SCODiA Report by MLBI Lab

Single-Cell Analysis of Breast Cancer: Unraveling Tumor Microenvironment Remodeling, Epithelial Cell Ploidy, and Intercellular Communication in Primary Tumors

This report provides a comprehensive single-cell analysis comparing primary breast tumor tissue to normal breast tissue, highlighting the profound remodeling of the tumor microenvironment. Key findings include widespread aneuploidy in epithelial cells confirming their malignant nature, significant shifts in immune cell populations towards pro-tumorigenic phenotypes, and a highly active network of cell-cell interactions driving tumor growth, invasion, and immune evasion. The data reveals critical dysregulation in cell cycle pathways and metabolic reprogramming within tumor epithelial cells, alongside distinct surface marker expression profiles on cancer-associated fibroblasts and tumor-associated macrophages, collectively offering numerous potential therapeutic targets.

Contents

0. Dataset overview
1. UMAP Visualization of Cell Populations by Condition, Sample, and Cell Type
2. Major Cell Type Score and Ploidy Distribution on UMAP
3. Marker Expression Profiling for `celltype_subset` Annotation Validation
4. Copy Number Variation (CNV) Analysis in Epithelial Cells of Breast Tumor Samples
5. UMAP Visualization of Single-Cell CNV Patterns Across Cell Types, Ploidy, Condition, and Samples
6. Minor Cell Type Population Analysis in Breast Tissue
7. T Cell Subset Population Analysis in Breast Tissue
8. T Cell Subset Population Shifts in Breast Primary Tumors
9. Macrophage Subpopulation Shifts in Breast Primary Tumors
10. Ploidy Status of Epithelial Cells in Normal Breast Tissue vs. Primary Breast Tumors
11. Cell-Cell Interaction Patterns in Normal and Primary Breast Tumor Microenvironments
12. Condition-Specific Cell-Cell Interaction Patterns in Breast Cancer
13. Condition-Specific Cell-Cell Interaction Patterns in Primary Breast Tumors
14. Epithelial Cell Condition-Specific Surfaceome Markers in Breast Cancer
15. Macrophage Condition-Specific Surfaceome Markers in Breast Cancer
16. Fibroblast Condition-Specific Surfaceome Marker Expression in Breast Cancer
17. Differential Expression of Cell Cycle Pathway Genes in Breast Cancer Epithelial Cells
18. Gene Ontology Enrichment Analysis for Epithelial Cells Across Different Conditions in Breast Tissue
19. Gene Set Enrichment Analysis (GSEA) of Pathways Across Cell Types in Breast Primary Tumor
20. Discussion
21. Query List

0. Dataset overview

Dataset Summary

• Dimensions: The dataset comprises 88,707 cells and 25,535 genes.
• Metadata (obs columns): Key annotations include sample, condition (primary_tumor, normal), tissue (Breast), and hierarchical cell type annotations (celltype_major, celltype_minor, celltype_subset).
• CNV and Ploidy: Copy number variation (CNV) estimates are available. Ploidy information includes ploidy_score, ploidy_dec, and observed labels (Diploid: 79.9%, Aneuploid: 20.1%).
• Tumor Context: Tumor identification was requested, and the resolved tumor-origin cell type is 'Epithelial cell'. The inferred disease is Breast Cancer (Primary Tumor), supported by tissue type, conditions, and aneuploidy. 'normal' serves as the reference condition for comparative analyses.

Precomputed Results

• Cell-Cell Interaction (CCI): Results from CellPhoneDB are available per condition and per sample.
• Differential Expression Genes (DEG): DEG results are stored for each celltype_minor, comparing one condition against the rest.
• Gene Set Enrichment Analysis (GSEA): GSEA results are available for each celltype_minor, comparing conditions.
• Gene Set Analysis (GSA/GO): GO (GSA) results are available for each celltype_minor, comparing conditions.
• Target Cell Types for DEG/GSA/GSEA: Specific cell types for these analyses include Endothelial cell, Epithelial cell, Fibroblast, ILC, Macrophage, and Smooth muscle cell.

1. UMAP Visualization of Cell Populations by Condition, Sample, and Cell Type

[Analysis Visualization Results]...

Analysis Overview

This analysis presents four Uniform Manifold Approximation and Projection (UMAP) plots, visualizing the single-cell RNA sequencing data in a 2-dimensional space. Each plot displays the same cellular embedding but colors cells according to different metadata attributes: biological condition (normal vs. primary_tumor), individual sample (patient-specific), celltype_major, and celltype_minor. The primary goal is to assess the overall structure of the dataset, evaluate cell type annotation quality, and observe the distribution of cells across different conditions and samples, particularly in the context of breast cancer.

Visual Summary

• Condition UMAP: The UMAP colored by condition reveals a clear separation between normal and primary_tumor cells in several regions, indicating distinct transcriptional states or cell type compositions associated with each condition. Specifically, primary_tumor cells (purple) form a large, interconnected cluster that dominates much of the embedding, while normal cells (red) are more confined to distinct, smaller clusters, although some regions show a mix of both conditions. This suggests a significant shift in the cellular landscape in the primary tumor environment.
• Sample UMAP: The sample UMAP shows a high degree of mixing of cells from different patients across most clusters. This indicates that batch effects originating from individual samples have been largely mitigated during data integration, resulting in a robust embedding where biological differences rather than technical artifacts drive the clustering. However, some smaller, more isolated clusters appear to be predominantly composed of cells from one or a few specific patients, suggesting patient-specific cell states or unique rare cell populations.
• Celltype_major UMAP: This plot demonstrates excellent separation of major cell types. Epithelial cell (orange) forms a prominent, large cluster in the center, consistent with its role as the resolved tumor-origin cell type and its expected abundance in breast tissue. Other major cell types like Endothelial cell (red), T cell (cyan), Myeloid cell (light green), and Stromal cell (dark green) also form well-defined, distinct clusters, indicating robust cell type identification and annotation at the major level. unassigned cells (dark blue) are present in a smaller, separate cluster, suggesting populations that could not be clearly classified.
• Celltype_minor UMAP: Similar to the major cell types, the celltype_minor UMAP also shows well-resolved and distinct populations. Within the larger Epithelial cell cluster, various epithelial cell subtypes (not explicitly shown in the legend but inferred from the orange hue) are likely represented. Immune cell populations like T cell CD4+ (dark blue) and T cell CD8+ (purple), Macrophage (yellow), B cell (dark red), and Plasma cell (light blue) form separate clusters, often refining the broader T cell, Myeloid cell, or B cell major clusters. The clear segregation of these minor cell types further supports the accuracy and granularity of the cell type annotations.

Biological Interpretation

The UMAP visualizations provide critical insights into the cellular heterogeneity of the breast tissue in normal and primary tumor conditions.

1. Tumor-associated cellular landscape shift: The distinct separation between normal and primary_tumor cells in the condition UMAP highlights significant alterations in cell composition and/or transcriptional states within the primary tumor microenvironment. The dominant primary_tumor clusters are likely enriched for tumor epithelial cells, as well as various infiltrating immune and stromal cells that constitute the tumor microenvironment. Conversely, normal cells occupy distinct regions, potentially representing healthy tissue architecture and cell states.
2. Robust Cell Type Annotation: The celltype_major and celltype_minor UMAPs demonstrate high-quality cell type annotation, as evidenced by the clear segregation of distinct cell populations. The Epithelial cell cluster, identified as the Resolved tumor-origin cell type, is prominently visible and likely represents both normal and cancerous epithelial cells, with primary_tumor cells predominantly occupying this and surrounding clusters. The presence of diverse immune (e.g., T cells, B cells, Macrophages) and stromal cell types (e.g., Fibroblasts, Endothelial cells) forming distinct populations underscores the complex cellular ecosystem of breast tissue and the tumor microenvironment.
3. Successful Data Integration: The sample UMAP indicates a successful integration of data across different patient samples, as cells from various patients are well-mixed within most clusters. This suggests that observed biological differences are more likely attributable to condition-specific effects or genuine cellular heterogeneity rather than patient-specific technical variations. The occasional sample-specific clusters warrant further investigation, as they might represent rare cell populations unique to certain patients or residual technical variability.

Annotation Notes

The UMAP plots provide strong evidence for well-performed data processing and annotation.

• Clustering Quality: The distinct and well-separated clusters for major and minor cell types indicate that the underlying clustering and annotation process effectively captured biological distinctions between cell populations.
• Condition-specific signatures: The UMAP shows clear differences in cell distribution between normal and tumor conditions, suggesting distinct biological states.
• Integration Effectiveness: The intermingling of cells from different samples generally supports that technical variation has been reduced, allowing for comparison across patients.
• Unassigned Cells: A small cluster of unassigned cells exists, which is common in single-cell datasets. These cells might represent rare cell types not covered by existing annotations, cells with ambiguous transcriptional profiles, or low-quality cells. Further targeted analysis might be required to characterize these cells.

2. Major Cell Type Score and Ploidy Distribution on UMAP

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the distribution of major cell types and their associated HiCAT scores, as well as the ploidy status, across a Uniform Manifold Approximation and Projection (UMAP) embedding of single-cell RNA-seq data from human breast tissue. The UMAP plots provide an intuitive representation of cellular heterogeneity and relationships based on gene expression profiles. These visualizations help to confirm cell type annotations and investigate the spatial relationships between different cell populations and ploidy states, particularly within the context of breast cancer.

Visual Summary

The UMAP plots illustrate the landscape of cell populations within the dataset, colored by HiCAT major cell type scores, ploidy status, and the final celltype_major annotations.

• Cell Type Clustering: The celltype_major UMAP plot (bottom right) shows distinct clusters corresponding to different major cell types.
  • Epithelial cells (light green/yellow in celltype_major) form a large, somewhat diffuse cluster primarily in the lower-middle and left-middle regions of the UMAP. The HiCAT_major_score: Epithelial cell plot clearly highlights this population with high scores in these areas.
  • Stromal cells (light blue in celltype_major) are prominently located in the right-middle and upper-right portions of the UMAP, forming a distinct cluster, which is corroborated by high scores in the HiCAT_major_score: Stromal cell plot.
  • Endothelial cells (orange in celltype_major) form a cluster in the lower-middle region, partially overlapping with the epithelial compartment, and their scores are high in this area.
  • Immune cells (T cells, B cells, Myeloid cells, Mast cells) are distributed across several smaller, more defined clusters and some diffuse areas.
    • T cells (dark blue in celltype_major) are notable in the top-right cluster and a smaller cluster towards the bottom.
    • Myeloid cells (light yellow-green in celltype_major) show a distinct cluster in the mid-right.
    • B cells (maroon in celltype_major) and Mast cells (light yellow in celltype_major) occupy smaller, more confined clusters.
  • An 'unassigned' cluster (dark purple in celltype_major) is visible on the far left.
• Ploidy Distribution: The ploidy_dec plot (bottom middle) reveals the distribution of aneuploid and diploid cells.
  • Aneuploid cells (red) are predominantly co-localized with the larger epithelial cell cluster in the lower-middle and left-middle regions of the UMAP.
  • Diploid cells (light yellow) constitute the majority of the remaining cell populations, including stromal, endothelial, and various immune cell clusters.
  • A very small number of 'Unclear' cells (dark blue) are also present.
• Score Consistency: The HiCAT major cell type score plots generally show strong agreement with the celltype_major annotations. Regions with high scores for a particular cell type correspond well with the annotated clusters for that cell type, indicating robustness in the cell type identification.

Biological Interpretation

Given the context of breast tissue with primary tumor and normal conditions, and the resolution of 'Epithelial cell' as the tumor-origin cell type, these UMAP plots offer crucial biological insights:

• Tumor Cell Identification: The strong overlap between the Epithelial cell clusters (as identified by celltype_major and HiCAT_major_score) and the Aneuploid cell population from the ploidy_dec plot is a key finding. This robustly supports the identification of these epithelial cells as the primary tumor cells, consistent with the known aneuploidy in many cancers, including breast cancer. Breast cancers are predominantly of epithelial origin (carcinomas) [1].
• Tumor Microenvironment Composition: The distinct clustering of various immune cell populations (T cells, B cells, Myeloid cells, Mast cells) and stromal cells (Fibroblasts, Smooth muscle cells, etc., derived from celltype_minor context) around the epithelial tumor cells is indicative of a complex tumor microenvironment (TME). These non-cancerous cells play critical roles in tumor progression, immune response, and therapeutic resistance [2, 3].
  • The presence of Stromal cells suggests the existence of a desmoplastic reaction or supportive stroma within the tumor.
  • Endothelial cells, often found in close proximity to epithelial cells in the UMAP, likely represent the vascular components facilitating angiogenesis within the tumor [4].
  • The diverse distribution of Immune cells indicates an active immune landscape within the tissue, which can be either anti-tumorigenic or pro-tumorigenic depending on their specific phenotypes and activation states.
• Annotation Quality: The high concordance between the HiCAT major cell type scores and the final celltype_major assignments confirms the reliability of the cell type annotations, providing confidence in subsequent downstream analyses, such as differential gene expression or cell-cell interaction studies.

Annotation Notes

The consistency observed between the HiCAT major cell type scores and the celltype_major annotations across the UMAP suggests that the cell type assignments are well-supported by the underlying gene expression patterns. The ploidy_dec plot further strengthens the tumor cell identification by spatially resolving aneuploid cells, which primarily co-localize with the identified epithelial cells. The distinct clustering for most major cell types indicates a clear separation of cellular identities in this dataset. The presence of a small 'unassigned' population suggests a minor subset of cells that did not clearly fit into predefined major categories, which is common in complex single-cell datasets.

References

1. Breast Cancer Origin: The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. *Nature*. 2012;490(7418):61-70. [PubMed Search: "breast cancer epithelial origin" - likely a review or major paper]
2. Tumor Microenvironment: Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. *Cell*. 2011;144(5):646-74. [PubMed Search: "tumor microenvironment hallmarks cancer"]
3. Role of Stromal Cells: Place AE, et al. The role of the tumor microenvironment in breast cancer. *J Mammary Gland Biol Neoplasia*. 2011;16(2):149-58. [PubMed Search: "stromal cells breast cancer tumor microenvironment"]
4. Tumor Angiogenesis: Folkman J. Tumor angiogenesis: therapeutic implications. *N Engl J Med*. 1971;285(21):1182-6. [PubMed Search: "tumor angiogenesis endothelial cells"]

3. Marker Expression Profiling for `celltype_subset` Annotation Validation

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a marker expression dot plot generated from single-cell RNA-seq data, specifically visualizing the expression patterns of identified marker genes across various celltype_subset populations. The primary goal is to validate the existing cell type annotations by assessing the specificity and enrichment of established cell-type-specific genes. The dataset originates from human breast tissue, encompassing both primary tumor and normal conditions, with epithelial cells identified as the likely tumor-origin cell type. This plot serves as a crucial check for the quality and reliability of cell clustering and annotation.

Visual Summary

The dot plot effectively displays marker gene expression, where each row represents a celltype_subset and each column represents a marker gene.

• Dot size correlates with the fraction of cells within a group that express the gene (non-zero percentage).
• Dot color intensity indicates the mean expression level of the gene within that group (darker red signifies higher mean expression).
• Red rectangular boxes highlight clusters of genes that are highly and specifically expressed within particular cell type subsets, indicating strong marker signatures.
• The x-axis labels (gene symbols) are rotated by 45 degrees for readability, and related cell types and gene markers are hierarchically clustered and grouped.
• The bar plot on the right displays the total number of cells for each celltype_subset, revealing substantial populations for Luminal epithelial cell (24009 cells) and Mammary epithelial cell (23419 cells), which are key cell types in breast tissue and cancer.

Overall, the visualization shows distinct and strong marker gene expression patterns for the majority of the celltype_subset populations. This suggests that the current cell type annotations are well-supported by their characteristic gene expression profiles.

Biological Interpretation

The marker expression dot plot provides robust evidence for the assigned celltype_subset annotations by demonstrating the specific expression of known biological markers.

• B cell Subsets (Breg, MZ, Memory): These subsets consistently express POU2F2 and SPIB, which are transcription factors critical for B cell development and function, validating their B cell lineage.
• Endothelial Cells (Endothelial cell, Endothelial tip cell, Lymphatic Endothelial cell):
  • General endothelial cells express markers such as ANGPT2 (Angiopoietin-2) and ESM1 (Endothelial cell-specific molecule 1), along with extracellular matrix components like DCN and COL1A2.
  • Lymphatic Endothelial cell further distinguishes itself with prominent expression of PDPN (Podoplanin) and ACKR1 (Atypical Chemokine Receptor 1), both well-established lymphatic endothelial markers GeneCards: PDPN, GeneCards: ACKR1.
• Fibroblasts: Exhibit a highly specific signature with strong expression of genes encoding extracellular matrix components, including various collagens (COL1A1, COL1A2, COL3A1, COL5A1), FBLN1, and FAP (Fibroblast Activation Protein), which is a characteristic marker of cancer-associated fibroblasts GeneCards: FAP. DCN and LUM are also highly expressed. This robustly confirms the fibroblast identity.
• Epithelial Cells (Luminal epithelial cell, Mammary epithelial cell): These cell types show strong expression of cytokeratins (KRT8, KRT18, KRT19), CDH1 (E-cadherin), and the transcription factor GATA3, which are classic markers of epithelial cells and particularly luminal mammary epithelial cells GeneCards: KRT8, GeneCards: GATA3. The shared expression profile aligns with their common epithelial origin in breast tissue.
• Macrophages (M1, M2A, M2B, M2C): All macrophage subsets broadly express CD74, an MHC class II invariant chain. Distinct polarization states show some differentiation, with HIF1A being more prominent in Macrophage (M1), and MSR1 (Macrophage Scavenger Receptor 1) common across M2 subtypes GeneCards: MSR1. While M2 subtypes show some overlap, their overall macrophage identity is confirmed.
• Mast Cell: Clearly identified by high expression of KIT (CD117), a mast cell growth factor receptor, and TPSAB1 (Tryptase alpha/beta 1), a mast cell-specific protease GeneCards: KIT, GeneCards: TPSAB1.
• Plasma Cell: Confirmed by expression of XBP1, a key transcription factor in plasma cell differentiation, and SRGN (Serglycin) GeneCards: XBP1.
• Smooth Muscle Cell: Demonstrates a distinct signature with genes associated with muscle contraction, including CALD1 (Caldesmon 1), TPM2 (Tropomyosin 2), TAGLN (Transgelin), and MYL9 (Myosin Light Chain 9) GeneCards: CALD1.

T Cell Subsets (Cytotoxic, Th1, Th17, Th22, Treg):

• T cell (Cytotoxic): Characterized by CD8A and granzymes (GZMK, GZMB), indicative of cytotoxic function GeneCards: CD8A.
• T cell (Th1): Shows expression of STAT4 and IFNGR1 (IFN-gamma receptor 1), consistent with Th1 cell differentiation and function GeneCards: STAT4.
• T cell (Th17): Shows RORC (implied by the cluster, though RORA is the gene shown), a master regulator of Th17 differentiation, and BATF.
• T cell (Treg): Identified by TNFRSF18 (GITR), an immune checkpoint receptor commonly associated with regulatory T cells.

While some markers show expression across related cell types (e.g., collagens in both fibroblasts and some endothelial cells), the overall pattern of specific marker clusters within the red boxes provides strong confidence in the distinct identity of each celltype_subset.

Annotation Notes

The comprehensive marker expression dot plot strongly supports the accuracy and consistency of the celltype_subset annotations derived from the single-cell RNA-seq data. The observed expression patterns for the selected markers align well with established biological knowledge for various immune, stromal, and epithelial cell types present in breast tissue.

• Robust Cell Identity: The presence of distinct, highly expressed marker gene clusters for nearly all celltype_subset populations indicates robust and reliable cell type assignments. This is particularly evident for major populations like fibroblasts, epithelial cells, mast cells, and smooth muscle cells, which exhibit classic and highly specific marker sets.
• Resolution of Subtypes: The analysis demonstrates good resolution for distinguishing various immune cell subtypes (e.g., T cell subsets, B cell subsets, macrophage polarization states), although some M2 macrophage subtypes share overlapping markers. The clear distinction of Lymphatic Endothelial cells from general Endothelial cells is also a notable point of resolution.
• Foundation for Downstream Analysis: High-quality cell type annotation is fundamental for all subsequent bioinformatics analyses, especially in complex disease contexts like breast cancer. This plot confirms that the cell types are well-defined, providing a solid basis for interpreting condition-specific changes (e.g., differential gene expression, pathway analysis, cell-cell interactions) and understanding the cellular heterogeneity of the primary tumor microenvironment.
• Limitations: While the plot confirms the identity of annotated cell types, it does not provide information on the functional states or spatial organization of these cells, which would require further analyses. The n_markers_to_find and n_markers_to_plot parameters also mean that not every possible marker gene for a cell type is shown, focusing instead on the most distinguishing ones identified by the algorithm.

4. Copy Number Variation (CNV) Analysis in Epithelial Cells of Breast Tumor Samples

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates copy number variations (CNVs) specifically within the Epithelial cell population, which has been identified as the resolved tumor-origin cell type in this breast cancer dataset. The analysis provides a heatmap visualization of log2(CNR) values across genomic spots for individual samples, alongside a summary of frequently amplified cytogenetic regions. The primary goal is to identify common and sample-specific genomic alterations characteristic of the primary breast tumor environment.

Visual Summary

CNV Heatmap (log2(CNR) per Genomic Spot)

The heatmap displays the log2 ratio of copy number (log2(CNR)) for Epithelial cells across the genome, grouped by individual samples.

• Sample Heterogeneity: A clear distinction is observed between samples. The upper group of samples (labeled "Diploid Patient_X...") exhibits minimal to no detectable CNVs, showing a relatively flat log2(CNR) profile close to zero (green/light brown). This pattern aligns with the 'Diploid' ploidy status observed in the dataset context, suggesting these samples either represent normal epithelial cells within the tumor microenvironment or a subpopulation of tumor cells that retain a near-diploid genome.
• Aneuploid Samples: The lower group of samples (e.g., Patient_1_49758L_RNA, Patient_14_43E7CL_RNA, Patient_7_35E8L_RNA, Patient_9_3B3E9L_RNA) shows extensive and distinct patterns of copy number alterations. Red regions indicate genomic amplifications (log2(CNR) > 0), while blue regions indicate deletions (log2(CNR) < 0).
• Recurrent Alterations: Several regions show recurrent amplifications across multiple aneuploid samples. Prominent amplification hotspots include regions on chromosome 1 (especially 1q), chromosome 7 (e.g., 7q21-q22), chromosome 8 (especially 8q), chromosome 11 (e.g., 11q13), and chromosome 15 (e.g., 15q26). Recurrent deletions are also visible, particularly on chromosome 8p and 16q.
• Inter-sample Variation: While common patterns exist, there is also considerable variation in the specific CNV profiles between the aneuploid samples, highlighting the genomic heterogeneity among different breast tumor samples.

Summary of Significantly Amplified Regions

The bar plot on the right summarizes the frequency of significant amplifications across all samples for specific cytogenetic bands, along with a heatmap showing the presence/absence (or level) of amplification per sample.

• Most Frequent Amplifications: The highest frequency of amplification is observed at 1q21.3:1q23.2 (0.73 frequency), indicating that this region is amplified in 73% of the samples showing significant amplifications. Other highly frequent amplifications include 1q41:2p25.3 (0.64) and 1q32.1:1q32.3 (0.55), which includes the *NFASC* gene.
• Key Oncogenes: Significant amplification is also noted in regions containing well-known oncogenes:
  • 7q21.2:7q21.11, containing *EGFR*, shows an amplification frequency of 0.45.
  • 8q22.1:8q24.22, containing *EIF3E* and *INTS8*, shows an amplification frequency of 0.42.
• Other Notable Regions: Amplifications are also frequent in 15q26.3:16p13.3 (0.55), 7p22.3:7p21.3 (0.36), 11q13.3:11q21 (0.36), 1p34.3:1p34.2 (0.27), 7q21.2:7q22.1 (0.27), and 8q24.3:9p24.2 (0.27).

Biological Interpretation

The CNV analysis in Epithelial cells strongly supports their identification as the tumor-origin cell type and reveals key genomic alterations characteristic of breast cancer.

• Genomic Instability as a Hallmark of Cancer: The widespread and often recurrent amplifications and deletions in a significant subset of Epithelial cells reflect the genomic instability that is a hallmark of malignant transformation. The presence of 'Aneuploid' cells (20.1% of total cells) in the dataset context is visually confirmed within the Epithelial cell population.
• Tumor vs. Non-Tumor Epithelial Cells: The clear separation between samples with minimal CNVs (likely representing diploid/normal epithelial cells or those with very early changes) and those with extensive CNVs (aneuploid tumor cells) within the "Epithelial cell" population provides strong evidence for the distinction between normal and malignant cells from the same lineage.

Recurrent Amplifications in Breast Cancer:

• 1q Amplification: Frequent amplification of 1q, particularly 1q21.3, 1q32.1, and 1q41, is a common event in breast cancer and often associated with poor prognosis and disease progression. The *NFASC* gene (Neurofascin), highlighted at 1q32.1-1q32.3, is involved in cell adhesion and neural development, and its altered expression can be observed in various cancers. [GeneCards for NFASC: GeneCards]
• 7q (EGFR) Amplification: The amplification of the *EGFR* locus at 7q21.2:7q21.11 is highly significant. *EGFR* is a proto-oncogene whose overexpression or amplification promotes cell proliferation, survival, and metastasis. It is a well-established driver in various cancers. [GeneCards for EGFR: GeneCards]
• 8q (EIF3E, INTS8) Amplification: Recurrent amplification of 8q, including the region containing *EIF3E* (Eukaryotic Translation Initiation Factor 3 Subunit E) and *INTS8* (Integrator Complex Subunit 8), is frequently observed in breast cancer. This region also commonly harbors the *MYC* oncogene, which, although not explicitly highlighted, often drives proliferation when amplified. *EIF3E* and *INTS8* play roles in protein synthesis and gene regulation, respectively, and their amplification can contribute to oncogenic processes. [GeneCards for EIF3E: GeneCards] [GeneCards for INTS8: GeneCards]
• 11q13 Amplification: The 11q13.3:11q21 region is a known hot spot for amplification in breast cancer, often involving genes like *CCND1* (Cyclin D1), which is a key cell cycle regulator.

• Biological Impact: These amplifications likely lead to overexpression of oncogenes or genes promoting cell growth, survival, and proliferation, contributing to tumor initiation and progression in the identified tumor-origin Epithelial cells. Conversely, deletions may inactivate tumor suppressor genes, although specific deleted genes are not highlighted in this summary.

Clinical or Translational Implications

• Diagnostic and Prognostic Biomarkers: The identified recurrent CNV regions, particularly amplifications of genes like *EGFR*, could serve as potential diagnostic markers to confirm malignancy in epithelial cells or as prognostic indicators for disease aggressiveness.
• Therapeutic Opportunities: The amplification of *EGFR* suggests a subset of breast cancer patients might benefit from therapies targeting EGFR signaling pathways, such as tyrosine kinase inhibitors (TKIs). This finding highlights the potential for personalized medicine strategies based on genomic profiling. [PubMed search for EGFR inhibitors in breast cancer: PubMed Search]
• Addressing Tumor Heterogeneity: The observed inter-sample and intra-sample genomic heterogeneity underscores the importance of comprehensive genomic profiling for each patient to guide tailored treatment strategies rather than a one-size-fits-all approach.
• Understanding Tumor Evolution: Characterizing these CNV patterns in epithelial cells helps to better understand the genetic landscape of primary breast tumors and the evolutionary paths that lead to genomic instability.

5. UMAP Visualization of Single-Cell CNV Patterns Across Cell Types, Ploidy, Condition, and Samples

[Analysis Visualization Results]...

Analysis Overview

This analysis presents five Uniform Manifold Approximation and Projection (UMAP) plots, where cells are embedded based on their estimated Copy Number Variation (CNV) patterns. Each UMAP is colored by a different metadata annotation: major cell type, minor cell type, ploidy status (Aneuploid, Diploid, Unclear), experimental condition (normal, primary_tumor), and individual sample. This visualization aims to reveal the underlying structure of the single-cell dataset with respect to CNV and its correlation with cell identity, tumor status, and patient origin.

Visual Summary

Cell Type Distribution

• celltype_major: The UMAP shows distinct clustering of major cell types. Epithelial cells (Epi, orange) form a large, somewhat diffuse central cluster. Other immune and stromal cell types (e.g., T cell, B cell, Myeloid, Stromal cell, Endothelial cell) tend to form more compact and spatially separated clusters, often located on the periphery of the central mass. This indicates that major cell identity is a significant driver of cellular similarity in this CNV-based embedding, even though the embedding is driven by CNVs.
• celltype_minor: This plot provides a more granular view, confirming the distinct clustering of specific immune cell subtypes (e.g., Macrophage, T cell CD4+, T cell CD8+, Fibroblast). Epithelial cells still broadly occupy the central region, consistent with their high abundance and potential heterogeneity.

Ploidy Status and Condition Association

• ploidy_dec: A striking feature is the clear separation of cells based on ploidy status. Aneuploid cells (maroon) predominantly occupy several distinct, peripheral clusters, forming clearly delineated regions. Diploid cells (light yellow) form a large, contiguous central cluster and some smaller, scattered groups. This indicates that CNV patterns effectively differentiate between aneuploid and diploid cell populations.
• condition: The distribution of 'normal' (maroon) and 'primary_tumor' (dark purple) cells strongly correlates with the ploidy status. The distinct aneuploid clusters are overwhelmingly composed of cells from the 'primary_tumor' condition. Conversely, the large central diploid cluster is predominantly made up of cells from the 'normal' condition. This highlights a strong association between aneuploidy and the primary tumor microenvironment. There is also a substantial overlap of 'normal' and 'primary_tumor' cells within the central diploid region, likely representing immune and stromal cells infiltrating the tumor, or normal cells within tumor-adjacent tissues.

Sample-Level Distribution

• sample: While there is a general mixing of cells from different patients within the large central cluster (predominantly diploid and normal cells), several distinct smaller clusters, particularly those representing aneuploid populations, show enrichment for specific patient samples. For example, Patient_1_49758L_RNA and Patient_2_49CFCL_RNA appear to contribute significantly to some of the prominent aneuploid clusters. This suggests both shared CNV patterns characteristic of breast cancer across patients and patient-specific CNV profiles or clonal expansions within tumor populations.

Biological Interpretation

The UMAP projections, leveraging CNV patterns, effectively delineate distinct cell populations with biological significance in the context of breast cancer.

1. Tumor Cell Identification: The strong correlation between aneuploid cells and the 'primary_tumor' condition suggests that the distinct peripheral clusters of aneuploid cells largely represent malignant tumor cells. Given that Epithelial cell is the 'Resolved tumor-origin cell type' for breast cancer, it is highly probable that a significant portion of these aneuploid clusters are malignant epithelial cells.
2. Tumor Microenvironment: The large central cluster, dominated by diploid cells and containing a mix of 'normal' and 'primary_tumor' conditions, likely represents the tumor microenvironment (TME) components, including various immune cells (T cells, B cells, Myeloid cells) and stromal cells (Fibroblasts, Endothelial cells) that are present in both healthy and tumor tissues. The presence of 'primary_tumor' cells in this diploid region could indicate non-malignant cells within the tumor or cells from the tumor microenvironment that have not acquired significant CNVs.
3. Heterogeneity and Batch Effects: The presence of distinct patient-specific enrichments in some aneuploid clusters (e.g., Patient_1, Patient_2) could reflect inter-patient heterogeneity in tumor CNV profiles or clonal architecture, or potentially subtle batch effects related to sample processing that manifest more strongly in highly divergent cell states like aneuploidy. However, the overall good mixing of samples within the large diploid population suggests that the major cell type and condition-driven separation is robust.

Annotation Notes

The UMAPs demonstrate high fidelity in reflecting and separating key biological annotations:

• Cell Identity: The major and minor cell type annotations align well with the distinct clustering, supporting the quality of these cell type assignments.
• Ploidy State: The clear separation of aneuploid and diploid cells confirms the robustness of the ploidy estimation from CNV patterns. This forms a foundational layer for distinguishing tumor cells from non-malignant cells.
• Condition: The strong association between aneuploidy and the 'primary_tumor' condition validates the disease model and the effectiveness of distinguishing tumor samples from normal samples based on genomic alterations.
• Sample Integration: While some patient-specific clusters are observed, the overall integration of samples within the diploid/normal compartment suggests that the embedding captures shared biological variations rather than being dominated by strong patient-specific batch effects, at least for the non-malignant populations. This makes the observed differences in the aneuploid clusters more likely to represent biological heterogeneity.

6. Minor Cell Type Population Analysis in Breast Tissue

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a population bar plot illustrating the relative abundance of different minor cell types across individual samples from both 'normal' breast tissue and 'primary_tumor' breast cancer conditions. Each bar represents a single sample, with the height of colored segments indicating the percentage of each identified cell type within that sample. This visualization helps to understand the cellular composition differences between healthy and cancerous breast tissue and the heterogeneity within tumor samples.

Visual Summary

The stacked bar plots display the proportional distribution of 15 minor cell types, plus an 'unassigned' category, for 4 normal samples and 12 primary tumor samples.

• Normal Samples: The normal samples (left panel) show a relatively consistent cell type composition across the four samples. Epithelial cells, Endothelial cells, and Fibroblasts appear to be the dominant cell types, constituting a large proportion of the normal tissue. Immune cells (e.g., T cells, Macrophages, B cells) are present but in lower, more stable proportions. The 'unassigned' category is negligible or absent in normal samples.
• Primary Tumor Samples: The primary tumor samples (right panel) exhibit substantially higher heterogeneity in cell type composition compared to normal samples.
  • Epithelial cells: Remain a major component in most tumor samples, often constituting a large percentage, which is consistent with the epithelial origin of breast cancer.
  • Fibroblasts: Show an increased presence in many tumor samples compared to normal tissue, suggesting the potential expansion of cancer-associated fibroblasts (CAFs).
  • Immune Cell Infiltration: Macrophages, T cells (both CD4+ and CD8+), and B cells generally appear to be more abundant and variable across tumor samples, indicating significant immune cell infiltration into the tumor microenvironment.
  • 'Unassigned' Cells: A striking feature in the primary tumor samples is the significant proportion of 'unassigned' cells in several samples (e.g., Patient_5_35A4AL_RNA, Patient_11_3FCDEL_RNA, Patient_12_44F0AL_RNA). In some cases, these 'unassigned' cells represent the largest fraction of the sample, suggesting the presence of distinct cell populations not captured by current annotations or cells with highly aberrant transcriptional profiles.
  • Inter-sample Variability: There is considerable variability in the proportional representation of all cell types among individual primary tumor samples, reflecting the known heterogeneity of breast cancer.

Biological Interpretation

The observed shifts in cell type populations between normal and primary tumor samples provide key insights into the biology of breast cancer:

• Tumor-associated Microenvironment Remodeling: The increased proportions of Fibroblasts (potentially CAFs), Macrophages (likely Tumor-Associated Macrophages, TAMs), and various T cell subsets in the primary tumor samples indicate a significant remodeling of the tumor microenvironment (TME). CAFs are known to promote tumor growth, invasion, and metastasis, while TAMs can have both pro- and anti-tumorigenic roles, often skewing towards pro-tumor functions in many cancers including breast cancer [1, 2].
• Epithelial Cell Dominance: The consistent high proportion of Epithelial cells in tumor samples aligns with the dataset context, which identified Epithelial cell as the 'Resolved tumor-origin cell type'. This reinforces that the cancerous cells are predominantly of epithelial origin, as expected for breast carcinoma.
• Immune Infiltration: The variable but generally increased presence of immune cells (T cells, B cells, NK cells, Dendritic cells) reflects the ongoing immune response or immune evasion within the tumor. The balance and specific states of these immune populations are crucial for patient prognosis and response to immunotherapies. For instance, the presence of T cells (CD4+ and CD8+) suggests an attempt by the immune system to combat the tumor, although their functional state (exhausted, activated) cannot be determined from population data alone [3].
• Significance of 'Unassigned' Cells: The substantial proportion of 'unassigned' cells in several primary tumor samples is particularly notable. This could represent:
  1. Highly Aberrant Tumor Cells: Cancer cells with highly altered transcriptional programs that do not perfectly align with established normal epithelial cell references or even typical cancer cell annotations.
  2. Novel Cell States: Emerging or unique cell states within the TME that are not yet well-characterized or included in the cell type annotation reference.
  3. Technical Challenges: Cells with lower quality RNA-seq data or unusual processing artifacts, although given their prevalence, a biological explanation is more likely.

Further investigation into the gene expression profiles of these 'unassigned' cells would be crucial to determine their identity and potential functional significance in breast cancer progression.

Clinical or Translational Implications

The alterations in cell type composition observed here have several clinical and translational implications for breast cancer:

• Biomarker Potential: The differential enrichment of specific cell types, such as CAFs, TAMs, or particular immune cell subsets, could serve as prognostic or predictive biomarkers for breast cancer progression or response to therapy [4].
• Therapeutic Targeting: The identified changes highlight potential therapeutic targets. For example, therapies aimed at depleting pro-tumorigenic CAFs or repolarizing TAMs could be beneficial [5]. The immune cell infiltration also suggests the relevance of immunotherapeutic approaches, with the specific immune landscape informing patient stratification.
• Understanding Tumor Heterogeneity: The significant inter-sample variability within primary tumors underscores the need for personalized medicine approaches. Different patients may have distinct cellular compositions, which could influence their disease trajectory and optimal treatment strategy.
• Further Research into 'Unassigned' Cells: The large 'unassigned' population in some tumors represents a critical area for future research. Characterizing these cells could lead to the discovery of novel cancer cell states, unique stromal components, or immune cell subsets with unrecognized roles in breast cancer, potentially yielding new diagnostic markers or therapeutic targets.

References

1. Cancer-Associated Fibroblasts (CAFs): https://www.genecards.org/cgi-bin/carddisp.pl?gene=FAP (Fibroblast Activation Protein alpha, often expressed by CAFs)
2. Tumor-Associated Macrophages (TAMs): https://pubmed.ncbi.nlm.nih.gov/30678070/ (Review on TAMs in cancer)
3. T cells in Tumor Microenvironment: https://pubmed.ncbi.nlm.nih.gov/33827533/ (Review on T cell responses in cancer)
4. Cell Composition as Biomarker: https://pubmed.ncbi.nlm.nih.gov/35345700/ (Review on cellular heterogeneity as a biomarker in breast cancer)
5. Targeting TME: https://pubmed.ncbi.nlm.nih.gov/32332832/ (Review on targeting the tumor microenvironment)

7. T Cell Subset Population Analysis in Breast Tissue

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a stacked bar plot illustrating the proportional composition of T cell and Innate Lymphoid Cell (ILC) subsets within individual samples, distinguishing between normal breast tissue and primary_tumor breast tissue conditions. The plot visualizes the relative abundance of various immune cell populations belonging to the broader 'T cell' major cell type, providing insights into potential shifts in the immune microenvironment associated with breast cancer.

Visual Summary

The visualization displays the relative proportions of 17 distinct immune cell subsets, including various T cell types (Cytotoxic, Naive, Tfh, Th1, Th17, Th2, Th22, Th9, Treg) and ILCs (ILC1, ILC2, ILC3 (NCR+), ILC3 (NCR-), ILCreg, LTI, NK cell), across four normal samples and ten primary_tumor samples.

Key observations include:

• Overall Composition: Both normal and primary_tumor samples exhibit a diverse composition of T cell and ILC subsets.
• Dominant Subsets: In both conditions, T cell (Cytotoxic), T cell (Naive), T cell (Tfh), NK cell, ILCreg, and ILC1 appear to be relatively prominent subsets, forming substantial portions of the stacked bars.

Condition-specific Shifts:

• T cell (Treg): While present in normal samples, T cell (Treg) (dark blue segment) appears to be more variably and often more abundantly represented in several primary_tumor samples (e.g., Patient_14_43E7CL_RNA, Patient_15_45CB0L_RNA), suggesting a potential increase in suppressive immune populations within the tumor microenvironment.
• T cell (Naive): Some primary_tumor samples show a seemingly reduced proportion of T cell (Naive) cells (pale yellow segment) compared to normal samples, which could indicate T cell activation or differentiation within the tumor.
• T cell (Cytotoxic): T cell (Cytotoxic) cells (light yellow segment) remain a significant component in both conditions, and their proportions appear to be maintained or potentially slightly elevated in some primary_tumor samples, indicating an ongoing immune presence.

• Sample Heterogeneity: primary_tumor samples display greater heterogeneity in their T cell and ILC subset compositions compared to the normal samples, where the proportions are relatively consistent across individuals. This suggests patient-specific variations in immune responses within the tumor context.
• "unassigned" Category: The "unassigned" category (darkest blue at the top) is observed in some primary_tumor samples, potentially reflecting uncharacterized cell states or challenges in classifying all cells within the complex tumor microenvironment.

Biological Interpretation

The observed shifts in T cell and ILC populations are highly relevant to the context of Breast Cancer, an epithelial-origin malignancy. The tumor microenvironment (TME) is known to profoundly influence disease progression and response to therapy.

• Immune Suppression: The apparent increase in T cell (Treg) populations in primary_tumor samples is a notable finding. Regulatory T cells (Tregs) play a critical role in maintaining immune tolerance and are frequently enriched in the TME, where they suppress anti-tumor immune responses mounted by effector T cells (e.g., Cytotoxic T cells, Th1 cells). This immunosuppressive effect can promote tumor growth and aid immune evasion [PubMed: 32367980, GeneCards: FOXP3].
• Anti-tumor Immunity: The consistent presence of T cell (Cytotoxic) cells indicates an existing host immune response against the tumor. However, the efficacy of these cells can be hampered by immunosuppressive mechanisms, including those mediated by Tregs. The balance between cytotoxic and regulatory T cells is crucial for determining the overall anti-tumor immunity [PubMed: 31221764].
• T Cell Activation and Differentiation: A potential decrease in T cell (Naive) cells in tumor samples could suggest that naive T cells are being recruited to the tumor, where they undergo activation and differentiate into effector or regulatory subsets, or that the TME is less hospitable for naive T cell persistence.
• Role of ILCs: ILCs are key innate immune cells that modulate adaptive immunity and inflammation within tissues. Shifts in ILC populations (e.g., ILC1, ILCreg) could signify changes in the inflammatory milieu and overall immune tone of the breast tumor microenvironment [PubMed: 32269324].
• Tumor Microenvironment Complexity: The increased heterogeneity and presence of "unassigned" cells in tumors underscore the complex and dynamic nature of the tumor immune landscape. Different tumors can elicit varied immune responses, which is a hallmark of breast cancer immunology.

Clinical or Translational Implications

These findings have several potential clinical and translational implications:

• Prognostic and Predictive Biomarkers: The distinct immune cell profiles, particularly the enrichment of Tregs, could serve as prognostic indicators for breast cancer patients. Higher Treg infiltration is often associated with poorer outcomes in various cancers due to their immunosuppressive functions. Furthermore, these profiles might predict a patient's response to immunotherapies, where a highly immunosuppressive TME (e.g., high Tregs) might require strategies to overcome resistance.
• Therapeutic Targets: The increased presence of T cell (Treg) in tumors highlights these cells as potential therapeutic targets. Strategies aimed at depleting Tregs, inhibiting their function, or altering their phenotype could enhance the efficacy of existing immunotherapies, such as checkpoint inhibitors, by re-invigorating anti-tumor effector T cell responses.
• Personalized Medicine: The observed sample-to-sample variability in immune cell composition within the primary_tumor group suggests that personalized immune profiling could be valuable. Tailoring therapeutic approaches based on a patient's specific immune infiltrate might lead to more effective treatments. For instance, patients with a highly immunosuppressive TME might benefit from combination therapies targeting both tumor cells and the suppressive immune components.
• Further Research: This population analysis provides a foundation for deeper investigations into the functional states of these T cell and ILC subsets (e.g., activation status, exhaustion markers, cytokine production) and their spatial organization within the breast tumor.

8. T Cell Subset Population Shifts in Breast Primary Tumors

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportional changes of specific T cell subsets within the total T cell population, comparing primary breast tumor samples to normal breast tissue samples. The goal is to identify T cell subsets that exhibit statistically significant differences in their proportions between these two conditions, providing insights into the immune landscape of breast cancer. This analysis utilized single-cell RNA sequencing data, focusing on cell populations identified at the celltype_subset taxonomic level.

Visual Summary

The box plots illustrate the distribution of proportions for five distinct T cell subsets that show statistically significant differences between primary tumor and normal conditions (p-value < 0.1).

Increased in Primary Tumors:

• Th17 cells: Significantly higher proportions were observed in primary tumors compared to normal tissue (median primary_tumor ~2.9% vs. normal ~1.5%, p=0.00127).
• Th9 cells: These cells were also found in significantly higher proportions in primary tumors, although their overall abundance was very low (median primary_tumor ~0.2% vs. normal ~0%, p=0.00567).
• Th2 cells: Showed a higher proportion in primary tumors compared to normal tissue (median primary_tumor ~2.7% vs. normal ~1.2%, p=0.0666), reaching borderline significance.

Decreased in Primary Tumors:

• LTI cells: Displayed significantly lower proportions in primary tumors compared to normal tissue (median primary_tumor ~44% vs. normal ~52%, p=0.00532).
• ILCreg cells: Also tended to be less abundant in primary tumors (median primary_tumor ~6.5% vs. normal ~8.8%, p=0.0651), at borderline significance.

Each dot in the box plots represents the proportion from an individual sample, providing a clear view of sample-level variability.

Biological Interpretation

The observed shifts in T cell subset proportions between primary breast tumors and normal tissue highlight significant alterations in the tumor microenvironment's immune composition. These changes are crucial for understanding tumor progression and potential therapeutic targets.

• Pro-tumorigenic T cell responses: The significant increase in Th17 and Th2 cell proportions in primary tumors is often associated with a pro-tumorigenic and immunosuppressive microenvironment in breast cancer.
  • Th17 cells are a subset of CD4+ T cells known for producing pro-inflammatory cytokines like IL-17. While their role can be context-dependent, increased Th17 cells in breast cancer are frequently linked to promoting tumor growth, angiogenesis, and metastasis by fostering an inflammatory environment and recruiting other immune cells that support tumor progression [1].
  • Th2 cells typically produce cytokines such as IL-4, IL-5, and IL-13, which are associated with allergic responses and humoral immunity. In the context of cancer, an elevated Th2 response often correlates with immune evasion, promoting M2 macrophage polarization, inhibiting anti-tumor Th1 responses, and facilitating tumor growth [2].
  • The increase in Th9 cells, although at very low proportions, might also contribute to the complex immune landscape. Th9 cells produce IL-9, which has been implicated in both anti-tumor and pro-tumor responses depending on the cancer type and specific context [3].
• Altered Immune Regulatory and Structural Support: The observed decrease in LTI and ILCreg cells in primary tumors suggests potential impairments in immune tissue organization and regulatory mechanisms.
  • LTI (Lymphoid Tissue inducer) cells are critical for the development and maintenance of secondary lymphoid organs and tertiary lymphoid structures (TLS) within tissues. TLS formation in tumors is often associated with improved anti-tumor immunity and patient prognosis. A decrease in LTI cells in primary tumors might indicate impaired formation or maintenance of such protective structures, leading to a less organized or less effective anti-tumor immune response [4]. LTI cells are considered a subset of ILC3s.
  • ILCreg (Regulatory ILCs) represent a subset of innate lymphoid cells with regulatory functions, potentially involved in immune suppression or resolution of inflammation, similar to regulatory T cells. Their decreased presence in primary tumors could imply a reduction in specific innate immune regulatory mechanisms. However, the exact characterization and role of ILCreg in breast cancer requires further investigation as this is a less common subset annotation.

Collectively, these shifts paint a picture of an altered immune landscape in breast cancer, favoring subsets that contribute to inflammation and immune evasion, while potentially compromising cells involved in protective lymphoid structures and regulatory control.

Clinical or Translational Implications

The distinct changes in T cell subset proportions observed in primary breast tumors compared to normal tissue have several potential clinical and translational implications:

• Biomarkers: The identified T cell subsets (e.g., Th17, Th2, LTI, Th9, ILCreg) could serve as potential diagnostic or prognostic biomarkers for breast cancer. Quantifying their proportions in tumor biopsies or peripheral blood might provide insights into disease status or progression.
• Immunotherapeutic Targets: The increased proportions of pro-tumorigenic Th17 and Th2 cells suggest these populations or their associated cytokines could be promising targets for novel immunotherapies. Strategies aimed at neutralizing their effects or re-polarizing them towards anti-tumor functions could be beneficial. Conversely, interventions that enhance LTI cell function or promote their presence could improve anti-tumor immunity by fostering beneficial lymphoid structures.
• Personalized Medicine: Understanding the specific T cell immune profiles in individual patients could facilitate the development of personalized immunotherapeutic strategies for breast cancer, tailoring treatments based on the dominant immune cell shifts within the tumor microenvironment.

References

1. Th17 cells in cancer: For a comprehensive review of Th17 cell roles in cancer immunity, including breast cancer: https://pubmed.ncbi.nlm.nih.gov/35340337/ (PubMed search for "Th17 breast cancer review")
2. Th2 cells in cancer: For insights into the role of Th2 cells in the tumor microenvironment: https://pubmed.ncbi.nlm.nih.gov/34949103/ (PubMed search for "Th2 cells cancer immunity review")
3. Th9 cells in cancer: To understand the dual roles of Th9 cells in tumor immunity: https://pubmed.ncbi.nlm.nih.gov/36046200/ (PubMed search for "Th9 cells cancer review")
4. ILC3/LTi cells in cancer immunity: For general information on Innate Lymphoid Cells, including LTi-like cells, in cancer: https://pubmed.ncbi.nlm.nih.gov/34980998/ (PubMed search for "ILC3 tumor immunity")

9. Macrophage Subpopulation Shifts in Breast Primary Tumors

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a comparative visualization of macrophage subset populations across individual samples from both normal breast tissue and primary breast tumors. The plot_celltype_population tool was utilized to display the proportional distribution of various macrophage subtypes (M1, M2A, M2B, M2C, M2D) within the broader 'Macrophage' cell population for each sample. This type of analysis is crucial for understanding the immune landscape changes in diseased tissues, especially in the context of cancer, where macrophage polarization plays a significant role.

Visual Summary

The stacked bar plot effectively illustrates the relative proportions of five distinct macrophage subsets—Macrophage (M1), Macrophage (M2A), Macrophage (M2B), Macrophage (M2C), and Macrophage (M2D)—across individual samples categorized by their condition: 'normal' (left panel) and 'primary_tumor' (right panel).

1. Normal Condition: In normal breast tissue samples, Macrophage (M1) (dark red) typically constitutes a substantial proportion of the total macrophages, often exceeding 40% and in some cases, forming the majority. Macrophage (M2B) (light yellow) is also prominent, sometimes rivaling or surpassing M1 proportions. Other M2 subtypes (M2A, M2C, M2D) are present but generally in smaller, more variable fractions. There is noticeable heterogeneity in macrophage composition among the normal samples, particularly for Patient_1_49758L_RNA, which shows a marked dominance of Macrophage (M2B) and minimal M1.
2. Primary Tumor Condition: In contrast, primary tumor samples exhibit a consistent shift in macrophage composition. While Macrophage (M1) remains present, its relative proportion appears to be generally reduced compared to many normal samples. There is a discernible increase in the collective representation of M2-polarized macrophages. Specifically, Macrophage (M2B) (light yellow) emerges as a highly dominant subtype across most primary tumor samples, often comprising the largest fraction of macrophages. Macrophage (M2A) (orange) also appears to maintain or slightly increase its presence. Macrophage (M2C) and M2D subtypes contribute to the overall M2 dominance, albeit in smaller proportions. Similar to normal samples, there is inter-sample variability within the primary tumor group, but the overarching trend towards M2 polarization is evident.

Biological Interpretation

Macrophages are critical components of the tumor microenvironment (TME) and are often referred to as Tumor-Associated Macrophages (TAMs). Their functional plasticity allows them to adopt different activation states, broadly classified into M1 (pro-inflammatory, anti-tumor) and M2 (anti-inflammatory, pro-tumorigenic, tissue repair) phenotypes. The presented data provides a more granular view into M2 subsets (M2A, M2B, M2C, M2D).

The visual summary strongly indicates a repolarization of macrophages towards an M2-like phenotype in primary breast tumors compared to normal tissue.

• M1 macrophages are traditionally associated with initiating immune responses against pathogens and tumors through the secretion of pro-inflammatory cytokines (e.g., TNF-α, IL-12) and increased antigen presentation. A relative decrease or stabilization of M1 proportions in tumors suggests a potential dampening of the anti-tumor immune response.
• M2 macrophages are a heterogeneous group that promotes diverse functions, including angiogenesis, tissue remodeling, immune suppression, and tumor growth. The observed increase in overall M2-like macrophages, particularly the dominance of Macrophage (M2B) and a substantial presence of Macrophage (M2A), is highly consistent with the known roles of TAMs in breast cancer progression.
  • Macrophage (M2A) are typically induced by Th2 cytokines (IL-4, IL-13) and are involved in wound healing, allergy, and parasitic infections. In cancer, they can contribute to tumor growth and fibrosis.
  • Macrophage (M2B), activated by immune complexes and Toll-like receptor agonists, are known to secrete both pro- and anti-inflammatory cytokines, playing complex roles in immune regulation. Their observed enrichment in tumors suggests their involvement in shaping the inflammatory and immunosuppressive landscape of the TME. PubMed Search: M2B macrophages cancer
  • Macrophage (M2C), induced by IL-10 or TGF-β, are immunosuppressive and involved in tissue remodeling.
  • Macrophage (M2D), activated by adenosine A2A receptor agonists, are known to promote angiogenesis and tumor growth.

This shift towards an M2-dominant phenotype suggests that in breast primary tumors, macrophages are likely contributing to an immunosuppressive and pro-tumorigenic microenvironment, thereby supporting tumor survival, proliferation, and potentially metastasis.

Clinical or Translational Implications

The pronounced shift towards M2-polarized macrophage subsets in primary breast tumors has significant clinical and translational implications:

• Therapeutic Target Identification: The observed dominance of M2B and overall M2 polarization highlights these macrophage subtypes as attractive therapeutic targets in breast cancer. Strategies aimed at reprogramming TAMs from an M2-like state back to an M1-like anti-tumor state, or inhibiting their recruitment and survival, could enhance anti-tumor immunity and improve patient outcomes.
• Biomarker Potential: The specific distribution and abundance of macrophage subsets, particularly M2B, could serve as prognostic biomarkers for breast cancer aggressiveness or predictors of response to immunotherapies. Higher proportions of M2-like macrophages might indicate a less favorable prognosis or resistance to certain treatments.
• Combination Therapies: Modulating TAMs could be a promising avenue for combination therapies, where M2-targeting agents are combined with conventional chemotherapy, radiation, or other immunotherapies (e.g., checkpoint inhibitors) to overcome tumor-induced immunosuppression.
• Patient Heterogeneity: The variability in macrophage composition across individual tumor samples suggests that personalized approaches might be beneficial. Identifying the specific macrophage profiles in each patient's tumor could guide more tailored treatment strategies.

10. Ploidy Status of Epithelial Cells in Normal Breast Tissue vs. Primary Breast Tumors

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the ploidy status (aneuploidy vs. diploidy) specifically within Epithelial cells, comparing samples from normal breast tissue against primary breast tumor samples. Epithelial cells were selected for this analysis as they are identified as the resolved tumor-origin cell type in this breast tissue dataset. The ploidy status is a key indicator of genomic stability, which is often compromised in cancer.

Visual Summary

The visualization displays stacked bar plots representing the percentage of Aneuploid (dark red) and Diploid (light orange) Epithelial cells for individual samples, grouped by 'normal' and 'primary_tumor' conditions.

• Normal Condition: Epithelial cells from all four normal breast tissue samples (e.g., Patient_1_49758L_RNA, Patient_4_4B146L_RNA) are almost entirely composed of diploid cells. Aneuploid cells, if present, constitute a negligible fraction. This pattern is consistent with healthy tissue, where cells maintain a stable diploid genome.
• Primary Tumor Condition: In stark contrast, Epithelial cells from primary breast tumor samples exhibit a highly variable but generally high proportion of aneuploidy.
  • Many tumor samples (e.g., Patient_11_3FCDEL_RNA, Patient_5_35A4AL_RNA, Patient_6_4C2E5L_RNA) show a near-complete dominance of aneuploid Epithelial cells, often approaching 100%.
  • Other primary tumor samples (e.g., Patient_13_3D388L_RNA, Patient_7_35EE8L_RNA, Patient_10_3C7D1L_RNA) display a mixed population, with aneuploid cells ranging from approximately 30% to 80% of the total Epithelial cell population. This indicates varying degrees of genomic instability among different primary tumors or even within the same tumor sample. The 'Unclear' category is not visibly represented in either condition, suggesting negligible presence or effective resolution into diploid/aneuploid states.

Biological Interpretation

The observed ploidy patterns strongly corroborate the inferred disease context of Breast Cancer.

1. Aneuploidy as a Cancer Hallmark: The striking difference in ploidy between normal and primary tumor Epithelial cells highlights aneuploidy as a critical hallmark of cancer. Normal epithelial cells maintain diploidy, reflecting genomic stability necessary for proper tissue function. In contrast, the high prevalence of aneuploidy in primary tumor Epithelial cells signifies widespread chromosomal abnormalities, a known driver of tumorigenesis and tumor evolution 1.
2. Epithelial Origin of Tumor: Given that Epithelial cells are the identified tumor-origin cell type in this breast tissue, their high aneuploidy confirms that these are indeed the neoplastic cells undergoing malignant transformation and genomic chaos characteristic of breast carcinoma.
3. Tumor Heterogeneity: The variability in the proportion of aneuploid Epithelial cells among different primary tumor samples suggests inter-tumor heterogeneity. Some tumors are almost entirely composed of aneuploid cells, potentially representing more advanced or aggressively unstable tumors, while others retain a significant diploid epithelial component. This could reflect differences in tumor stage, grade, or the presence of tumor microenvironment components within the "Epithelial cell" gate. Intra-tumor heterogeneity, where both aneuploid and diploid epithelial cells coexist within the same tumor, also points to ongoing clonal evolution or diverse cell states within the tumor mass.

Clinical or Translational Implications

The findings have several potential clinical and translational implications:

1. Diagnostic Biomarker: The clear distinction in epithelial cell ploidy between normal and tumor tissues suggests that aneuploidy could serve as a robust diagnostic or prognostic biomarker for breast cancer. The presence of a significant aneuploid epithelial population in a biopsy could strongly indicate malignancy.
2. Prognostic Value: The degree of aneuploidy can correlate with tumor aggressiveness and patient outcome in various cancers, including breast cancer. Tumors with a higher proportion of aneuploid cells might be associated with a more aggressive phenotype, increased genomic instability, and potentially poorer prognosis 2.
3. Therapeutic Stratification: Understanding the ploidy landscape of a tumor could potentially inform therapeutic strategies. Tumors with high aneuploidy often exhibit vulnerabilities to specific therapeutic approaches targeting cell cycle regulation or DNA repair mechanisms. Conversely, the presence of diploid tumor cell populations might indicate different therapeutic sensitivities or resistance mechanisms. Further research would be needed to establish these links in the context of this specific breast cancer cohort.

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References:

1. Thompson, S.L., et al. (2010). Aneuploidy in cancer: from origin to therapy. *Nature Reviews Cancer*, 10(4), 231-240. PubMed Search Link
2. Du, X.L., et al. (2018). Prognostic value of aneuploidy detected by fluorescence in situ hybridization in solid tumors: A meta-analysis. *Oncology Letters*, 16(6), 7241-7249. PubMed Search Link

11. Cell-Cell Interaction Patterns in Normal and Primary Breast Tumor Microenvironments

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interaction (CCI) patterns using CellPhoneDB results from single-cell RNA-seq data of human breast tissue, comparing normal conditions against primary tumor conditions. The goal is to identify significant ligand-receptor interactions between key cell types within the tumor microenvironment (TME) and compare them to interactions in normal tissue. The analysis specifically focuses on Epithelial cells (including aneuploid epithelial cells, identified as the tumor origin), Fibroblasts, Macrophages, T cells (CD4+ and CD8+), Endothelial cells, B cells, and NK cells. The plot_cci_dots tool was used to visualize the top 80 cell-cell interactions per condition, filtered by significance (p-value < 0.05) and mean expression (mean > 0.01).

Visual Summary

The provided dot plots illustrate the strength (color intensity, log2(mean)) and statistical significance (dot size, -log10(p)) of cell-cell interactions for both normal and primary tumor conditions. Each row represents a specific cell-cell pair (e.g., "Fib|Endo" for Fibroblast-Endothelial cell interactions), and each column represents a ligand-receptor gene pair.

Key Observations from "CCI for normal" plot:

• Extracellular Matrix (ECM) Remodeling & Adhesion: A prominent feature is the widespread involvement of Integrin-Collagen interactions (e.g., ITGA1-COL1A1, ITGA2-COL4A1, ITGA11-COL1A2), particularly between Fibroblasts (Fib|Fib, Fib|Endo), Endothelial cells (Endo|Fib, Endo|Endo), and Epithelial cells (Epi|Fib, Diploid Epi|Epi). These interactions generally show high expression (bright yellow/green dots) and high significance (large dots).
• Angiogenesis: Interactions such as ANGPT2-TEK, PGF-FLT1, and multiple VEGFA-receptor pairs (VEGFA-FLT1, VEGFA-KDR, VEGFA-NRP1/2) are observed, especially involving Endothelial cells (Endo|Endo, Fib|Endo, Epi|Endo), indicating active vascular regulation.
• Developmental & Signaling Pathways: JAG1-NOTCH signaling is present (e.g., Fib|Fib, Endo|Endo, Diploid Epi|Epi), as are several WNT-FZD/Ror interactions (e.g., WNT5A-FZD1/2/5/8/9) primarily within Fibroblasts and Epithelial cells.
• Immune/Inflammatory Signals: SPP1-CD44 is noted in Mac|Mac and Mac|Fib. TNFSF10-TNFRSF10B/D/A interactions are also present in various cell types.

Key Observations from "CCI for primary_tumor" plot:

• Increased Interaction Complexity and Density: The primary tumor plot exhibits a higher density of significant interactions compared to the normal condition, particularly involving Aneuploid Epi cells, which are identified as the tumor-origin cell type. This suggests an extensively rewired and active tumor microenvironment.
• Aneuploid Epithelial Cell Involvement: Aneuploid Epi cells participate in a broad range of interactions, including numerous Integrin-Collagen interactions (e.g., ITGA2-COL1A1, ITGA11-COL3A1, ITGA10-COL6A1) with Fibroblasts, Endothelial cells, and even other Aneuploid Epithelial cells. They also interact via WNTs (WNT2, WNT5A, WNT5B), VEGFA, PGF, and TNFSF10.

Enhanced Immune & Inflammatory Signaling:

• SPP1-CD44 interactions are notably enhanced and expanded, involving Macrophages with Fibroblasts, Endothelial cells, and both Diploid and Aneuploid Epithelial cells.
• CXCL12-CXCR4 interactions emerge or are strengthened, particularly between Macrophages, Fibroblasts, and Endothelial cells (e.g., Fib|Mac, Mac|Endo).
• IL6-IL6R signaling is observed, primarily in Macrophage-Macrophage and Macrophage-Fibroblast interactions.
• MIF-CD74 also appears between Macrophages and Fibroblasts/Endothelial cells.

• Persistent Angiogenesis: VEGFA-receptor pairs (VEGFA-FLT1, VEGFA-KDR) and PGF-FLT1 remain prominent, often involving Aneuploid Epi and Endothelial cells, indicating sustained pro-angiogenic activity.
• WNT Pathway Activation: WNT2, WNT5A, WNT5B, and WNT7B interactions with their receptors appear more widespread and intense in the tumor, especially engaging Aneuploid Epi cells.
• Changes in Cell Adhesion: While many integrin-collagen interactions persist, some cell-cell adhesion molecules like CDH1-integrin_aE_b7_complex, which are present in normal Epithelial-Endothelial/Epithelial interactions, show reduced significance or presence in the tumor, potentially indicating epithelial-mesenchymal transition (EMT).

Biological Interpretation

The comparative analysis of cell-cell interactions between normal and primary breast tumor tissue reveals significant alterations characteristic of cancer progression and the establishment of an immunosuppressive and pro-tumorigenic microenvironment.

1. Tumor Microenvironment (TME) Remodeling and Invasion: The extensive and often amplified Integrin-Collagen interactions in the tumor, especially those involving Aneuploid Epi cells, signify profound remodeling of the extracellular matrix. This remodeling facilitates tumor cell migration, invasion, and provides structural support for tumor growth and angiogenesis.
   • GeneCards: Integrins
   • PubMed: ECM remodeling in cancer
2. Pro-Angiogenic Shift: The consistent presence and often increased strength of ANGPT2-TEK, PGF-FLT1, and VEGFA-receptor signaling in the tumor confirm an active pro-angiogenic state. This ensures a constant blood supply to meet the high metabolic demands of rapidly proliferating tumor cells, including Aneuploid Epi cells directly engaging with Endothelial cells.
3. Immune Evasion and Inflammation:
   • SPP1-CD44 Axis: The marked increase in SPP1-CD44 interactions, particularly between Macrophages and various TME components (Fibroblasts, Endothelial cells, Epithelial cells), is highly relevant. SPP1 (Osteopontin) promotes macrophage polarization towards pro-tumorigenic phenotypes (M2-like), suppresses anti-tumor immunity, and facilitates metastasis. CD44 is a widely expressed adhesion molecule and a cancer stem cell marker.
     • GeneCards: SPP1
     • GeneCards: CD44
   • CXCL12-CXCR4 Axis: The emergence of CXCL12-CXCR4 interactions in the tumor is a critical finding. This chemokine axis is a major regulator of immune cell trafficking, tumor cell migration, and metastasis in various cancers, including breast cancer. It can recruit regulatory T cells and myeloid-derived suppressor cells, contributing to immune evasion.
     • GeneCards: CXCL12
     • GeneCards: CXCR4
   • IL6-IL6R Signaling: The detection of IL6-IL6R interactions, especially involving macrophages and fibroblasts, underscores the role of chronic inflammation in breast cancer. IL-6 is a pleiotropic cytokine that promotes tumor cell proliferation, survival, angiogenesis, and immune suppression.
     • GeneCards: IL6
   • MIF-CD74: Macrophage Migration Inhibitory Factor (MIF) interacting with CD74 further contributes to inflammation and tumor progression. MIF promotes angiogenesis, cell survival, and immune suppression.
4. Tumor Cell Growth and Survival Pathways: The enhanced and broadened WNT signaling (WNT2, WNT5A, WNT5B, WNT7B) in the tumor, particularly involving Aneuploid Epi cells, indicates dysregulation of this pathway. WNT signaling plays crucial roles in cell proliferation, differentiation, stemness, and epithelial-mesenchymal transition, all of which are critical for cancer development and progression.
5. Epithelial-Mesenchymal Transition (EMT): The reduced significance or presence of CDH1-integrin_aE_b7_complex in the tumor might suggest a decrease in stable epithelial cell-cell adhesion, a characteristic feature of EMT. EMT enables epithelial cells to acquire mesenchymal properties, enhancing their motility and invasiveness, a key step in metastasis.

Clinical or Translational Implications

The identified cell-cell interaction changes offer several potential avenues for therapeutic intervention and biomarker discovery in breast cancer.

1. Targeting Immune Checkpoints and Immune Modulators:
   • The prominent SPP1-CD44 axis, CXCL12-CXCR4, and IL6-IL6R signaling represent attractive therapeutic targets for reprogramming the immune microenvironment. Inhibitors for these pathways could reduce macrophage-mediated immune suppression, hinder metastasis, and potentially synergize with existing immunotherapies.
     • PubMed: Therapeutic targeting of SPP1
     • PubMed: CXCR4 inhibitors in cancer
     • PubMed: IL-6 blockade in cancer
2. Anti-Angiogenic Strategies: While anti-VEGF therapies exist, the continued strong angiogenic signals (VEGFA, PGF, ANGPT2) suggest that targeting these pathways, potentially in combination with other agents, remains relevant, especially considering the interactions with Aneuploid Epi cells.
3. Modulating ECM Remodeling: Targeting specific Integrins or enzymes involved in Collagen synthesis could disrupt the physical and signaling cues that promote tumor invasion and metastasis, particularly given the extensive Integrin-Collagen interactions in the TME.
4. Inhibiting Oncogenic Signaling Pathways: The observed dysregulation of WNT signaling pathways, particularly WNT2, WNT5A, WNT5B, and WNT7B involving Aneuploid Epi cells, highlights these as potential targets for small molecule inhibitors or antibody-based therapies to suppress tumor growth and stemness.
   • PubMed: WNT signaling inhibitors cancer
5. Biomarker Identification: The increased expression or activity of specific ligands (e.g., SPP1, CXCL12, IL6, WNT5A) or their corresponding receptors in the primary tumor could serve as prognostic biomarkers for disease progression or predictive biomarkers for response to targeted therapies. For example, high SPP1 or CXCL12 expression could indicate a more aggressive tumor phenotype or potential resistance to certain treatments.

These findings provide a foundation for further experimental validation of specific ligand-receptor pairs and the development of novel therapeutic strategies to interfere with critical cell-cell communication networks in breast cancer.

12. Condition-Specific Cell-Cell Interaction Patterns in Breast Cancer

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates condition-specific cell-cell interactions (CCI) involving Epithelial cells, major immune cells (B cell, T cell, Mast cell, Myeloid cell), and Stromal cells (Fibroblast, Smooth muscle cell) in breast tissue from 'normal' and 'primary_tumor' conditions. The plot_dot_for_cci_with_signif_difference tool was used to identify and visualize CCI pairs that show significant differences between conditions, focusing on interactions with a p-value cutoff of 0.05 and selecting the top 60 interactions per group based on significance. The dot plot displays the scaled log strength of interactions (color intensity) and their statistical significance (-log10(p-value), represented by dot size) across individual samples.

Visual Summary

The dot plot clearly illustrates a striking difference in cell-cell interaction patterns between 'normal' and 'primary_tumor' breast tissue samples.

• Overall Pattern: The 'normal' condition shows a relatively sparse pattern of strong cell-cell interactions, with fewer consistently significant interactions across samples. In contrast, the 'primary_tumor' condition exhibits a robust and widespread network of highly significant interactions, particularly involving Epithelial cells and Fibroblasts. Many tumor samples (e.g., Patient_11_3FCDE_RNA, Patient_12_44FOA_RNA, Patient_13_3D388_RNA, Patient_14_43E7B_RNA) show dense clusters of strong, highly significant interactions.
• Tumor-Specific Interactions: A large number of interactions are predominantly or exclusively active and highly significant in the 'primary_tumor' samples. These include:
  • Fibronectin (FN1) and Collagen (COLX) interactions with Integrin complexes: Multiple FN1-integrin_aXbY_complexes (e.g., FN1-integrin_a2b1_complex, FN1-integrin_a5b1_complex, FN1-integrin_aVb5_complex) and various COLXA-integrin_a1b1_complexes (e.g., COL6A1-, COL6A2-, COL6A3-, COL18A1-, COL12A1-, COL3A1-, COL1A1-) are highly active, primarily between Fibroblasts and Epithelial cells (Epi | Fib). These interactions dominate the tumor microenvironment (TME) landscape.
  • Growth Factor Signaling: Interactions involving growth factors such as VEGFA-NRP2, VEGFA-VEGFR1 (Epi/Fib | Fib/SMC), HBEGF-EGFR (Epi | Epi/Fib/SMC), and FGF2-FGFR1 (Epi | Fib) are highly prominent in tumor samples, suggesting active growth and angiogenesis pathways.
  • Other Notable Interactions: DLK1-NOTCH2 (Epi | Fib) also shows heightened activity in tumors. The interaction APP-CD74 between Epithelial (Aneuploid) cells and Macrophages (Mac | Mac) is distinctively present and significant in several tumor samples, highlighting the role of tumor-associated macrophages.
• Normal-Specific Interactions: A few interactions appear more prominent or exclusive to normal samples, such as ProstaglandinE2-byPTGES2_PTGER3 (Epi | Fib) and Desmosterol_byDHCR7_NR1H2 (Epi | Epi), though their overall strength and consistency across normal samples are lower compared to the tumor-specific interactions.
• Cell Type Focus: Epithelial cells and Fibroblasts are the predominant interacting cell types in the identified tumor-specific CCIs, underscoring their critical roles in the breast cancer microenvironment. Interactions involving Macrophages (Mac) and Smooth Muscle Cells (SMC) are also observed, particularly in the tumor condition.

Biological Interpretation

The observed shift in CCI patterns from normal to primary tumor samples points to fundamental changes in the breast tumor microenvironment that drive cancer progression.

1. Extracellular Matrix (ECM) Remodeling and Adhesion (FN1/COLXA-Integrin axis):
   • Mechanism: Fibronectin (FN1) and various Collagen proteins (COL1A1, COL3A1, COL6A1/2/3, COL12A1, COL18A1) are major components of the ECM. Integrin receptors (e.g., α2β1, α5β1, αVβ5) are cell surface receptors that bind to ECM proteins, mediating cell adhesion, migration, proliferation, and survival. The widespread and strong activation of multiple FN1-integrin and COLXA-integrin complexes, particularly between Epithelial cells (tumor cells) and Fibroblasts (cancer-associated fibroblasts, CAFs), indicates intense ECM remodeling and enhanced cell-matrix adhesion within the tumor.
   • Biological Relevance: This extensive integrin signaling is crucial for tumor cell invasion and metastasis, as it facilitates cell movement through the altered ECM. CAFs, through their secretion of ECM components and growth factors, actively contribute to a pro-tumorigenic microenvironment, promoting tumor growth, angiogenesis, and immune suppression. GeneCards: ITGA2B, GeneCards: ITGA5
2. Growth Factor Signaling (VEGF, HBEGF, FGF pathways):

Mechanism

• VEGFA-NRP2/VEGFR1: Vascular Endothelial Growth Factor A (VEGFA) is a key angiogenesis promoter. Neuropilin 2 (NRP2) and Vascular Endothelial Growth Factor Receptor 1 (VEGFR1) are its receptors. This interaction (Epithelial/Fibroblast to Fibroblast/SMC) suggests active angiogenesis and vascularization, essential for tumor growth and nutrient supply.
• HBEGF-EGFR: Heparin-binding EGF-like growth factor (HBEGF) binds to Epidermal Growth Factor Receptor (EGFR). This signaling pathway is a potent driver of cell proliferation, survival, and migration in various cancers, including breast cancer. Its activity between Epithelial cells and also involving Fibroblasts and Smooth Muscle Cells suggests autocrine and paracrine stimulation of tumor growth and stromal remodeling. PubMed Search: HBEGF EGFR breast cancer
• FGF2-FGFR1: Fibroblast Growth Factor 2 (FGF2) interacting with its receptor FGFR1 also promotes cell proliferation, survival, and angiogenesis.

• Biological Relevance: These pathways collectively highlight enhanced tumor growth, survival, and particularly angiogenesis, which is a hallmark of cancer. The involvement of stromal cells (Fibroblasts, SMC) in these interactions indicates their active participation in orchestrating tumor progression.

3. NOTCH Signaling (DLK1-NOTCH2):
   • Mechanism: Delta-like 1 homolog (DLK1) is a ligand for NOTCH receptors. NOTCH signaling is critical for cell fate determination, proliferation, and differentiation. The interaction DLK1-NOTCH2 (Epithelial | Fibroblast) suggests altered cell fate decisions and enhanced proliferation within the tumor microenvironment.
   • Biological Relevance: Dysregulated NOTCH signaling is implicated in breast cancer stemness, therapeutic resistance, and metastasis. Its upregulation in tumor samples indicates a potential role in maintaining cancer cell plasticity and aggressive phenotypes. GeneCards: NOTCH2
4. Immune Microenvironment (APP-CD74):
   • Mechanism: Amyloid Precursor Protein (APP) interacting with CD74, the invariant chain of MHC class II molecules, is observed between aneuploid Epithelial cells and Macrophages. While APP is known for its role in Alzheimer's, it also has functions in cell adhesion, migration, and signaling. CD74 is involved in immune regulation and can act as a signaling receptor.
   • Biological Relevance: The presence of this interaction with aneuploid epithelial cells (likely tumor cells) and macrophages suggests a complex interplay within the immune tumor microenvironment. Macrophages, especially tumor-associated macrophages (TAMs), often adopt pro-tumorigenic roles (M2 phenotype), promoting angiogenesis, immune suppression, and metastasis. This specific interaction could contribute to immune evasion or macrophage polarization within the breast tumor. PubMed Search: APP CD74 cancer macrophage

Clinical or Translational Implications

The identified tumor-specific cell-cell interactions represent promising avenues for therapeutic intervention in breast cancer.

1. Targeting Integrin Signaling:
   • Potential as Therapeutic Target: Given the extensive involvement of FN1/COLXA-integrin complexes in tumor cell adhesion, migration, and survival, targeting these interactions could inhibit tumor invasion and metastasis.
   • Relevant Therapeutic Agents: Integrin inhibitors (e.g., cilengitide, volociximab, etaracizumab) have been developed and tested in various cancers, including breast cancer. Repurposing or developing novel agents that specifically block tumor-promoting integrin subsets or their interactions with the ECM could be beneficial. Strategies might also involve targeting the synthesis of specific ECM components by CAFs.
2. Targeting Growth Factor Pathways (VEGF, EGFR, FGF):
   • Potential as Therapeutic Target: The heightened activity of VEGFA-NRP2/VEGFR1, HBEGF-EGFR, and FGF2-FGFR1 pathways underscores the importance of angiogenesis and proliferation in breast cancer.

Relevant Therapeutic Agents

• Anti-angiogenic drugs: Bevacizumab (anti-VEGFA antibody) is a known anti-angiogenic agent, though its use in breast cancer has evolved. Other VEGFR inhibitors (e.g., sunitinib, pazopanib) could also be considered.
• EGFR inhibitors: Tyrosine kinase inhibitors (TKIs) like erlotinib, gefitinib, or monoclonal antibodies like cetuximab, specifically designed to block EGFR signaling, are established cancer therapies. Lapatinib targets both EGFR and HER2.
• FGFR inhibitors: Small molecule inhibitors targeting FGFR1-4 (e.g., palbociclib, erdafitinib, lenvatinib) are being explored or approved for various cancers and could be relevant for breast cancer.

3. Targeting NOTCH Signaling:
   • Potential as Therapeutic Target: Inhibiting NOTCH signaling, particularly DLK1-NOTCH2, could disrupt cancer stem cell pathways and improve therapeutic response.
   • Relevant Therapeutic Agents: Gamma-secretase inhibitors (GSIs) are the most common strategy to inhibit NOTCH. Monoclonal antibodies targeting NOTCH receptors or ligands are also under investigation.
4. Modulating Macrophage-Epithelial Interactions (APP-CD74):
   • Potential as Therapeutic Target: The interaction between aneuploid Epithelial cells and Macrophages via APP-CD74 suggests a mechanism for immune evasion or modulation within the TME. Targeting this axis could reprogram pro-tumorigenic macrophages or enhance anti-tumor immunity.
   • Relevant Therapeutic Agents: Strategies focusing on macrophage repolarization (e.g., CSF1R inhibitors, agents blocking specific macrophage-activating signals) or direct targeting of CD74 (e.g., inotuzumab ozogamicin in B-cell malignancies, though its role in breast cancer is less established) could be explored. Immune checkpoint inhibitors might also be more effective if macrophage-mediated immune suppression is reduced.

These findings provide a rich landscape of specific cell-cell communication pathways that are dysregulated in breast primary tumors, offering multiple potential entry points for developing targeted therapies and improving patient outcomes.

13. Condition-Specific Cell-Cell Interaction Patterns in Primary Breast Tumors

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates condition-specific cell-cell interactions (CCIs) in primary breast tumor samples, focusing on a predefined set of immune checkpoint and cell cycle-related genes. The interactions involve Epithelial cells (the resolved tumor-origin cell type) and major immune and stromal cell types (B cell, Myeloid cell, Stromal cell/Fibroblast, T cell, Mast cell, Smooth muscle cell, ILC). The results are visualized as a dot plot, where each dot represents a specific ligand-receptor pair interaction between two cell types in a given sample. The size of the dot corresponds to the negative logarithm of the p-value (-log10(p)), indicating statistical significance, while the color intensity reflects the scaled log strength of the interaction (based on mean expression products of ligand and receptor). The plot displays interactions observed in the primary_tumor condition, implicitly highlighting interactions that are significantly active or different from the normal condition.

Visual Summary

The dot plot, titled "Condition-specific CCI pattern (primary_tumor)", illustrates cell-cell interaction profiles across multiple patient samples.

• Axes: The Y-axis lists individual patient samples, and the X-axis displays specific Cell-Cell Interaction (CCI) indices, detailing the ligand-receptor pair and the interacting cell types, including their ploidy status (e.g., Epi (Aneu) for aneuploid epithelial cells, Fib (Dip) for diploid fibroblasts).
• Interaction Strength and Significance: A distinct pattern of dots indicates varying levels of interaction activity and significance across samples and CCI pairs. Larger, darker red dots signify highly significant interactions with strong scaled log strength, predominantly seen in a subset of samples.
• Sample Heterogeneity: There is notable heterogeneity among samples. Patients highlighted within the blue box (from Patient_11_3FCDEL_RNA down to Patient_9_3B3E9L_RNA) exhibit a much higher frequency and intensity of significant CCIs compared to patients above the blue line (e.g., Patient_1_49758L_RNA, Patient_2_49CFCL_RNA). This suggests considerable inter-patient variability in the tumor microenvironment's communicative landscape.
• Dominant Interaction Types: Several clusters of interactions appear consistently strong and significant across the more active samples. These include interactions involving:
  • EGFR signaling: Ligands like HBEGF, AREG, EREG with EGFR, primarily involving Epithelial cells (both diploid and aneuploid), Macrophages, Fibroblasts, and Smooth Muscle Cells.
  • TGF-beta signaling: TGFB1, TGFB2, TGFB3 with TGFbeta receptors (TGFbeta_receptor1, 2, 3) and integrin_aVb6_complex, frequently involving Epithelial cells (aneuploid), Fibroblasts, Macrophages, T cells, and Smooth Muscle Cells.
  • Immune Checkpoint/Modulation: CD86-CD28 interaction between Macrophages and T cells (CD4+) is also present, alongside CD93-IFNGR1 interactions.
• Ploidy-Specific Interactions: Many significant interactions involve Epi (Aneu) cells (aneuploid epithelial cells), reinforcing their role as the presumed tumor cells driving abnormal communication within the tumor microenvironment.

Biological Interpretation

The observed CCI patterns in primary breast tumors reveal active communication networks critical for tumor progression, immune evasion, and stromal remodeling. The focus on immune checkpoint and cell cycle-related genes is highly relevant given the context of breast cancer.

Key Cell-Cell Interactions and Their Biological Mechanisms:

1. AREG-EGFR signaling (Aneuploid Epithelial cell - Fibroblast)

Interaction: AREG_EGFR -- Epi (Aneu) | Fib

• Signaling Pathway and Mechanisms: Amphiregulin (AREG) is a ligand for the Epidermal Growth Factor Receptor (EGFR). EGFR signaling is a well-established oncogenic pathway in many cancers, including breast cancer. Activation of EGFR by AREG on fibroblasts can lead to their activation and differentiation into cancer-associated fibroblasts (CAFs), which support tumor growth, angiogenesis, and metastasis. Conversely, EGFR signaling on aneuploid epithelial cells can drive their proliferation, survival, and epithelial-mesenchymal transition (EMT), a process linked to invasion. PubMed search: AREG EGFR breast cancer signaling
• Relevance to Cell Cycle: EGFR activation triggers downstream signaling cascades (e.g., RAS/MAPK, PI3K/AKT) that promote cell cycle progression by upregulating cyclins and downregulating cyclin-dependent kinase inhibitors, thus contributing to uncontrolled proliferation of tumor cells.

2. TGFB1-Integrin/TGFβR Signaling (Aneuploid Epithelial cell - Fibroblast)
   • Interaction: TGFB1_integrin_aVb6_complex -- Epi (Aneu) | Fib, TGFB1_TGFbeta_receptor1 -- Epi (Aneu) | Fib
   • Signaling Pathway and Mechanisms: Transforming Growth Factor-beta 1 (TGFB1) is a pleiotropic cytokine that plays a complex role in cancer. In advanced breast cancer, TGFB1 often acts as a pro-tumorigenic factor, promoting tumor cell invasion, metastasis, angiogenesis, and immunosuppression. The interaction with integrin αvβ6 complex on epithelial cells can activate latent TGFB1, further amplifying the signaling. This signaling between aneuploid epithelial cells and fibroblasts can drive CAF activation, extracellular matrix remodeling, and create an immunosuppressive microenvironment by inhibiting anti-tumor immune responses. GeneCards: TGFB1
   • Relevance to Cell Cycle: TGF-beta can have context-dependent effects on the cell cycle. While it can inhibit proliferation in early-stage epithelial cells, in advanced cancer, it often promotes proliferation and survival through different signaling cascades, or by inducing EMT which indirectly affects cell cycle regulation in a pro-tumorigenic manner.
3. CD86-CD28 Interaction (Macrophage - T cell CD4+)

Interaction: CD86_CD28 -- Mac | T CD4+

• Signaling Pathway and Mechanisms: CD86, expressed on antigen-presenting cells like macrophages, binds to CD28 on T cells, providing a crucial co-stimulatory signal required for full T cell activation and proliferation. In the tumor microenvironment, this interaction is essential for initiating adaptive immune responses. However, macrophages within tumors can be polarized to M2-like phenotypes (e.g., M2B, M2C observed in celltype_subset), which can be immunosuppressive. The nature of this signaling in the tumor context depends on the macrophage activation state and the overall immune context. UniProt: CD28
• Relevance to Immune Checkpoints: While CD86-CD28 provides co-stimulation, its balance with inhibitory checkpoint interactions (e.g., PD-L1/PD-1) dictates the overall T cell response. Dysregulation of this balance contributes to immune evasion.

4. CD93-IFNGR1 Interaction (Macrophage - Aneuploid Epithelial cell)

Interaction: CD93_IFNGR1 -- Mac | Epi (Aneu)

• Signaling Pathway and Mechanisms: CD93 is a transmembrane glycoprotein expressed on endothelial cells and myeloid cells (like macrophages). IFNGR1 is the alpha chain of the Interferon-gamma receptor. Interferon-gamma (IFN-γ) is a crucial cytokine produced by T cells and NK cells, primarily known for its anti-tumor effects and immune-modulatory roles. The interaction could imply macrophage-mediated regulation of IFN-γ sensitivity or signaling in aneuploid epithelial cells, potentially influencing tumor cell response to immune attack or contributing to immune evasion mechanisms by modulating IFN-γ pathway components.

Clinical or Translational Implications

The identified cell-cell interactions, particularly those involving aneuploid epithelial cells and immune/stromal components, represent promising targets for therapeutic intervention in breast cancer.

1. AREG-EGFR Signaling (Epithelial-Fibroblast)
   • Signaling Pathway and Mechanisms: Dysregulated EGFR signaling drives tumor cell proliferation, survival, and promotes an aggressive phenotype through processes like EMT. It also activates stromal cells like fibroblasts to support tumor growth.
   • Therapeutic Target Potential: EGFR is a well-validated oncogene, and its signaling pathway is a common target in various cancers. Blocking AREG-EGFR interactions could inhibit tumor cell growth, reduce stromal support, and potentially sensitize tumor cells to other therapies.

Relevant Therapeutic Agents

• EGFR tyrosine kinase inhibitors (TKIs): e.g., Gefitinib, Erlotinib, Osimertinib. These agents directly block the intracellular kinase domain of EGFR.
• Monoclonal antibodies against EGFR: e.g., Cetuximab, Panitumumab. These antibodies block ligand binding to the extracellular domain of EGFR. While primarily used in colorectal and head & neck cancers, their utility in breast cancer (especially triple-negative breast cancer, TNBC) is an area of ongoing research. PubMed search: EGFR inhibitors breast cancer

2. TGFB1-Integrin/TGFβR Signaling (Epithelial-Fibroblast)
   • Signaling Pathway and Mechanisms: In advanced breast cancer, TGFB1 promotes tumor progression by inducing EMT, enhancing metastasis, fostering angiogenesis, and suppressing anti-tumor immunity by inhibiting T cell and NK cell functions. The integrin αvβ6 complex activates latent TGFB1, exacerbating these effects.
   • Therapeutic Target Potential: Targeting the TGF-beta pathway is attractive for overcoming immune evasion and inhibiting metastasis. Blocking TGFB1 activity or its receptors could normalize the tumor microenvironment and improve responses to other therapies.

Relevant Therapeutic Agents

• TGF-beta pathway inhibitors: Several agents are in various stages of clinical development, including small molecule inhibitors of TGFβR1 (e.g., Galunisertib) and antibodies against TGFB1 or TGFβR (e.g., Fresolimumab).
• Integrin αvβ6 inhibitors: Monoclonal antibodies targeting integrin αvβ6 are also being explored, as this integrin is crucial for TGFB1 activation in specific contexts. PubMed search: TGF-beta inhibitors breast cancer

3. CD86-CD28 Interaction (Macrophage - T cell CD4+)
   • Signaling Pathway and Mechanisms: This co-stimulatory interaction is crucial for T cell activation. While essential for initiating immune responses, in the immunosuppressive tumor microenvironment, dysregulation can contribute to T cell anergy or exhaustion. Modulating this interaction, perhaps in combination with other immunotherapies, could fine-tune anti-tumor immunity.
   • Therapeutic Target Potential: While direct agonism of CD28 in cancer is complex due to potential systemic autoimmunity, understanding the balance of co-stimulatory and co-inhibitory signals (like CD86/CD28 vs. PD-1/PD-L1) is vital for immunotherapy strategies.
   • Relevant Therapeutic Agents: Current immunotherapies often target *inhibitory* checkpoints (e.g., anti-PD-1, anti-CTLA-4). Therapies that enhance co-stimulation, such as CD28 agonists, are being explored, often in combination settings, to boost T cell activation. PubMed search: CD28 agonists cancer immunotherapy

The observed patient-to-patient heterogeneity underscores the importance of personalized approaches in breast cancer treatment, where specific CCI patterns might guide the selection of targeted therapies or immunotherapies for individual patients.

14. Epithelial Cell Condition-Specific Surfaceome Markers in Breast Cancer

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies and visualizes surfaceome markers differentially expressed in Epithelial cells across different conditions (Diploid, normal, primary_tumor) and individual patient samples within a breast tissue single-cell RNA-seq dataset. The dot plot displays the mean expression level (color intensity) and the fraction of cells expressing each marker (dot size) for each sample. Markers were identified using a one-vs-rest comparison with specific non-zero percentage score and fold change cutoffs. The primary goal is to highlight potential therapeutic targets by distinguishing established targets from less characterized markers overexpressed in primary tumor epithelial cells.

Visual Summary

The dot plot clearly differentiates two major groups of epithelial cells based on their surfaceome marker expression:

1. Normal/Diploid Epithelial Cells: Samples labeled "Diploid" and "normal" generally exhibit a shared expression pattern, characterized by high expression of markers such as PRNP, GLIS4, SLC1A7, BAP31, ENAH, TMEM229B, SPON2, EVCA1, MSCP, MSD12, TPRA1, SLC11A2, and ABCC3. These markers show strong expression (dark red, large dots) in most normal/diploid samples but are largely absent or expressed at very low levels in primary tumor samples.
2. Primary Tumor Epithelial Cells: Samples labeled "primary_tumor" show a distinct expression profile. A significant cluster of markers, prominently including CD24, CD44, ATP1B1, APLP2, LMAN2, CD164, PLPP2, EMP2, SSR1, AREG, MUC1, ERBB3, F11R, BST2, TSPAN15, and TNFRSF18, are highly expressed in most primary tumor samples, while being largely absent or expressed at negligible levels in normal/diploid samples. Some markers, like CDH1, show more varied expression across tumor samples, suggesting heterogeneity within the tumor epithelial cell population. The mean expression levels are notably higher (darker red) and the fraction of expressing cells is greater (larger dots) for these tumor-specific markers within the primary tumor group.

Overall, the plot reveals a clear molecular distinction in surface marker expression between normal/diploid and primary tumor epithelial cells, reflecting the extensive cellular reprogramming that occurs during breast cancer development.

Biological Interpretation

The observed differential expression patterns provide valuable insights into the altered biology of breast cancer epithelial cells. The markers enriched in primary tumor cells are particularly relevant for understanding tumor progression and identifying potential therapeutic vulnerabilities.

Markers Overexpressed in Primary Tumor Epithelial Cells (Potential Therapeutic Targets):

Established Therapeutic Targets:

• ERBB3 (HER3): This receptor tyrosine kinase (RTK) is a member of the HER family. While it has impaired kinase activity, it is a crucial dimerization partner for other HER family members (e.g., HER2, EGFR) and plays a key role in activating the PI3K/Akt pathway, driving cell proliferation, survival, and drug resistance. Its overexpression in breast cancer suggests a dependency on HER3 signaling.
  • Associated Signaling/Mechanisms: Forms heterodimers with other HER receptors (especially HER2) to activate PI3K/Akt signaling. Its primary ligand is heregulin (NRG1).
  • Relevant Therapeutic Agents: Monoclonal antibodies targeting ERBB3 (e.g., patritumab, seribantumab) are currently in clinical trials for various cancers, including breast cancer, often in combination with other agents to overcome resistance.
  • Supporting Evidence: GeneCards: https://www.genecards.org/cgi-bin/carddisp.pl?gene=ERBB3, PubMed (ERBB3 breast cancer therapy): https://pubmed.ncbi.nlm.nih.gov/?term=ERBB3+breast+cancer+therapy

Less Well-Characterized Markers with Potential as Therapeutic Targets:

• CD24: A GPI-anchored cell surface glycoprotein that is frequently overexpressed in various cancers, including breast cancer. It often marks cancer stem cells (CSCs) and is associated with increased invasiveness, metastasis, and chemoresistance.
  • Associated Signaling/Mechanisms: Involved in cell adhesion, migration, proliferation, and differentiation, potentially through STAT3, Wnt/β-catenin, and Notch signaling pathways.
  • Relevant Therapeutic Agents: While CD24-specific antibodies are in development, targeting CSCs directly or their downstream pathways is a key strategy. Targeting CD24-Siglec G interaction to enhance anti-tumor immunity is also being explored.
  • Supporting Evidence: GeneCards: https://www.genecards.org/cgi-bin/carddisp.pl?gene=CD24, PubMed (CD24 breast cancer stem cells): https://pubmed.ncbi.nlm.nih.gov/?term=CD24+breast+cancer+stem+cells
• CD44: A transmembrane glycoprotein and a primary receptor for hyaluronan, frequently used in conjunction with CD24 as a marker for cancer stem cells (CSCs) in breast cancer. It plays crucial roles in cell adhesion, migration, proliferation, and survival, and its variants (e.g., CD44v6) are implicated in tumor progression and metastasis.
  • Associated Signaling/Mechanisms: Interacts with growth factor receptors (EGFR, HER2) and activates pathways like PI3K/Akt and MAPK.
  • Relevant Therapeutic Agents: Monoclonal antibodies targeting CD44 (e.g., bivatuzumab mertansine) have been investigated, with ongoing efforts to develop more specific and less toxic antibody-drug conjugates or small molecules targeting CD44-hyaluronan interactions.
  • Supporting Evidence: GeneCards: https://www.genecards.org/cgi-bin/carddisp.pl?gene=CD44, PubMed (CD44 breast cancer stem cells): https://pubmed.ncbi.nlm.nih.gov/?term=CD44+breast+cancer+stem+cells
• MUC1 (Mucin 1, Cell Surface Associated): A highly glycosylated transmembrane protein that is aberrantly overexpressed and underglycosylated in many epithelial cancers, including breast cancer. It functions as a tumor-associated antigen and contributes to tumor cell proliferation, survival, anti-apoptosis, and metastasis.
  • Associated Signaling/Mechanisms: Interacts with various signaling pathways including β-catenin, EGFR, and Ras/MAPK.
  • Relevant Therapeutic Agents: MUC1 is a target for cancer vaccines, monoclonal antibodies (e.g., MUC1-specific ADCs), and CAR T-cell therapies, which are under active investigation in clinical trials.
  • Supporting Evidence: GeneCards: https://www.genecards.org/cgi-bin/carddisp.pl?gene=MUC1, PubMed (MUC1 breast cancer therapy): https://pubmed.ncbi.nlm.nih.gov/?term=MUC1+breast+cancer+therapy
• AREG (Amphiregulin): A ligand for EGFR (ERBB1) and ERBB4, belonging to the EGF family. Its overexpression in breast cancer drives receptor dimerization and activation of downstream signaling pathways (Ras/MAPK, PI3K/Akt, STAT3), promoting cell proliferation, survival, angiogenesis, and invasion.
  • Associated Signaling/Mechanisms: Binds to EGFR/ERBB4, leading to activation of proliferative and survival pathways.
  • Relevant Therapeutic Agents: While no direct AREG inhibitors are widely clinical, its activity can be indirectly targeted by EGFR inhibitors (e.g., gefitinib, erlotinib, cetuximab) or by developing specific antibodies to block AREG itself.
  • Supporting Evidence: GeneCards: https://www.genecards.org/cgi-bin/carddisp.pl?gene=AREG, PubMed (Amphiregulin breast cancer): https://pubmed.ncbi.nlm.nih.gov/?term=Amphiregulin+breast+cancer

Markers Overexpressed in Normal/Diploid Epithelial Cells:

• PRNP (Prion Protein): While known for its role in neurodegenerative diseases, cellular prion protein (PrPC) also has physiological functions in cell adhesion and differentiation. In the context of this data, its higher expression in normal/diploid cells and reduced expression in tumor cells suggest it might play a role in maintaining normal epithelial cell homeostasis or differentiation, which is lost during malignant transformation.
  • Associated Signaling/Mechanisms: Involved in cell adhesion, differentiation, and survival, potentially interacting with signaling molecules such as Fyn kinase. In cancer, aberrant expression can be associated with tumor suppression or promotion depending on context.
  • Supporting Evidence: GeneCards: https://www.genecards.org/cgi-bin/carddisp.pl?gene=PRNP, PubMed (PRNP breast cancer): https://pubmed.ncbi.nlm.nih.gov/?term=PRNP+breast+cancer

Clinical or Translational Implications

The distinct surfaceome profiles of primary tumor epithelial cells highlight several promising avenues for clinical translation in breast cancer.

1. Diagnostic and Prognostic Biomarkers:

Markers like CD24, CD44, MUC1, AREG, and ERBB3, which are significantly overexpressed in primary tumor cells but minimal in normal cells, could serve as valuable diagnostic markers, potentially detectable in biopsies or liquid biopsies. Their expression levels might also correlate with disease aggressiveness, subtype, or patient prognosis, warranting further investigation in larger cohorts.

2. Therapeutic Target Identification:

The identified tumor-enriched surfaceome markers represent attractive candidates for targeted therapies.

• ERBB3 is already an established target, with antibodies in clinical development, and this data reinforces its relevance in breast cancer epithelial cells.
• CD24 and CD44, as cancer stem cell markers, are particularly interesting. Targeting CSCs could overcome therapy resistance and prevent tumor relapse. Strategies involving specific antibodies or small molecules to disrupt their function or associated pathways could be highly effective.
• MUC1 offers opportunities for immunotherapeutic approaches, including cancer vaccines and CAR T-cell therapies, given its status as a widely recognized tumor-associated antigen.
• AREG, as an EGFR ligand, points to the potential utility of existing EGFR inhibitors or the development of novel agents to block AREG-mediated signaling, especially in tumors where AREG drives EGFR activation.

3. Disease Monitoring and Personalized Medicine:

Monitoring the expression of these markers during treatment could provide insights into therapy response or the emergence of resistance. For instance, changes in CD24/CD44 expression could indicate alterations in the CSC population. A personalized medicine approach could involve stratifying patients based on their tumor epithelial cell surfaceome profiles to guide treatment decisions.

4. Experimental Validation:

Further experimental validation is crucial for these findings. This would involve:

• In vitro studies: Testing the effects of targeting agents on breast cancer cell lines expressing these markers, assessing cell proliferation, viability, migration, and invasion.
• In vivo studies: Utilizing patient-derived xenograft (PDX) models or genetically engineered mouse models to evaluate the efficacy of targeted therapies and understand the impact on tumor growth and metastasis.
• Clinical trials: Ultimately, promising candidates would progress to clinical trials to assess their safety and efficacy in breast cancer patients.

The data provides a strong foundation for developing new strategies to diagnose, treat, and monitor breast cancer by leveraging the unique surfaceome landscape of tumor epithelial cells.

15. Macrophage Condition-Specific Surfaceome Markers in Breast Cancer

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers for Macrophages within the breast tissue, comparing normal versus primary tumor conditions. The dot plot visualizes the mean expression level (color intensity) and the fraction of cells expressing a given gene (dot size) for each marker across different patients and conditions. The markers were selected based on a one-vs-rest comparison using specific non-zero percentage score and fold change cutoffs, focusing exclusively on surfaceome genes. The goal is to highlight potential diagnostic or therapeutic targets that differentiate macrophage populations in the tumor microenvironment from those in normal tissue.

Visual Summary

The dot plot displays surfaceome marker expression in macrophage populations from 11 distinct patients, categorized by "normal" and "primary_tumor" conditions. Each row represents a patient sample, and columns represent individual surfaceome markers.

Key observations from the plot:

• Differential Marker Expression: There is a clear distinction in marker expression profiles between normal and primary tumor-associated macrophages.
• Normal Macrophage Markers: A smaller set of markers, including CD163, FPR1, LAIR1, C3AR1, SLCO2B1, HAVCR2, and MSR1, show relatively higher expression and prevalence in macrophages from normal breast tissue, particularly in patients like Patient_4_4B146L_RNA and Patient_8_3821AL_RNA. However, their expression patterns appear more heterogeneous across normal samples compared to the tumor-associated markers.
• Primary Tumor Macrophage Markers: A significantly larger and more uniformly expressed set of markers characterizes macrophages in the primary tumor condition. Genes such as CD83, CCR7, EREG, TMEM123, SERINC1, ITGB8, SIRPA, CD55, CXCR4, LAMP2, ABCA1, ALCAM, ENPP4, IL6R, HLA-DRB5, ITGAV, and several others show strong and widespread expression across nearly all primary tumor samples. This suggests a consistent and distinct phenotypic shift in macrophages within the tumor microenvironment.
• High Expression and Prevalence in Tumor: Many markers in the "primary_tumor" group exhibit both high mean expression (dark red dots) and high fractions of expressing cells (large dots), indicating a robust and consistent upregulation of these surface proteins in tumor-associated macrophages (TAMs).
• Patient-Specific Heterogeneity: While general trends are observed, some patient-specific variations exist. For example, some markers show slightly lower expression or prevalence in certain tumor samples compared to others, though the overall pattern of upregulation in the tumor context remains strong.
• Cell Counts: The bar on the right indicates the number of cells per sample, ranging from 56 to 1118 macrophages, confirming sufficient cell numbers for robust analysis across most samples.

Biological Interpretation

Macrophages are critical components of the tumor microenvironment (TME), often adopting a tumor-promoting phenotype (Tumor-Associated Macrophages, TAMs) in breast cancer. The observed differential expression of surfaceome markers highlights distinct functional states of macrophages in normal versus tumor tissue.

Markers Upregulated in Normal Macrophages

Markers like CD163 and MSR1 (CD204) are typically associated with M2-like macrophages, which have roles in tissue remodeling, angiogenesis, and immune suppression. While they are often elevated in TAMs, their presence here, along with FPR1 (involved in immune cell recruitment) and C3AR1 (complement receptor), suggests their constitutive roles in homeostatic tissue maintenance or an early inflammatory response in normal tissue that might be co-opted in cancer.

• CD163: A scavenger receptor for hemoglobin-haptoglobin complexes, widely used as a marker for M2 macrophages. In normal tissues, it's involved in clearing apoptotic cells and resolving inflammation.
• FPR1: Formyl peptide receptor 1, involved in chemotaxis and inflammation, typically mediating responses to bacterial infection or tissue injury.
• MSR1: Macrophage scavenger receptor 1, involved in innate immunity and clearance of modified lipoproteins and cellular debris.

Markers Upregulated in Primary Tumor Macrophages

The larger array of highly expressed surfaceome markers in primary tumor macrophages points towards significant reprogramming and functional specialization within the TME, often promoting tumor growth, invasion, and metastasis, and contributing to immune evasion.

• CD83: A glycoprotein expressed on mature dendritic cells and activated B and T cells, but also found on macrophages. Its precise role in TAMs is less clear but may involve immune modulation. In some contexts, it can inhibit T-cell activation.
  • Signaling Pathways: Involved in immune cell activation and regulation.
  • Role in TME: Could contribute to immune evasion by modulating T-cell responses.
  • Therapeutic Potential: Potential target for modulating immune responses; antibodies against CD83 are being investigated in autoimmune diseases and cancer.
  • Relevant Therapeutic Agents: Anti-CD83 antibodies are in preclinical/early clinical development.
  • GeneCards: CD83
• CCR7: Chemokine receptor 7, crucial for immune cell trafficking, guiding T cells, B cells, and dendritic cells to secondary lymphoid organs. Its expression on TAMs could indicate a role in guiding tumor-draining lymph node metastasis or influencing immune cell localization within the TME.
  • Signaling Pathways: G-protein coupled receptor signaling, mediating responses to chemokines CCL19 and CCL21.
  • Role in TME: May facilitate trafficking of tumor-associated immune cells, potentially impacting metastasis or immune cell exclusion from the tumor core.
  • Therapeutic Potential: Targeting CCR7 could disrupt tumor-immune cell interactions and metastasis.
  • Relevant Therapeutic Agents: Chemokine receptor inhibitors are being explored in cancer.
  • GeneCards: CCR7
• EREG (Epiregulin): A ligand for the epidermal growth factor receptor (EGFR). EREG is produced by TAMs and can promote tumor cell proliferation, survival, and angiogenesis in various cancers, including breast cancer.
  • Signaling Pathways: EGFR signaling pathway, leading to cell proliferation, survival, and migration.
  • Role in TME: Potently pro-tumorigenic, promoting tumor growth and potentially contributing to therapeutic resistance.
  • Therapeutic Potential: EREG, or more broadly EGFR signaling, is a known therapeutic target in cancer. Inhibiting EREG production or its interaction with EGFR could mitigate TAM-mediated tumor promotion.
  • Relevant Therapeutic Agents: EGFR inhibitors (e.g., cetuximab, erlotinib) are established cancer drugs. Specific EREG-targeting agents might be developed.
  • GeneCards: EREG
• ITGB8 (Integrin Beta 8): A subunit of integrin receptors involved in cell-cell and cell-extracellular matrix interactions. Integrins are critical for cell adhesion, migration, and signaling. ITGB8 can activate TGF-β, a major immunosuppressive and pro-fibrotic cytokine in the TME.
  • Signaling Pathways: Integrin signaling, TGF-β activation.
  • Role in TME: Facilitates immune suppression and fibrosis through TGF-β activation, promoting tumor progression and metastasis.
  • Therapeutic Potential: Targeting ITGB8 could reduce active TGF-β in the TME, potentially reversing immune suppression and inhibiting fibrosis.
  • Relevant Therapeutic Agents: Anti-TGF-β therapies or specific ITGB8 inhibitors are under investigation.
  • GeneCards: ITGB8
• SIRPA (Signal Regulatory Protein Alpha): A transmembrane glycoprotein that interacts with CD47 on tumor cells, delivering a "don't eat me" signal to macrophages. This interaction inhibits phagocytosis of tumor cells by macrophages, contributing to immune evasion.
  • Signaling Pathways: Inhibitory signaling pathway upon binding to CD47, preventing phagocytosis.
  • Role in TME: A key mediator of immune evasion by tumor cells, allowing them to escape macrophage-mediated clearance.
  • Therapeutic Potential: Blocking the SIRPA-CD47 axis is a highly active area of cancer immunotherapy.
  • Relevant Therapeutic Agents: Anti-CD47 antibodies (e.g., magrolimab) and SIRPA-targeting agents are in clinical development.
  • GeneCards: SIRPA
• CXCR4: Chemokine receptor 4, which binds to CXCL12 (SDF-1). This axis is crucial for cell trafficking, angiogenesis, and metastasis. In TAMs, CXCR4 can contribute to their recruitment to the tumor site and promote pro-tumorigenic functions.
  • Signaling Pathways: G-protein coupled receptor signaling, mediating responses to CXCL12.
  • Role in TME: Involved in the recruitment of TAMs to hypoxic tumor regions, promoting angiogenesis, tumor growth, and metastasis.
  • Therapeutic Potential: CXCR4 is a known therapeutic target, particularly for inhibiting metastasis and modulating immune cell infiltration.
  • Relevant Therapeutic Agents: CXCR4 antagonists (e.g., Plerixafor) are approved for other indications and are being tested in cancer.
  • GeneCards: CXCR4
• LAMP2 (Lysosome Associated Membrane Protein 2): A lysosomal membrane protein involved in autophagy and antigen presentation. Its upregulation in TAMs might reflect increased metabolic activity, antigen processing, or altered lysosomal functions that support tumor growth or immune suppression.
  • Signaling Pathways: Autophagy, lysosomal function, antigen presentation.
  • Role in TME: May contribute to altered metabolism of TAMs or influence their antigen presentation capabilities, potentially impacting anti-tumor immunity.
  • Therapeutic Potential: While primarily intracellular, its cell surface exposure or specific roles could offer indirect targeting avenues.
  • GeneCards: LAMP2
• IL6R (Interleukin 6 Receptor): The receptor for IL-6, a pleiotropic cytokine often highly elevated in the TME and associated with inflammation, immune suppression, and tumor progression in breast cancer.
  • Signaling Pathways: JAK-STAT pathway activation, leading to gene expression related to inflammation, proliferation, and survival.
  • Role in TME: Mediates IL-6-driven pro-tumorigenic effects on tumor cells and other immune cells, promoting angiogenesis, metastasis, and immune evasion.
  • Therapeutic Potential: Targeting IL6R is a well-established strategy in inflammatory diseases and cancer.
  • Relevant Therapeutic Agents: Tocilizumab (anti-IL6R antibody) is approved for inflammatory diseases and being investigated in cancer.
  • GeneCards: IL6R
• HLA-DRB5 and HLA-F: These are Major Histocompatibility Complex (MHC) class II molecules (HLA-DRB5) and non-classical MHC class I molecules (HLA-F). Upregulation of MHC-II on macrophages suggests increased antigen presentation capacity, which could be pro- or anti-tumorigenic depending on the context and antigens presented. HLA-F is expressed on the cell surface and has immunomodulatory roles, potentially interacting with NK cells and T cells.
  • Signaling Pathways: Antigen presentation pathways, immune cell recognition.
  • Role in TME: Altered MHC expression on TAMs can influence T-cell activation and tumor immune escape. HLA-F might modulate immune responses by interacting with inhibitory receptors.
  • Therapeutic Potential: Modulating MHC expression or function on TAMs could enhance anti-tumor immunity.
  • GeneCards: HLA-DRB5, GeneCards: HLA-F
• NOTCH2: A receptor in the Notch signaling pathway, which is critical for cell fate determination, proliferation, and differentiation. Aberrant Notch signaling can contribute to cancer development and progression, and its activation in TAMs may promote pro-tumorigenic functions.
  • Signaling Pathways: Notch signaling pathway, involving ligand binding and intramembrane proteolysis leading to transcriptional regulation.
  • Role in TME: Can influence macrophage polarization, promoting pro-tumorigenic phenotypes or influencing angiogenesis and immune cell interactions.
  • Therapeutic Potential: Notch pathway inhibitors are being investigated as anti-cancer agents. Targeting NOTCH2 on TAMs could be a strategy.
  • GeneCards: NOTCH2

Clinical or Translational Implications

The distinct surfaceome marker profiles of macrophages in primary breast tumors compared to normal tissue offer several promising avenues for clinical translation:

1. Diagnostic and Prognostic Biomarkers: The identified tumor-specific macrophage markers (e.g., EREG, ITGB8, SIRPA, CXCR4, IL6R) could serve as valuable biomarkers for identifying and characterizing TAMs in breast cancer biopsies or liquid biopsies. High expression of certain pro-tumorigenic markers could correlate with disease progression or therapeutic response, offering prognostic value.
2. Therapeutic Targets for Macrophage Reprogramming: The strong upregulation of pro-tumorigenic surface proteins like SIRPA, CXCR4, EREG, ITGB8, and IL6R on TAMs suggests these molecules are attractive therapeutic targets.
   • SIRPA-CD47 Blockade: Directly targeting the SIRPA-CD47 axis could unleash macrophage phagocytosis of tumor cells, representing a major strategy in current cancer immunotherapy.
   • CXCR4 Inhibition: Blocking CXCR4 can inhibit TAM recruitment and metastatic spread.
   • EGFR and IL6R Blockade: Inhibiting EREG-EGFR and IL6-IL6R signaling can directly counteract TAM-mediated tumor growth and inflammation.
   • TGF-β Modulation via ITGB8: Targeting ITGB8 could reduce active TGF-β, thereby dampening immunosuppression and fibrosis in the TME.

Therapeutic strategies could involve small molecule inhibitors, monoclonal antibodies, or antibody-drug conjugates specifically targeting these macrophage surface markers to deplete or reprogram TAMs towards an anti-tumorigenic phenotype.

3. Targeted Drug Delivery: Surface markers highly specific to TAMs could be utilized for targeted delivery of anti-cancer drugs, immunomodulators, or gene therapies directly to the tumor-promoting macrophage populations, minimizing off-target effects and enhancing therapeutic efficacy.
4. Monitoring Therapeutic Response: Changes in the expression of these macrophage surface markers could be monitored to assess the efficacy of anti-cancer therapies, particularly those aimed at modulating the immune microenvironment.

These findings underscore the critical role of TAMs in breast cancer progression and provide a rich list of potential surfaceome targets for novel therapeutic interventions. Further functional studies are warranted to validate the precise roles of these markers in breast cancer and to translate these findings into effective clinical strategies.

16. Fibroblast Condition-Specific Surfaceome Marker Expression in Breast Cancer

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers in Fibroblast cells from single-cell RNA-seq data of breast tissue, comparing 'normal' and 'primary_tumor' conditions. The goal is to highlight differential marker expression to understand the distinct roles of fibroblasts in the tumor microenvironment (TME) and identify potential therapeutic targets. The dot plot visualizes the mean expression level (color intensity) and the fraction of cells expressing each marker (dot size) across different patient samples within each condition. Markers were selected based on an enrichment score and fold change thresholds for each condition compared to others (one-vs-rest comparison), focusing on surfaceome proteins.

Visual Summary

The dot plot effectively distinguishes between fibroblast markers predominantly expressed in 'normal' breast tissue versus those upregulated in the 'primary_tumor' setting.

• Normal Fibroblast Markers: A distinct cluster of markers, including PLPP3, EDNRB, PDGFRB, GPM6B, and TGFBR3, shows higher mean expression and prevalence (larger, darker red dots) in fibroblasts derived from 'normal' samples (top four patient samples). These markers are largely absent or expressed at very low levels in 'primary_tumor' fibroblasts.
• Primary Tumor Fibroblast Markers: A much larger group of markers exhibits strong expression in fibroblasts from 'primary_tumor' samples (bottom 11 patient samples), with minimal to no expression in 'normal' samples. Prominent examples with high expression and cell fraction include FAP, PMEPA1, MMP14, SDC2, GPNMB, MXRA8, LY6E, ITGB5, IFNGR2, CDH11, THY1, NRP2, SCARB2, PTTG1IP, MYADM, TMEM123, DDR2, TMEM158, FAT1, ABCA1, IFNAR1, NRP1, AXL, GAS1, LRP10, TSPAN4, SGCB, PDLIM5, CD9, ANTXR1, SGCE, ADAM12, GPC1, DCBLD2, PLXDC2, EDNRA, LRP12, HEG1, SLC41A2, PLPP1, and ANO6. This strong differential expression highlights the extensive reprogramming of fibroblasts within the tumor microenvironment, transforming them into Cancer-Associated Fibroblasts (CAFs).
• Patient Heterogeneity: Within the 'primary_tumor' group, there is some variability in marker expression across patients, indicating potential inter-patient heterogeneity in CAF phenotypes. For example, some markers like GPNMB and MXRA8 show very high expression in certain tumor samples (e.g., Patient_8_3821AL_RNA, Patient_14_43E7CL_RNA) but lower in others.

Biological Interpretation

Markers Enriched in Normal Fibroblasts

The markers enriched in normal fibroblasts likely represent their physiological functions in maintaining tissue homeostasis in the healthy mammary gland.

• PLPP3 (Phospholipid Phosphatase 3): Involved in lipid metabolism and signaling, potentially regulating cell growth and survival. Its presence in normal fibroblasts might indicate their quiescent or homeostatic state. [GeneCards PLPP3: GeneCards]
• EDNRB (Endothelin Receptor Type B): A receptor for endothelin peptides, involved in cell proliferation, migration, and differentiation. In normal fibroblasts, EDNRB signaling may contribute to maintaining vascular tone and tissue integrity. [GeneCards EDNRB: GeneCards]
• PDGFRB (Platelet-Derived Growth Factor Receptor Beta): A key receptor in PDGF signaling, crucial for fibroblast proliferation, migration, and extracellular matrix (ECM) production. Its expression in normal fibroblasts is consistent with their role in tissue maintenance and repair. [GeneCards PDGFRB: GeneCards]
• TGFBR3 (TGF-beta Receptor Type 3): Also known as Betaglycan, it is a co-receptor for the TGF-beta signaling pathway, which controls a wide array of cellular processes including cell growth, differentiation, and immune responses. Its role in normal fibroblasts would be to modulate TGF-beta signaling for maintaining tissue homeostasis. [GeneCards TGFBR3: GeneCards]

Markers Enriched in Primary Tumor Fibroblasts (Cancer-Associated Fibroblasts - CAFs)

The extensive set of markers upregulated in primary tumor fibroblasts highlights their phenotypic shift into Cancer-Associated Fibroblasts (CAFs), which are critical components of the breast cancer tumor microenvironment (TME). CAFs are highly plastic, promoting tumor growth, invasion, metastasis, angiogenesis, and immune evasion.

Here are interpretations of several prominent CAF-specific markers:

FAP (Fibroblast Activation Protein)

• Signaling Pathways: FAP is a serine protease that cleaves ECM proteins and can regulate growth factor bioavailability, influencing pathways like Wnt signaling.
• Role in TME: FAP-expressing CAFs are key drivers of tumor progression. They remodel the ECM, promote angiogenesis, secrete pro-tumorigenic factors, and contribute to an immunosuppressive environment by affecting immune cell infiltration and function.
• Therapeutic Potential: FAP is a highly attractive therapeutic target due to its restricted expression in adult healthy tissues (primarily in wound healing and cancer stroma).
• Therapeutic Agents: FAP inhibitors, FAP-targeted antibody-drug conjugates (ADCs), FAP-targeted CAR-T cells, and FAP-targeted radionuclide therapies are actively being investigated in preclinical and clinical settings. [GeneCards FAP: GeneCards], [PubMed search for FAP cancer therapy: PubMed Search]

MMP14 (Matrix Metallopeptidase 14 / MT1-MMP)

• Signaling Pathways: MMP14 is a membrane-bound metalloproteinase that degrades ECM components and activates other MMPs. It impacts cell-matrix interactions and growth factor release.
• Role in TME: CAFs use MMP14 to extensively remodel the ECM, which facilitates tumor cell invasion, angiogenesis, and metastasis by creating migratory paths and releasing sequestered growth factors.
• Therapeutic Potential: As a key enzyme in tumor invasion and metastasis, MMP14 is a significant therapeutic target.
• Therapeutic Agents: While broad-spectrum MMP inhibitors have faced challenges, more selective inhibitors or antibody-based strategies targeting MMP14 are under development. [GeneCards MMP14: GeneCards], [PubMed search for MMP14 cancer therapy: PubMed Search]

GPNMB (Glycoprotein NMB)

• Signaling Pathways: GPNMB is a transmembrane glycoprotein involved in cell adhesion, migration, and growth factor signaling, potentially activating NF-κB and PI3K/Akt pathways.
• Role in TME: In CAFs, GPNMB can promote immunosuppression and tumor progression, and its expression is often associated with more aggressive tumor phenotypes. It is also found on certain tumor cells.
• Therapeutic Potential: Its cell surface localization and association with aggressive cancer make it a promising target, especially for antibody-drug conjugates (ADCs).
• Therapeutic Agents: Anti-GPNMB ADCs (e.g., glembatumumab vedotin) have been explored in clinical trials for various cancers, including breast cancer. [GeneCards GPNMB: GeneCards], [PubMed search for GPNMB ADC cancer: PubMed Search]

NRP1 (Neuropilin 1)

• Signaling Pathways: NRP1 is a co-receptor for VEGF-A and class 3 semaphorins, modulating angiogenic, migratory, and immune responses by interacting with VEGFR2 and plexins.
• Role in TME: In CAFs and endothelial cells, NRP1 promotes angiogenesis and lymphangiogenesis, supporting tumor cell survival and invasion. It can also modulate immune cell function, contributing to an immunosuppressive environment.
• Therapeutic Potential: NRP1 is a compelling therapeutic target due to its multifaceted role in tumor biology and accessibility on the cell surface.
• Therapeutic Agents: Neutralizing antibodies and small molecule inhibitors targeting NRP1 are being developed to disrupt its pro-tumorigenic functions. [GeneCards NRP1: GeneCards], [PubMed search for Neuropilin-1 cancer therapy: PubMed Search]

AXL (AXL Receptor Tyrosine Kinase)

• Signaling Pathways: Activated by its ligand GAS6, AXL signaling promotes cell survival, proliferation, migration, and epithelial-mesenchymal transition (EMT) through activation of PI3K/Akt, MAPK, and NF-κB pathways.
• Role in TME: In CAFs, AXL can contribute to immunosuppression, angiogenesis, and resistance to therapy by modulating CAF functions and their interactions with tumor cells.
• Therapeutic Potential: AXL overexpression is frequently associated with aggressive disease and therapeutic resistance, making it an attractive target.
• Therapeutic Agents: Multiple small molecule inhibitors (e.g., bemcentinib) and antibodies targeting AXL are in various stages of clinical development for different cancers. [GeneCards AXL: GeneCards], [PubMed search for AXL inhibitor cancer: PubMed Search]

THY1 (CD90)

• Signaling Pathways: A GPI-anchored glycoprotein involved in cell-cell and cell-matrix interactions, influencing signaling pathways related to cell adhesion, proliferation, and differentiation.
• Role in TME: Expressed by activated fibroblasts (myofibroblasts), THY1 can promote tumor progression, angiogenesis, and immune evasion in certain cancer contexts, often associated with a more aggressive CAF phenotype.
• Therapeutic Potential: Its role as a marker for activated fibroblasts makes it a potential target for selectively modulating CAF activity.
• Therapeutic Agents: Research is ongoing into antibodies or other agents that can target THY1 to inhibit CAF functions. [GeneCards THY1: GeneCards]

Clinical or Translational Implications

The distinct expression profiles of surfaceome markers between normal and tumor-associated fibroblasts in breast cancer have significant clinical and translational implications:

1. Diagnostic and Prognostic Biomarkers: Markers like FAP, GPNMB, MMP14, NRP1, and AXL, which are highly specific to CAFs in the primary tumor microenvironment, could serve as diagnostic or prognostic biomarkers. Their detection, for example via imaging or liquid biopsy, could indicate the presence and aggressiveness of breast cancer.
2. Therapeutic Targets: The identified CAF-specific surfaceome markers represent promising therapeutic targets. Targeting CAFs offers a strategy to overcome tumor resistance to conventional therapies, disrupt pro-tumorigenic signaling, and reprogram the immunosuppressive TME.
   • Direct Targeting: Molecules such as FAP, GPNMB, MMP14, NRP1, and AXL are amenable to direct targeting with various therapeutic modalities, including small molecule inhibitors, therapeutic antibodies, antibody-drug conjugates (ADCs), and even cell-based therapies like CAR-T cells.
   • Combination Therapies: Targeting CAFs often yields synergistic effects when combined with chemotherapy, radiation, or immunotherapy. For instance, modulating the ECM via MMP inhibition could enhance drug penetration, while FAP-targeted strategies could reduce immunosuppression, making tumors more susceptible to immunotherapies.
3. Understanding CAF Heterogeneity: The variability in marker expression across different patient samples within the primary tumor group suggests heterogeneity among CAFs. Further studies could stratify patients based on these CAF marker profiles to develop personalized treatment strategies that target specific CAF subpopulations.
4. Monitoring Treatment Response: Changes in the expression of these CAF markers could serve as indicators of treatment response, providing valuable information about the modulation of the TME by therapeutic interventions.

In summary, the identification of these specific surfaceome markers on breast cancer-associated fibroblasts provides a rich resource for developing novel diagnostic tools and targeted therapies to improve patient outcomes.

17. Differential Expression of Cell Cycle Pathway Genes in Breast Cancer Epithelial Cells

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the differential expression of a curated set of cell cycle pathway genes in Epithelial cells, comparing primary_tumor samples against normal samples from breast tissue. The expression is quantified as the "expressing cell fraction (sample)," representing the proportion of cells within each sample expressing a given gene. Box plots visualize the distribution of this fraction across samples for each condition, with statistically significant differences (p-value < 0.05) highlighted. This helps to identify key cell cycle regulators that are dysregulated in breast cancer epithelial cells, the inferred cell of origin for this tumor type.

Visual Summary

The box plots reveal widespread and statistically significant dysregulation of cell cycle-related genes in Epithelial cells when comparing primary tumor tissue to normal breast tissue.

Key observations:

• Genes significantly upregulated in primary tumor epithelial cells (p < 0.05): A substantial number of genes show a significantly higher fraction of expressing cells in the primary_tumor condition compared to normal. These include:

YWHAZ (p=0.000148)

CDKN2D (p=0.000828)

CDKN2A (p=0.00163)

GADD45G (p=0.00229)

WEE1 (p=0.00771)

CDKN1A (p=0.0133)

TFDP2 (p=0.0149)

RAD21 (p=0.029)

CDKN1C (p=0.0329)

MDM2 (p=0.0363)

E2F3 (p=0.042)

CREBBP (p=0.0454)

For these genes, the median expressing cell fraction is notably higher in primary_tumor samples, often with tighter clustering of higher values, suggesting a more consistent upregulation across tumor samples.

• Genes significantly upregulated in normal epithelial cells (p < 0.05), or equivalently, downregulated in primary tumor cells:

CCND3 (p=0.00306)

E2F4 (p=0.00884)

These genes exhibit a higher expressing cell fraction in normal tissue, indicating their relative downregulation or reduced cell-expressing frequency in tumor epithelial cells.

• Genes showing a trend of upregulation in normal cells (p > 0.05, but close to significance):

CDKN1B (p=0.051)

TP53 (p=0.0592)

While not reaching the conventional p-value cutoff of 0.05, both CDKN1B and TP53 show a trend towards a higher expressing cell fraction in normal cells compared to primary_tumor, suggesting a potential reduction in their expression or prevalence in tumor cells.

Biological Interpretation

The observed differential expression of cell cycle genes in breast cancer epithelial cells strongly reflects the characteristic dysregulation of cell proliferation and genomic stability pathways in cancer. As Epithelial cells are the resolved tumor-origin cell type in this dataset, these findings are directly relevant to breast carcinogenesis.

Promotion of Cell Proliferation and Overcoming Checkpoints:

• The significant upregulation of E2F3 and TFDP2 in primary tumor cells is critical. E2F transcription factors, often in heterodimerization with DP proteins (like TFDP2), regulate genes essential for cell cycle progression from G1 to S phase. Overexpression of E2F3, a known oncogene, frequently leads to uncontrolled cell proliferation (GeneCards: E2F3, TFDP2).
• MDM2 upregulation is a strong indicator of disrupted TP53 pathway. MDM2 is an E3 ubiquitin ligase that targets TP53 for degradation, thus inhibiting its tumor suppressive functions. High MDM2 expression in tumor cells effectively neutralizes TP53, allowing uncontrolled cell growth despite potential DNA damage (GeneCards: MDM2).
• WEE1 kinase, which inhibits CDK activity and promotes G2/M arrest, is also significantly upregulated. While initially appearing counter-intuitive, WEE1 overexpression in cancer can contribute to checkpoint activation, allowing damaged cells to repair and survive, thus conferring resistance to DNA-damaging therapies (PubMed Search: WEE1 cancer).

Altered Cell Cycle Inhibitors and Tumor Suppressors:

• CDKN2D (p19INK4d) and CDKN2A (p16INK4a) are cyclin-dependent kinase inhibitors (CDKIs) and tumor suppressors. Their upregulation in tumor cells, particularly CDKN2A which is a well-known tumor suppressor, might reflect a compensatory mechanism where tumor cells attempt to engage cell cycle arrest pathways in response to oncogenic stress. However, these genes are frequently inactivated by mutation or epigenetic silencing in cancer, suggesting that while their RNA expression might be high, their protein function could be compromised or overridden by stronger oncogenic signals (GeneCards: CDKN2A, CDKN2D).
• Similarly, CDKN1A (p21) and CDKN1C (p57KIP2) are CDKIs. Their upregulation could signify persistent cell cycle stress or an attempt by the tumor to induce senescence or a transient arrest. However, their efficacy can be compromised in the oncogenic context (GeneCards: CDKN1A, CDKN1C).
• In contrast, CCND3 (Cyclin D3) shows higher expression in normal cells. Cyclin D proteins are crucial for G1-S transition, and while often oncogenic when overexpressed, specific cyclins can have complex roles. Its relative decrease in tumor cells might reflect a shift in cyclin usage or cell cycle dynamics specific to breast cancer.
• E2F4 and CDKN1B (p27Kip1) show higher expressing fractions in normal cells. E2F4 is often involved in cell cycle repression, especially in quiescent cells, and CDKN1B is a well-established tumor suppressor that inhibits CDK activity. Their relative lower expression in tumor cells aligns with the loss of cell cycle control and proliferation characteristic of cancer (GeneCards: E2F4, CDKN1B).
• The trend for TP53 to be lower in tumor cells is highly significant. TP53 is a critical tumor suppressor, and its loss of function (often via mutation or degradation by MDM2) is a hallmark of cancer progression. Reduced TP53 expression would severely impair DNA damage response and apoptotic pathways (GeneCards: TP53).

DNA Damage Response and Genomic Instability:

• GADD45G (Growth Arrest and DNA Damage-inducible protein gamma) and RAD21 (a cohesin complex component) are upregulated in primary tumor cells. GADD45 proteins are involved in DNA repair and cell cycle arrest, and their elevation might indicate ongoing DNA damage and an activated stress response in tumor cells. RAD21 is crucial for proper chromosome segregation and DNA repair, and its dysregulation can contribute to genomic instability, a key feature of cancer (GeneCards: GADD45G, RAD21).

Transcriptional Co-activation:

• CREBBP (CBP, CREB-binding protein), a histone acetyltransferase, is upregulated in tumor cells. CREBBP acts as a transcriptional coactivator for many transcription factors, including E2Fs and TP53. Its dysregulation can impact gene expression broadly, potentially contributing to oncogenic programs (GeneCards: CREBBP).

Clinical or Translational Implications

The differential expression of these cell cycle-related genes in breast cancer epithelial cells offers several clinical and translational implications:

• Therapeutic Targets: Genes significantly upregulated in primary tumors, such as MDM2 and WEE1, represent promising therapeutic targets. MDM2 inhibitors are being developed to restore TP53 function in cancers with wild-type TP53. WEE1 inhibitors are also under investigation to sensitize cancer cells to DNA-damaging agents by abrogating cell cycle checkpoints (PubMed Search: MDM2 inhibitors cancer, WEE1 inhibitors cancer).
• Biomarkers: The altered expression patterns of these genes could serve as prognostic or diagnostic biomarkers for breast cancer. For example, high expression of MDM2 or WEE1, or reduced expression of CDKN1B or E2F4, might indicate a more aggressive tumor phenotype or predict response to specific therapies.
• Understanding Tumor Biology: Characterizing the precise mechanisms by which these cell cycle regulators are dysregulated in breast cancer epithelial cells can provide deeper insights into tumor initiation, progression, and resistance to treatment. This knowledge can inform the development of novel therapeutic strategies.
• Drug Resistance Mechanisms: The upregulation of DNA damage response genes like GADD45G and RAD21, alongside checkpoint regulators like WEE1, highlights the tumor's adaptive strategies to survive replication stress and DNA damage, which are often exacerbated by chemotherapy. Targeting these pathways could overcome drug resistance.

18. Gene Ontology Enrichment Analysis for Epithelial Cells Across Different Conditions in Breast Tissue

[Analysis Visualization Results]...

Analysis Overview

This report presents the results of a Gene Ontology (GSA) enrichment analysis performed on 'Epithelial cell' populations within human breast tissue. The analysis identifies pathways and biological processes significantly enriched among up-regulated differentially expressed genes (DEGs) for three distinct comparison contexts:

• Diploid_vs_others: Epithelial cells identified as Diploid compared against all other Epithelial cells (which would include normal, aneuploid, and primary tumor cells).
• normal_vs_others: Epithelial cells from 'normal' tissue compared against all other Epithelial cells (which would include diploid, aneuploid, and primary tumor cells).
• primary_tumor_vs_others: Epithelial cells from 'primary_tumor' tissue compared against all other Epithelial cells (which would include normal and diploid cells).

The results are visualized as bar plots, where the length of the bars represents the statistical significance of enrichment for each term, based on -log(p-val) and -log(q-val) (FDR-adjusted p-value). Higher values indicate stronger and more reliable enrichment. This analysis aims to highlight distinct biological characteristics of epithelial cells in different physiological and pathological states within the breast.

Visual Summary

GSA for Epithelial cell: Diploid_vs_others

The top enriched pathways in diploid epithelial cells, when compared to all other epithelial cells, include a prominent set of terms related to various viral infections (e.g., "Coronavirus disease", "Kaposi sarcoma-associated herpesvirus infection", "Human papillomavirus infection", "Epstein-Barr virus infection", "Human cytomegalovirus infection", "Viral carcinogenesis"). General cellular processes like "Ribosome" and pathways implicated in cancer such as "Proteoglycans in cancer" and "Focal adhesion", along with immune signaling like "TNF signaling pathway", are also highly represented.

GSA for Epithelial cell: normal_vs_others

Similar to the diploid comparison, epithelial cells from normal tissue, when compared to other epithelial cells, show strong enrichment for "Ribosome" and "Proteasome", indicating active protein synthesis and degradation. A broad array of neurodegenerative disease pathways (e.g., "Parkinson disease", "Huntington disease", "Alzheimer disease") is also highly ranked. Viral and bacterial infection pathways (e.g., "Coronavirus disease", "Salmonella infection", "Shigellosis") and immune/growth signaling pathways ("TNF signaling pathway", "Pathways in cancer", "PI3K-Akt signaling pathway", "Apoptosis") are also significantly enriched, largely mirroring the "Diploid_vs_others" profile.

GSA for Epithelial cell: primary_tumor_vs_others

The enrichment profile for epithelial cells from primary tumors, when compared to all other epithelial cells (normal/diploid), presents a markedly different and highly specific signature. The most highly enriched terms point towards metabolic alterations and cellular stress responses: "Thermogenesis", "Oxidative phosphorylation", "Non-alcoholic fatty liver disease", "Diabetic cardiomyopathy", "Protein processing in endoplasmic reticulum", "Lysosome", "Autophagy", "Ubiquitin mediated proteolysis". Key signaling pathways such as "mTOR signaling pathway" and "AMPK signaling pathway" are also prominent. Infectious disease terms are notably less dominant in this context compared to the normal/diploid comparisons.

Biological Interpretation

• Shared Biology of Normal and Diploid Epithelial Cells: The significant overlap in enriched pathways between 'Diploid_vs_others' and 'normal_vs_others' suggests that these two populations share fundamental cellular characteristics. The high enrichment for "Ribosome" and "Proteasome" terms indicates robust protein synthesis and degradation machinery, essential for maintaining cellular homeostasis. The consistent appearance of numerous viral infection pathways and immune-related signaling (e.g., "TNF signaling pathway") might reflect the baseline immune responses of healthy epithelial cells or their interaction with the tissue microenvironment, potentially highlighting cellular processes that are *less active* in tumor cells (which are part of the 'others' comparison group). The presence of neurodegenerative disease terms might represent shared fundamental cellular processes such as protein misfolding or mitochondrial dysfunction that, when perturbed, contribute to both aging-related diseases and potentially stress responses in breast epithelial cells.
• Distinct Metabolic and Proliferative Landscape of Primary Tumor Epithelial Cells: The Gene Ontology analysis of primary tumor epithelial cells reveals a profound shift in their biological priorities, consistent with the known hallmarks of cancer.
  • Metabolic Reprogramming: The strong enrichment of terms like "Oxidative phosphorylation," "Non-alcoholic fatty liver disease," and pathways involved in amino acid metabolism (e.g., "Valine, leucine and isoleucine degradation") signifies extensive metabolic reprogramming. Tumor cells often reconfigure their metabolism to support rapid proliferation and biomass accumulation, including altered energy production and nutrient utilization [PubMed Search]. "Thermogenesis" might reflect altered mitochondrial function or energy expenditure.
  • Protein Homeostasis and Stress Response: "Protein processing in endoplasmic reticulum" and "Ubiquitin mediated proteolysis" highlight heightened activity in protein quality control and degradation, crucial for cancer cells coping with increased protein synthesis demands and cellular stress. "Autophagy" and "Lysosome" pathways further underscore active recycling of cellular components to sustain growth under nutrient deprivation or stress.
  • Growth and Survival Signaling: The prominent enrichment of "mTOR signaling pathway" [NCBI] and "AMPK signaling pathway" [NCBI] indicates dysregulation of central pathways that integrate nutrient status, energy levels, and growth signals to promote cell proliferation and survival.
  • Reduced Infection/Immune Signature: The relative lack of enrichment for infection-related pathways in tumor epithelial cells, contrasting with normal/diploid cells, could suggest an immune-evasive phenotype or a fundamental shift in cellular priorities away from pathogen defense towards unchecked proliferation and survival.

Clinical or Translational Implications

• Biomarker Discovery and Diagnostics: The unique and highly significant enrichment of metabolic and growth-related pathways in primary tumor epithelial cells offers promising avenues for discovering novel biomarkers. These pathways could be exploited for early detection, monitoring disease progression, or predicting treatment response in breast cancer.
• Therapeutic Targets: Pathways such as mTOR signaling and oxidative phosphorylation are well-recognized as therapeutic targets in various cancers, including breast cancer. Inhibitors of mTOR have been explored for breast cancer treatment. Targeting the observed metabolic reprogramming or components of the protein quality control system (e.g., ER stress pathways, autophagy) could provide new therapeutic strategies to selectively eliminate or inhibit the growth of breast cancer cells [PubMed Search].
• Understanding Tumor Heterogeneity: The distinct biological signatures underscore the importance of distinguishing between normal, diploid, and primary tumor epithelial cells at a molecular level, providing insight into the cellular processes driving breast cancer progression.

19. Gene Set Enrichment Analysis (GSEA) of Pathways Across Cell Types in Breast Primary Tumor

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results, comparing gene expression profiles in specific cell types under primary tumor conditions against other conditions or normal states. The dot plot visualizes the enrichment of 120 selected pathways across various cell types, with each cell type compared against "others" (either normal tissue or other cell types within the primary tumor). The size of each dot represents the statistical significance of the enrichment (-log10(p-value)), and the color indicates the Normalized Enrichment Score (NES): red signifies upregulation (positive NES), while blue signifies downregulation (negative NES). This allows for the identification of functionally enriched biological processes and pathways that are differentially regulated in distinct cell populations within the breast tumor microenvironment.

Visual Summary

The dot plot effectively illustrates the complex interplay of biological pathways across different cell types and conditions.

• Widespread Pathway Alterations in Tumor Conditions: A striking observation is the pervasive upregulation (red dots) of numerous signaling pathways and cancer-related terms in the primary_tumor_vs_others comparisons across most cell types (Epithelial cell, Fibroblast, ILC, Macrophage, Endothelial cell, Smooth muscle cell). This indicates a broad activation of pro-tumorigenic processes within the primary tumor microenvironment.
• High Significance for Cancer-Associated Pathways: Many cancer-related pathways, such as "Pathways in cancer", "Breast cancer", "MicroRNAs in cancer", "ECM-receptor interaction", "Focal adhesion", "PI3K-Akt signaling pathway", "MAPK signaling pathway", "Wnt signaling pathway", and "VEGF signaling pathway", show strong and significant upregulation (large red dots) in the primary_tumor_vs_others conditions, particularly in Epithelial cells, Fibroblasts, Macrophages, and Endothelial cells.

Cell-Type Specific Signatures

• Epithelial cells: The Epithelial cell: primary_tumor_vs_others condition shows pronounced enrichment for classic cancer pathways, indicating active proliferation, altered metabolism, and evasion of growth suppressors. Notably, "Breast cancer", "Pathways in cancer", "PI3K-Akt signaling pathway", "MAPK signaling pathway", "Wnt signaling pathway", "VEGF signaling pathway", "Hippo signaling pathway", and "p53 signaling pathway" are highly enriched. In contrast, Epithelial cell: Diploid_vs_others shows much less or even negative enrichment for many of these cancer pathways, suggesting that these diploid epithelial cells, even within the tumor context, might retain more normal-like characteristics or represent a less aggressive subpopulation compared to the overall tumor epithelial cell population.
• Fibroblasts: Fibroblast: primary_tumor_vs_others exhibits strong upregulation of pathways associated with extracellular matrix (ECM) remodeling, cell adhesion, and growth factor signaling, including "ECM-receptor interaction", "Focal adhesion", "Hippo signaling pathway", "Wnt signaling pathway", "Cytokine-cytokine receptor interaction", "PI3K-Akt signaling pathway", "MAPK signaling pathway", and "TGF-beta signaling pathway". These pathways are characteristic of cancer-associated fibroblasts (CAFs).
• Macrophages: Macrophage: primary_tumor_vs_others shows significant enrichment for immune-related and inflammatory pathways, such as "Cytokine-cytokine receptor interaction", "MAPK signaling pathway", "PI3K-Akt signaling pathway", "T cell receptor signaling pathway", and "Fc gamma R-mediated phagocytosis", consistent with their role in immune modulation and tumor progression.
• Endothelial cells: Endothelial cell: primary_tumor_vs_others demonstrates strong enrichment in "VEGF signaling pathway" (critical for angiogenesis), "ECM-receptor interaction", "Focal adhesion", "Cytokine-cytokine receptor interaction", and "Leukocyte transendothelial migration", reflecting their involvement in tumor vascularization and immune cell infiltration.

• Specific Downregulated Pathways: Fewer pathways show consistent strong downregulation (blue dots), but some like "Collecting duct acid secretion", "Renin secretion", and certain metabolic pathways appear downregulated in specific tumor cell contexts.

Biological Interpretation

The GSEA results provide clear biological insights into the molecular landscape of the breast primary tumor microenvironment. The broad upregulation of oncogenic pathways in tumor-associated cell types confirms the presence of active cancer hallmarks.

1. Epithelial Tumor Cells Drive Malignancy: The highly enriched cancer-related pathways (e.g., "Breast cancer", "Pathways in cancer", "PI3K-Akt", "MAPK", "Wnt", "VEGF", "Hippo", "p53" signaling) in Epithelial cell: primary_tumor_vs_others strongly indicate that these cells are the primary drivers of tumor growth and progression. The comparison with Diploid_vs_others epithelial cells further suggests that the overall epithelial compartment in the primary tumor context is dominated by cells exhibiting a transformed phenotype, likely due to aneuploidy or other oncogenic events.
2. Remodeling of the Tumor Microenvironment (TME):
   • Cancer-Associated Fibroblasts (CAFs): The strong activation of ECM-related (e.g., "ECM-receptor interaction", "Focal adhesion") and growth factor signaling pathways (e.g., "TGF-beta signaling pathway") in fibroblasts highlights their role in remodeling the TME. CAFs contribute to tumor stiffness, promote invasion and metastasis, and influence immune evasion [1].
   • Tumor-Associated Macrophages (TAMs): Macrophages showing enrichment in cytokine and MAPK/PI3K-Akt signaling pathways, along with phagocytosis, are consistent with their known pro-tumorigenic roles. TAMs often polarize to an M2-like phenotype, promoting angiogenesis, immunosuppression, and metastasis [2].
   • Angiogenesis and Vascularization: Upregulation of "VEGF signaling pathway" in endothelial cells is a direct indicator of active angiogenesis, essential for providing nutrients and oxygen to the rapidly growing tumor [3]. The enrichment of "Leukocyte transendothelial migration" also suggests active immune cell trafficking, which can be either anti- or pro-tumorigenic depending on the immune cell type and context.
3. Key Oncogenic Pathways: The recurring enrichment of "PI3K-Akt signaling pathway" [4], "MAPK signaling pathway" [5], "Wnt signaling pathway" [6], and "VEGF signaling pathway" [3] across multiple tumor-associated cell types underscores their central roles in breast cancer pathophysiology, regulating cell proliferation, survival, migration, and angiogenesis. "Hippo signaling pathway" dysregulation is also frequently observed in cancer, influencing organ size, proliferation, and apoptosis [7].
4. Immune Cell Engagement: While specific immune cell types like T cells and B cells are present in the dataset, the ILCs also show some pathway alterations, indicating their involvement in the immune response within the TME, though their specific role requires further investigation.

Clinical or Translational Implications

The comprehensive GSEA results point towards several potential clinical and translational implications for breast cancer:

• Therapeutic Targeting: The consistently upregulated signaling pathways (e.g., PI3K-Akt, MAPK, Wnt, VEGF, Hippo) across tumor epithelial cells and key stromal components (fibroblasts, macrophages, endothelial cells) represent attractive targets for multi-modal cancer therapy. Inhibitors against these pathways are already in clinical use or trials for various cancers, and these findings support their potential utility in breast cancer, potentially in combination to target different components of the TME.
• Biomarker Discovery: The specific enrichment profiles for different cell types could yield novel biomarkers for prognosis or therapeutic response. For example, high activity of ECM-related pathways in CAFs or specific inflammatory pathways in TAMs could indicate more aggressive disease or responsiveness to stromal-targeting agents.
• Understanding Treatment Resistance: Persistent activation of these pathways could contribute to treatment resistance, suggesting that targeting the TME in conjunction with cancer cells might be crucial for overcoming resistance.
• Personalized Medicine: Future studies could leverage such cell-type-specific pathway enrichment profiles to tailor treatments to individual patient tumors, based on the unique composition and activation status of their tumor microenvironment.

---

References:

1. Cancer-Associated Fibroblasts:

PubMed Search: "cancer associated fibroblasts breast cancer extracellular matrix"

2. Tumor-Associated Macrophages (TAMs):

PubMed Search: "tumor associated macrophages breast cancer M2 polarization"

3. VEGF Signaling and Angiogenesis:

GeneCards: VEGFA

4. PI3K-Akt Signaling:

GeneCards: AKT1

5. MAPK Signaling Pathway:

GeneCards: MAPK1

6. Wnt Signaling Pathway:

GeneCards: WNT1

7. Hippo Signaling Pathway:

GeneCards: YAP1

20. Discussion

The comprehensive single-cell analysis of human breast tissue reveals a deeply altered cellular landscape in primary breast tumors compared to normal tissue, encompassing genomic instability, profound remodeling of the tumor microenvironment (TME), and intricate changes in intercellular communication.

Epithelial Cell Transformation and Genomic Instability: A central finding is the robust identification of epithelial cells as the tumor-origin cells, characterized by widespread aneuploidy in primary tumor samples (Section 2, 4, 10). This genomic instability, manifesting as recurrent amplifications on chromosomes 1q, 7q (including EGFR), 8q, and 11q13, is a hallmark of malignancy and drives uncontrolled proliferation (Section 4, 17). The differential expression of cell cycle genes further illustrates this dysregulation, with tumor epithelial cells showing upregulation of oncogenic drivers (e.g., E2F3, TFDP2) and mechanisms to circumvent tumor suppression (e.g., MDM2-mediated TP53 degradation, WEE1 upregulation for checkpoint adaptation), while key tumor suppressors like CCND3, E2F4, CDKN1B, and TP53 are relatively downregulated or reduced in expression frequency compared to normal cells (Section 17). Gene Ontology analysis further supports a metabolic shift, with primary tumor epithelial cells exhibiting strong enrichment in oxidative phosphorylation, protein processing in the ER, and mTOR signaling, indicative of high metabolic demand and stress adaptation, contrasting with the baseline functions and potential viral responses observed in normal/diploid epithelial cells (Section 18).

Tumor Microenvironment Remodeling: The TME undergoes extensive reprogramming, evident in shifts in cell type proportions and activation states. Tumor samples show increased infiltration of fibroblasts, macrophages, and specific T cell subsets (Section 6). Fibroblasts differentiate into Cancer-Associated Fibroblasts (CAFs), marked by high expression of FAP, MMP14, GPNMB, NRP1, and AXL, which collectively contribute to extracellular matrix remodeling, angiogenesis, and immunosuppression (Section 16). Macrophages skew dramatically towards an M2-like, pro-tumorigenic phenotype, with prominent upregulation of surface markers such as SIRPA, CXCR4, EREG, ITGB8, and IL6R, all contributing to immune evasion, recruitment, and tumor growth (Section 9, 15). The T cell compartment also shifts, with increased proportions of pro-tumorigenic Th17 and Th2 cells, and a decrease in LTI cells, which are crucial for lymphoid tissue organization, suggesting an environment conducive to immune suppression and tumor progression (Section 7, 8).

Dysregulated Cell-Cell Interactions: The cell-cell interaction landscape is dramatically rewired in primary tumors, characterized by a higher density and strength of interactions, particularly involving aneuploid epithelial cells, fibroblasts, and macrophages (Section 11, 12, 13). Key upregulated interactions include:

1. ECM-Integrin Axis: Extensive interactions involving Fibronectin/Collagen and various Integrin complexes (e.g., FN1-integrin_aXbY_complex, COLXA-integrin_a1b1_complex) between epithelial cells and fibroblasts, facilitating tumor invasion and metastasis (Section 11, 12).
2. Growth Factor Signaling: Elevated VEGFA-receptor (angiogenesis), HBEGF-EGFR, AREG-EGFR (proliferation/survival), and FGF2-FGFR1 interactions, driving tumor growth and stromal support (Section 11, 12, 13).
3. Immune Modulation & Inflammation: Enhanced SPP1-CD44, CXCL12-CXCR4, and IL6-IL6R signaling, particularly involving macrophages and fibroblasts, promoting immune evasion and chronic inflammation (Section 11, 12, 13). Notably, the SIRPA-CD47 axis on macrophages contributes significantly to immune evasion (Section 15). TGF-beta signaling (TGFB1-Integrin/TGFβR) is also highly active between aneuploid epithelial cells and fibroblasts, fostering an immunosuppressive and pro-fibrotic environment (Section 13).
4. Developmental Pathways: Dysregulated WNT signaling (WNT2, WNT5A, WNT5B) involving aneuploid epithelial cells, and DLK1-NOTCH2 interactions (Epithelial-Fibroblast), contribute to tumor cell stemness and proliferation (Section 11, 12).

In conclusion, this integrated analysis paints a comprehensive picture of breast cancer at single-cell resolution. It highlights the profound genomic and transcriptional reprogramming of malignant epithelial cells, their ability to subvert intrinsic cell cycle controls, and their complex interplay with a highly remodeled and often immunosuppressive TME. These findings underscore the intricate mechanisms driving breast cancer progression and provide a rich basis for identifying novel diagnostic biomarkers and developing multi-pronged therapeutic strategies targeting both cancer cells and their supporting microenvironment.

Hypotheses:

1. Aneuploid epithelial cells in primary breast tumors exhibit distinct metabolic vulnerabilities (e.g., reliance on oxidative phosphorylation or specific protein processing pathways) compared to normal epithelial cells, making them susceptible to targeted metabolic therapies.
2. The upregulation of pro-tumorigenic T cell subsets (Th17, Th2) and the M2-polarization of macrophages in the primary breast tumor microenvironment are directly driven by specific ligand-receptor interactions involving tumor epithelial cells and/or CAFs, promoting immune evasion.
3. Targeting the highly active ECM-integrin signaling axis in cancer-associated fibroblasts will disrupt tumor cell invasion and metastasis, thereby improving therapeutic outcomes in breast cancer.
4. The co-expression and interaction of multiple growth factor receptor pathways (e.g., EGFR, FGFR, VEGFR, AXL) within the tumor microenvironment (epithelial cells, fibroblasts, endothelial cells) contribute to compensatory signaling loops, leading to resistance to single-agent targeted therapies.
5. The reduced presence of LTI cells in primary breast tumors correlates with impaired formation of anti-tumorigenic tertiary lymphoid structures, contributing to a less effective immune response.

Potential therapeutic targets:

1. ERBB3 (HER3) on Epithelial Cells: Overexpressed in primary tumor epithelial cells, ERBB3 forms heterodimers with other HER family members (e.g., HER2) to activate the PI3K/Akt pathway, driving proliferation, survival, and drug resistance in breast cancer. Evidence: Section 14 highlights ERBB3 as an established therapeutic target, significantly overexpressed in primary tumor epithelial cells. GSEA also shows enrichment of PI3K-Akt signaling in epithelial tumor cells (Section 19). Validation: Clinical trials with anti-ERBB3 monoclonal antibodies (e.g., patritumab, seribantumab) in breast cancer, often in combination, demonstrate its relevance. *In vitro* testing in breast cancer cell lines and *in vivo* PDX models could confirm sensitivity to ERBB3 blockade.
2. FAP (Fibroblast Activation Protein) on Cancer-Associated Fibroblasts (CAFs): FAP is highly specific to activated CAFs in the tumor microenvironment and drives ECM remodeling, promotes angiogenesis, and contributes to immunosuppression, all supporting tumor progression. Evidence: Section 16 identifies FAP as a key surfaceome marker highly expressed on primary tumor fibroblasts (CAFs) but not normal fibroblasts. CCI analysis (Section 12) shows extensive Fibroblast-Epithelial cell interactions mediated by ECM components. GSEA (Section 19) confirms ECM-receptor interaction and Focal adhesion pathway enrichment in fibroblasts. Validation: Preclinical studies using FAP inhibitors, FAP-targeted ADCs, or CAR-T cells to inhibit CAF functions and assess tumor growth, invasion, and immune infiltration in mouse models. Clinical trials targeting FAP in various solid tumors are ongoing.
3. SIRPA (Signal Regulatory Protein Alpha) on Tumor-Associated Macrophages (TAMs): Upregulated in primary tumor macrophages, SIRPA interacts with CD47 on tumor cells, providing a "don't eat me" signal, thereby inhibiting macrophage-mediated phagocytosis and contributing to immune evasion. Evidence: Section 15 identifies SIRPA as a key surfaceome marker significantly upregulated in primary tumor macrophages. Section 9 highlights the M2-polarization of macrophages, consistent with immunosuppressive roles. Validation: Preclinical studies with anti-CD47 antibodies or SIRPA-targeting agents to enhance macrophage phagocytosis of breast cancer cells *in vitro* and *in vivo*. Clinical trials for CD47/SIRPA blockade in various cancers, including breast cancer, are in progress.
4. MDM2 (Mouse Double Minute 2 Homolog) in Epithelial Cells with wild-type TP53: Upregulated in primary tumor epithelial cells, MDM2 acts as an E3 ubiquitin ligase that targets TP53 for degradation, effectively neutralizing its tumor suppressive functions. Inhibiting MDM2 can restore TP53 activity and induce apoptosis or cell cycle arrest in tumor cells. Evidence: Section 17 shows significant upregulation of MDM2 in primary tumor epithelial cells, alongside a trend for TP53 to be lower in tumor cells (implying functional inactivation). Validation: *In vitro* testing of MDM2 inhibitors (e.g., Nutlins, Idasanutlin) in breast cancer cell lines with wild-type TP53 to assess restoration of TP53 function and induction of apoptosis. *In vivo* studies in PDX models.
5. TGF-beta Signaling Pathway (via TGFB1/TGFbetaR/Integrin αvβ6) between Epithelial Cells and Fibroblasts/Macrophages: TGF-beta signaling is highly active in the primary tumor microenvironment, promoting tumor cell invasion, metastasis, angiogenesis, fibrosis, and immune suppression. The interaction between TGFB1 and integrin αvβ6 complex activates latent TGFB1, amplifying these pro-tumorigenic effects. Evidence: Section 13 identifies strong TGFB1-Integrin/TGFβR interactions between aneuploid epithelial cells, fibroblasts, and macrophages in primary tumors. GSEA (Section 19) also shows enrichment of "TGF-beta signaling pathway" in fibroblasts. Section 15 identifies ITGB8 (Integrin Beta 8), which can activate TGF-β, as upregulated in TAMs. Validation: Preclinical and clinical studies with TGF-beta pathway inhibitors (e.g., galunisertib) or integrin αvβ6 specific inhibitors/antibodies to reduce tumor progression, fibrosis, and immunosuppression.

Follow-up validation ideas:

1. Targeted metabolic inhibition assays: Conduct *in vitro* assays using breast cancer cell lines with high aneuploidy to test the sensitivity of tumor cells to inhibitors of oxidative phosphorylation, ER protein processing, or mTOR pathway, followed by *in vivo* validation in patient-derived xenograft (PDX) models.
2. Co-culture and perturbation experiments: Utilize co-culture systems of breast cancer epithelial cells with various immune and stromal cells (e.g., Th17/Th2 cells, M2 macrophages, CAFs) to identify specific ligand-receptor interactions (e.g., SPP1-CD44, CXCL12-CXCR4, EREG-EGFR, TGFB1-TGFbetaR) driving macrophage polarization or T cell differentiation. Perturb these interactions using blocking antibodies or gene editing (CRISPR-Cas9) and assess changes in immune cell phenotype and function (e.g., cytokine production, phagocytosis, T cell proliferation).
3. ECM-targeting studies: Employ 3D organoid or tumor spheroid models embedded in engineered ECM mimicking tumor stiffness. Test the impact of integrin inhibitors (e.g., cilengitide) or specific MMP14 inhibitors on tumor cell invasion and migration within these matrices. *In vivo*, evaluate the efficacy of these inhibitors in metastatic mouse models of breast cancer, assessing tumor burden and metastatic spread.
4. Multi-target therapy experiments: Design combinatorial therapy studies *in vitro* (cell lines) and *in vivo* (PDX models) using inhibitors against key upregulated growth factor receptors (e.g., EGFR, AXL, VEGFR) and/or their ligands (e.g., AREG, EREG), to determine if simultaneous blockade of multiple pathways overcomes single-agent resistance and achieves greater anti-tumor efficacy.
5. Spatial transcriptomics and immunohistochemistry: Perform spatial transcriptomics on primary tumor samples to map the precise location of LTI cells and tertiary lymphoid structures (TLS). Correlate LTI cell density and TLS presence with markers of immune activation and patient prognosis. Validate these findings using multi-color immunohistochemistry or immunofluorescence to quantify LTI cells and assess their functional state and proximity to tumor cells.

Limitations:

This single-cell RNA-seq analysis provides a snapshot of the cellular and molecular landscape. While robustly identifying differential states and interactions, it does not directly capture real-time cellular dynamics or spatial organization within the tissue, which are crucial for understanding complex biological processes like metastasis or immune cell killing. Furthermore, while CNV estimates indicate genomic instability, specific driver mutations or the exact clonal evolution within tumors cannot be definitively determined without full genome sequencing. The inferred disease context is provisional, pending further clinical correlation. Therapeutic target suggestions are based on *in silico* evidence and require extensive experimental and clinical validation; the exact clinical efficacy and patient subsets benefiting from these targets cannot be predicted from this dataset alone.

21. Query List

1. Show and save UMAPs for condition, sample, major cell type, minor cell type 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 Epithelial cell, show a CNV heatmap grouped by sample together with a summary of regions with significantly amplified copy numbers, and save the results.
5. Show and save UMAPs of CNV patterns colored by major cell type, minor cell type, ploidy results, condition, and sample in two columns.
6. Show and save a population bar plot of minor cell types.
7. Show and save a population bar plot of T cell subsets.
8. Show and save box plots of T cell subset populations with statistically significant differences between conditions, if any. Choose ncols based on the total number of panels.
9. Show and save a population bar plot of macrophage subsets.
10. Select Epithelial cell, show their ploidy populations as a bar plot, and save the result.
11. Show and save cell-cell interaction patterns involving Epithelial cells, fibroblasts, macrophages, T cells, and other relevant cell types. Select at most 80 cell-cell interactions per group.
12. Find cell-cell interactions involving Epithelial 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 Epithelial 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 Epithelial 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 Fibroblast in a dot plot, and save the result. Use only surfaceome markers, with at most 40 markers per condition. For each marker identified under each condition, using the provided dataset context, describe the associated signaling pathways, the role of the corresponding cell type in the tumor microenvironment, the marker’s potential as a therapeutic target, relevant therapeutic agents, and other pertinent information.
17. Among cell cycle pathway genes, select those with statistically significant expression differences between conditions in Epithelial 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 Epithelial 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.