available for each celltype_minor, comparing one condition versus the rest.
• Gene Ontology (GO/GSA) results (GSA_up) are available for each celltype_minor, comparing one condition versus the rest.

Inferred Biological Context:

• The dataset is provisionally inferred to concern Pancreatic Ductal Adenocarcinoma (PDAC), based on the explicit 'PDAC' condition, the presence of aneuploid cells, and 'Ductal cell' identified as the tumor-origin cell type within pancreatic tissue.

1. UMAP Visualization of Pancreatic Single-Cell RNA-seq Data by Key Metadata

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Uniform Manifold Approximation and Projection (UMAP) visualizations of 49,221 single cells from pancreatic tissue, derived from single-cell RNA sequencing data. The cells are colored according to six distinct metadata categories: experimental condition, individual sample origin, major cell type, minor cell type, ploidy status, and detailed cell type subset. These visualizations provide an intuitive, two-dimensional representation of cell similarity based on their gene expression profiles, allowing for the assessment of cell population structure, annotation quality, and the distribution of key biological features across the dataset. The primary goal is to understand how cells group together and how these groupings relate to disease status (PDAC vs. Adj_normal), cellular identity, and genomic integrity (ploidy).

Visual Summary

The UMAP plots reveal the following key patterns:

• Condition: Cells from the 'Adj_normal' condition (maroon) predominantly occupy distinct clusters, particularly smaller, more peripheral regions, and one central region. In contrast, cells from the 'PDAC' condition (purple) are widely distributed, populating most of the UMAP space, including the largest central clusters and regions overlapping with normal tissue components. This suggests a significant shift in cellular composition and/or state in PDAC, with tumor-associated cells influencing a broad spectrum of the cellular landscape.
• Sample: The distribution of cells across individual samples shows good integration, meaning cells from different samples generally mix within larger biological clusters rather than forming sample-specific isolated clusters. This indicates a robust UMAP embedding with minimal strong batch effects. However, some areas show higher enrichment of specific samples, reflecting biological variability between patients or specific contributions of certain samples to particular cell states.
• Celltype_major: The major cell types show distinct clustering, indicating successful identification and separation of broad cell lineages. Prominent clusters include 'Ductal cell' (orange), 'Stromal cell' (light green), 'T cell' (light blue), and 'Myeloid cell' (yellow). 'Acinar cells' (maroon) form a distinct, smaller cluster. 'B cell' and 'Mast cell' also occupy well-defined, albeit smaller, regions. The 'unassigned' category (purple) is scattered, suggesting a small proportion of cells that could not be confidently classified at this level.
• Celltype_minor: Further resolution into minor cell types maintains generally clear and coherent clusters. For example, within the 'T cell' major cluster, 'T cell CD4+' and 'T cell CD8+' form distinct sub-regions. Similarly, 'Macrophage' (yellow) forms a clear group within the 'Myeloid cell' region, and 'Fibroblast' (light orange) and 'Stellate cell' (dark green) delineate within the 'Stromal cell' region. 'Ductal cell' remains a large and prominent cluster, consistent with its biological importance in pancreatic tissue and PDAC.
• Ploidy_dec: The 'ploidy_dec' plot reveals a striking pattern: 'Aneuploid' cells (maroon) are highly concentrated in a specific, large region of the UMAP. This region largely overlaps with the dominant 'Ductal cell' cluster observed in the celltype_major and celltype_minor plots. 'Diploid' cells (yellow) are widely distributed across the remaining UMAP space, representing the majority of cells, as expected for normal immune and stromal populations. 'Unclear' cells (purple) are very few and widely scattered, suggesting robust ploidy assignments.
• Celltype_subset: This highest level of resolution demonstrates that even fine-grained cell type subsets largely form distinct and coherent clusters. Various specific immune cell subtypes (e.g., T cell (Cytotoxic), Macrophage (M1), DC (Classical)) are resolved into recognizable groups. The large 'Ductal cell' cluster also exhibits internal substructure, potentially indicating different functional states or disease-associated transformations within this cell type.

Biological Interpretation

The UMAP visualizations provide critical insights into the cellular landscape of PDAC.

1. Ductal Cell Origin of Tumor and Aneuploidy: The most compelling finding is the clear co-localization of 'Aneuploid' cells almost exclusively with the large 'Ductal cell' cluster. This strongly supports the inference that 'Ductal cell' is the tumor-origin cell type for PDAC, and that these tumor cells exhibit significant genomic instability, a hallmark of cancer progression. The vast majority of aneuploid cells are associated with the PDAC condition, reinforcing their pathological nature.
   • Aneuploidy is a common feature of human cancers, including PDAC, and is associated with tumor initiation, progression, and therapeutic resistance PubMed search: aneuploidy cancer PDAC.
2. PDAC-Associated Cellular Heterogeneity: The 'PDAC' condition encompasses a broad range of cell types, extending beyond the aneuploid tumor cells. This highlights the complex tumor microenvironment (TME) in PDAC, which is known to consist of diverse stromal (e.g., fibroblasts, stellate cells, endothelial cells) and immune (e.g., T cells, macrophages, B cells, NK cells) components. The widespread distribution of PDAC cells across the UMAP indicates that the disease significantly alters the entire cellular ecosystem of the pancreas.
3. Distinct Immune and Stromal Infiltrates: The clear separation of various major, minor, and subset cell types (e.g., Macrophage subtypes, T cell subsets, Fibroblasts, Stellate cells) demonstrates the dataset's capacity to resolve the intricate cellular heterogeneity within both normal and diseased pancreatic tissue. This resolution is crucial for studying the roles of specific immune and stromal cell populations in tumor progression and response to therapy. Pancreatic stellate cells and tumor-associated macrophages (TAMs) are well-known contributors to the desmoplastic stroma and immunosuppressive environment in PDAC, respectively.
   • The tumor microenvironment in PDAC is characterized by a dense desmoplastic stroma, largely driven by activated pancreatic stellate cells, which fosters tumor growth and limits therapeutic delivery PubMed search: PDAC tumor microenvironment stellate cells.
   • Tumor-associated macrophages contribute significantly to immune suppression and disease progression in PDAC PubMed search: PDAC tumor associated macrophages.
4. Robust Cell Type Annotation: The consistently distinct clustering of cell populations across various levels of annotation (major, minor, subset) validates the quality and resolution of the cell type assignments in the AnnData object. This strong separation suggests that the gene expression profiles are sufficiently distinct to confidently differentiate cell identities, providing a solid foundation for downstream differential expression or pathway analyses.

Annotation Notes

• The UMAP embedding appears robust, with clear separation of most major and minor cell types and minimal evidence of severe batch effects across samples.
• The high confidence in ploidy assignment (very few 'Unclear' cells) adds significant value for identifying tumor-derived cells.
• The presence of an 'unassigned' category in the celltype_major and celltype_minor plots, though small, indicates cells that did not perfectly fit predefined categories, which is common in complex biological samples and may represent rare cell types or transient cell states.
• The fine granularity of celltype_subset annotations further supports the depth of characterization possible with this dataset, enabling detailed studies of specific immune and stromal cell states.

2. Major Cell Type and Ploidy Distribution on UMAP for Pancreatic Ductal Adenocarcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis presents several UMAP (Uniform Manifold Approximation and Projection) plots derived from single-cell RNA-sequencing data of the Pancreas. The primary goal is to visualize the distribution of major cell types based on their computed scores (HiCAT_major_score), alongside the final major cell type annotations (celltype_major) and the inferred ploidy status (ploidy_dec) across the entire single-cell landscape. This provides a crucial visual assessment of cell type identification accuracy, the spatial organization of different cell populations, and the localization of aneuploid cells within the context of Pancreatic Ductal Adenocarcinoma (PDAC).

Visual Summary

The UMAP visualizations effectively segment the 49,221 cells into distinct clusters, each generally corresponding to a specific major cell type, as indicated by the HiCAT_major_score plots and the final celltype_major annotation.

1. Cell Type Scoring (HiCAT_major_score):
   • Individual UMAPs display the score for each major cell type. Highly scoring cells for a particular type typically coalesce into well-defined, spatially separated clusters.
   • Immune Cells: T cells, B cells, Myeloid cells, and Mast cells form distinct, often interconnected, clusters, indicating diverse immune cell populations within the dataset. T cells and Myeloid cells show strong, localized scores in particular regions.
   • Stromal and Endothelial Cells: Stromal cells and Endothelial cells also occupy well-defined regions, suggesting their distinct transcriptional profiles and anatomical niches.
   • Pancreatic Epithelial Cells: Acinar cells and Ductal cells each form prominent and mostly distinct clusters. Notably, the Ductal cell cluster, identified as the tumor-origin cell type, shows a strong, localized signal.
   • Pancreatic Endocrine Cells: Alpha, Beta, Delta, Epsilon, and Gamma (PP) cells (islet cells) are observed to cluster together in a relatively confined region, consistent with their co-localization in pancreatic islets. Their scores are generally lower than the more abundant cell types but still clearly delineate their populations.
   • Other Cell Types: Pancreatic progenitor cells and Schwann cells appear as smaller, more diffuse, or less intensely scoring populations, but their presence is detectable.
2. Ploidy Status (ploidy_dec):
   • The ploidy_dec UMAP highlights the distribution of cells classified as Diploid (light yellow), Aneuploid (maroon), and Unclear (light blue).
   • A significant, dense cluster of Aneuploid cells (maroon) is prominently located in the central-upper region of the UMAP. These cells represent 15.0% of the total cell population.
   • Diploid cells (light yellow) are widely distributed across the remaining UMAP space, forming the bulk of the non-aneuploid cell populations.
3. Major Cell Type Annotation (celltype_major):
   • This final UMAP provides the integrated cell type annotation, visually confirming the distinct clustering patterns suggested by the individual HiCAT_major_score plots.
   • The 'Ductal cell' population (orange) largely co-localizes with the main Aneuploid cluster identified in the ploidy_dec UMAP. This strong overlap is a critical observation.
   • Other major cell types, such as Acinar (maroon), T cell (teal), Myeloid cell (light green), Stromal cell (dark green), and Endothelial cell (light orange), are clearly segregated, demonstrating robust cell type annotation.

Biological Interpretation

The UMAP visualizations provide compelling evidence for the distinct identity and spatial organization of various cell populations within the pancreatic tissue, particularly in the context of PDAC.

1. Robust Cell Type Identification: The high HiCAT_major_score for each cell type, resulting in well-separated clusters that align with the final celltype_major annotations, suggests a high quality of cell type identification and annotation for this single-cell dataset. This robust annotation is foundational for downstream analyses such as differential expression or cell-cell interaction studies.
2. Tumor Cell Localization and Identity:
   • The striking co-localization of the Ductal cell population (identified as the tumor-origin cell type) with the dense Aneuploid cluster is a strong indicator of malignant ductal cells in PDAC. Aneuploidy, a hallmark of cancer characterized by an abnormal number of chromosomes, is expected to be prevalent in tumor cells.
   • This finding supports the inference that the primary tumor mass in this PDAC sample is largely composed of transformed ductal cells exhibiting aneuploidy. While the total percentage of aneuploid cells is 15%, their concentration within the ductal cell compartment provides specific evidence for the tumor lineage.
3. Tumor Microenvironment (TME) Composition:
   • The presence of distinct clusters for various immune cells (T cells, B cells, Myeloid cells, Mast cells) and stromal components (Stromal cells, Endothelial cells) highlights the complexity of the PDAC tumor microenvironment. These non-malignant cells are known to play crucial roles in tumor progression, immune evasion, and therapeutic resistance in PDAC.
   • The spatial segregation of these immune and stromal populations from the core aneuploid ductal cell cluster suggests distinct functional compartments, though cell-cell interactions within these compartments would require further investigation (e.g., using the precomputed CCI results).
4. Normal Pancreatic Cell Architecture:
   • The clustering of Acinar cells, along with the tight grouping of pancreatic endocrine cells (Alpha, Beta, Delta, Epsilon, Gamma cells), demonstrates the presence of the normal pancreatic parenchyma and islet structures within the sample. This indicates that the dataset likely captures both tumor and adjacent normal tissue components, as suggested by the Adj_normal condition.

Annotation Notes

• The consistency between the individual HiCAT_major_score plots and the aggregated celltype_major plot confirms the efficacy of the cell type scoring and clustering algorithms used.
• The clear separation of most major cell types on the UMAP supports high-resolution cellular phenotyping for subsequent analyses.
• The distinct aneuploid cluster primarily residing within the ductal cell compartment is a key finding that validates the identification of tumor cells and is consistent with the biology of PDAC where ductal cells are the cells of origin.
• While a significant portion of cells are aneuploid and co-localize with ductal cells, other ductal cells remain diploid. This could represent normal ductal cells, early transformed cells without full aneuploidy, or limitations of the aneuploidy calling algorithm. Further analysis of these subpopulations may be warranted.

3. Marker Expression Profile across Pancreatic Cell Subsets

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a dot plot illustrating the expression patterns of marker genes across various celltype_subset populations identified in the single-cell RNA-seq dataset from human pancreas, including both "Adj_normal" and "PDAC" conditions. The primary goal of this visualization is to verify the distinct identity of each cell type based on the expression of specific, defining marker genes, aligning with the celltype_subset annotation. The tool plot_markers_and_expression_dot was used with parameters configured to identify and display highly specific surface markers that are not broadly expressed across many cell groups, making it ideal for cell type annotation validation.

Visual Summary

The dot plot effectively visualizes the expression of marker genes (x-axis) across different celltype_subset populations (y-axis).

• Mean Expression and Fraction of Cells: The color intensity of each dot represents the mean expression level of a gene within a given cell group (darker red indicates higher mean expression), while the size of the dot corresponds to the fraction of cells within that group expressing the gene (larger dot indicates a higher percentage of expressing cells).
• Distinct Cell Type Signatures: Overall, the plot reveals well-defined, block-like patterns of gene expression, with distinct sets of markers highly expressed in specific cell type subsets. This indicates strong separation and unique identities for most annotated celltype_subset groups.
• Cell Counts: The bar chart on the right side of the plot shows the number of cells belonging to each celltype_subset, ranging from 70 cells (B cell (Breg)) to 10935 cells (Ductal cell).
• Marker Specificity: The analysis parameters (especially rem_mkrs_common_in_N_groups_or_more: 3 and surfaceome_only: True) emphasize markers that are largely unique to one or two cell type subsets, leading to a clear, diagonal-like structure where each cell type has its own set of distinguishing markers.
• Shared Markers: While most markers are highly specific, some genes, particularly those related to extracellular matrix components (e.g., various COL genes) or contractile elements (e.g., ACTA2, MYL9, CALD1), show expression across related stromal cell types like Fibroblasts, Smooth muscle cells, and Stellate cells, which is biologically expected. Similarly, some B cell related markers are shared between B cell subsets and Plasma cells.

Biological Interpretation

The marker expression patterns observed strongly support the assigned celltype_subset identities within the pancreatic tissue.

• Acinar Cells (2335 cells): These cells show exclusive and high expression of genes characteristic of pancreatic exocrine function, including various digestive enzymes and related proteins such as CPA1, PRSS1, CPB1, CLPS, CPA2, CTRB2, CELA3A, REG1A, CTRB1, CTRC, PNLIP, SPINK1, CELA3B, PRSS3, CELA2A, and SYCN. This signature unequivocally confirms their identity.
• B Cell Subsets (Breg: 70 cells, MZ: 257 cells, Memory: 183 cells) and Plasma Cells (1332 cells): These lymphoid populations exhibit a shared but also distinct set of markers. Genes like POU2F2, SPIB, and IRF8 are prominent in B cell subsets. Plasma cells show strong expression of canonical markers like XBP1, PRDM1, TNFRSF17, and SDC1, which are critical for antibody production and plasma cell differentiation. Some markers, such as MZB1, PRDM1, SDC1, and TNFRSF17, are shared between B cells and Plasma cells, reflecting their developmental lineage.
• DC (Plasmacytoid) (168 cells): These specialized dendritic cells are identified by strong expression of S100A10 and IRF7, which are characteristic of plasmacytoid DCs and their role in antiviral immunity.
• Ductal Cells (10935 cells): As the resolved tumor-origin cell type in PDAC, these cells exhibit high and specific expression of epithelial markers, including KRT19, MUC1, ANXA4, KRT7, CLDN4, and SLPI. The robust expression of keratins (KRT7, KRT19) is a hallmark of ductal epithelium.
• Endothelial Tip Cells (204 cells): These cells, crucial for angiogenesis, are marked by genes like DLL4, a key regulator of tip cell specification and vessel sprouting [1]. They also express various collagen genes (COL1A2, COL1A1, COL3A1, COL6A2) and extracellular matrix (ECM) related genes such as DCN, LUM, and FBLN1, consistent with their role in matrix interaction during vessel formation.
• Fibroblasts (604 cells): A prominent stromal component, fibroblasts are characterized by a broad set of ECM and mesenchymal markers including COL1A1, COL3A1, COL6A2, SPARC, FN1, MGP, PDGFRA, PDGFRB, A2M, FAP, FBLN2, and ELN. Their role in tissue remodeling and fibrosis, particularly relevant in PDAC, is well supported by this gene signature.
• Innate Lymphoid Cells (ILC1: 345 cells, ILCreg: 109 cells, LTI: 260 cells): These immune cells show expression of lymphoid-associated markers such as CD69, ID3, CD7, IL2RG, IFNGR1, and IFNGR2, which are consistent with various ILC subsets involved in innate immunity and tissue homeostasis [2].
• Macrophage Subsets (M1: 7406 cells, M2C: 2151 cells): Macrophages are a significant immune population. While specific M1/M2 polarization markers might be subtle at this resolution, both subsets show expression of MSR1 (macrophage scavenger receptor 1), and components of the complement system like C1S and C1R, reflecting their phagocytic and immune-modulatory roles.
• Mast Cells (1738 cells): These immune cells are clearly distinguished by markers such as KIT (CD117), TPSAB1 (Tryptase alpha/beta 1), SRGN (Serglycin), and GATA2, which are essential for mast cell development and function [3].
• NK Cells (825 cells): Natural Killer cells, important for innate immunity, express characteristic markers like KLRD1 (CD94), FCGR3A (CD16a), and KLRF1 (NKG2D), confirming their cytotoxic potential [4].
• Smooth Muscle Cells (680 cells): These contractile cells are identified by canonical markers of smooth muscle differentiation and function, including ACTA2 (alpha-smooth muscle actin), MYH11 (myosin heavy chain 11), MYL9 (myosin light chain 9), CALD1 (caldesmon 1), CNN1 (calponin 1), NOTCH3, and TAGLN (transgelin).
• Stellate Cells (1626 cells): Pancreatic stellate cells are crucial in PDAC desmoplasia. Their marker profile, including ACTA2, FAP (fibroblast activation protein), ELN (elastin), SPARC, FN1, and various collagen genes (COL4A1, COL6A3), indicates their myofibroblast-like characteristics and significant contribution to ECM remodeling in the tumor microenvironment [5]. There is an expected overlap with Fibroblasts and Smooth muscle cells for general mesenchymal and contractile markers.
• T Cell Subsets (Cytotoxic: 4697 cells, Th17: 342 cells, Treg: 592 cells): These adaptive immune cells show highly specific markers: CD8A for cytotoxic T cells, RORA (ROR gamma t) for Th17 cells, and CTLA4 for regulatory T cells, which are consistent with their known roles in immune surveillance and regulation.

Annotation Notes

The dot plot provides strong evidence for the robustness and accuracy of the celltype_subset annotations within this AnnData object. The selected marker genes clearly delineate distinct cell populations, with minimal ambiguity between most groups. The specificity is enhanced by the analytical approach that prioritizes unique markers.

• The clear, non-overlapping blocks of high expression for most celltype_subset groups indicate that the current annotations are well-supported by their transcriptional profiles.
• Expected biological overlaps in marker expression, such as between B cells and Plasma cells, or among Fibroblasts, Stellate cells, and Smooth muscle cells (due to shared mesenchymal or contractile properties and roles in ECM), are observed but do not diminish the overall distinctiveness of each subset. These overlaps reflect genuine biological relationships rather than annotation errors.
• The presence of canonical markers for specialized cells like Endothelial tip cells (DLL4) and pDCs (IRF7, S100A10) further validates the fine-grained resolution of the celltype_subset annotations.
• The significant number of cells in the Ductal cell population, identified as the tumor-origin cell type, and their distinct epithelial marker profile (KRT7, KRT19) is consistent with the PDAC context and suggests a strong representation of malignant cells.

Overall, this marker expression dot plot serves as an excellent validation of the current celltype_subset annotations, demonstrating that each group is defined by a coherent and biologically meaningful gene signature.

---

References

[1] DLL4: GeneCards entry for DLL4. https://www.genecards.org/cgi-bin/carddisp.pl?gene=DLL4

[2] ILCs: Innate lymphoid cells: a new paradigm in immune regulation. PubMed search. https://pubmed.ncbi.nlm.nih.gov/?term=innate+lymphoid+cells+immune+regulation&sort=relevance&size=200

[3] Mast Cells: GeneCards entry for KIT. https://www.genecards.org/cgi-bin/carddisp.pl?gene=KIT

[4] NK Cells: Natural killer cells: markers and function. PubMed search. https://pubmed.ncbi.nlm.nih.gov/?term=natural+killer+cells+markers+function&sort=relevance&size=200

[5] Stellate Cells: Pancreatic stellate cells in pancreatic cancer. PubMed search. https://pubmed.ncbi.nlm.nih.gov/?term=pancreatic+stellate+cells+pancreatic+cancer&sort=relevance&size=200

4. Genomic Copy Number Variation Analysis in Pancreatic Ductal Cells

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates copy number variations (CNVs) in Ductal cells from Pancreatic Ductal Adenocarcinoma (PDAC) patients and adjacent normal pancreatic tissue. The plot_cnv_heatmap tool was used to visualize log2(Copy Number Ratio) values across the genome, grouped by sample, specifically for Ductal cells identified as the tumor-origin cell type. A complementary heatmap summarizes the frequency of significantly amplified regions across different PDAC samples, highlighting specific cytogenetic bands and associated genes.

Visual Summary

Figure 1: Copy Number Variation Heatmap for Ductal Cells

The heatmap illustrates the log2(Copy Number Ratio) across the genome for Ductal cells, categorized by sample and their inferred ploidy status.

• Normal vs. PDAC Samples: A stark contrast is observed between cells from "Adj_normal" samples and most "PDAC" samples. "Adj_normal" samples and "Diploid PDAC" samples generally exhibit a neutral (close to zero) log2(CNR) profile, indicated by blue and light blue colors, suggesting a diploid state with minimal CNVs.
• Widespread Genomic Instability in PDAC: In contrast, most "PDAC" samples (e.g., PDAC_1, PDAC_3-9, PDAC_11A/B, PDAC_13, PDAC_15-16) display extensive regions of both amplifications (red) and deletions (dark blue/purple). This indicates significant genomic instability and aneuploidy, a hallmark of cancer.
• Recurrent CNV Patterns: Several recurrent amplification and deletion patterns are noticeable across multiple PDAC samples. For instance, amplifications are frequently observed on chromosomes 3, 8, 12, 17, and 19. Deletions are visible on chromosomes 6 and 9.
• Sample Heterogeneity: There is considerable heterogeneity in CNV profiles among individual PDAC samples. Some PDAC samples, labeled "Diploid PDAC" (e.g., PDAC_10), show fewer CNV events, resembling the adjacent normal samples more closely, which could represent non-malignant Ductal cells within the tumor microenvironment or tumors with minimal genomic alterations.

Figure 2: Frequency of Significantly Amplified Cytogenetic Bands in PDAC Ductal Cells

This heatmap and accompanying bar chart provide a quantitative summary of the most frequently amplified cytogenetic regions across the PDAC samples.

• High-Frequency Amplifications: Several genomic regions show high frequencies of amplification (overall frequency > 0.4) across the PDAC samples:

1q21.3:1q23.3 (0.64)

• 1q42.11:2p25.2 (0.57) - *Note: This label spanning two chromosomes is unusual for a single cytogenetic band and may represent a larger region identified to be frequently amplified across chromosomal boundaries in the linearized genomic view.*

3q26.33:3q29, containing the oncogene SOX2 (0.43)

7p13:7q21.1, containing the oncogene EGFR (0.43)

• 8q21.2:8q13.1, containing genes like LSM1, DDHD2, GSDMD, EIF3E, INTS8 (0.43)

17q12:17q21.2, containing the oncogene ERBB2 (0.43)

• Sample-Specific Amplification Burdens: While certain regions are frequently amplified across many samples, the specific pattern and extent of amplifications vary by sample. For example, PDAC_4 shows particularly strong amplifications in 3q26.33:3q29 (SOX2) and 17q12:17q21.2 (ERBB2), while PDAC_1 and PDAC_3 exhibit broader amplification patterns across multiple regions.

Biological Interpretation

The analysis of copy number variations in Ductal cells strongly corroborates the inferred diagnosis of Pancreatic Ductal Adenocarcinoma (PDAC). The "Ductal cell" origin, explicitly identified as the tumor-origin cell type, combined with the observed genomic instability, provides robust evidence for malignant transformation.

• Genomic Instability as a Cancer Hallmark: The pervasive amplifications and deletions in PDAC Ductal cells, absent in adjacent normal tissue, are characteristic features of cancer, indicating severe genomic instability and widespread aneuploidy. This aligns with the observed 15% aneuploid cells in the overall dataset.
• Oncogene Amplifications Drive PDAC Pathogenesis: The frequent amplification of key oncogenes in PDAC Ductal cells highlights their potential roles in driving tumor development and progression:
  • SOX2 (3q26.33:3q29): This transcription factor is crucial for pluripotency and self-renewal. Its amplification in PDAC suggests a role in maintaining cancer stem cell properties, contributing to tumor growth and resistance to therapy. GeneCards: SOX2
  • EGFR (7p13:7q21.1): A receptor tyrosine kinase involved in cell growth, proliferation, and survival. EGFR amplification or overexpression is a well-known driver event in many cancers, including subsets of PDAC, promoting uncontrolled cellular signaling. GeneCards: EGFR
  • ERBB2 (17q12:17q21.2): Also known as HER2, this receptor tyrosine kinase is another potent oncogene. ERBB2 amplification leads to enhanced cell signaling and proliferation and is a validated therapeutic target in other cancers like breast and gastric cancer. Its presence here indicates a potential oncogenic driver in a subset of PDAC. GeneCards: ERBB2
  • Genes on 8q21.2:8q13.1: This region contains several genes (LSM1, DDHD2, GSDMD, EIF3E, INTS8) whose amplification may collectively or individually contribute to oncogenesis. EIF3E, for instance, promotes translation and is often overexpressed in cancers, while GSDMD's role in pyroptosis can be complex in tumor settings. GeneCards: EIF3E
• Tumor Suppressor Gene Deletions: While not explicitly summarized, the observed deletions on chromosomes 6 and 9 could indicate the loss of tumor suppressor genes, such as CDKN2A/B (on 9p21), which are frequently inactivated in PDAC.
• Intratumoral Heterogeneity: The variation in CNV patterns among different PDAC samples, including the presence of "Diploid PDAC" cells, highlights the significant intratumoral and intertumoral heterogeneity in PDAC. This cellular and genomic diversity can impact disease progression and therapeutic responses.

Clinical or Translational Implications

• Biomarker Potential: The recurrent amplifications of oncogenes like SOX2, EGFR, and ERBB2 in PDAC Ductal cells suggest they could serve as valuable biomarkers. These could be used for identifying specific subsets of PDAC patients, guiding prognosis, or predicting response to targeted therapies.
• Therapeutic Opportunities: The identification of EGFR and ERBB2 amplifications in PDAC points to potential therapeutic strategies. Patients with such amplifications might be candidates for existing EGFR or HER2-targeted therapies (e.g., erlotinib, trastuzumab, or trastuzumab deruxtecan) that have shown efficacy in other cancers. Further clinical validation in PDAC is warranted. PubMed search: EGFR HER2 PDAC therapy
• Personalized Medicine: The observed genomic heterogeneity underscores the need for personalized approaches in PDAC treatment. Identifying the specific CNV landscape in a patient's tumor could inform tailored therapeutic decisions, moving beyond a one-size-fits-all strategy.
• Understanding Disease Progression: The distinct CNV profiles between typical PDAC and "Diploid PDAC" samples could offer insights into different stages of tumor evolution or less aggressive forms of the disease, which might respond differently to treatment. This heterogeneity could also inform strategies to prevent or overcome drug resistance.

5. CNV-Based UMAP Analysis of Pancreatic Single-Cell Landscape

[Analysis Visualization Results]...

Analysis Overview

This analysis presents five UMAP (Uniform Manifold Approximation and Projection) plots, generated using inferred Copy Number Variation (CNV) patterns from single-cell RNA-seq data. The UMAPs visualize the cellular landscape of the pancreas, with cells colored according to different metadata attributes: major cell type (celltype_major), minor cell type (celltype_minor), inferred ploidy status (ploidy_dec), experimental condition (condition), and individual sample (sample). The embedding was performed with a focus on CNVs, allowing for the segregation of cells based on their genomic alteration profiles, which is particularly relevant in the context of Pancreatic Ductal Adenocarcinoma (PDAC).

Visual Summary

General Embedding Structure

The UMAPs display a complex and heterogeneous cellular landscape. A large, central, somewhat amorphous cluster is evident, representing a diverse population of cells. Several distinct, smaller clusters and "arms" extend from this central mass, indicating populations with unique CNV profiles. The use of CNV information for embedding appears to effectively separate cells based on genomic characteristics.

celltype_major and celltype_minor

• Cell Type Distribution: The central dense region of the UMAP is highly mixed, containing various major and minor cell types such as Stromal cells, Myeloid cells, and Ductal cells.
• Ductal Cell Prominence: Ductal cell populations (orange in celltype_major, orange in celltype_minor) are notably widespread across the UMAP, appearing in the central mixed region as well as forming significant portions of the more distinct clusters on the left and bottom-right of the embedding. This broad distribution is consistent with their role as the tumor-origin cell type.
• Acinar Cell Segregation: Acinar cell populations (burgundy) tend to form more distinct clusters, particularly in the top-left region, suggesting a relatively unique and homogeneous CNV profile compared to other cell types.
• Immune and Stromal Cells: T cells (blue) appear in several discrete clusters, reflecting their diverse subtypes. Stromal cells (teal) and Myeloid cells (light yellow/green) are broadly distributed, often co-localizing with other cell types in the central area, which could represent components of the tumor microenvironment or normal tissue.

ploidy_dec

• Aneuploid Cell Segregation: The ploidy_dec UMAP provides a striking visualization of aneuploidy. Diploid cells (light yellow) constitute the vast majority of the central, mixed population. In contrast, Aneuploid cells (burgundy) are highly localized and enriched in specific, well-defined clusters, predominantly on the left side of the UMAP (e.g., top-left arm, middle-left small cluster) and a distinct cluster at the bottom-right. This clear spatial separation strongly suggests that CNV patterns are effective in distinguishing aneuploid (putative malignant) cells from diploid (putative normal or non-malignant) cells.
• "Unclear" Cells: A very small number of "Unclear" cells (dark blue) are scattered, consistent with their low count (0.1% of total cells).

condition

• PDAC Enrichment: Cells from PDAC samples (dark blue) are extensively distributed across the UMAP, heavily populating the regions where aneuploid cells are concentrated (left-side and bottom-right clusters). This indicates that these distinct clusters represent tumor cells and associated microenvironment cells derived from cancer samples.
• Adj_normal Contribution: Adj_normal cells (burgundy) are primarily found within the central, mixed population, and some peripheral clusters. They also show some overlap with PDAC cells in the central region, which is expected as PDAC samples also contain normal and non-malignant stromal/immune cells.

sample

• Sample Integration and Specificity: The sample UMAP shows a mosaic pattern across the embedding, indicating that cells from different samples (both Adj_N and PDAC) are generally well-integrated, reducing concerns about strong batch effects solely driven by sample origin in the CNV space.
• Heterogeneity within PDAC: Specific PDAC samples (e.g., PDAC_1, PDAC_2, etc.) contribute to distinct aneuploid clusters, highlighting inter-patient heterogeneity in CNV profiles within the PDAC cohort. Some regions also show a mix of Adj_N and PDAC samples, particularly in the central, diploid-rich areas.

Biological Interpretation

The CNV-based UMAP provides critical insights into the cellular composition and genomic alterations within the pancreatic tissue in the context of PDAC.

• Tumor Cell Identification and Origin: The distinct clustering of aneuploid cells, predominantly overlapping with Ductal cell populations and originating from PDAC samples, strongly supports the identification of Ductal cell as the tumor-origin cell type. This aligns with PDAC being a ductal carcinoma, characterized by genetic instability and aneuploidy.
• Genomic Heterogeneity of PDAC: The distribution of PDAC cells across multiple UMAP clusters, including both aneuploid-rich and mixed regions, underscores the high genomic and cellular heterogeneity typical of PDAC. This suggests that even within tumor samples, there is a complex mix of malignant cells with varying CNV profiles, along with associated non-malignant stromal and immune cells.
• Tumor Microenvironment: The co-localization of Stromal cell and Myeloid cell populations with Ductal cell clusters in both normal and PDAC-associated regions highlights the integral role of the tumor microenvironment. The UMAP suggests that while the malignant cells are segregated by their CNVs, the surrounding cells (e.g., Fibroblasts, Macrophages) may adopt distinct CNV-defined states or simply represent non-malignant components co-existing with the tumor.
• Distinguishing Normal vs. Tumor Components: The effective separation of diploid and aneuploid cells by CNV patterns validates the power of this approach to dissect the distinct genomic landscapes of normal and malignant cell populations within a complex tissue. Acinar cells, a major functional component of the pancreas, also show distinct CNV-based clustering, suggesting that even normal cell types might have unique, stable CNV signatures or lack significant alterations distinguishing them clearly from other cell types.

Annotation Notes

• The clear segregation of aneuploid cells in specific UMAP regions, which largely correspond to Ductal cell populations from PDAC samples, strongly validates the accuracy of both the ploidy calls and the tumor-origin cell type identification.
• The embedding effectively captures meaningful biological variation related to CNVs, allowing for the distinction of tumor cells from normal cells and the visualization of inter-sample heterogeneity within PDAC.
• While overall sample integration appears robust, the presence of sample-specific clusters within the aneuploid regions indicates that inter-patient variability in CNV patterns is a significant factor in PDAC, which is biologically expected.
• The "unassigned" category is present but not significantly clustered or widespread, indicating that most cells have been successfully assigned a cell type.

6. Pancreatic Minor Cell Type Population Analysis in PDAC vs. Adjacent Normal Tissue

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a stacked bar plot visualizing the relative proportions of minor cell types across individual samples from both "Adj_normal" (adjacent normal pancreas tissue) and "PDAC" (Pancreatic Ductal Adenocarcinoma) conditions. The data is derived from single-cell RNA sequencing, allowing for a detailed examination of the cellular composition within each sample. The objective is to identify shifts in cell type populations associated with the disease state, providing insights into the tumor microenvironment of PDAC.

Visual Summary

The bar plot clearly delineates the cellular heterogeneity between adjacent normal pancreatic tissue and PDAC samples.

• Adjacent Normal Samples (AdjN_1, AdjN_2, AdjN_3): These samples show a relatively consistent cellular composition, characterized by a substantial proportion of Acinar cells (dark red), followed by considerable fractions of Macrophages (light yellow), and various immune cells including T cells (CD4+ and CD8+, teal/blue shades). Ductal cells (orange-red) and Stellate cells (mint green) are present but in smaller proportions.
• PDAC Samples (e.g., PDAC_16, PDAC_6, PDAC_3, etc.): In stark contrast, PDAC samples exhibit significant alterations in cell type proportions, which are also highly heterogeneous across individual tumor samples.
  • Ductal cells (orange-red): This cell type shows a pronounced increase in most PDAC samples, often becoming the predominant cell population, consistent with their identification as the tumor-origin cell type.
  • Acinar cells (dark red): Conversely, Acinar cells are markedly reduced, sometimes nearly absent, in the majority of PDAC samples.
  • Stellate cells (mint green) and Fibroblasts (orange): These stromal cell types are generally increased in PDAC samples compared to adjacent normal tissue, contributing significantly to the tumor mass in many instances.
  • Macrophages (light yellow): Macrophages remain a notable component in PDAC samples, with their proportions often sustained or even appearing increased compared to normal tissue.
  • Lymphocytes (T cells, B cells, NK cells): T cells (CD4+ and CD8+) are consistently present in PDAC samples, although their relative proportions vary. Other immune cells like B cells and NK cells are generally minor populations in both conditions but show sample-specific fluctuations.

Biological Interpretation

The observed shifts in cell populations provide critical biological insights into the pathogenesis and microenvironment of Pancreatic Ductal Adenocarcinoma.

• Tumor Cell Expansion: The dramatic increase in Ductal cells in PDAC samples directly reflects the malignant transformation and proliferation of pancreatic ductal epithelial cells, confirming the dataset's inference that Ductal cells are the resolved tumor-origin cell type in PDAC. This expansion displaces normal pancreatic parenchyma.
• Loss of Normal Parenchyma: The substantial reduction in Acinar cells in PDAC samples is consistent with the destruction and replacement of normal exocrine pancreatic tissue by the growing tumor and its associated stroma.
• Desmoplastic Stroma Formation: The significant increase in Stellate cells (likely pancreatic stellate cells, PSCs) and Fibroblasts (likely cancer-associated fibroblasts, CAFs) is a hallmark of PDAC. These cells are key drivers of the extensive desmoplastic stroma, a dense extracellular matrix that is a defining feature of PDAC. This desmoplasia contributes to tumor hardness, poor drug penetration, and creates a pro-tumorigenic microenvironment.
• Immune Microenvironment Remodeling: The sustained or increased presence of Macrophages within the PDAC samples suggests the recruitment and accumulation of tumor-associated macrophages (TAMs). TAMs are known to play diverse roles in PDAC progression, often promoting angiogenesis, immune suppression, and metastasis. The variable proportions of T cells indicate the presence of an adaptive immune response, but the overall immune landscape is heterogeneous, typical of the complex and often immune-suppressive PDAC microenvironment.

Clinical or Translational Implications

The distinct cellular composition of PDAC, characterized by expanded tumor (Ductal) cells and a prominent desmoplastic stroma (Stellate cells, Fibroblasts), alongside an altered immune infiltrate (Macrophages), has several clinical and translational implications:

• Diagnostic Potential: Changes in the proportion of Ductal, Acinar, Stellate, and Fibroblast cells could serve as diagnostic or prognostic markers for PDAC, potentially detectable through advanced biopsy analysis or spatial transcriptomics.
• Therapeutic Targeting of the Stroma: The pervasive increase in Stellate cells and Fibroblasts highlights the tumor stroma as a critical therapeutic target. Strategies aimed at depleting or reprogramming these stromal components, or disrupting their interactions with tumor cells, could enhance drug delivery and efficacy.
• Immunotherapy Challenges: The presence of macrophages and variable T-cell infiltration underscores the complex immune landscape of PDAC. Understanding the specific phenotypes and functional states of these immune cells (e.g., M1 vs. M2 macrophages, exhausted T cells) is crucial for developing effective immunotherapeutic strategies, which have historically faced challenges in PDAC due to its highly immunosuppressive microenvironment.
• Disease Heterogeneity: The sample-to-sample variability in cell type proportions within the PDAC group emphasizes the high inter-patient heterogeneity of the disease, suggesting that personalized therapeutic approaches might be necessary based on individual tumor microenvironment profiles.

7. T Cell and Innate Lymphoid Cell Subpopulation Analysis in Pancreatic Ductal Adenocarcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a stacked bar plot visualizing the relative proportions of T cell and Innate Lymphoid Cell (ILC) subsets within the overall 'T cell' major cell type. The visualization compares samples from 'Adj_normal' (adjacent normal pancreatic tissue) and 'PDAC' (Pancreatic Ductal Adenocarcinoma) conditions, providing insights into the composition of these immune cell populations in the tumor microenvironment versus healthy tissue.

The data originates from single-cell RNA-seq of human pancreas, encompassing 3 adjacent normal samples (AdjN_1, AdjN_2, AdjN_3) and 14 PDAC tumor samples (e.g., PDAC_1, PDAC_2, ..., PDAC_16). The plot focuses on a detailed breakdown of cell types including various T helper subsets (Cytotoxic, Naive, Tfh, Th1, Th17, Th2, Th22, Th9, Treg), different ILC types (ILC1, ILC2, ILC3 (NCR+), ILC3 (NCR-), ILCreg, LTI), NK cells, and any 'unassigned' cells within the T cell major cluster.

Visual Summary

The stacked bar plot effectively illustrates the compositional differences of T cell and ILC subsets across individual samples from both conditions:

• Adjacent Normal (Adj_normal) Samples: These samples exhibit a relatively consistent immune cell composition. They are predominantly characterized by T cell (Cytotoxic) and T cell (Naive) populations, forming the majority of the T cell compartment. Other T cell subsets (e.g., T cell (Th1), T cell (Treg)) and ILCs (e.g., ILC1, ILC3 (NCR+)) are present in very low, almost negligible, proportions.
• Pancreatic Ductal Adenocarcinoma (PDAC) Samples: In stark contrast to the normal samples, PDAC samples display considerable heterogeneity in their immune cell composition:
  • Increased ILC Populations: A notable observation is the significant increase in Innate Lymphoid Cell (ILC) populations, particularly ILC1 (dark red) and ILC3 (NCR+) (red-orange), in several PDAC samples (e.g., PDAC_1, PDAC_13, PDAC_15, PDAC_5). These subsets are almost absent in the adjacent normal tissue.
  • Shifts in T cell Subsets: While T cell (Cytotoxic) and T cell (Naive) still constitute a substantial portion in many PDAC samples, their relative dominance appears to be reduced in some, often replaced by expanded ILC or other T cell subsets.
  • T regulatory (Treg) Cells: T cell (Treg) populations (dark blue) also appear slightly more prominent in several PDAC samples compared to their low abundance in adjacent normal tissue.
  • Other T Helper Subsets: Other T helper cell types (e.g., T cell (Th1), T cell (Th17)) are present in varying, generally smaller, proportions across PDAC samples, contributing to the overall heterogeneity.
  • NK cells: NK cells are present in low to moderate proportions in PDAC samples, similar to or slightly higher than in normal samples.

Biological Interpretation

The observed shifts in T cell and ILC subpopulation distribution provide critical biological insights into the immune landscape of PDAC:

• Remodeling of the Tumor Immune Microenvironment: The most striking finding is the profound remodeling of the T cell and ILC compartment in PDAC. The adjacent normal pancreas shows a relatively quiescent T cell profile, primarily composed of conventional cytotoxic and naive T cells. In contrast, the PDAC samples exhibit a much more diverse and often skewed immune composition, indicative of an active, albeit potentially dysregulated, immune response to the tumor.
• Emergence of Innate Lymphoid Cell Responses: The significant expansion of ILC1 and ILC3 (NCR+) cells in PDAC is a key observation.
  • ILC1s are often associated with type 1 immunity, producing IFN-γ, and can contribute to anti-tumor responses, similar to cytotoxic T cells [1]. Their elevated presence could signify an ongoing attempt by the innate immune system to combat the tumor, though their functional status (e.g., exhausted vs. active) requires further investigation.
  • ILC3 (NCR+) cells are known for producing IL-17 and IL-22, which can have complex roles in cancer, sometimes promoting anti-tumor immunity, but also contributing to chronic inflammation and tumor progression, particularly in inflammation-driven cancers like PDAC [2]. Their enrichment suggests a shift towards inflammatory pathways.

Adaptive Immune Cell Dysregulation:

• The relative decrease in cytotoxic T cells in some PDAC samples might indicate immune evasion mechanisms by the tumor or T cell exhaustion within the immunosuppressive tumor microenvironment, a common feature in many cancers.
• The increased presence of T regulatory cells (Tregs) is consistent with their established role in suppressing anti-tumor immune responses, thereby fostering an immunosuppressive environment that promotes tumor growth and progression in PDAC [3].

• Inter-tumor Heterogeneity: The substantial sample-to-sample variability among PDAC patients highlights the heterogeneous nature of the tumor immune microenvironment. This suggests that distinct immune subtypes may exist within PDAC, potentially influencing disease progression and therapeutic responsiveness.

Clinical or Translational Implications

The findings from this analysis have several important clinical and translational implications for PDAC:

• Biomarker Potential: The distinct profiles of ILC1 and ILC3 (NCR+) cells in PDAC, which are largely absent in normal tissue, could serve as novel biomarkers for PDAC diagnosis, prognosis, or even for stratifying patients into different immune subtypes.

Therapeutic Target Identification:

• Further research into the functional state of ILC1s in PDAC is warranted. If they are dysfunctional, strategies to reactivate or enhance their anti-tumor activity could be explored.
• The expansion of ILC3 (NCR+) suggests that targeting the IL-17/IL-22 axis or other pro-inflammatory pathways mediated by these cells might offer therapeutic opportunities in specific PDAC subsets.
• The presence of an increased T cell (Treg) population points towards immunotherapeutic strategies aimed at depleting or inhibiting Treg function, such as checkpoint inhibitors or specific anti-Treg therapies, to unleash anti-tumor immunity.

• Personalized Immunotherapy: The observed heterogeneity in immune cell composition underscores the necessity for personalized approaches to PDAC immunotherapy. Profiling the immune microenvironment of individual patients could help predict response to specific treatments (e.g., immune checkpoint inhibitors, cytokine therapies) and guide the selection of combination therapies.

---

References:

[1] Vivier, E., et al. (2018). Innate Lymphoid Cells: 10 Years On. *Cell*, 174(5), 1045-1056. PubMed search for "Innate Lymphoid Cells cancer review"

[2] Yang, W., et al. (2021). Innate lymphoid cells in the tumor microenvironment. *Frontiers in Immunology*, 12, 706497. PubMed search for "ILC3 cancer review"

[3] Plitas, G., & Rudensky, A. Y. (2016). Regulatory T cells in cancer. *Immunity*, 44(6), 1215-1226. PubMed search for "T regulatory cells cancer review"

8. Differentially Abundant T Cell Subsets in Pancreatic Ductal Adenocarcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportional representation of various T cell subsets (as defined by celltype_subset) within the Pancreas tissue, comparing Pancreatic Ductal Adenocarcinoma (PDAC) samples against adjacent normal (Adj_normal) tissue samples. The box plots display the celltype proportion for each T cell subset across conditions, highlighting statistically significant differences with corresponding p-values.

Visual Summary

The visualization presents box plots for eight distinct T cell subsets, all of which show statistically significant differences in their proportions between PDAC and Adj_normal conditions.

• T_Cyto (Cytotoxic T cells): Display a markedly lower proportion in PDAC samples (median ~25-30%) compared to Adj_normal samples (median ~80%). This difference is highly significant (p=7.65e-11).
• T_Naive (Naive T cells): Exhibit a substantially higher proportion in PDAC samples (median ~30%) compared to Adj_normal samples (median ~3-5%). This difference is highly significant (p=5.63e-07).
• Treg (Regulatory T cells): Are present in significantly higher proportions in PDAC samples (median ~5%) compared to Adj_normal samples (median ~0.5%). This difference is highly significant (p=0.00065).
• Th9: Show a higher proportion in PDAC samples (median ~0.3%) than in Adj_normal samples (median near 0). This difference is significant (p=0.00142).
• Tfh (Follicular Helper T cells): Are significantly more abundant in PDAC samples (median ~8%) compared to Adj_normal samples (median ~2.5%). This difference is significant (p=0.00249).
• Th17: Display a higher proportion in PDAC samples (median ~2.5%) compared to Adj_normal samples (median ~0.2%). This difference is significant (p=0.00254).
• Th2: Show an elevated proportion in PDAC samples (median ~1.0%) relative to Adj_normal samples (median ~0.1%). This difference is significant (p=0.00604).
• Th22: Are present in higher proportions in PDAC samples (median ~2.0%) compared to Adj_normal samples (median ~0.7%). This difference is significant (p=0.00655).

In summary, across all plotted T cell subsets, Cytotoxic T cells are significantly reduced in PDAC, while Naive T cells, Regulatory T cells, and all observed Helper T cell subsets (Th9, Tfh, Th17, Th2, Th22) are significantly increased in PDAC compared to adjacent normal tissue.

Biological Interpretation

The observed shifts in T cell subset proportions between PDAC and adjacent normal tissue provide critical insights into the immune landscape of pancreatic cancer, which is known for its immunosuppressive tumor microenvironment (TME).

1. Reduced Cytotoxic T Cells (T_Cyto) in PDAC: The drastic reduction of cytotoxic T cells in PDAC is a hallmark of immune evasion. Cytotoxic T cells (CD8+ T cells) are the primary effectors of anti-tumor immunity, directly killing cancer cells. Their diminished presence suggests a compromised anti-tumor immune response within the PDAC TME, contributing to tumor progression and resistance to immunotherapies. PubMed Search: PDAC cytotoxic T cell exhaustion infiltration
2. Increased Naive T Cells (T_Naive) in PDAC: An elevated proportion of naive T cells could indicate an influx of unprimed T cells into the TME or a failure of T cell activation and differentiation into effective anti-tumor effector cells. In an immunosuppressive environment like PDAC, these naive cells may not be effectively activated or might differentiate into pro-tumorigenic phenotypes.
3. Increased Regulatory T Cells (Treg) in PDAC: The significant enrichment of Tregs in PDAC is a well-established mechanism of immune suppression. Tregs actively suppress the function of effector T cells and other immune cells, thereby promoting immune tolerance towards tumor cells and hindering effective anti-tumor responses. This is a strong indicator of an immunosuppressive TME. GeneCards: FOXP3 (marker for Treg)
4. Increased Helper T Cell Subsets (Th9, Tfh, Th17, Th2, Th22) in PDAC:
   • Th2 and Th17 Cells: These subsets often play pro-tumorigenic roles in many cancers, including PDAC. Th2 cells can promote a humoral (antibody-mediated) and immunosuppressive (M2 macrophage polarization) microenvironment, while Th17 cells are associated with chronic inflammation, angiogenesis, and tumor growth.
   • Th9 Cells: Their role in cancer is context-dependent. While they can contribute to anti-tumor immunity in some settings, their increase in PDAC, alongside other immunosuppressive elements, might suggest a pro-tumorigenic or anergic state.
   • Tfh Cells: Typically found in lymphoid follicles, Tfh cells help B cells produce high-affinity antibodies. Their presence in the tumor context, potentially within tertiary lymphoid structures, can sometimes correlate with anti-tumor responses, but in an overall immunosuppressive environment like PDAC, their functional impact on tumor control is complex and warrants further investigation.
   • Th22 Cells: Involved in tissue inflammation and repair. Their increased presence in PDAC might contribute to the chronic inflammatory state known to foster tumor development and progression.

Collectively, these findings paint a picture of an immune microenvironment in PDAC that is highly skewed towards immune evasion and suppression. The dramatic decrease in tumor-killing cytotoxic T cells, coupled with the significant increase in immune-suppressive Tregs and several helper T cell subsets that can promote chronic inflammation or contribute to immune tolerance, underscores the formidable immunological barriers to effective anti-tumor responses in PDAC.

Clinical or Translational Implications

The distinct T cell subset profiles observed in PDAC versus adjacent normal tissue have several important clinical implications:

1. Biomarkers for Disease Progression/Prognosis: The relative proportions of these T cell subsets, particularly the T_Cyto/Treg ratio, could serve as prognostic biomarkers for PDAC patients. A lower T_Cyto proportion and higher Treg proportion in the tumor microenvironment are generally associated with poorer prognosis and reduced response to therapies.
2. Immunotherapy Resistance and Targets: The severely depleted cytotoxic T cell population and expanded immunosuppressive Treg population highlight a major mechanism of immunotherapy resistance in PDAC. Strategies aimed at increasing cytotoxic T cell infiltration and activity (e.g., CAR T cells, oncolytic viruses, specific vaccines) or depleting/inhibiting Tregs (e.g., anti-CTLA-4, small molecule inhibitors) could be crucial for improving patient outcomes.
3. Therapeutic Strategies Targeting Helper T Cells: The increased Th2, Th17, Th9, and Th22 populations suggest that targeting the signaling pathways that drive the differentiation and function of these pro-tumorigenic/inflammatory helper T cell subsets could be a viable therapeutic approach to modulate the TME. For instance, modulating cytokine environments (e.g., IL-4, IL-17) that support these cell types might shift the immune balance.
4. Combination Therapies: Given the multifaceted immunosuppression, combination immunotherapies that simultaneously enhance anti-tumor immunity (e.g., checkpoint blockade to reinvigorate T cells) and counteract pro-tumorigenic/immunosuppressive elements (e.g., Treg inhibition, myeloid-derived suppressor cell targeting) are likely to be more effective in PDAC.

9. Population Analysis of Macrophage Subsets in Pancreatic Ductal Adenocarcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a bar plot illustrating the relative proportions of different macrophage subsets within individual samples, distinguishing between 'Adj_normal' (adjacent normal pancreatic tissue) and 'PDAC' (Pancreatic Ductal Adenocarcinoma) conditions. The macrophage populations are categorized into five subsets: Macrophage (M1), Macrophage (M2A), Macrophage (M2B), Macrophage (M2C), and Macrophage (M2D). This visualization allows for a direct comparison of macrophage polarization states across different samples and conditions, providing insight into the immune microenvironment in PDAC.

Visual Summary

The stacked bar plot effectively displays the cellular composition of macrophage subsets for each sample.

• Adjacent Normal Samples (Adj_normal): The three adjacent normal samples (AdjN_1, AdjN_3, AdjN_2) show a relatively consistent macrophage subset distribution. Macrophage (M1) (burgundy) constitutes the largest fraction, typically ranging from approximately 35% to 55%. Macrophage (M2A) (orange) is the second most abundant subset, generally around 20-30%. Macrophage (M2B), (M2C), and (M2D) collectively make up the remaining smaller proportions.
• PDAC Samples: The PDAC samples exhibit substantial heterogeneity in their macrophage subset composition.
  • M1 Dominance: A significant number of PDAC samples (e.g., PDAC_1, PDAC_16, PDAC_4, PDAC_2, PDAC_I2, PDAC_8, PDAC_3, PDAC_5) show a pronounced increase in the Macrophage (M1) population, often exceeding 60% and sometimes reaching close to 80% of total macrophages. This suggests a strong presence of M1-polarized macrophages in these tumors.
  • M2A Prominence: Conversely, other PDAC samples (e.g., PDAC_9, PDAC_11B, PDAC_7, PDAC_11A, PDAC_10, PDAC_6) display a relatively higher proportion of Macrophage (M2A) compared to the normal samples, often surpassing 30% and sometimes becoming the most dominant subset, exceeding M1 levels.
  • Other M2 Subsets: While M2B, M2C, and M2D subsets are present across both conditions, their individual proportions remain relatively minor, though shifts in their contributions are noticeable in some PDAC samples, particularly when M1 and M2A are less dominant.
  • Overall Heterogeneity: The most striking feature is the considerable inter-sample variability within the PDAC group, highlighting diverse immune landscapes among individual tumors.

Biological Interpretation

Macrophages are critical components of the tumor microenvironment (TME) and are broadly classified into pro-inflammatory (M1-like) and anti-inflammatory/pro-tumorigenic (M2-like) phenotypes.

• M1 Macrophages: Classically activated M1 macrophages are characterized by their ability to produce pro-inflammatory cytokines, present antigens, and directly kill tumor cells, thus being associated with anti-tumor immunity [1]. The observed increase in M1 macrophage proportion in many PDAC samples, compared to adjacent normal tissue, is an intriguing finding. While often considered anti-tumorigenic, the persistent presence of M1-like macrophages in the TME might also indicate chronic inflammation that can, in some contexts, contribute to tumor progression or reflect an ongoing, yet potentially ineffective, anti-tumor immune response.
• M2 Macrophages (M2A, M2B, M2C, M2D): M2 macrophages are a heterogeneous group generally associated with immune suppression, tissue repair, angiogenesis, and tumor growth and metastasis [2].
  • M2A Macrophages: These are typically induced by Th2 cytokines (IL-4, IL-13) and are involved in allergic responses and parasitic infections, as well as tissue repair and fibrosis. In cancer, M2A macrophages often promote tumor progression, angiogenesis, and dampen anti-tumor immunity [3]. The elevated M2A populations in a subset of PDAC samples align with the known pro-tumorigenic roles of tumor-associated macrophages (TAMs) in many cancers, including PDAC.
  • M2B, M2C, M2D Macrophages: These subsets also contribute to the complex immune suppressive and pro-tumorigenic functions within the TME, with roles in regulating inflammation and promoting tissue remodeling. While their individual contributions appear smaller, their collective presence still contributes to the overall immune context.
• PDAC Heterogeneity: The substantial patient-to-patient variability in macrophage polarization states within PDAC is a key biological insight. This suggests that PDAC does not present a uniform immune landscape. Some tumors appear to harbor a TME with a relatively stronger M1 signature, potentially indicative of an active immune response or a specific disease subtype. In contrast, other tumors exhibit a more pronounced M2A signature, characteristic of an immunosuppressive and pro-tumorigenic environment. This heterogeneity underscores the complexity of immune responses in PDAC and highlights the potential for distinct underlying biological mechanisms driving tumor progression in different patients.

Clinical or Translational Implications

The observed heterogeneity in macrophage polarization across PDAC samples has significant clinical and translational implications:

• Patient Stratification and Prognosis: The specific macrophage subset composition in a patient's tumor could serve as a valuable biomarker for stratifying patients into groups with distinct prognoses or predicted responses to therapy. For instance, tumors with higher M1 infiltration might respond differently to immunotherapies compared to those dominated by M2A macrophages.
• Targeted Immunotherapy: Understanding the predominant macrophage phenotype in an individual tumor could guide personalized therapeutic strategies.
  • For tumors with a high M2A signature, strategies aimed at reprogramming M2 macrophages to an M1-like phenotype (e.g., via CD40 agonists, CSF1R inhibitors) or depleting M2 populations could be beneficial [4, 5].
  • In tumors with dominant M1 populations, further investigations would be needed to understand why these M1 macrophages are not effectively clearing the tumor, potentially leading to combination therapies that enhance M1 effector functions or overcome inhibitory signals within the TME.
• Monitoring Therapeutic Response: Changes in macrophage polarization profiles could be monitored during treatment to assess the efficacy of immunomodulatory therapies and predict patient outcomes.

References

1. Mills, C.D., Kincaid, K.M., Juntilla, J.M., et al. (2000). M-1/M-2 macrophages and the Th1/Th2 paradigm. *J Immunol*, 164(11), 5013-5023. [PubMed Search: M1 M2 macrophages]
2. Mantovani, A., Sica, A., Sozzani, S., et al. (2004). The chemokine system in cancer biology and therapy. *Cytokine Growth Factor Rev*, 15(4), 239-247. [PubMed Search: M2 macrophages cancer]
3. Gordon, S., & Martinez, F.O. (2010). Alternative activation of macrophages: mechanism and functions. *Immunity*, 32(5), 593-604. [PubMed Search: M2A macrophages]
4. Ruffell, B., & Coussens, L.M. (2015). Macrophages and therapeutic resistance in cancer. *Cancer Cell*, 27(4), 462-472. [PubMed Search: Macrophage reprogramming cancer therapy]
5. Najafi, M., Farhood, B., & Mortezaee, K. (2019). Macrophage polarity in cancer: a review. *J Cell Biochem*, 120(1), 273-283. [PubMed Search: Macrophage polarization cancer therapy]

10. Ploidy Analysis of Tumor-Origin Ductal Cells in Pancreatic Ductal Adenocarcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the ploidy status (Aneuploid, Diploid, or Unclear) of Ductal cells, which have been identified as the tumor-origin cell type, across samples from both adjacent normal pancreatic tissue (Adj_normal) and Pancreatic Ductal Adenocarcinoma (PDAC) conditions. The aim is to visualize the distribution of ploidy populations within this specific cell type across individual samples to understand genomic stability changes associated with PDAC.

Visual Summary

The bar plot displays the percentage of Ductal cells classified as Aneuploid (maroon), Diploid (orange), or Unclear (light green) for each sample.

• Adjacent Normal Samples (Adj_normal): The three adjacent normal samples (AdjN_1, AdjN_2, AdjN_3) show a nearly homogeneous population of Ductal cells that are overwhelmingly Diploid, comprising close to 100% of the cells. A minimal fraction of cells, if any, is classified as Unclear, and virtually no Aneuploid cells are observed.
• PDAC Samples: In stark contrast, the PDAC samples exhibit a significant and highly variable presence of Aneuploid Ductal cells.
  • Some PDAC samples (e.g., PDAC_11A, PDAC_10, PDAC_4) show a mixed population, with a smaller but notable proportion of Aneuploid cells (ranging from approximately 10% to 25%) alongside a dominant Diploid population.
  • As observed in samples like PDAC_9, PDAC_5, PDAC_8, and PDAC_11B, there's a progressive increase in the proportion of Aneuploid Ductal cells, reaching approximately 40-50% in PDAC_11B.
  • A majority of the PDAC samples (e.g., PDAC_7, PDAC_1, PDAC_6, PDAC_15, PDAC_3, PDAC_2, PDAC_13, PDAC_16) are predominantly composed of Aneuploid Ductal cells, often exceeding 90-95% of the population, with only a small fraction remaining Diploid.
• Unclear Population: The "Unclear" ploidy classification remains consistently low across all samples, both normal and PDAC, suggesting robust ploidy determination.

Biological Interpretation

The observed ploidy profiles strongly support the identification of Ductal cells as the tumor-origin cells in PDAC and highlight critical aspects of tumor biology:

• Genomic Stability in Normal Cells: The adjacent normal Ductal cells maintain a stable diploid genome, which is characteristic of healthy somatic cells and a baseline for comparison.
• Hallmark of Cancer: The presence and high proportion of aneuploid Ductal cells in PDAC samples is a classic hallmark of cancer, indicating significant genomic instability and chromosomal abnormalities within the malignant cell population. Aneuploidy is frequently associated with tumor initiation, progression, and resistance to therapy PubMed Search: Aneuploidy cancer hallmark.
• Tumor Cell Identification: Given that Ductal cells were resolved as the tumor-origin cell type, these aneuploid Ductal cells are very likely the true cancer cells within the PDAC tumors. This finding provides direct evidence of the malignant transformation occurring in these specific cells.
• Intra-Tumor Heterogeneity and Evolution: The substantial variability in the proportion of aneuploid Ductal cells across different PDAC samples suggests significant intra-tumor heterogeneity and potentially different stages of tumor progression or clonal evolution. Samples with a lower percentage of aneuploidy might represent earlier stages, less aggressive tumors, or samples with a higher admixture of non-malignant cells. Conversely, samples with a very high percentage of aneuploidy likely represent advanced tumors dominated by highly unstable cancer clones.

Clinical or Translational Implications

The ploidy analysis of Ductal cells offers several clinical and translational implications for PDAC:

• Diagnostic Biomarker: The detection of a significant aneuploid Ductal cell population can serve as a strong diagnostic indicator for PDAC, differentiating malignant from normal pancreatic tissue.
• Prognostic Indicator: The degree of aneuploidy within Ductal cell populations could potentially correlate with tumor aggressiveness, clinical stage, or patient prognosis. Tumors with a higher proportion of aneuploid cells might be associated with more aggressive disease behavior.
• Therapeutic Targeting: Understanding the genomic instability and aneuploidy landscape in individual PDAC tumors might inform patient stratification for therapies targeting DNA repair pathways or cell cycle checkpoints that are often dysregulated in aneuploid cells. The heterogeneity observed suggests that a "one-size-fits-all" approach to such therapies might be less effective.
• Monitoring Tumor Evolution: Changes in ploidy populations over time or in response to treatment could potentially be used to monitor disease progression or therapeutic efficacy.

11. Cell-Cell Interaction Patterns in Pancreatic Ductal Adenocarcinoma (PDAC) Tumor Microenvironment

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interaction (CCI) patterns using CellPhoneDB across two conditions: 'Adj_normal' (adjacent normal pancreas tissue) and 'PDAC' (Pancreatic Ductal Adenocarcinoma tissue). The focus is on interactions involving key cell types: Ductal cells (specifically 'Aneuploid Ductal' cells in the tumor context), Macrophages, and T cells (CD8+ and CD4+). The dot plots visualize significant ligand-receptor interactions, where dot size represents the statistical significance (-log10(p-value)) and dot color indicates the average expression level (log2(mean)). A maximum of 80 top interactions per group are displayed.

Visual Summary

Adj_normal Condition:

• The 'Adj_normal' plot primarily highlights interactions among immune cells, specifically T cells (CD8+) and Macrophages, as well as T cell self-interactions.
• Key interactions observed include various HLA-E related pairs (e.g., HLA-E-CD94/NKG2A, HLA-E-KLRC1/2, HLA-E-LILRB2), cell adhesion molecules (e.g., CD58-CD2, ICAM1-integrin complexes), and cytokine signaling (e.g., IL18-IL18R1, TGFB1-TGFBR1).
• The overall density of highly significant interactions appears moderate, reflecting a balanced immune surveillance and tissue homeostasis environment.

PDAC Condition:

• The 'PDAC' plot shows a dramatic increase in the complexity and number of significant cell-cell interactions, particularly involving 'Aneuploid Ductal' cells (identified as the tumor-origin cell type).
• Extensive interactions are evident between 'Aneuploid Ductal' cells and Macrophages, and 'Aneuploid Ductal' cells and T cells (CD8+ and CD4+). Strong 'Aneuploid Ductal' self-interactions are also prominent.
• Several interactions involving growth factors, extracellular matrix components, and immune modulators are highly upregulated or uniquely observed in the PDAC context:
  • SPP1 (Osteopontin) with CD44 and various integrins (e.g., ITGAV/ITGB1/ITGB5/ITGB6): These are highly enriched in interactions between Aneuploid Ductal cells and Macrophages, and also Aneuploid Ductal self-interactions.
  • CEACAM1 and CEACAM5: Significant interactions, particularly Aneuploid Ductal self-interactions and Aneuploid Ductal-Macrophage interactions.
  • MIF (Macrophage Migration Inhibitory Factor) with CD74, CXCR2, CXCR4: Prominent in Macrophage self-interactions and Macrophage-Aneuploid Ductal interactions.
  • FN1 (Fibronectin) with integrin complexes: Observed in Aneuploid Ductal self-interactions and Aneuploid Ductal-Macrophage interactions.
  • TGFB1-TGFBR1: Shows increased intensity and involvement of Aneuploid Ductal cells interacting with themselves, Macrophages, and T cells.
  • GAS6-AXL: Appears in Macrophage-Aneuploid Ductal interactions.
  • MMP9-LRP1: Seen in Aneuploid Ductal-Macrophage interactions.
  • HLA-E interactions: Continue to be present, now involving Aneuploid Ductal cells with T cells and Macrophages, alongside immune cell interactions.
• Compared to 'Adj_normal', the 'PDAC' TME exhibits a landscape dominated by pro-tumorigenic and immunosuppressive interactions, primarily driven by the tumor cells and tumor-associated macrophages.

Biological Interpretation

The contrasting CCI patterns between 'Adj_normal' and 'PDAC' conditions underscore the profound reorganization of cell communication networks within the pancreatic tumor microenvironment.

1. Emergence of Tumor-Macrophage and Tumor-T cell Cross-Talk: The most striking feature in PDAC is the extensive and diverse set of interactions involving 'Aneuploid Ductal' cells (tumor cells) with Macrophages and T cells. This highlights the critical role of tumor-stroma interactions in PDAC progression. Macrophages, in particular, appear to be heavily recruited and reprogrammed by tumor cells, likely adopting pro-tumorigenic phenotypes (e.g., M2-like) that promote tumor growth, immune evasion, and metastasis.
2. Key Pro-tumorigenic Pathways Activated in PDAC:
   • SPP1-CD44/Integrin axis: Osteopontin (SPP1) is a secreted glycoprotein highly expressed in many cancers, including PDAC, and interacts with CD44 and various integrins. This signaling axis is known to promote tumor cell adhesion, migration, invasion, survival, and angiogenesis, and can also modulate immune responses towards a pro-tumorigenic phenotype [PubMed Search: SPP1 cancer]. Its strong presence in tumor-macrophage interactions suggests a mechanism for macrophage polarization and recruitment.
   • CEACAM1/CEACAM5 signaling: Carcinoembryonic antigen-related cell adhesion molecules (CEACAMs) are often overexpressed in adenocarcinomas like PDAC and are associated with tumor progression, metastasis, and immune evasion [PubMed Search: CEACAM1 CEACAM5 PDAC]. Their frequent self-interactions among Aneuploid Ductal cells suggest roles in tumor cell aggregation and survival, while interactions with macrophages could influence immune responses.
   • MIF-CD74/CXCR2/CXCR4: Macrophage Migration Inhibitory Factor (MIF) is a pleiotropic cytokine implicated in inflammation, immunity, and cancer. It promotes tumor growth, angiogenesis, and immune suppression by binding to CD74 and CXCR2/CXCR4 receptors [PubMed Search: MIF cancer pathway]. This interaction network, especially prevalent in Macrophage-Aneuploid Ductal communication, indicates its contribution to the inflammatory and pro-tumorigenic TME.
   • TGFB1-TGFBR1 pathway: Transforming Growth Factor-beta 1 (TGFB1) is a critical cytokine in PDAC, driving extensive fibrosis and suppressing anti-tumor immune responses [PubMed Search: TGFB1 PDAC TME]. Its prominent role in interactions involving tumor cells, macrophages, and T cells underscores its central function in creating an immunosuppressive and pro-fibrotic microenvironment that hinders effective immune surveillance.
   • FN1-integrin and MMP9-LRP1: Fibronectin (FN1) is a major extracellular matrix component. Its interactions with integrins, alongside Matrix Metalloproteinase 9 (MMP9) and its receptor LRP1, are indicative of extensive extracellular matrix remodeling, which facilitates tumor cell invasion and metastasis.
   • GAS6-AXL: Growth Arrest-specific 6 (GAS6) and its receptor AXL are involved in cell survival, proliferation, and metastasis, and are often upregulated in aggressive cancers, promoting resistance to therapy [GeneCards: AXL].
3. Immune Evasion Mechanisms: The continued presence and potentially altered context of HLA-E interactions (with NKG2A, LILRB2 on immune cells) in PDAC, now involving tumor cells, suggest active mechanisms of immune evasion. HLA-E can present peptides derived from classical HLA molecules, protecting tumor cells from NK cell and T cell-mediated killing. The strong TGFB1 signaling also directly suppresses cytotoxic T cell function.
4. Shift in Immune Cell Roles: While immune cell-immune cell interactions (T cell-Macrophage, Macrophage-Macrophage) are present in both conditions, their overall context shifts in PDAC. Macrophages appear to be co-opted by tumor cells to support cancer progression, rather than primarily mediate anti-tumor immunity. The observed T cell interactions with tumor cells may represent an attempt at immune response, but the co-occurrence of immunosuppressive signals suggests their function might be compromised.

Clinical or Translational Implications

The identified cell-cell interaction patterns in PDAC offer several promising avenues for therapeutic intervention and translational research:

1. Targeting Pro-tumorigenic Ligand-Receptor Axes:
   • SPP1-CD44/Integrin blockade: Inhibiting SPP1, CD44, or relevant integrins could disrupt tumor cell adhesion, migration, and survival, as well as modulate the pro-tumorigenic functions of tumor-associated macrophages (TAMs). This could be explored through monoclonal antibodies or small molecule inhibitors [PubMed Search: SPP1 CD44 integrin cancer therapy].
   • CEACAM1/CEACAM5 inhibition: Blocking these molecules could interfere with tumor cell growth, aggregation, and their interactions with immune cells, potentially sensitizing tumor cells to other therapies.
   • MIF-CD74/CXCR2/CXCR4 inhibition: Targeting MIF or its receptors could dampen the inflammatory and pro-tumorigenic signaling cascade in the TME, thereby inhibiting tumor growth and angiogenesis.
   • GAS6-AXL inhibition: AXL inhibitors are already under investigation in clinical trials for various cancers. Targeting this pathway in PDAC could overcome resistance mechanisms and inhibit tumor progression [GeneCards: AXL pathway].
2. Overcoming Immunosuppression in the TME:
   • TGFB1 pathway inhibition: Given the pervasive role of TGFB1 in PDAC fibrosis and immunosuppression, combination therapies targeting TGFB1 (e.g., with anti-PD-1/PD-L1) could be highly beneficial in re-activating anti-tumor immunity and reducing stromal barriers [PubMed Search: TGFB1 inhibition PDAC immunotherapy].
   • Modulating HLA-E interactions: Investigating agents that interfere with HLA-E mediated immune evasion (e.g., by targeting NKG2A or LILRB2 on immune cells) could enhance NK and T cell activity against tumor cells.
3. Reprogramming Tumor-Associated Macrophages (TAMs): The central role of Macrophages in tumor-cell interactions suggests that strategies aimed at depleting TAMs, blocking their recruitment, or reprogramming them from a pro-tumorigenic (M2-like) to an anti-tumorigenic (M1-like) phenotype could significantly improve therapeutic outcomes in PDAC.

These findings highlight specific molecular targets that are highly active in the PDAC tumor microenvironment, providing a data-driven basis for prioritizing novel therapeutic strategies, especially combination immunotherapies, to combat this aggressive cancer.

12. Condition-Specific Cell-Cell Interaction Patterns in Pancreatic Ductal Adenocarcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates condition-specific cell-cell interactions (CCIs) within the pancreas, comparing Pancreatic Ductal Adenocarcinoma (PDAC) samples against adjacent normal (Adj_normal) tissue. The analysis focuses on interactions involving Ductal cells, major immune cells (T cells, Macrophages, Mast cells, B cells, ILC, NK cells, Dendritic cells, Plasma cells), and stromal cells (Smooth muscle cells, Stellate cells, Fibroblasts, Endothelial cells). The dot plot visualizes the scaled interaction strength and significance (-log10(p)) for identified ligand-receptor pairs across individual samples within each condition. The primary goal is to identify CCIs that are significantly different between conditions, understand their associated signaling pathways, and explore their potential as therapeutic targets in PDAC.

Visual Summary

The dot plot, titled "Condition-specific CCI pattern," displays numerous cell-cell interaction pairs on the X-axis and individual samples on the Y-axis, grouped by condition (Adj_normal and PDAC).

• Overall Pattern: A striking difference in the density and strength of interactions is observed between the two conditions. The "Adj_normal" panel shows a relatively sparse pattern of interactions, with fewer and generally weaker signals (smaller, lighter red dots), primarily concentrated in the top few samples (AdjN_1, AdjN_2, AdjN_3). In contrast, the "PDAC" panel exhibits a pervasive and significantly stronger pattern of interactions (larger, darker red dots) across nearly all PDAC samples. This indicates a highly active and remodelled cellular communication network in the PDAC tumor microenvironment.
• Interaction Types in Adj_normal: In the adjacent normal tissue, notable interactions include VEGFA_FLT1 and VEGFA_NRP1 pairs primarily involving Endothelial cells with other Endothelial, NK, or Mast cells. Other interactions involve immune cells, such as HLA-E_KLRK1 between Endothelial/NK/T CD8+ cells and CCL4_CCR5 within T CD8+ cells. These interactions likely represent homeostatic processes related to vascular maintenance and basal immune surveillance.
• Interaction Types in PDAC: The PDAC microenvironment is characterized by a massive upregulation of interactions, predominantly involving Macrophages and T CD8+ cells. Key categories of upregulated interactions include:
  • Adhesion and Immune Checkpoint Molecules: Numerous ICAM-integrin complexes (e.g., ICAM1_ITGAL -- Mac | T CD8+), FN1-integrin complexes, CD99_PILRA, and critical immune checkpoint axes like SIRPA_CD47 (Mac-Mac, Mac-NK) and LGALS9_HAVCR2 (Mac-Mac).
  • Non-classical MHC and Inhibitory Receptors: Multiple interactions involving HLA-E and HLA-F with inhibitory receptors such as LILRB2 and CD94:NKG2C (e.g., HLA-F_LILRB2 -- Mac | T CD8+, HLA-E_CD94:NKG2C -- Mac | T CD8+) are prominent.
  • Semaphorin-Plexin Signaling: Interactions like SEMA4D_PLXNB2 (Mac-T CD8+) and SEMA4A_PLXND1 (Mac-Mac) are highly active.
  • TNF Superfamily Interactions: TNFSF14_TNFRSF14 (Mac-T CD8+) shows strong signals.
  • Urokinase Plasminogen Activator Receptor System: PLAU_PLAUR_integrin_a4b1_complex -- Mac | Mac interactions are highly evident.
• Sample Heterogeneity: While general trends are clear, some heterogeneity is observed across samples within the PDAC group, with some samples displaying an even greater intensity and breadth of interactions (e.g., PDAC_1, PDAC_10, PDAC_11A, PDAC_11B).

Biological Interpretation

The observed shift from a relatively quiescent set of interactions in adjacent normal tissue to a highly dynamic and intense network in PDAC samples underscores the profound remodeling of the tumor microenvironment (TME) in pancreatic cancer. The dominance of Macrophage- and T cell-related interactions, many with known immunomodulatory functions, points to key mechanisms driving disease progression and immune evasion.

1. SIRPA-CD47 Axis (Macrophage-Macrophage, Macrophage-NK):
   • Signaling Pathways/Mechanisms: CD47, often overexpressed on cancer cells and tumor-associated macrophages (TAMs), acts as a "don't eat me" signal by binding to SIRPα on phagocytic cells (macrophages, dendritic cells). This interaction inhibits phagocytosis and promotes immune evasion. The strong presence of this interaction between macrophages themselves and between macrophages and NK cells suggests a broader role in shaping the immunosuppressive myeloid compartment and inhibiting NK cell anti-tumor functions within the PDAC TME.
   • Relevance in PDAC: Cancer cells exploit this pathway to avoid destruction by innate immune cells. Within the TME, this interaction can contribute to the survival, differentiation, and polarization of pro-tumorigenic TAMs (M2-like phenotypes) and blunt the anti-tumor activity of NK cells.
   • Therapeutic Potential: Blocking the CD47-SIRPα axis has emerged as a promising strategy to reactivate macrophage-mediated phagocytosis of cancer cells and enhance anti-tumor immunity. Several anti-CD47 antibodies (e.g., magrolimab) and SIRPα fusion proteins are currently in clinical trials for various cancers, including PDAC. [PubMed Search: "CD47 SIRPA cancer therapy"]
2. ICAM-Integrin Complexes (Macrophage-T CD8+, Macrophage-Macrophage):
   • Signaling Pathways/Mechanisms: Intercellular Adhesion Molecules (ICAMs), such as ICAM1 and ICAM3, are critical adhesion molecules that bind to integrin receptors (e.g., ITGAL, various α/β integrin complexes) on immune cells. These interactions mediate cell-cell adhesion, cell migration, immune cell activation, and synapse formation.
   • Relevance in PDAC: The extensive upregulation of ICAM-integrin interactions between macrophages and CD8+ T cells, and among macrophages, suggests enhanced cell-cell contact within the PDAC TME. While necessary for immune responses, in a tumor context, these sustained interactions can contribute to T cell dysfunction and exhaustion by prolonged antigen presentation in an immunosuppressive environment. They can also facilitate the retention of immune cells within the tumor.
   • Therapeutic Potential: Modulating integrin signaling is complex due to their widespread roles. However, specific targeting of integrin pathways involved in pro-tumorigenic immune cell adhesion or migration could be explored, potentially in combination with other immunotherapies. [GeneCards: ICAM1, ITGAL]
3. HLA-E/F and LILRB2/KLRK1/CD94:NKG2C Interactions (Macrophage-T CD8+, NK-Mac):
   • Signaling Pathways/Mechanisms: Non-classical MHC class I molecules like HLA-E and HLA-F interact with various receptors on NK cells (e.g., KLRK1/NKG2D, CD94/NKG2A/C) and T cells (e.g., LILRB2/ILT4). HLA-E typically presents peptides derived from classical MHC-I molecules and can engage inhibitory receptors (like NKG2A) to suppress NK and T cell activity. LILRB2 (ILT4) is an inhibitory receptor on myeloid cells that binds a range of HLA molecules, suppressing immune responses.
   • Relevance in PDAC: The strong presence of interactions like HLA-F_LILRB2 -- Mac | T CD8+ strongly indicates a potent mechanism of immune suppression. Tumor-associated macrophages can upregulate HLA-F or other inhibitory ligands to disarm anti-tumor CD8+ T cells by engaging LILRB2. Similarly, HLA-E interactions with inhibitory NK cell receptors (HLA-E_CD94:NKG2A) can lead to NK cell inactivation.
   • Therapeutic Potential: Targeting these inhibitory pathways, particularly LILRB2/ILT4, is a promising area for cancer immunotherapy. Blockade of LILRB2 aims to restore immune cell activation and function in the TME. [UniProt: LILRB2, HLA-F]
4. LGALS9-HAVCR2 (Galectin-9-TIM-3) (Macrophage-Macrophage):
   • Signaling Pathways/Mechanisms: LGALS9 (Galectin-9) is a ligand for HAVCR2 (TIM-3), a well-established immune checkpoint receptor expressed on exhausted T cells, NK cells, and myeloid cells. This interaction delivers inhibitory signals, leading to T cell exhaustion and dysfunction of other immune cells.
   • Relevance in PDAC: The strong expression of this immune checkpoint axis highlights a key mechanism by which the PDAC TME suppresses effective anti-tumor immunity, promoting T cell exhaustion and allowing tumor progression.
   • Therapeutic Potential: Anti-TIM-3 antibodies are under investigation as novel immune checkpoint inhibitors, often in combination with PD-1/PD-L1 blockade, to reverse T cell exhaustion and reinvigorate anti-tumor immune responses in various cancers. [PubMed Search: "Galectin-9 TIM-3 cancer immunotherapy"]
5. Semaphorin-Plexin Pathways (e.g., SEMA4D-PLXNB2, SEMA4A-PLXND1):
   • Signaling Pathways/Mechanisms: Semaphorins and Plexins are a family of ligands and receptors originally identified for their roles in neuronal guidance, but now recognized for their involvement in angiogenesis, immune regulation, and cancer. SEMA4D-PLXNB2 can influence T cell activation and differentiation, while SEMA4A-PLXND1 can modulate macrophage polarization and function.
   • Relevance in PDAC: These interactions likely contribute to the immunosuppressive TME by promoting M2-like macrophage polarization, inhibiting anti-tumor T cell responses, or guiding immune cell migration patterns that favor tumor growth.
   • Therapeutic Potential: Given their multifaceted roles in cancer progression and immunity, semaphorin-plexin pathways are emerging as potential therapeutic targets, with ongoing research into inhibitors designed to disrupt their pro-tumorigenic activities.

Clinical or Translational Implications

The distinct and highly active cell-cell interaction landscape in PDAC, particularly the extensive involvement of macrophages and CD8+ T cells through various immunomodulatory pathways, offers significant clinical and translational insights:

1. Biomarker Potential: The differential expression patterns of specific CCI pairs observed in PDAC versus adjacent normal tissue could serve as valuable biomarkers. These could potentially be used for:
   • Diagnosis/Prognosis: Identifying early stage disease or predicting patient outcome.
   • Therapeutic Response Prediction: Stratifying patients who are more likely to respond to targeted immunotherapies.
2. Novel Therapeutic Targets and Strategies: The highly upregulated immune checkpoint and adhesion molecule interactions in PDAC identify several promising therapeutic targets:
   • Immune Checkpoint Blockade: The prominent SIRPA-CD47 and LGALS9-HAVCR2 (TIM-3) axes suggest that targeting these pathways could be highly effective in reactivating anti-tumor immunity in PDAC. Clinical trials for anti-CD47 and anti-TIM-3 agents are ongoing, and their evaluation in PDAC, perhaps in combination with existing therapies, is crucial.
   • Modulation of Myeloid Cell Function: Given the pervasive role of macrophages in these interactions, strategies aimed at reprogramming tumor-associated macrophages (TAMs) from an immunosuppressive M2-like phenotype to an anti-tumor M1 phenotype could be beneficial.
   • Disrupting Adhesion and Migration: Targeting key ICAM-integrin interactions or semaphorin-plexin pathways could interfere with the physical and functional integration of immune cells within the TME, potentially enhancing the access and efficacy of cytotoxic T cells.
   • Combination Therapies: The complexity of the PDAC TME suggests that monotherapies might have limited efficacy. Rational combination therapies, such as combining anti-CD47 with anti-PD-1/PD-L1 or anti-TIM-3, or agents that reprogram TAMs, might be necessary to achieve durable anti-tumor responses.

Overall, this analysis provides a detailed map of cell-cell communication changes in PDAC, highlighting critical interactions that contribute to immune evasion and disease progression, thereby offering a foundation for developing more effective, targeted immunotherapeutic strategies for this challenging cancer.

13. Condition-Specific Cell-Cell Interaction Patterns in Pancreatic Ductal Adenocarcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates condition-specific cell-cell interaction (CCI) patterns in Pancreatic Ductal Adenocarcinoma (PDAC) compared to adjacent normal pancreatic tissue (Adj_normal), focusing on interactions involving immune checkpoint and cell cycle pathway genes. The interactions were identified between Ductal cells (the resolved tumor-origin cell type), major immune cells (T cells, Myeloid cells, B cells, Macrophages, Mast cells, NK cells, Dendritic cells, Plasma cells), and stromal cells (Stromal cells, Smooth muscle cells, Stellate cells, Fibroblasts). The results are visualized as a dot plot, where dot color indicates interaction strength (scaled log strength) and dot size reflects statistical significance (-log10(p-value)). The analysis aimed to uncover critical communication pathways that are differentially regulated in PDAC, with a view toward identifying potential therapeutic targets.

Visual Summary

The dot plot strikingly reveals a profound difference in cell-cell communication profiles between 'Adj_normal' and 'PDAC' samples.

• Differential Activity: Samples from the 'Adj_normal' condition exhibit relatively sparse and weaker interactions (lighter red, smaller dots), indicating a less active or distinct intercellular communication landscape. In contrast, 'PDAC' samples show a dramatic increase in the number, strength (darker red), and statistical significance (larger dots) of CCIs across a broad spectrum of gene and cell pairs. This suggests a highly reprogrammed and intensified communication network within the PDAC tumor microenvironment.
• Pervasive Activation in PDAC: The entire 'PDAC' section of the plot is densely populated with large, dark red dots, indicating that many identified interactions involving immune checkpoint and cell cycle-related genes are strongly upregulated and highly significant in tumor samples.
• Cell Type Specificity: While specific interactions are numerous, a general pattern emerges where Ductal cells (particularly aneuploid ones, denoted "Aneu") are extensively involved in strong interactions with macrophages, T cells, and stellate cells in PDAC. Autocrine signaling within Ductal (Aneuploid) cells also appears highly active.
• Sample Consistency in PDAC: Most 'PDAC' samples exhibit consistently high levels of these interactions, although minor variations exist. The 'Adj_normal' samples show some variability, but overall, their interaction landscape remains considerably less active than that of PDAC.

Biological Interpretation

The observed upregulation of specific cell-cell interactions in PDAC highlights key biological processes contributing to tumor progression, immune evasion, and the formation of the desmoplastic tumor microenvironment. Several critical pathways involving Ductal cells, macrophages, T cells, and stellate cells are prominently activated.

Key Interaction Pathways and Mechanisms in PDAC:

1. Immune Checkpoint & Evasion: CD47-SIRPα Axis

Interaction: SIRPA_CD47 -- Ductal (Aneuploid) | Macrophage

• Signaling/Mechanism: CD47, often overexpressed on cancer cells including Ductal cells in PDAC, interacts with SIRPα on myeloid cells like macrophages. This interaction delivers a potent "don't eat me" signal, preventing phagocytic clearance of tumor cells by macrophages and contributing significantly to immune evasion in PDAC. The prominent activity of this axis indicates a key mechanism by which PDAC cells escape innate immune surveillance.
• Therapeutic Target Potential: High. Disrupting this interaction aims to unleash macrophage-mediated anti-tumor immunity.
• Therapeutic Agents: CD47-blocking monoclonal antibodies (e.g., Magrolimab) or SIRPα-Fc fusion proteins are actively being investigated in clinical trials for various solid tumors, including pancreatic cancer, often in combination with chemotherapy or other immunotherapies.
  • [PubMed search: "CD47 SIRPalpha pancreatic cancer therapy" PubMed Search]

2. Growth Factor Signaling: EGFR Family Activation
   • Interactions: HBEGF_EGFR -- Macrophage | Ductal (Aneuploid); EREG_EGFR -- Ductal (Aneuploid) | Ductal (Aneuploid); BTC_EGFR -- Ductal (Aneuploid) | Ductal (Aneuploid)
   • Signaling/Mechanism: The epidermal growth factor receptor (EGFR) signaling pathway is a well-known driver of cell proliferation, survival, and metastasis. In PDAC, the strong presence of interactions involving EGFR ligands (HBEGF, EREG, BTC) and their receptors (EGFR, ERBB2) indicates both autocrine (within Ductal tumor cells) and paracrine (from macrophages to Ductal cells) stimulation. Macrophages, a major component of the PDAC microenvironment, can secrete HBEGF, further promoting tumor growth. This robust activation suggests a persistent proliferative stimulus for aneuploid tumor cells.
   • Therapeutic Target Potential: High. EGFR is a validated oncogenic driver in many cancers.
   • Therapeutic Agents: EGFR tyrosine kinase inhibitors (TKIs) like Erlotinib, Afatinib, or monoclonal antibodies (e.g., Cetuximab) are used clinically to block this pathway. Given the involvement of ERBB2, pan-HER inhibitors or ERBB2-specific therapies (e.g., Trastuzumab) might also be relevant for specific cases.
     • [GeneCards: EGFR GeneCards]
3. Tumor Microenvironment Remodeling: TGF-β Signaling
   • Interactions: TGFB1_TGFbeta_receptor1 -- Ductal (Aneuploid) | Stellate cell / T cell / Macrophage; TGFB3_TGFbeta_receptor1 -- Ductal (Aneuploid) | Stellate cell
   • Signaling/Mechanism: Transforming Growth Factor-beta (TGF-β) signaling is critically involved in the extensive desmoplasia (fibrotic stroma) characteristic of PDAC. Ductal tumor cells secrete TGF-β ligands (TGFB1, TGFB3) that activate pancreatic stellate cells (PSCs), leading to their differentiation into myofibroblast-like cells that produce excessive extracellular matrix. TGF-β also plays a significant immunosuppressive role by inhibiting anti-tumor T cell responses and promoting an immunosuppressive macrophage phenotype.
   • Therapeutic Target Potential: High. Targeting TGF-β aims to reduce fibrosis, enhance drug delivery, and reverse immune suppression in the TME.
   • Therapeutic Agents: TGF-β inhibitors (e.g., galunisertib, fresolimumab) are under investigation in clinical trials for PDAC, often in combination strategies to overcome the highly immunosuppressive and fibrotic TME.
     • [PubMed search: "TGF-beta pancreatic cancer therapy" PubMed Search]
4. T Cell Co-stimulation: CD86-CD28

Interaction: CD86_CD28 -- Macrophage | T CD4+

• Signaling/Mechanism: The interaction between CD86 (on antigen-presenting cells like macrophages) and CD28 (on T cells, including CD4+) provides a crucial co-stimulatory signal necessary for full T cell activation, proliferation, and differentiation. While essential for initiating immune responses, in the chronic inflammatory and immunosuppressive environment of PDAC, sustained or aberrant co-stimulation can also contribute to T cell exhaustion or the generation of regulatory T cells, further dampening anti-tumor immunity.
• Therapeutic Target Potential: Moderate. Modulating co-stimulatory pathways requires careful consideration of the specific T cell context within the TME.
• Therapeutic Agents: While direct CD28 agonists have had safety concerns, strategies to enhance co-stimulation are being explored in combination with checkpoint blockade. Conversely, antagonists like Abatacept (CTLA4-Ig, which binds CD80/CD86) are used in autoimmune diseases, highlighting the delicate balance of this pathway.

5. Adhesion and Communication: ICAM1-Integrin interactions
   • Interactions: ICAM1_integrin_aLb2_complex -- Mast cell | Macrophage; ICAM1_integrin_aMb2_complex -- Mast cell | Macrophage, and others involving different integrin subunits.
   • Signaling/Mechanism: Intercellular Adhesion Molecule 1 (ICAM1) and its integrin receptors (e.g., αLβ2, αMβ2) mediate critical cell-cell adhesion processes involved in immune cell trafficking, extravasation, and stable cell contact. Their strong presence among mast cells and macrophages suggests enhanced cell-cell adhesion and potential inflammatory cell infiltration and activation within the PDAC microenvironment, facilitating cross-talk between these immune cell populations.
   • Therapeutic Target Potential: Moderate. Targeting adhesion molecules could disrupt immune cell infiltration or cell-cell interactions crucial for tumor progression.
   • Therapeutic Agents: While less directly targeted in cancer, anti-integrin antibodies have been explored in various inflammatory and cancerous conditions.
     • [GeneCards: ICAM1 GeneCards]

Clinical or Translational Implications

The robust and distinct patterns of cell-cell interactions in PDAC compared to adjacent normal tissue offer significant clinical and translational implications:

• Diagnostic and Prognostic Biomarkers: The specific and strong activation of these CCI pathways could serve as diagnostic or prognostic biomarkers for PDAC, potentially detectable through spatial transcriptomics or proteomic analyses of the TME.
• Therapeutic Stratification: Understanding which patients exhibit highly active CD47-SIRPα, EGFR, or TGF-β signaling could help stratify patients for targeted therapies. For example, patients with high EGFR-mediated signaling might benefit more from EGFR inhibitors.
• Combination Therapies: The complex interplay of these pathways suggests that combination therapies targeting multiple nodes might be more effective in PDAC. For instance, combining CD47 blockade (to unleash macrophages) with TGF-β inhibition (to reduce desmoplasia and immune suppression) and standard chemotherapy could be a promising strategy.
• Overcoming Treatment Resistance: The observed autocrine EGFR signaling within aneuploid Ductal cells and paracrine signaling from macrophages highlights mechanisms of intrinsic and extrinsic resistance to therapies. Targeting these compensatory pathways may overcome resistance.
• Immunotherapy Enhancement: Given the strong involvement of immune cells and immune checkpoint pathways, these findings reinforce the rationale for developing novel immunotherapies or improving existing ones for PDAC, which has historically been resistant to such treatments. Modulating the immune context, potentially by altering macrophage function or T cell activation through these CCIs, could prime the tumor for better responses to conventional or checkpoint therapies.
• Focus on Stromal-Immune-Tumor Interactions: The involvement of stellate cells (stroma) alongside immune cells and Ductal cells underscores the importance of a holistic approach to targeting the entire tumor ecosystem, not just the tumor cells themselves.

14. Ductal Cell Condition-Specific Surfaceome Marker Analysis in Pancreatic Ductal Adenocarcinoma (PDAC)

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies surfaceome markers differentially expressed in Ductal cells from Pancreatic Ductal Adenocarcinoma (PDAC) samples compared to adjacent normal pancreatic tissue (Adj_normal), as well as between different ploidy states within PDAC. The analysis focuses on surfaceome markers, which are particularly relevant for therapeutic targeting due to their accessibility on the cell surface. The dot plot visualizes the mean expression level (color intensity) and the fraction of expressing cells (dot size) for selected markers across various individual samples grouped by condition (Adj_normal, Diploid PDAC, PDAC).

Visual Summary

The dot plot clearly delineates two distinct clusters of Ductal cell samples based on their surfaceome marker expression:

1. Adjacent Normal (Adj_normal) Samples: These samples (AdjN_1, AdjN_2, AdjN_3) show high expression and high frequency for a relatively small set of markers such as GP2, CLDN3, BCAM, and EPACAM. Expression of most other markers is low or absent in these samples.
2. PDAC Samples (Diploid PDAC and Aneuploid PDAC): The majority of markers displayed are highly expressed across both Diploid PDAC and Aneuploid PDAC samples, exhibiting large, dark red circles, indicating both a high fraction of expressing cells and high mean expression.
   • Diploid PDAC samples (e.g., Diploid PDAC_4, Diploid PDAC_10) show a mixed pattern, with some markers (e.g., EPACAM, CLDN4, CD44) showing moderate to high expression, while others (e.g., MSLN, CEACAM6, CD47) are less consistently expressed or at lower levels compared to the aneuploid PDAC group.
   • Aneuploid PDAC samples (e.g., PDAC_1, PDAC_15, PDAC_9) demonstrate a particularly strong upregulation of a broad panel of surfaceome markers. These samples consistently display high mean expression and a large fraction of expressing cells for nearly all markers starting from SPINT2 to ERBB3. This suggests a highly active and dysregulated surface proteome in aneuploid tumor cells.

A strong distinction is observed between Adj_normal Ductal cells and both Diploid and Aneuploid PDAC Ductal cells, with the latter showing a dramatically expanded repertoire of highly expressed surface markers. Within the PDAC group, aneuploid samples generally exhibit a more pronounced upregulation of these cancer-associated surface markers compared to diploid PDAC samples, although many markers are shared. This pattern suggests that the identified markers are largely tumor-associated.

Biological Interpretation

The analysis reveals numerous surfaceome markers significantly upregulated in PDAC Ductal cells, particularly in aneuploid samples. These markers represent potential diagnostic, prognostic, and therapeutic targets in PDAC. Below, we highlight several key markers, distinguishing between established and less well-characterized candidates.

Established Therapeutic Targets

1. EPCAM (Epithelial Cell Adhesion Molecule)
   • Expression Pattern: Highly expressed in Adj_normal Ductal cells, but its expression pattern appears more heterogeneous across PDAC samples, with some PDAC samples retaining high expression while others show reduced expression compared to adjacent normal. However, it is also highly expressed in many PDAC samples, indicating its continued relevance.
   • Signaling Pathways/Mechanisms: EPCAM is a transmembrane glycoprotein involved in cell adhesion, proliferation, and differentiation. It can activate the Wnt signaling pathway and facilitate cell-cell adhesion, which can be altered during cancer progression. In cancer, EPCAM overexpression is often associated with increased proliferation and migration.
   • Therapeutic Agents: Due to its broad expression in many epithelial cancers, EPCAM has been an attractive target for immunotherapy. Catumaxomab (a bispecific antibody targeting EpCAM and CD3) was previously approved in Europe for malignant ascites, although it has since been withdrawn from the market for commercial reasons. Other anti-EPCAM antibody-drug conjugates and CAR-T cell therapies are under investigation.

Supporting Evidence:

• GeneCards: https://www.genecards.org/cgi-bin/carddisp.pl?gene=EPCAM
• PubMed (EPCAM cancer therapy): https://pubmed.ncbi.nlm.nih.gov/?term=EPCAM+cancer+therapy

2. MUC1 (Mucin 1)
   • Expression Pattern: Strongly and broadly upregulated across most PDAC samples, both diploid and aneuploid, with high mean expression and high cell fraction.
   • Signaling Pathways/Mechanisms: MUC1 is a transmembrane mucin glycoprotein that is aberrantly overexpressed and glycosylated in many adenocarcinomas, including PDAC. It acts as an oncogene, promoting cell proliferation, survival, invasion, and metastasis. MUC1 can interact with various signaling molecules, including EGFR, HER2, and β-catenin, impacting pathways like MAPK, PI3K/Akt, and Wnt.
   • Therapeutic Agents: MUC1's overexpression and altered glycosylation in cancer make it a prominent target. Therapeutic strategies include MUC1-targeting antibodies, peptide vaccines, and CAR-T cell therapies. Clinical trials investigating MUC1-targeting agents are ongoing for various cancers.

Supporting Evidence:

• UniProt (MUC1): https://www.uniprot.org/uniprotkb/P15941/entry
• PubMed (MUC1 pancreatic cancer therapy): https://pubmed.ncbi.nlm.nih.gov/?term=MUC1+pancreatic+cancer+therapy

3. MSLN (Mesothelin)
   • Expression Pattern: Highly and broadly expressed in most PDAC samples, with consistently high mean expression and fraction of cells, particularly in aneuploid samples.
   • Signaling Pathways/Mechanisms: Mesothelin is a glycosylphosphatidylinositol-anchored protein overexpressed in several aggressive cancers, including PDAC. It is involved in cell adhesion and signaling, promoting tumor growth, survival, and metastasis. It can interact with MUC16 (CA-125) and contribute to epithelial-mesenchymal transition (EMT).
   • Therapeutic Agents: MSLN is a well-validated therapeutic target in PDAC. Several mesothelin-targeting immunotherapies are in development or clinical trials, including antibody-drug conjugates (e.g., anetumab ravtansine, mirvetuximab soravtansine for ovarian cancer), CAR-T cell therapies, and immunotoxins (e.g., SS1P, LMB-100).

Supporting Evidence:

• GeneCards: https://www.genecards.org/cgi-bin/carddisp.pl?gene=MSLN
• PubMed (Mesothelin pancreatic cancer therapy): https://pubmed.ncbi.nlm.nih.gov/?term=Mesothelin+pancreatic+cancer+therapy

4. CD47 (Integrin Associated Protein)
   • Expression Pattern: Upregulated across most PDAC samples, with high expression and cell fraction, indicating its prominence in tumor cells.
   • Signaling Pathways/Mechanisms: CD47 is a transmembrane protein that acts as a "don't eat me" signal by binding to SIRPα on phagocytic cells (e.g., macrophages), thereby inhibiting phagocytosis and allowing tumor cells to evade immune surveillance. It is also involved in cell adhesion, migration, and apoptosis.
   • Therapeutic Agents: CD47 is a major focus for cancer immunotherapy. Anti-CD47 antibodies aim to block the CD47-SIRPα interaction, enabling macrophages to engulf tumor cells. Several CD47-targeting antibodies (e.g., magrolimab) are in various stages of clinical development for different solid tumors and hematological malignancies, including PDAC.

Supporting Evidence:

• UniProt (CD47): https://www.uniprot.org/uniprotkb/Q08722/entry
• PubMed (CD47 pancreatic cancer therapy): https://pubmed.ncbi.nlm.nih.gov/?term=CD47+pancreatic+cancer+therapy

Less Well-Characterized Markers with Potential as Therapeutic Targets

1. CEACAM6 (Carcinoembryonic Antigen Related Cell Adhesion Molecule 6)
   • Expression Pattern: Consistently highly expressed in nearly all PDAC samples, particularly prominent in aneuploid groups.
   • Signaling Pathways/Mechanisms: CEACAM6 is a glycosylphosphatidylinositol-anchored cell adhesion molecule that is frequently overexpressed in various cancers, including PDAC. It promotes cell proliferation, survival (by inhibiting anoikis), invasion, and resistance to chemotherapy. CEACAM6 can activate signaling pathways such as PI3K/Akt and ERK.
   • Therapeutic Agents: While not an established target with approved drugs, CEACAM6 is being investigated as a promising target. Preclinical studies show that antibodies or small molecule inhibitors targeting CEACAM6 can suppress tumor growth and metastasis in PDAC models.

Supporting Evidence:

• GeneCards: https://www.genecards.org/cgi-bin/carddisp.pl?gene=CEACAM6
• PubMed (CEACAM6 pancreatic cancer therapy): https://pubmed.ncbi.nlm.nih.gov/?term=CEACAM6+pancreatic+cancer+therapy

2. CD44 (CD44 Molecule)
   • Expression Pattern: Strongly and broadly expressed across most PDAC samples, with high mean expression and cell fraction, indicating its prevalence in tumor cells.
   • Signaling Pathways/Mechanisms: CD44 is a transmembrane glycoprotein and a major receptor for hyaluronan. It plays critical roles in cell adhesion, migration, proliferation, and survival. CD44, especially its variant isoforms (CD44v), is often associated with cancer stem cell properties, epithelial-mesenchymal transition (EMT), and metastasis in PDAC. It can activate signaling pathways such as PI3K/Akt and Wnt/β-catenin.
   • Therapeutic Agents: CD44 is considered a challenging but important target due to its involvement in cancer stemness. Therapeutic strategies include anti-CD44 antibodies, hyaluronan synthesis inhibitors, and small molecules targeting CD44 signaling. These are largely in preclinical or early clinical development for various cancers, including PDAC.

Supporting Evidence:

• UniProt (CD44): https://www.uniprot.org/uniprotkb/P16070/entry
• PubMed (CD44 pancreatic cancer therapy): https://pubmed.ncbi.nlm.nih.gov/?term=CD44+pancreatic+cancer+therapy

3. ERBB3 (ERBB Receptor Tyrosine Kinase 3)
   • Expression Pattern: Highly expressed in most PDAC samples, particularly in aneuploid groups, showing high mean expression and high cell fraction.
   • Signaling Pathways/Mechanisms: ERBB3 (HER3) is a member of the epidermal growth factor receptor (EGFR/ERBB) family of receptor tyrosine kinases. Unlike other ERBB family members, ERBB3 has minimal intrinsic kinase activity but functions as a critical co-receptor, forming heterodimers with ERBB2 (HER2), EGFR, or ERBB4. Upon ligand binding (e.g., heregulin), these heterodimers activate downstream signaling pathways, notably the PI3K/Akt pathway, driving cell proliferation, survival, and drug resistance. ERBB3 signaling is crucial in PDAC progression and therapeutic resistance.
   • Therapeutic Agents: ERBB3 is an attractive therapeutic target, especially in cancers with ERBB2/EGFR overexpression or resistance to EGFR inhibitors. Antibodies targeting ERBB3 (e.g., seribantumab, patritumab deruxtecan) and tyrosine kinase inhibitors are being investigated in clinical trials for various cancers, including PDAC, to block downstream oncogenic signaling.

Supporting Evidence:

• GeneCards: https://www.genecards.org/cgi-bin/carddisp.pl?gene=ERBB3
• PubMed (ERBB3 pancreatic cancer therapy): https://pubmed.ncbi.nlm.nih.gov/?term=ERBB3+pancreatic+cancer+therapy

Clinical or Translational Implications

The identification of a comprehensive panel of surfaceome markers overexpressed in PDAC Ductal cells has significant clinical and translational implications:

• Therapeutic Target Identification: The prominently upregulated markers like MSLN, MUC1, CD47, and ERBB3 represent validated or highly promising therapeutic targets for PDAC. Existing immunotherapies, antibody-drug conjugates, and CAR-T cell therapies targeting these molecules could be further explored for PDAC treatment. The distinction between established targets (like MSLN, MUC1, CD47) and emerging targets (like CEACAM6, CD44, ERBB3) helps prioritize drug development and clinical trial design.
• Biomarker Development: These surface markers could serve as diagnostic or prognostic biomarkers for PDAC. Their detection in liquid biopsies (e.g., circulating tumor cells or extracellular vesicles) could facilitate early detection, disease monitoring, and assessment of treatment response.
• Imaging and Drug Delivery: Surface markers offer avenues for targeted imaging agents to better visualize tumors and for specific delivery of cytotoxic drugs, minimizing off-target effects.
• Understanding Tumor Biology: The broad upregulation of numerous adhesion molecules (EPCAM, ITGB1, CLDN4, CD44, CEACAM6), immune modulators (CD47, HLA-E, CD55), and signaling receptors (ERBB3, APP) points to a highly aggressive and immune-evasive phenotype of PDAC Ductal cells. This underscores the complex interplay of cell-intrinsic and microenvironmental factors driving PDAC progression.
• Heterogeneity of PDAC: The varying expression patterns observed across different PDAC samples, especially between diploid and aneuploid groups, highlight the inherent heterogeneity of PDAC. This suggests that a personalized medicine approach, where specific surface markers are targeted based on an individual patient's tumor profile, may be more effective. For example, tumors with very high MSLN expression might respond better to MSLN-targeting therapies. Further characterization of individual patient samples will be crucial.

15. Macrophage Condition-Specific Surfaceome Markers in Pancreatic Ductal Adenocarcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies surfaceome markers differentially expressed in Macrophages from Pancreatic Ductal Adenocarcinoma (PDAC) tissue compared to adjacent normal (Adj_normal) tissue. The markers were selected using a one-vs-rest comparison with specific criteria for expression score (nz_pct_score) and fold change (nz_pct FC), focusing exclusively on surfaceome genes. The results are visualized as a dot plot, illustrating the mean expression level and the fraction of cells expressing each marker across various individual samples within both conditions.

Visual Summary

The dot plot displays a clear distinction in macrophage surfaceome marker expression between 'Adj_normal' and 'PDAC' samples.

• Adj_normal Samples (AdjN_2, AdjN_3, AdjN_1): Macrophages from adjacent normal tissue generally exhibit very low mean expression (light or absent red color) and a small fraction of expressing cells (small dot size) for most of the identified markers. This suggests a relatively quiescent or non-activated state for these macrophages or that the identified markers are not prominently expressed in this context.
• PDAC Samples (PDAC_5 through PDAC_11A): In stark contrast, macrophages within PDAC samples show widespread and often high expression of numerous surfaceome markers. Many markers display dark red coloration, indicating high mean expression, and large dot sizes, signifying a high fraction of expressing cells across most PDAC samples. This pattern strongly suggests a significant activation, differentiation, or altered state of macrophages in the PDAC tumor microenvironment (TME).
• Prominent PDAC-associated Markers: A substantial number of markers, including GP2, ITGAX, CD46, SIRPA, PTPRA, MYADM, SERINC1, ITGAM, TNFRSF14, LILRB3, TGFBR2, ADAM10, TMEM154, CD47, ADAM17, CCR1, HLA-DRB5, CD58, and ICAM1, exhibit particularly strong upregulation in PDAC macrophages. These markers are highly expressed in a large proportion of cells across nearly all PDAC samples, suggesting their critical roles in the tumor context.

Biological Interpretation

Macrophages, specifically Tumor-Associated Macrophages (TAMs), are critical components of the Pancreatic Ductal Adenocarcinoma (PDAC) tumor microenvironment (TME). They contribute to tumor progression, immunosuppression, angiogenesis, and metastasis. The observed upregulation of specific surfaceome markers in PDAC macrophages points towards their altered phenotype and active involvement in tumor-supportive functions.

Here is an interpretation of some prominent markers and their biological relevance:

CD47

• Associated Signaling Pathways: CD47 interacts with Signal Regulatory Protein Alpha (SIRPA) on phagocytic cells, activating intracellular signaling cascades that inhibit phagocytosis. It also signals through integrins and other receptors.
• Role in PDAC TME: CD47 acts as a "don't eat me" signal, protecting tumor cells (and potentially tumor-associated immune cells like macrophages) from phagocytosis by host macrophages. Upregulation on macrophages might contribute to immune evasion by preventing their own clearance or influencing the behavior of other phagocytes. More commonly, tumor cells upregulate CD47, but its expression on macrophages themselves can also modulate immune interactions within the TME.
• Potential as a Therapeutic Target: Targeting the CD47-SIRPA axis is a major strategy in cancer immunotherapy. Blocking CD47 can unleash phagocytosis of tumor cells. While primarily focused on tumor cell CD47, understanding its role on TAMs could inform combination therapies.
• Relevant Therapeutic Agents: Monoclonal antibodies targeting CD47 (e.g., magrolimab, lemzoparlimab) are in clinical development for various cancers. GeneCards: CD47

SIRPA

• Associated Signaling Pathways: As the primary ligand for CD47, SIRPA binding to CD47 initiates downstream signaling that inhibits phagocytosis through tyrosine phosphorylation of ITIM domains and recruitment of SHP-1/SHP-2 phosphatases.
• Role in PDAC TME: SIRPA is highly expressed on macrophages and dendritic cells. Its interaction with CD47 on tumor cells (or potentially other immune cells) mediates the "don't eat me" signal, contributing to immune evasion. Its upregulation here suggests active engagement in inhibitory signaling.
• Potential as a Therapeutic Target: Given its central role in the CD47-SIRPA axis, SIRPA is also a therapeutic target. Blocking SIRPA or developing SIRPA fusion proteins can disrupt this inhibitory interaction and promote anti-tumor immunity.
• Relevant Therapeutic Agents: Various SIRPA-blocking antibodies or SIRPA-Fc fusion proteins are being investigated. GeneCards: SIRPA

ITGAX (CD11c) / ITGAM (CD11b)

• Associated Signaling Pathways: These are integrin alpha subunits that form heterodimers with ITGB2 (CD18) to create MAC-1 (CD11b/CD18) and CD11c/CD18. They mediate cell adhesion, migration, and phagocytosis, interacting with various ligands (e.g., ICAMs, C3bi). Signaling involves focal adhesion kinase (FAK) and other kinases.
• Role in PDAC TME: CD11c and CD11b are canonical markers for myeloid cells, including macrophages and dendritic cells. Their increased expression in PDAC macrophages suggests enhanced adhesive properties, migration capabilities, and phagocytic potential, consistent with their active recruitment and function within the TME. CD11c is often associated with pro-inflammatory M1-like macrophages or specific dendritic cell subsets, while CD11b is broadly expressed.
• Potential as a Therapeutic Target: While direct targeting of these broad integrins is challenging due to their wide physiological roles, modulation of integrin-mediated adhesion or signaling could impact macrophage infiltration and function in the TME.
• Relevant Therapeutic Agents: Integrin antagonists exist for other diseases, but specific targeting for TAMs in cancer is still an area of research. GeneCards: ITGAX, GeneCards: ITGAM

TGFBR2

• Associated Signaling Pathways: TGFBR2 is a receptor for Transforming Growth Factor Beta (TGF-β). Upon ligand binding, it forms a complex with TGFBR1, leading to phosphorylation of SMAD proteins (SMAD2/3) and subsequent gene transcription, primarily mediating immunosuppression and fibrosis.
• Role in PDAC TME: TGF-β signaling is highly active and plays a critical pro-tumorigenic and immunosuppressive role in the PDAC TME. Macrophages with upregulated TGFBR2 are likely more responsive to TGF-β, leading to their differentiation into pro-tumorigenic TAMs, reduced anti-tumor activity, and enhanced secretion of pro-fibrotic and immunosuppressive factors. This contributes to the dense desmoplastic stroma characteristic of PDAC.
• Potential as a Therapeutic Target: Inhibiting TGF-β signaling is a promising strategy to counteract immunosuppression and fibrosis in PDAC. Targeting TGFBR2 directly or its downstream signaling could reprogram TAMs and reduce their pro-tumorigenic functions.
• Relevant Therapeutic Agents: Several TGF-β pathway inhibitors, including receptor kinase inhibitors (e.g., galunisertib) and ligand-trapping antibodies (e.g., fresolimumab, bintrafusp alfa), are being evaluated in clinical trials. GeneCards: TGFBR2

ADAM17 (TACE)

• Associated Signaling Pathways: ADAM17 (A Disintegrin And Metalloproteinase 17), also known as TACE (TNF-alpha Converting Enzyme), is a 'sheddase' that cleaves and releases the extracellular domains of many membrane-bound proteins, including TNF-α, EGFR ligands (e.g., HB-EGF, amphiregulin), and Notch ligands. It activates various downstream signaling pathways via these released ligands.
• Role in PDAC TME: Upregulated ADAM17 on macrophages in PDAC could lead to increased shedding of pro-inflammatory cytokines, growth factors, and immune checkpoints, thus shaping the TME. For example, increased soluble TNF-α or activation of EGFR signaling could promote tumor growth, angiogenesis, and immune suppression.
• Potential as a Therapeutic Target: Inhibition of ADAM17 can modulate multiple pro-tumorigenic pathways. It could reduce the release of pro-tumorigenic ligands and cytokines, potentially sensitizing tumors to other therapies and reprogramming the TME.
• Relevant Therapeutic Agents: ADAM17 inhibitors are under investigation, though challenges remain in achieving specificity and avoiding off-target effects. GeneCards: ADAM17

CCR1

• Associated Signaling Pathways: CCR1 is a G-protein coupled receptor (GPCR) that binds to various CC chemokines (e.g., CCL3, CCL5, CCL7, CCL8, CCL13, CCL15, CCL16). Ligand binding activates G-proteins, leading to downstream signaling pathways involved in cell migration, adhesion, and inflammation.
• Role in PDAC TME: CCR1 plays a significant role in recruiting various immune cells, including monocytes/macrophages, to sites of inflammation and tumors. Upregulation of CCR1 on PDAC macrophages suggests their active recruitment to the tumor site in response to chemokine gradients produced by tumor cells and stromal cells. This directed migration is crucial for TAM accumulation, which supports tumor progression.
• Potential as a Therapeutic Target: Blocking CCR1 could inhibit the recruitment of pro-tumorigenic macrophages to the PDAC TME, thereby reducing tumor growth and metastasis.
• Relevant Therapeutic Agents: CCR1 antagonists have been developed and tested in inflammatory diseases, and their potential in cancer, particularly for inhibiting TAM recruitment, is being explored. GeneCards: CCR1

Clinical or Translational Implications

The identification of highly expressed surfaceome markers on macrophages within PDAC highlights potential avenues for therapeutic intervention and biomarker development.

1. Therapeutic Targeting of TAMs: Markers like CD47, SIRPA, TGFBR2, ADAM17, and CCR1 represent attractive targets for therapies aimed at reprogramming or depleting pro-tumorigenic macrophages. Modulating the CD47-SIRPA axis could enhance phagocytic clearance of tumor cells. Inhibiting TGFBR2 could reduce immunosuppression and fibrosis. Targeting ADAM17 might reduce the availability of pro-tumorigenic soluble factors. Blocking CCR1 could prevent TAM infiltration.
2. Combination Therapies: Given the complex nature of the PDAC TME, therapies targeting these macrophage surface markers could be particularly effective when combined with conventional chemotherapy, radiation, or other immunotherapies. For instance, combining CD47/SIRPA blockade with T-cell-directed immunotherapies could enhance overall anti-tumor responses.
3. Biomarker Potential: The distinct expression profiles of these surface markers could serve as diagnostic or prognostic biomarkers for PDAC, indicating the presence and activity of tumor-promoting macrophages. Furthermore, monitoring their expression could help assess treatment response.
4. Phenotypic Characterization: These markers provide insights into the specific activation states and functions of macrophages in the PDAC TME, which can guide further research into their precise roles and vulnerabilities. For example, the co-expression of CD11c and CD11b along with immunosuppressive markers like TGFBR2 suggests a highly adaptive and pro-tumorigenic macrophage phenotype.

16. T cell CD4+ Condition-Specific Surfaceome Markers in Pancreatic Tissue

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers for CD4+ T cells derived from single-cell RNA sequencing data of human pancreas samples, comparing 'Adj_normal' (adjacent normal tissue) with 'PDAC' (Pancreatic Ductal Adenocarcinoma) conditions. The dot plot visualizes the mean expression level (color intensity) and the fraction of cells expressing each marker (dot size) across individual samples grouped by condition. Markers were selected based on a one-vs-rest comparison, applying thresholds for non-zero percentage score (nz_pct_score) and fold change (nz_pct FC) to ensure robust differential expression and prevalence in the respective conditions. Specifically, for Adj_normal-specific markers, nz_pct_score >= 0.133 and nz_pct FC > 1.31 were used, while for PDAC-specific markers, nz_pct_score >= 0.183 and nz_pct FC > 1.25 were applied. The analysis focuses on surfaceome markers, which are particularly relevant for cell-cell interactions, therapeutic targeting, and diagnostic applications.

Visual Summary

The dot plot clearly differentiates two sets of markers based on their expression patterns in 'Adj_normal' versus 'PDAC' samples.

• Adj_normal-Enriched Markers: A distinct cluster of markers, including CD69, SLC2A3, PTGER4, GPR171, GP2, KLRB1, PTGER2, TMX4, AREG, IFNGR1, GPR183, and TMEM123, shows high expression and prevalence primarily in the 'Adj_normal' samples (AdjN_2, AdjN_1). While some of these markers (e.g., CD69, SLC2A3) show some expression in certain PDAC samples (e.g., PDAC_10), their overall enrichment is in the normal context.
• PDAC-Enriched Markers: A much larger set of markers exhibits strong expression and high cell fraction specifically within the 'PDAC' samples. These include CD27, ITGB1, SLC38A1, TNFRSF14, CCR7, S1PR4, CD164, SELL, CLEC2D, BTN3A2, CD46, M6PR, TNFRSF25, IL10RA, SORL1, CD4, TSPAN14, LNPEP, FLT3LG, SUSD3, SPN, PTPRA, and TM9SF3. The expression and prevalence of these markers vary across individual PDAC samples, indicating heterogeneity within the tumor microenvironment.
• Common Markers: CD4 is expressed uniformly across almost all cells in both conditions, as expected for CD4+ T cells, confirming the cell type identity.
• Heterogeneity: Notably, some PDAC samples (e.g., PDAC_15, PDAC_2, PDAC_16, PDAC_11A) show lower expression or prevalence for many of the identified PDAC-enriched markers compared to others (e.g., PDAC_10, PDAC_1, PDAC_12). This highlights inter-patient variability in the immune landscape of PDAC.

Biological Interpretation

The differential expression of surfaceome markers on CD4+ T cells provides insights into their distinct functional states and roles in the normal pancreatic tissue versus the PDAC tumor microenvironment.

Markers Enriched in Adj_normal CD4+ T cells

• CD69: An early activation marker and a C-type lectin involved in lymphocyte retention in non-lymphoid tissues.
  • Signaling Pathways: Involved in T cell activation and retention, often linked to TGF-β and S1P signaling, which modulate lymphocyte egress.
  • Role in TME (normal pancreas): Suggests a population of recently activated or tissue-resident CD4+ T cells within the normal pancreatic environment, potentially involved in immune surveillance or homeostatic tissue maintenance.
  • Therapeutic Potential: While not a direct therapeutic target, CD69 expression can indicate immune activation or tissue-resident memory T cells (Trm), which are crucial for local immunity. Modulating tissue residency could be explored for enhancing local anti-tumor immunity if Trm cells are dysfunctional in cancer.
  • Relevant Agents: Modulators of S1P signaling could influence CD69-mediated retention [PubMed Search: CD69 S1P retention].
• SLC2A3 (GLUT3): Glucose Transporter Type 3. A high-affinity glucose transporter.
  • Signaling Pathways: Critical for cellular metabolism, particularly glucose uptake. Important for supporting the energetic demands of activated immune cells.
  • Role in TME (normal pancreas): Suggests that CD4+ T cells in the normal pancreas are metabolically active, potentially supporting their homeostatic or immune surveillance functions.
  • Therapeutic Potential: Metabolic reprogramming is a hallmark of cancer and immune cells. Targeting glucose metabolism in T cells could modulate their function, though its specific role in normal tissue versus tumor would need careful consideration to avoid off-target effects.
• PTGER4 (EP4): Prostaglandin E2 Receptor 4.
  • Signaling Pathways: Binds Prostaglandin E2 (PGE2), activating G-protein coupled signaling pathways, including cAMP. PGE2 often has immunosuppressive effects.
  • Role in TME (normal pancreas): CD4+ T cells in normal tissue may express EP4, making them responsive to PGE2, potentially influencing their inflammatory or regulatory functions in a homeostatic context.
  • Therapeutic Potential: Inhibiting EP4 could reverse PGE2-mediated immunosuppression. EP4 antagonists are under investigation in cancer to boost anti-tumor immunity [GeneCards: PTGER4].
• KLRB1 (CD161): Killer cell lectin-like receptor subfamily B member 1.
  • Signaling Pathways: Involved in NK cell and T cell activation, can act as a co-receptor. Ligand is CLEC2D (LLT1).
  • Role in TME (normal pancreas): Expressed on NK cells, NKT cells, and some T cell subsets. Its presence on CD4+ T cells in normal tissue suggests a potential for interaction with CLEC2D-expressing cells and involvement in innate-like immune responses or regulation.
  • Therapeutic Potential: Modulating CD161 signaling could enhance or suppress immune responses. Given its ligand CLEC2D is enriched in PDAC (see below), this could be a relevant axis.
• AREG (Amphiregulin): An EGF receptor (EGFR) ligand.
  • Signaling Pathways: Activates EGFR, leading to proliferation, survival, and migration.
  • Role in TME (normal pancreas): While often produced by stromal or epithelial cells, its expression by CD4+ T cells in normal tissue could imply a role in paracrine signaling, tissue repair, or modulating local cell growth, potentially distinct from its pro-tumorigenic role in cancer [UniProt: AREG].
  • Therapeutic Potential: EGFR inhibitors are used in cancer. If T cell-derived AREG contributes to tumor growth, targeting AREG or EGFR could be relevant, though T cell specific targeting might be challenging.
• IFNGR1: Interferon Gamma Receptor 1. Component of the receptor for IFN-γ.
  • Signaling Pathways: Binds IFN-γ, initiating JAK-STAT signaling, leading to gene expression involved in antiviral, anti-proliferative, and immunomodulatory responses.
  • Role in TME (normal pancreas): Indicates that normal pancreatic CD4+ T cells are responsive to IFN-γ, a key cytokine in host defense and anti-tumor immunity.
  • Therapeutic Potential: IFN-γ is used therapeutically in some cancers. Enhancing IFNGR1 signaling or its downstream effects could augment anti-tumor responses.

Markers Enriched in PDAC CD4+ T cells

• CD27: A co-stimulatory receptor of the TNF receptor superfamily.
  • Signaling Pathways: Upon binding its ligand CD70, activates NF-κB and JNK pathways, promoting T cell activation, differentiation, and survival, especially of memory T cells.
  • Role in TME (PDAC): High expression suggests a population of activated or memory CD4+ T cells within the tumor. CD27-CD70 interactions can be crucial for sustaining anti-tumor T cell responses, but also for generating regulatory T cells. Its upregulation could signify active, albeit potentially dysfunctional, immune engagement.
  • Therapeutic Potential: Agonistic anti-CD27 antibodies (e.g., Varlilumab) are being explored in cancer immunotherapy to enhance T cell activation and anti-tumor immunity [PubMed Search: Varlilumab CD27 cancer].
• ITGB1 (CD29): Integrin Beta-1 subunit. Forms various heterodimeric integrins (e.g., VLA-4, VLA-5) with alpha subunits.
  • Signaling Pathways: Integrin signaling, mediating cell-extracellular matrix (ECM) and cell-cell adhesion, cell migration, and T cell activation/survival.
  • Role in TME (PDAC): Integrins are vital for T cell trafficking into and retention within tissues, including the tumor. Elevated ITGB1 may facilitate CD4+ T cell interactions with the dense ECM characteristic of PDAC, or with other cell types, influencing their localization and effector functions.
  • Therapeutic Potential: Integrin inhibitors exist for other inflammatory conditions. Targeting ITGB1 could modulate T cell infiltration or interactions within the PDAC TME, potentially impacting therapeutic delivery or immune evasion [GeneCards: ITGB1].
• SLC38A1: Solute Carrier Family 38 Member 1 (SNAT1), a neutral amino acid transporter.
  • Signaling Pathways: Involved in nutrient uptake, particularly amino acids. Linked to mTOR signaling, which regulates cell growth and metabolism.
  • Role in TME (PDAC): Suggests that CD4+ T cells in PDAC have altered metabolic needs or are adapting to the nutrient-deprived tumor environment, similar to cancer cells upregulating nutrient transporters. This could support their survival or specific effector functions.
  • Therapeutic Potential: Modulating amino acid metabolism is an emerging area in cancer therapy. Inhibiting SLC38A1 could starve tumor-infiltrating T cells, but also potentially tumor cells, warranting further study.
• TNFRSF14 (HVEM): TNF Receptor Superfamily Member 14 (Herpesvirus Entry Mediator).
  • Signaling Pathways: Interacts with multiple ligands (e.g., BTLA, CD160, LIGHT), playing dual roles in both co-stimulation and co-inhibition of T cells. Involved in NF-κB activation.
  • Role in TME (PDAC): Its expression highlights complex immunoregulatory networks in the PDAC TME. Depending on the ligand context, it can either promote or inhibit CD4+ T cell responses, influencing anti-tumor immunity.
  • Therapeutic Potential: Modulating TNFRSF14 interactions (e.g., blocking inhibitory interactions or enhancing stimulatory ones) could be a strategy to fine-tune T cell responses in cancer [UniProt: TNFRSF14].
• CCR7: C-C Chemokine Receptor Type 7.
  • Signaling Pathways: Binds chemokines CCL19 and CCL21, guiding T cells to secondary lymphoid organs. Involved in G-protein coupled signaling pathways.
  • Role in TME (PDAC): CCR7 is typically expressed by naive and central memory T cells, facilitating their homing to lymph nodes. Its presence on CD4+ T cells in PDAC could indicate a population of less differentiated T cells or those involved in recirculation between the tumor and draining lymph nodes. In the TME, it might contribute to immune cell exclusion from the tumor core or migration to specific niches.
  • Therapeutic Potential: Targeting CCR7 could potentially alter lymphocyte trafficking, either to enhance tumor infiltration or to reduce immune escape via lymphatic routes.
• S1PR4: Sphingosine-1-phosphate Receptor 4.
  • Signaling Pathways: A G-protein coupled receptor for Sphingosine-1-phosphate (S1P), influencing lymphocyte trafficking and activation.
  • Role in TME (PDAC): S1P signaling is critical for lymphocyte egress from lymphoid organs. Upregulation of S1PR4 on CD4+ T cells in PDAC might reflect an altered migratory phenotype or adaptation to the S1P-rich tumor microenvironment, potentially contributing to T cell retention or dysfunction within the tumor.
  • Therapeutic Potential: S1P receptor modulators (e.g., Fingolimod) are approved for autoimmune diseases and are being investigated for cancer. Targeting S1PR4 could modulate T cell trafficking and anti-tumor responses [GeneCards: S1PR4].
• CD164: Sialomucin.
  • Signaling Pathways: Involved in cell adhesion, migration, and signaling through poorly defined pathways.
  • Role in TME (PDAC): May play a role in regulating cell-cell interactions, particularly with stromal cells or other immune cells, influencing CD4+ T cell behavior and retention within the tumor niche.
  • Therapeutic Potential: Less characterized as a therapeutic target compared to other markers, but its role in adhesion could be exploited to alter cell trafficking or interactions.
• SELL (CD62L): L-Selectin.
  • Signaling Pathways: An adhesion molecule involved in lymphocyte homing to high endothelial venules in lymph nodes.
  • Role in TME (PDAC): Primarily expressed by naive and central memory T cells. Its presence on CD4+ T cells in PDAC could indicate an unactivated or central memory phenotype, or T cells that have recently migrated from lymphoid organs. Its downregulation typically occurs upon activation.
  • Therapeutic Potential: While not a direct target, SELL expression can serve as a marker for T cell subsets and their trafficking patterns.
• CLEC2D (LLT1): C-type Lectin Domain Family 2 Member D. Ligand for NKR-P1A (CD161).
  • Signaling Pathways: Interacts with CD161 (KLRB1) on NK cells and some T cells, typically leading to inhibitory signaling in NK cells.
  • Role in TME (PDAC): If expressed by CD4+ T cells (which is less common than on other immune cells), it suggests a potential immune suppressive mechanism where CD4+ T cells could dampen NK cell activity or modulate CD161-expressing T cells within the PDAC microenvironment.
  • Therapeutic Potential: Blocking the CLEC2D-CD161 interaction could enhance NK cell and potentially T cell anti-tumor responses. This axis is an emerging immunotherapeutic target [UniProt: CLEC2D].
• BTN3A2: Butyrophilin Subfamily 3 Member A2.
  • Signaling Pathways: Part of the butyrophilin family, involved in immune regulation, particularly in activating Vγ9Vδ2 T cells.
  • Role in TME (PDAC): Suggests that CD4+ T cells in PDAC may have a specific immunoregulatory function or interact with gamma-delta T cells, influencing the broader immune landscape.
  • Therapeutic Potential: Modulating BTN3A family members is an emerging area, potentially to enhance or suppress specific T cell subsets, particularly gamma-delta T cells known for anti-tumor activity [PubMed Search: BTN3A2 gamma delta T cells cancer].
• IL10RA: Interleukin 10 Receptor Subunit Alpha.
  • Signaling Pathways: Binds IL-10, an immunosuppressive cytokine, activating JAK-STAT signaling, leading to inhibition of inflammatory responses.
  • Role in TME (PDAC): CD4+ T cells expressing IL10RA are susceptible to IL-10-mediated immunosuppression. Given the high levels of IL-10 often found in PDAC, this suggests that these T cells are likely being suppressed, contributing to immune evasion.
  • Therapeutic Potential: Blocking IL-10 or its receptor (IL10RA) could be a strategy to reverse immunosuppression in the PDAC TME and enhance anti-tumor immunity. Anti-IL-10R antibodies are being explored in clinical trials [GeneCards: IL10RA].
• FLT3LG: FMS-like Tyrosine Kinase 3 Ligand.
  • Signaling Pathways: Ligand for FLT3 receptor tyrosine kinase, primarily stimulating proliferation and differentiation of hematopoietic stem cells and dendritic cell precursors.
  • Role in TME (PDAC): While typically secreted by stromal cells, its expression *on* CD4+ T cells is unusual and could suggest a novel mechanism of immune cell cross-talk, possibly influencing the differentiation or activation of other immune cells (e.g., dendritic cells) within the TME, or an autocrine/paracrine role for these specific CD4+ subsets.
  • Therapeutic Potential: FLT3 inhibitors are used in leukemia. Understanding FLT3LG's role on CD4+ T cells in PDAC could reveal new ways to modulate immune cell development or function in the TME.
• SPN (CD43): Sialophorin.
  • Signaling Pathways: A highly glycosylated transmembrane protein involved in cell adhesion, T cell activation, and migration. Can have both pro- and anti-adhesive functions depending on the context.
  • Role in TME (PDAC): Modulates T cell interactions with other cells and the ECM within the tumor. Its specific functions in the PDAC TME are complex and may impact T cell trafficking, retention, and effector functions.
  • Therapeutic Potential: Not a primary therapeutic target, but its modulation could influence T cell behavior.

Clinical or Translational Implications

The identification of condition-specific surfaceome markers on CD4+ T cells holds significant clinical and translational implications for PDAC:

1. Diagnostic and Prognostic Biomarkers: Markers selectively enriched in PDAC CD4+ T cells (e.g., CD27, ITGB1, S1PR4, IL10RA) could serve as novel diagnostic or prognostic biomarkers. Their presence or specific expression levels might correlate with tumor stage, response to therapy, or patient outcomes.
2. Therapeutic Targets: Surfaceome proteins are ideal drug targets as they are accessible to antibodies or small molecules.
   • Immunomodulation: Upregulation of co-stimulatory (CD27) and immunoregulatory molecules (TNFRSF14, CLEC2D, BTN3A2) in PDAC CD4+ T cells suggests opportunities for immunotherapeutic interventions. Agonistic anti-CD27 antibodies could enhance anti-tumor responses, while blocking the inhibitory CLEC2D-CD161 axis might unleash NK cell activity.
   • Overcoming Immunosuppression: The high expression of IL10RA points to a mechanism of immune suppression in PDAC. Blocking IL10RA could reverse IL-10-mediated immune inhibition, restoring effective anti-tumor immunity.
   • Targeting Trafficking and Metabolism: Markers involved in cell adhesion (ITGB1, CD164, SPN) and trafficking (S1PR4, CCR7) highlight altered migratory patterns of CD4+ T cells in PDAC. Modulating these pathways could improve T cell infiltration into tumors or prevent immune escape. SLC38A1 indicates metabolic adaptations that could be exploited.
3. Understanding PDAC Immune Evasion: The differential expression profiles offer insights into how CD4+ T cells adapt and are potentially dysregulated within the immunosuppressive PDAC microenvironment. The presence of markers associated with immunosuppression (e.g., IL10RA, potential inhibitory roles of TNFRSF14/CLEC2D) suggests active mechanisms by which the tumor evades immune destruction.
4. Stratification for Immunotherapy: The observed inter-patient heterogeneity in marker expression among PDAC samples suggests that stratifying patients based on their T cell surfaceome profiles could optimize personalized immunotherapy approaches, selecting patients most likely to respond to specific immune-modulating agents.
5. Experimental Validation: Further experimental validation using techniques like flow cytometry, immunohistochemistry, or functional assays with patient samples would be crucial to confirm the protein expression of these markers, their functional relevance, and their potential as therapeutic targets in PDAC.

17. Deregulated Cell Cycle Gene Expression in Pancreatic Ductal Adenocarcinoma Ductal Cells

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the expression patterns of a curated set of cell cycle pathway genes within Ductal cells, comparing samples from Pancreatic Ductal Adenocarcinoma (PDAC) patients to adjacent normal pancreatic tissue (Adj_normal). The plot_box_for_gene_expression_with_signif_difference tool was used to identify and visualize genes with statistically significant differences in expression between these two conditions. The y-axis represents the "Expressing cell fraction (sample)", indicating the proportion of Ductal cells within each sample that express the gene. This analysis focuses on the tumor-origin cell type (Ductal cells) in the context of PDAC, a disease characterized by uncontrolled proliferation and often aneuploidy.

Visual Summary

The visualization displays box plots for 24 selected cell cycle-related genes, all of which show statistically significant differences in expression between the 'Adj_normal' and 'PDAC' conditions within Ductal cells.

• Consistent Upregulation in PDAC: Across all 24 genes presented (e.g., ANAPC1, MCM7, GSK3B, RB1, TGFB1, SFN, CDC16, CDC25B, ORC2, MAD1L1, TP53, FZR1, CREBBP, ANAPC10, CCND1, SMAD3, ZBTB17, CDC23, SMAD2, CDK7, MYC, CDKN2D, WEE1, BUB3), the expressing cell fraction is markedly and significantly higher in PDAC samples compared to Adj_normal samples.
• Statistical Significance: All displayed genes have very low p-values (e.g., ANAPC1 p=6.75e-05, MCM7 p=8.59e-05, CCND1 p=0.00247, MYC p=0.00563), reinforcing the robustness of these observed differences.
• Expression Distribution: In Adj_normal samples, the expressing cell fraction for these genes is generally low, often below 0.2, with narrow distributions. In contrast, PDAC samples exhibit a much broader range and higher median expressing cell fractions, frequently between 0.2 and 0.6, indicating that a larger proportion of Ductal cells in PDAC tumors express these cell cycle genes. This suggests increased cellular heterogeneity and a higher proliferative state in the tumor microenvironment.

Biological Interpretation

The widespread and significant upregulation of cell cycle pathway genes in Ductal cells from PDAC samples points to a fundamental deregulation of cell cycle control, a hallmark of cancer. Given that Ductal cells are identified as the tumor-origin cell type and 15% of cells are aneuploid in this dataset, these findings are highly relevant to PDAC pathogenesis.

• Cell Cycle Hyperactivation: Genes such as MCM7 (a DNA replication helicase component) [GeneCards: MCM7], CCND1 (Cyclin D1, a key G1/S transition regulator) [GeneCards: CCND1], CDC16, CDC23, CDC25B, and CDC7 (critical regulators of DNA replication and cell division) are highly expressed in PDAC. This collectively indicates an accelerated and unchecked progression through the cell cycle, driving tumor cell proliferation.
• Mitotic Checkpoint and Stability: Components of the Anaphase-Promoting Complex/Cyclosome (APC/C) like ANAPC1 and ANAPC10, and spindle assembly checkpoint proteins like MAD1L1 and BUB3, show elevated expression. While these genes are crucial for accurate chromosome segregation, their dysregulation or overexpression in a context of rapid proliferation and aneuploidy (15% aneuploid cells in this dataset) suggests an attempt to manage increased mitotic stress or may reflect an altered, potentially unstable, cell division process in cancer.

Oncogenic Signaling and Tumor Suppressors:

• MYC, a well-known proto-oncogene that promotes cell growth and proliferation [GeneCards: MYC], is significantly upregulated, which is a common driver in PDAC.
• The upregulation of TGFB1, SMAD2, and SMAD3 suggests active TGF-beta signaling. While TGF-beta can act as a tumor suppressor in early stages, it often switches to a pro-tumorigenic role in advanced PDAC, promoting cell proliferation, EMT, and immune evasion [PubMed search: TGFB signaling PDAC].
• TP53 and RB1, critical tumor suppressor genes, are also observed to have higher expression fractions in PDAC. This can be complex; while functional loss of these proteins is common in cancer, increased mRNA levels might reflect compensatory mechanisms, cellular stress, or the presence of mutated but transcribed genes, especially given the observed aneuploidy [GeneCards: TP53].

• Other Key Regulators: Genes like GSK3B (involved in various signaling pathways, including cell cycle regulation) [GeneCards: GSK3B], CREBBP (a transcriptional coactivator), and WEE1 (a G2 checkpoint kinase) also show increased expression, highlighting the complex network of transcriptional and cell cycle control altered in PDAC. The upregulation of WEE1, which typically prevents premature mitosis, might indicate an ongoing struggle within cancer cells to maintain some level of cell cycle fidelity despite rampant proliferation, or it could be exploited by cancer cells to allow for DNA repair.

Clinical or Translational Implications

The pervasive upregulation of cell cycle pathway genes in PDAC Ductal cells offers several clinical and translational insights:

• Therapeutic Targets: Many of the upregulated genes, such as CCND1, CDK7, MYC, MCM7, and WEE1, represent attractive therapeutic targets for PDAC. Inhibitors against these cell cycle regulators are being developed or are already in clinical use for various cancers. Targeting these specific pathways could potentially halt the uncontrolled proliferation of PDAC cells.
• Biomarkers for Diagnosis and Prognosis: The significantly increased expression of these cell cycle genes could serve as robust biomarkers for PDAC. Detecting elevated levels of these genes in Ductal cells, perhaps through liquid biopsies or tissue analysis, could aid in early diagnosis, monitoring disease progression, or predicting response to therapy.
• Resistance Mechanisms: Alterations in cell cycle regulation pathways could also contribute to resistance to chemotherapy or radiation therapy in PDAC. Understanding these specific changes may inform the development of combination therapies to overcome such resistance.
• Targeting Pro-tumorigenic Signaling: The elevated expression of TGFB1 and its downstream effectors (SMAD2, SMAD3) suggests that targeting the TGF-beta pathway could be a viable therapeutic strategy in PDAC, particularly in patients where this pathway is promoting tumor progression.

18. Gene Ontology (GSA) Analysis of Ductal Cells in Pancreatic Ductal Adenocarcinoma (PDAC)

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Ontology (GO) enrichment results for Ductal cells in three different comparisons, based on up-regulated differentially expressed genes (DEGs). The comparisons are:

1. Adj_normal_vs_others: Identifying GO terms enriched in Ductal cells from adjacent normal tissue compared to all other Ductal cells (including those from PDAC).
2. Diploid_vs_others: Identifying GO terms enriched in Diploid Ductal cells compared to all other Ductal cells (including Aneuploid cells).
3. PDAC_vs_others: Identifying GO terms enriched in Ductal cells from PDAC tissue compared to all other Ductal cells (including those from adjacent normal tissue).

The results are displayed as bar plots, showing the top significantly enriched GO terms ranked by their negative logarithm of the p-value (-log(p-val)) and corresponding -log(q-val) (FDR adjusted p-value). A p-value cutoff of 0.05 was applied, and only up-regulated genes were considered for enrichment.

Visual Summary

1. GSA for Ductal cell: Adj_normal_vs_others

This plot shows a diverse set of GO terms primarily related to normal pancreatic function and metabolism. The most significant terms include "Pancreatic secretion", "Protein digestion and absorption", "Fatty acid degradation", and various metabolic pathways (e.g., "Pyruvate metabolism", "Arginine and proline metabolism", "Bile secretion"). Several immune and signaling pathways are also enriched, albeit with lower significance. The p-values range up to ~4.5 (-log(p-val)), indicating moderate significance for most terms.

2. GSA for Ductal cell: Diploid_vs_others

This plot reveals a much smaller set of significantly enriched GO terms compared to the other two comparisons. The top terms are "Bile secretion", "Proximal tubule bicarbonate reclamation", "Pancreatic secretion", "Complement and coagulation cascades", "cAMP signaling pathway", and "PI3K-Akt signaling pathway". These terms largely overlap with the top terms observed in the "Adj_normal_vs_others" comparison, suggesting that diploid Ductal cells share common functional characteristics with normal Ductal cells. The significance levels are comparable to the top terms in the Adj_normal comparison.

3. GSA for Ductal cell: PDAC_vs_others

This plot displays a substantially larger number of highly significant GO terms. The p-values for many terms are notably higher, with some reaching ~20 (-log(p-val)). This indicates a very strong and widespread enrichment of distinct biological processes in PDAC Ductal cells. Key enriched terms include "Ubiquitin mediated proteolysis", "Protein processing in endoplasmic reticulum", "Autophagy", "Spliceosome", "Ribosome", "RNA transport", and various pathways directly linked to cancer such as "Pancreatic cancer", "Pathways in cancer", "ErbB signaling pathway", "TNF signaling pathway", "AMPK signaling pathway", "Apoptosis", and "Cellular senescence". Many terms related to neurodegenerative diseases and infectious diseases are also present, often reflecting shared dysregulated cellular processes or stress responses.

Biological Interpretation

The GO enrichment analysis highlights profound biological differences between normal/diploid Ductal cells and PDAC-associated Ductal cells, consistent with the inferred disease context of Pancreatic Ductal Adenocarcinoma.

• Normal/Diploid Ductal Cell Function: The "Adj_normal_vs_others" and "Diploid_vs_others" comparisons predominantly show enrichment of pathways essential for the physiological function of pancreatic exocrine cells. "Pancreatic secretion" and "Protein digestion and absorption" are critical roles of the pancreas, reflecting the healthy digestive enzymatic activity. Various metabolic pathways, including those for amino acids, carbohydrates, and lipids, indicate an active metabolic state supporting normal cellular operations. The overlap between Adj_normal and Diploid cell profiles suggests that cells with normal ploidy largely maintain these homeostatic functions, even in the tumor microenvironment or adjacent tissues.
• PDAC-associated Ductal Cell Dysfunction and Oncogenic Reprogramming: The "PDAC_vs_others" comparison reveals a dramatic shift towards hallmarks of cancer.
  • Elevated Protein Homeostasis and Turnover: The prominent enrichment of terms like "Ubiquitin mediated proteolysis", "Protein processing in endoplasmic reticulum", "Spliceosome", "Ribosome", and "RNA transport" indicates high protein synthesis, folding, quality control, and degradation demands. This reflects the increased proliferative rate, metabolic activity, and altered proteostasis characteristic of cancer cells, which often experience ER stress and activate adaptive unfolded protein responses to cope with rapidly accumulating misfolded proteins [1].
  • Dysregulated Cell Survival and Proliferation: Pathways such as "Apoptosis" and "Cellular senescence" are commonly dysregulated in cancer, allowing cells to evade programmed cell death and sustain proliferation. The enrichment of "Pathways in cancer" and specific oncogenic signaling cascades like "ErbB signaling pathway" (which includes EGFR signaling, a known driver in PDAC [2]), "TNF signaling pathway" (involved in inflammation and cancer progression), and "AMPK signaling pathway" (critical for metabolic adaptation in cancer) underscores the oncogenic transformation of these cells.
  • Metabolic Reprogramming: While not as explicitly metabolic as the normal cell profiles, the implication of high protein turnover and energy demands points towards altered metabolism to fuel rapid growth.
  • Broader Stress Responses: The appearance of terms related to various neurodegenerative diseases and infectious diseases, alongside terms like "Autophagy" and "Lysosome", suggests a general activation of cellular stress responses, waste management, and altered immune interactions within the tumor microenvironment. These processes are often hijacked or dysregulated in cancer for survival and progression. The explicit enrichment of "Pancreatic cancer" terms confirms the specificity of the observed changes to the disease.

Clinical or Translational Implications

The distinct GO profiles provide valuable insights into the pathobiology of PDAC Ductal cells and offer potential avenues for translational research:

• Biomarker Discovery: The significantly up-regulated pathways in PDAC Ductal cells (e.g., protein processing, specific signaling pathways) could yield novel diagnostic or prognostic biomarkers for PDAC. Genes within these pathways could be investigated as indicators of tumor presence or progression.
• Therapeutic Targets: Pathways highly active in PDAC cells but less so in normal cells, particularly those involved in protein homeostasis ("Ubiquitin mediated proteolysis", "Protein processing in endoplasmic reticulum") or specific oncogenic signaling ("ErbB signaling pathway"), represent promising therapeutic targets. Inhibitors against components of these pathways could selectively impact cancer cell survival and growth while minimizing off-target effects on healthy tissue. For example, strategies targeting ER stress or proteasome activity have shown promise in other cancers and could be explored for PDAC [3, 4].
• Understanding Tumor Microenvironment: The presence of immune-related terms in both normal and PDAC contexts (e.g., "Complement and coagulation cascades", "TNF signaling pathway") highlights the complex interplay between Ductal cells and their microenvironment. Further investigation into these interactions could uncover mechanisms influencing tumor growth, metastasis, and immune evasion.

References

1. ER Stress and Cancer: Wang M, Kaufman RJ. The unfolded protein response and cell death. *Annu Rev Biochem*. 2012;81:455-478. PubMed Search: "ER stress cancer"
2. ErbB Signaling in PDAC: GeneCards entry for EGFR. GeneCards: EGFR
3. Proteasome Inhibitors in Cancer: Bortezomib for example. PubMed Search: "proteasome inhibitor cancer"
4. Targeting ER Stress in Cancer: PubMed Search: "targeting ER stress cancer therapy"

19. Gene Set Enrichment Analysis (GSEA) in Pancreatic Ductal Adenocarcinoma (PDAC) Cell Types

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results across various cell types identified from single-cell RNA-sequencing data of the human pancreas, comparing cells under Pancreatic Ductal Adenocarcinoma (PDAC) conditions against 'Adj_normal' conditions, or in the case of Ductal cells, comparing 'Diploid' cells against 'others' (aneuploid/unclear cells). The objective is to identify significantly enriched or depleted biological pathways within specific cell populations, thereby elucidating the molecular alterations associated with PDAC and its tumor microenvironment. Each comparison is based on gene rankings derived from a one-vs-rest differential expression analysis within each cell type.

Visual Summary

The dot plot effectively visualizes the Normalized Enrichment Score (NES) and statistical significance for 120 selected gene sets across multiple cell types.

• Overall Disease Signature: A prominent pattern is the widespread enrichment (red dots, positive NES) of various cancer-related pathways (e.g., "Breast cancer", "Central carbon metabolism in cancer", "MicroRNAs in cancer", "Proteoglycans in cancer", "Pathways in cancer") across different cell types in the PDAC condition.

Ductal Cell Remodeling (Tumor-Origin):

• "Pancreatic secretion" is consistently and significantly downregulated (large blue dots, negative NES) in Ductal cells across all comparisons, including 'Adj_normal_vs_others', 'PDAC_vs_others', and 'Diploid_vs_others'. This indicates a profound loss of normal exocrine function in these cells.
• Conversely, Ductal cells in the PDAC condition show strong upregulation (large red dots, positive NES) of key oncogenic signaling pathways such as "ErbB signaling pathway", "MAPK signaling pathway", "PI3K-Akt signaling pathway", and "Ras signaling pathway". Pathways involved in protein synthesis and degradation, like "Proteasome", "Protein processing in endoplasmic reticulum", and "Protein export", are also notably enriched.

Immune Cell Activation and Dysregulation in TME:

• Macrophages: Exhibit robust activation (red dots) of inflammatory and immune-modulating pathways including "Cytokine-cytokine receptor interaction", "HIF-1 signaling pathway", "JAK-STAT signaling pathway", "NOD-like receptor signaling pathway", and "PD-L1 expression and PD-1 checkpoint pathway in cancer" in the PDAC context.
• T Cells (CD4+ and CD8+): Show upregulation of "T cell receptor signaling pathway", "Cytokine-cytokine receptor interaction", and "TNF signaling pathway" in PDAC, suggesting active immune responses. "Th17 cell differentiation" is also enriched in CD4+ T cells.
• NK Cells: A critical observation is the significant downregulation (large blue dots, negative NES) of "Natural killer cell mediated cytotoxicity" in NK cells from the PDAC condition, pointing towards impaired anti-tumor immunity.
• Mast Cells: Demonstrate enrichment in "Fc epsilon RI signaling pathway", "Cytokine-cytokine receptor interaction", and "JAK-STAT signaling pathway", indicating their activation and involvement in the tumor microenvironment.

• Stromal Cell Contribution: Endothelial cells in PDAC show upregulation of pathways like "HIF-1 signaling pathway", "Cell adhesion molecules", and "MAPK signaling pathway", reflecting their role in angiogenesis and tumor support.
• Metabolic Shifts: "Oxidative phosphorylation" shows mixed enrichment, being upregulated in Ductal and Endothelial cells in PDAC, but downregulated in some immune cells. "AGE-RAGE signaling pathway in diabetic complications" is broadly activated across various cell types in PDAC.
• Ploidy-Specific Patterns in Ductal Cells: Interestingly, "Ductal cell: Diploid_vs_others" (comparing diploid Ductal cells to non-diploid Ductal cells) reveals a similar enrichment pattern for many cancer-associated pathways (e.g., PI3K-Akt, MAPK, Ras, Proteasome, MicroRNAs in cancer) as seen in Ductal cells from the overall PDAC condition. This suggests that even diploid Ductal cells within the tumor context exhibit substantial oncogenic pathway activation and functional aberrations.

Biological Interpretation

The GSEA results delineate a complex interplay of cellular and molecular changes in PDAC, extending beyond the tumor cells to encompass the entire tumor microenvironment.

1. Oncogenic Signaling and Metabolic Reprogramming in Ductal Cells: The robust activation of central oncogenic pathways (ErbB, MAPK, PI3K-Akt, Ras) in PDAC Ductal cells underscores their role in uncontrolled proliferation, survival, and differentiation [PubMed Search]. The enhanced protein synthesis and degradation machinery (Proteasome, ER processing) supports the high metabolic demand of rapidly dividing cancer cells. The concurrent loss of "Pancreatic secretion" function signifies dedifferentiation and functional impairment characteristic of tumor progression.
2. Pro-Tumorigenic and Immunosuppressive TME:
   • Inflammation and Immune Suppression: The highly activated state of macrophages, with enriched "Cytokine-cytokine receptor interaction", "HIF-1 signaling pathway", "JAK-STAT signaling pathway", and "PD-L1 expression and PD-1 checkpoint pathway in cancer", indicates a pro-tumorigenic, inflammatory phenotype. These macrophages likely contribute to immune suppression and tumor progression, potentially by promoting angiogenesis and stromal remodeling [UniProt].
   • Impaired Anti-tumor Immunity: The observed downregulation of "Natural killer cell mediated cytotoxicity" in NK cells and the upregulation of "PD-L1 expression and PD-1 checkpoint pathway in cancer" across multiple cell types (Ductal, Macrophage, T cells) are strong indicators of tumor-mediated immune evasion strategies [PubMed Search]. While T cells show signs of activation, the checkpoint activation suggests potential exhaustion, limiting their anti-tumor efficacy.
3. Metabolic Adaptations: Differential activity in "Oxidative phosphorylation" and broad activation of "AGE-RAGE signaling pathway" reflect metabolic adaptations within the tumor and its microenvironment to support the high energy demands of cancer cells and to cope with inflammatory stress [PubMed Search].
4. Heterogeneity of Tumor Cells: The GSEA findings in diploid Ductal cells, showing similar oncogenic pathway activation as the general PDAC Ductal population, are crucial. This suggests that significant tumorigenic activity and functional shifts are not exclusive to highly aneuploid cells. It implies that a subset of diploid cells within the tumor context might represent actively transforming cells, or a highly proliferative subpopulation, challenging the simplistic view that only aneuploid cells are drivers of malignancy.

Clinical or Translational Implications

1. Targeted Therapies: The consistent activation of key oncogenic pathways (PI3K-Akt, MAPK, Ras, ErbB) in PDAC Ductal cells and components of the TME highlights these as promising therapeutic targets. Developing or repurposing inhibitors against these pathways could offer new treatment avenues for PDAC patients [PubMed Search].
2. Immunotherapy Combinations: The evidence of immune evasion (downregulated NK cell cytotoxicity, upregulated PD-1/PD-L1) suggests that while single-agent immunotherapies like PD-1/PD-L1 blockade may have limited efficacy in PDAC, combination strategies targeting multiple immune checkpoints or modulating the pro-tumorigenic phenotype of macrophages could be more effective.
3. Biomarker Discovery: Pathways related to the loss of "Pancreatic secretion" and the activation of specific oncogenic and inflammatory pathways could serve as potential diagnostic, prognostic, or predictive biomarkers for PDAC. Monitoring the activity of these pathways might help track disease progression or response to therapy.
4. Addressing Tumor Heterogeneity: The findings regarding diploid Ductal cells emphasize the importance of considering tumor heterogeneity in treatment strategies. Therapies should aim to target the diverse cellular states and underlying mechanisms active in both diploid and aneuploid tumor cell populations to achieve comprehensive disease control.
5. Metabolic Interventions: The observed metabolic reprogramming suggests that strategies targeting specific metabolic vulnerabilities of cancer cells or their microenvironment could be explored as complementary therapies to enhance the efficacy of existing treatments [PubMed Search].

20. Discussion

This comprehensive single-cell analysis provides deep insights into the cellular and molecular characteristics of Pancreatic Ductal Adenocarcinoma (PDAC), revealing a profoundly reprogrammed tumor ecosystem. The foundational observation is the identification of Ductal cells as the tumor-origin cell type, confirmed by their co-localization with aneuploid cells and pervasive genomic instability characterized by recurrent amplifications of oncogenes such as SOX2, EGFR, and ERBB2. This genomic instability is a hallmark of cancer and underscores the aggressive nature of PDAC.

Remarkably, the tumor microenvironment (TME) undergoes dramatic remodeling. There is a significant expansion of Ductal tumor cells and a notable reduction in normal Acinar cells. The characteristic desmoplastic stroma is evident through the substantial increase in stromal cells, particularly activated Stellate cells and Fibroblasts, which are known to create a physical barrier and an immunosuppressive milieu. The immune landscape is also significantly altered: a striking decrease in cytotoxic T cells (T_Cyto) is observed, coupled with a significant increase in immunosuppressive regulatory T cells (Treg) and various helper T cell subsets (Th9, Tfh, Th17, Th2, Th22). This pronounced shift towards immune suppression is a major contributor to immune evasion in PDAC. Macrophages, critical components of the TME, exhibit diverse polarization states, with a frequent dominance of M1-like macrophages in many tumors, alongside notable M2A populations in others. However, despite the presence of M1 macrophages, the overall immune environment remains immunosuppressive, suggesting either a dysfunctional M1 phenotype or overwhelming suppressive signals.

Cell-cell interaction (CCI) analyses reveal a highly active and complex communication network in PDAC, prominently featuring interactions between aneuploid Ductal cells and macrophages, as well as T cells and stromal cells. Key pro-tumorigenic and immunosuppressive pathways are highly activated, including the SPP1-CD44/Integrin axis, CEACAM1/5, MIF-CD74/CXCR2/4, GAS6-AXL, and most notably, the TGFB1-TGFBR1 pathway, which drives fibrosis and immune suppression. Crucially, immune checkpoint interactions such as SIRPA-CD47 and LGALS9-HAVCR2 (Galectin-9-TIM-3) are highly upregulated, providing direct mechanisms for tumor cells and tumor-associated myeloid cells to disarm anti-tumor immunity. The downregulation of 'Natural killer cell mediated cytotoxicity' in NK cells further highlights pervasive immune evasion. Pathway analyses (GSA/GSEA) corroborate these findings, showing a profound loss of normal pancreatic function in Ductal cells (e.g., 'Pancreatic secretion') and robust activation of oncogenic signaling (ErbB, MAPK, PI3K-Akt, Ras) alongside pathways for protein homeostasis and stress responses.

Interestingly, even diploid Ductal cells within the PDAC microenvironment show signs of oncogenic pathway activation, suggesting they may represent early transformed cells or a highly proliferative, non-aneuploid tumor subpopulation, challenging a simplistic view that only aneuploid cells are drivers of malignancy. The observed heterogeneity across individual PDAC samples, both in cellular composition and molecular pathway activation, underscores the complex and individualized nature of this disease, necessitating personalized therapeutic approaches.

Hypotheses:

1. Aneuploid Ductal cells are the primary drivers of aggressive tumor progression and therapy resistance in PDAC, establishing an immunosuppressive and pro-fibrotic microenvironment through extensive cell-cell interactions.
2. The extensive desmoplastic stroma, largely mediated by activated pancreatic stellate cells and fibroblasts, actively suppresses anti-tumor immune responses and creates a physical barrier that limits immune cell infiltration and therapeutic agent delivery in PDAC.
3. The diverse macrophage populations in the PDAC tumor microenvironment (TME), despite exhibiting an M1-like polarization in many cases, are functionally reprogrammed by tumor-stromal interactions to contribute to overall immune evasion and tumor growth.
4. The observed reduction in cytotoxic T cells and increase in regulatory T cells, coupled with the upregulation of multiple immune checkpoint pathways (e.g., CD47-SIRPα, TIM-3), leads to a functionally exhausted and anergic T cell compartment in PDAC.
5. Early activation of oncogenic pathways (e.g., PI3K-Akt, MAPK, Ras, ErbB) in diploid Ductal cells within the PDAC microenvironment indicates that these cells represent an actively transforming or highly proliferative non-aneuploid tumor subpopulation, contributing to tumor heterogeneity and progression.

Potential therapeutic targets:

1. EGFR/ERBB2 (HER2): Key oncogenic drivers that, when amplified or overexpressed, promote uncontrolled cell proliferation, survival, and metastasis in PDAC. Targeting these receptors can inhibit a central mechanism of tumor growth. Evidence: CNV analysis showed frequent amplifications of EGFR (7p13) and ERBB2 (17q12) in PDAC Ductal cells. GSEA and GSA revealed robust enrichment of 'ErbB signaling pathway' in PDAC Ductal cells. CCI analysis highlighted activation of EGFR family ligands (HBEGF, EREG, BTC) stimulating Ductal cells. ERBB3 (a co-receptor for EGFR/ERBB2) was also highly expressed on PDAC Ductal cells. Validation: Test existing EGFR tyrosine kinase inhibitors (e.g., Erlotinib, Afatinib) or monoclonal antibodies (e.g., Cetuximab) and HER2-targeted therapies (e.g., Trastuzumab, trastuzumab deruxtecan) in PDAC patient-derived organoids, xenografts, or clinical trials for patients with EGFR/ERBB2 amplifications.
2. CD47: Functions as a 'don't eat me' signal, binding to SIRPα on phagocytic cells (macrophages) to inhibit phagocytosis of tumor cells, thereby enabling immune evasion. Blocking CD47 can re-engage innate immunity against cancer. Evidence: Highly upregulated on PDAC Ductal cells in surfaceome marker analysis. CCI analysis showed strong SIRPA-CD47 interactions between aneuploid Ductal cells and macrophages, and among macrophages, indicating its role in immune suppression within the TME. Validation: Evaluate anti-CD47 monoclonal antibodies (e.g., Magrolimab) in PDAC models or clinical trials, potentially in combination with chemotherapy or other immunotherapies. Functional assays could confirm enhanced macrophage-mediated phagocytosis of PDAC cells upon CD47 blockade.
3. TGF-β Pathway (TGFB1/TGFBR1/TGFBR2): A central mediator of the desmoplastic stroma and potent immunosuppressor in PDAC. Drives fibroblast activation, extracellular matrix production, and suppresses anti-tumor immune responses, thus hindering drug delivery and promoting immune evasion. Evidence: CCI analysis showed prominent TGFB1-TGFBR1 interactions involving aneuploid Ductal cells with stellate cells, T cells, and macrophages. GSA and GSEA highlighted significant activation of TGF-β signaling in PDAC Ductal cells. TGFBR2 was also upregulated on PDAC macrophages, indicating their enhanced responsiveness to TGF-β. Validation: Test TGF-β pathway inhibitors (e.g., galunisertib, fresolimumab) in PDAC models. Monitor for reduction in stromal density, increased immune cell infiltration, and enhanced anti-tumor activity, especially in combination with chemotherapy or checkpoint inhibitors.
4. MSLN (Mesothelin): A glycosylphosphatidylinositol-anchored protein aberrantly overexpressed in many aggressive cancers, including PDAC, where it promotes tumor growth, survival, and metastasis. Evidence: Surfaceome marker analysis showed strong and broad expression of MSLN in PDAC Ductal cells, particularly in aneuploid samples, indicating its widespread presence on tumor cells. Validation: Evaluate mesothelin-targeting immunotherapies such as antibody-drug conjugates (ADCs, e.g., anetumab ravtansine), CAR-T cell therapies, or immunotoxins (e.g., SS1P) in PDAC preclinical models and clinical trials.
5. TIM-3 Pathway (HAVCR2/LGALS9): TIM-3 is an immune checkpoint receptor expressed on exhausted T cells and myeloid cells. Its interaction with ligands like Galectin-9 delivers inhibitory signals, leading to T cell exhaustion and dysfunction of other immune cells, contributing to immunosuppression. Evidence: CCI analysis identified strong LGALS9-HAVCR2 (Galectin-9-TIM-3) interactions between macrophages in PDAC, highlighting a key immune checkpoint axis mediating suppression within the TME. Validation: Evaluate anti-TIM-3 antibodies as novel immune checkpoint inhibitors in PDAC, potentially in combination with PD-1/PD-L1 blockade, to reverse T cell exhaustion and reinvigorate anti-tumor immune responses.
6. IL-10 / IL10RA: IL-10 is a highly immunosuppressive cytokine often found in the PDAC TME. Blocking its receptor can reverse IL-10-mediated immune inhibition and restore effective anti-tumor immunity by reactivating T cells. Evidence: Surfaceome marker analysis showed upregulation of IL10RA on PDAC CD4+ T cells, indicating their susceptibility to IL-10-mediated suppression within the tumor microenvironment. Validation: Test anti-IL-10 or anti-IL10RA antibodies in PDAC models to assess their ability to reduce immunosuppression and enhance anti-tumor responses, particularly in combination with other immunotherapies.

Follow-up validation ideas:

1. Spatial Transcriptomics/Proteomics: Apply spatial omics technologies to PDAC tissue sections to precisely map the colocalization of aneuploid Ductal cells with specific stromal and immune cell types, and to visualize the spatial distribution and activation of the identified cell-cell interactions and marker expressions (e.g., CD47, SIRPα, TGFB1, MUC1, MSLN) in situ.
2. Flow Cytometry/Immunohistochemistry (IHC): Conduct multi-color flow cytometry and IHC on larger cohorts of fresh/frozen and FFPE PDAC patient samples to validate the protein expression levels and localization of key surfaceome markers (e.g., MSLN, MUC1, CD47, ERBB3 on Ductal cells; SIRPα, TGFBR2, CCR1, ADAM17 on Macrophages; CD27, IL10RA on CD4+ T cells) and to confirm the observed cell population shifts (e.g., T cell subsets, macrophage polarization states).
3. FISH/Digital PCR for CNVs: Use Fluorescence In Situ Hybridization (FISH) or digital PCR to orthogonally validate specific copy number amplifications of oncogenes (e.g., EGFR, ERBB2, SOX2) and ploidy status in Ductal cells. Correlate these genomic alterations with protein expression levels of corresponding genes and with clinical outcomes (e.g., patient survival, response to therapy).
4. In vitro/Ex vivo Functional Assays: Utilize patient-derived organoids or co-culture systems to perform functional assays:

• Cell-cell interaction blockade assays: Block specific ligand-receptor interactions (e.g., CD47-SIRPα, TGFB1-TGFBR1, MIF-CD74) using neutralizing antibodies or small molecule inhibitors and assess their impact on tumor cell survival, macrophage phagocytosis, T cell activation/exhaustion, and fibroblast activation/matrix production.
• Macrophage reprogramming assays: Treat patient-derived tumor-associated macrophages (TAMs) with inhibitors (e.g., CSF1R, ADAM17, CCR1 antagonists) and analyze their polarization (M1/M2 markers), phagocytic activity, and T cell stimulatory/suppressive capacity.
• T cell functional assays: Assess cytotoxic T cell activity, proliferation, and cytokine production in response to tumor cells after inhibiting immune checkpoints (e.g., TIM-3, IL10RA, LILRB2) or activating co-stimulatory receptors (e.g., CD27).

5. Patient-Derived Xenograft (PDX) Models or Genetically Engineered Mouse Models (GEMMs): Employ these in vivo models to evaluate the preclinical efficacy of therapeutic agents targeting identified pathways (e.g., EGFR/ERBB2 inhibitors, anti-CD47 antibodies, anti-MUC1/MSLN agents, TGF-β inhibitors, cell cycle inhibitors), potentially in combination. Monitor changes in tumor growth, metastasis, and the composition/function of the tumor microenvironment in response to treatment.

Limitations:

This report is based on a single-cell RNA-seq dataset, providing transcriptional insights but lacking direct protein-level validation for all findings. While robust computational methods were used for cell type annotation and CNV inference, these remain inferences. The dataset does not provide clinical staging or detailed patient metadata, limiting the correlation of findings with disease progression or treatment outcomes. The observed associations are correlative, and direct causality cannot be definitively established without functional perturbation studies. The 'unassigned' cell population and inter-sample heterogeneity within PDAC highlight the complex and dynamic nature of the tumor microenvironment, which may not be fully captured by this single dataset.

21. Query List

1. Show and save UMAPs for condition, sample, celltype_major, celltype_minor, 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 Ductal cells as 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 celltype_major, celltype_minor, ploidy_dec, 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. Select Ductal cells as tumor-origin cells, show their ploidy populations as a bar plot, and save the result.
11. Show and save cell-cell interaction patterns involving Ductal cells, Fibroblasts, Macrophages, and T cells. Select at most 80 cell-cell interactions per group.
12. Find cell-cell interactions involving Ductal 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.
13. Find cell-cell interactions containing immune checkpoint and cell cycle pathway genes and involving Ductal 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. Show marker expressions for Ductal 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.
15. 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.
16. 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.
17. Among cell cycle pathway genes, select those with statistically significant expression differences between conditions in Ductal 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.
18. Show and save Gene Ontology (GSA) analysis results for Ductal cells as a bar plot.
19. 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.