SCODiA Report by MLBI Lab

Single-Cell Landscape of Breast Cancer: Cellular Heterogeneity, Genomic Alterations, and Therapeutic Opportunities

This analysis of single-cell RNA-seq data from breast tissue reveals significant cellular heterogeneity and distinct microenvironmental shifts between normal tissue and breast cancer subtypes (ER+, HER2+, TNBC). UMAP visualizations confirm the epithelial origin of aneuploid tumor cells, which form prominent clusters characterized by extensive copy number variations, including ERBB2 amplification in HER2+ cases. The tumor microenvironment exhibits dynamic changes in immune and stromal cell populations, with varying degrees of immune infiltration and specific macrophage polarization patterns across cancer subtypes. Condition-specific cell-cell interactions and marker expression profiles highlight critical pathways involved in tumor growth, immune evasion, and stromal remodeling, offering insights into subtype-specific therapeutic vulnerabilities.

Contents

  1. Dataset overview
  2. UMAP Visualization of Cell Identity, Ploidy, and Sample/Condition in Breast Cancer Single-Cell Data
  3. Major Cell Type Scores and Annotation on UMAP
  4. Marker Gene Expression Validation Across Breast Tissue Cell Subsets
  5. Copy Number Variation Analysis of Epithelial Cells in Breast Cancer Subtypes
  6. UMAP Visualization of Single-Cell CNV Patterns Across Cell Types, Ploidy, Conditions, and Samples
  7. Population Bar Plot of Minor Cell Types Across Breast Cancer Subtypes and Normal Tissue
  8. T Cell and Innate Lymphoid Cell Subset Composition Across Breast Cancer Subtypes and Normal Tissue
  9. T cell Subset Population Differences Across Breast Cancer Conditions
  10. Cell Population Analysis of Macrophage Subsets and Other Cell Types Across Breast Cancer Subtypes and Normal Tissue
  11. Macrophage Subset Population Changes Across Breast Cancer Conditions
  12. Ploidy Status of Epithelial Cells Across Breast Cancer Subtypes and Normal Breast Tissue
  13. Cell-Cell Interaction Patterns Across Breast Cancer Subtypes and Normal Tissue
  14. Condition-Specific Cell-Cell Interaction Patterns in Breast Cancer
  15. Condition-Specific Cell-Cell Interaction Patterns in Breast Cancer Subtypes
  16. Epithelial Cell Surfaceome Markers in Breast Cancer Subtypes
  17. Macrophage Surfaceome Marker Expression Across Breast Cancer Subtypes
  18. Fibroblast Condition-Specific Surfaceome Marker Expression in Breast Cancer
  19. T cell CD4+ Condition-Specific Surfaceome Marker Expression Analysis
  20. Differential Expression of Cell Cycle Pathway Genes in Epithelial Cells Across Breast Cancer Subtypes
  21. Gene Ontology (GSA) Analysis of Epithelial Cells in Breast Cancer Subtypes
  22. Gene Set Enrichment Analysis Reveals Condition- and Cell Type-Specific Pathway Alterations in Breast Cancer
  23. Discussion
  24. Query List

0. Dataset overview

Dataset Summary

Precomputed Results

1. UMAP Visualization of Cell Identity, Ploidy, and Sample/Condition in Breast Cancer Single-Cell Data

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents a series of UMAP (Uniform Manifold Approximation and Projection) plots generated from single-cell RNA-seq data of breast tissue, visualizing cell distribution based on various metadata categories: disease condition, sample origin, major cell type, minor cell type, ploidy status (diploid/aneuploid), and cell type subsets. The goal is to visually assess the clustering patterns and relationships between these biological characteristics within the high-dimensional gene expression space.

Visual Summary

Condition-based UMAP

The UMAP colored by condition shows a clear separation between cells from Normal tissue (light green), which generally cluster on the left and bottom-left regions, and cells from breast cancer subtypes (ER+, HER2+, TNBC), which largely occupy the central and right-hand side of the embedding. Among the cancer conditions:

Sample-based UMAP

The sample UMAP displays a high degree of intermixing of cells from different samples (indicated by the extensive color palette) across the major clusters. This suggests that the primary structure of the UMAP is driven by biological variation (e.g., cell type and disease status) rather than strong batch effects related to individual samples. While some regions might show enrichment for specific samples, the overall integration appears successful.

Major Cell Type UMAP

This plot reveals distinct and well-separated clusters for each celltype_major.

Minor Cell Type UMAP

Further refinement into celltype_minor maintains good separation. Within the broad major cell type clusters, finer populations become apparent.

Ploidy Status UMAP

The ploidy_dec UMAP provides critical insight into the malignant nature of cells.

Cell Type Subset UMAP

The celltype_subset UMAP presents the most granular level of cell type annotation.

Biological Interpretation

The UMAP visualizations provide a comprehensive overview of the cellular landscape in breast tissue across normal and cancerous conditions.

Annotation Notes

2. Major Cell Type Scores and Annotation on UMAP

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[Analysis Visualization Results]...

Analysis Overview

This analysis provides a visual representation of cell populations within the single-cell RNA-seq dataset from breast tissue, projected onto a Uniform Manifold Approximation and Projection (UMAP) embedding. The primary aim is to visualize the distribution of major cell type scores (derived using the HiCAT method), alongside the inferred ploidy status and the final assigned major cell type labels. This helps to assess the quality of cell type annotations, identify major cellular compartments, and understand the genomic stability (diploidy vs. aneuploidy) of these populations in the context of breast cancer.

Visual Summary

The UMAP plots display the spatial organization of 85,449 cells based on their gene expression profiles.

HiCAT Major Cell Type Scores:

Ploidy Status (ploidy_dec):

Major Cell Type Annotation (celltype_major):

Biological Interpretation

The UMAP visualization provides critical insights into the cellular landscape of the breast tissue samples.

  1. Tumor Cell Identification and Characterization: The strong co-localization of high HiCAT Epithelial cell scores with aneuploid cells on the UMAP strongly suggests that these epithelial cells represent the malignant tumor cell population. This is highly consistent with breast cancer being predominantly of epithelial origin (carcinoma) and the high prevalence of aneuploidy in cancer cells [PubMed Search: aneuploidy cancer]. The resolved tumor-origin cell type being Epithelial cell further supports this interpretation.
  2. Tumor Microenvironment (TME) Composition: The distinct clusters of stromal cells, endothelial cells, and various immune cell types (T cells, B cells, myeloid cells, mast cells) highlight the complex and heterogeneous nature of the tumor microenvironment. These non-epithelial cell populations appear largely diploid, as expected for host cells contributing to the TME rather than being transformed themselves.
  3. Robustness of Cell Type Annotation: The high concordance between the continuous HiCAT major cell type scores and the discrete celltype_major assignments reinforces the reliability and quality of the cell type annotations within this dataset. This consistency indicates that the underlying gene expression patterns robustly define these major cell identities.

Annotation Notes

3. Marker Gene Expression Validation Across Breast Tissue Cell Subsets

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents a marker expression dot plot generated for various celltype_subset populations identified in the single-cell RNA-seq dataset from human breast tissue. The primary goal of this visualization is to validate the accuracy and specificity of the assigned cell type annotations by examining the expression patterns of known and discovered marker genes. Each dot represents a gene within a specific cell type, with its color intensity reflecting the mean expression level and its size indicating the fraction of cells expressing that gene within the group. The markers were identified across all cell types (target_cell=None), and the gene labels were rotated by 45 degrees for improved readability.

Visual Summary

The dot plot displays cell type subsets on the y-axis and marker genes on the x-axis, grouped by the cell type they characterize. A prominent diagonal pattern of dark red, large dots is observed, with red boxes highlighting these enriched gene clusters for each cell type. This pattern signifies that specific sets of marker genes are highly and broadly expressed within their designated cell type subsets, demonstrating strong specificity.

The color scale on the right indicates mean expression in group, ranging from light red (low expression) to dark red (high expression). The dot size scale shows the fraction of cells in a group expressing the gene, with larger dots representing a higher percentage of expressing cells. Numbers on the far right provide the total cell count for each celltype_subset, with 'Luminal epithelial cell' being the most abundant (39969 cells).

Biological Interpretation

The observed marker expression patterns largely support the robust annotation of the various celltype_subset populations in the breast tissue dataset.

Immune Cell Subsets:

Non-Immune Cell Subsets:

Annotation Notes

The dot plot provides strong visual evidence that the celltype_subset annotations are accurate and well-supported by the expression of characteristic marker genes. The distinct diagonal pattern confirms that each identified subset possesses a unique and biologically coherent transcriptional signature. While some markers may show low-level expression in other cell types (off-diagonal dots), the overall specificity and enrichment within the annotated groups are high. This robust annotation is critical for downstream analyses, particularly given the complex cellular heterogeneity of breast tissue in both normal and pathological conditions.

4. Copy Number Variation Analysis of Epithelial Cells in Breast Cancer Subtypes

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents a comprehensive view of copy number variations (CNVs) specifically within Epithelial cells derived from single-cell RNA-seq data of breast tissue. The results are visualized through two heatmaps: the first displays the log2(Copy Number Ratio) across genomic spots for each sample, allowing for the identification of amplifications and deletions. The second heatmap summarizes regions with significantly amplified copy numbers, indicating their frequency within each sample and across all analyzed samples. The samples are grouped by their condition (Normal, ER+, HER2+, TNBC) and ploidy status, providing a detailed comparison of genomic alterations relevant to breast cancer.

Visual Summary

log2(CNR) Heatmap (Copy Number Ratio)

Subtype-Specific CNV Signatures

Summary of Significant Amplified Regions

Biological Interpretation

The observed CNV landscape in Epithelial cells offers crucial biological insights into the molecular underpinnings of breast cancer, aligning with the inferred disease context and observed aneuploidy.

Role of Other Amplified Oncogenes/Genes

Clinical or Translational Implications

The detailed CNV profiling of Epithelial cells in breast cancer has several important clinical and translational implications:

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

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents five UMAP (Uniform Manifold Approximation and Projection) plots, generated based on single-cell copy number variation (CNV) patterns. These visualizations map the high-dimensional CNV landscape of 85,449 single cells into two dimensions, where proximity indicates similarity in CNV profiles. Each plot is colored by a distinct metadata category (celltype_major, celltype_minor, ploidy_dec, condition, and sample) to reveal underlying cellular organization, ploidy status, disease context, and inter-sample variability informed by genomic alterations.

Visual Summary

Cell Type Distribution (celltype_major and celltype_minor)

Ploidy Status (ploidy_dec)

Condition-Specific Patterns (condition)

Sample-Level Heterogeneity (sample)

Biological Interpretation

The UMAPs, derived from CNV profiles, provide compelling insights into the cellular and genomic architecture of breast tissue in health and disease.

  1. Tumor Cell Identification and Purity: The strong segregation of "Aneuploid" cells from "Diploid" cells, with aneuploid cells largely overlapping with "Epithelial cell" populations, robustly confirms the identification of tumor cells. This aligns with the resolved tumor-origin cell type: Epithelial cell and the inferred disease: Breast Cancer, as carcinomas originate from epithelial cells and typically accumulate aneuploidies. Non-epithelial cells (stromal, immune) predominantly reside in the diploid regions, suggesting their normal, non-malignant state within the tumor microenvironment.
  2. Subtype-Specific Genomic Signatures: The distinct clustering of ER+, HER2+, and TNBC cells within the aneuploid space highlights that different breast cancer subtypes are characterized by unique underlying CNV landscapes. This genomic heterogeneity between subtypes likely contributes to their diverse biological behaviors and therapeutic responses. For instance, the more widespread distribution of ER+ aneuploid cells could indicate greater genomic diversity within this subtype, or more widespread involvement of ER+ cells in aneuploidy.
  3. Microenvironment Context: The consistent clustering of diverse immune and stromal cell types in the diploid regions, largely distinct from the aneuploid epithelial clusters, indicates that these populations maintain their normal genomic integrity even within the tumor-bearing tissue. This underscores the importance of analyzing the tumor microenvironment as a separate, albeit interacting, component.
  4. Inter-Patient Heterogeneity: The distinct clustering of cells from individual samples, particularly within the tumor conditions, illustrates significant inter-patient heterogeneity in CNV patterns. Even within the same breast cancer subtype, each patient's tumor can harbor unique genomic alterations, which is a known characteristic of cancer and crucial for personalized medicine approaches.

Annotation Notes

6. Population Bar Plot of Minor Cell Types Across Breast Cancer Subtypes and Normal Tissue

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents a stacked bar plot visualizing the proportional distribution of minor cell types within each sample, grouped by condition (ER+, HER2+, Normal, TNBC). The goal is to identify differences in cellular composition that characterize different breast cancer subtypes compared to normal breast tissue, and to observe sample-to-sample heterogeneity. The 'minor' cell type classification provides a granular view of cell populations.

Visual Summary

The stacked bar plots display the relative abundance of various minor cell types across individual samples, categorized by their respective conditions: ER+ breast cancer, HER2+ breast cancer, Normal breast tissue, and Triple-Negative Breast Cancer (TNBC).

Normal vs. Cancer Conditions:

Differences Among Cancer Subtypes:

Biological Interpretation

The observed cell type distributions provide critical insights into the tissue architecture and microenvironmental shifts in breast cancer.

Tumor Microenvironment (TME) Alterations:

Clinical or Translational Implications

The distinct cellular compositions observed across breast cancer subtypes have several clinical and translational implications:

7. T Cell and Innate Lymphoid Cell Subset Composition Across Breast Cancer Subtypes and Normal Tissue

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents a population bar plot illustrating the relative proportions of various T cell subsets and other lymphocyte populations (including Innate Lymphoid Cells and Natural Killer cells) within individual samples, grouped by tissue condition (Normal, ER+, HER2+, and TNBC breast cancer subtypes). Each stacked bar represents the total lymphocyte population for a given sample, with different colors indicating the proportional contribution of each specific cell subset as defined in the legend. The purpose is to identify shifts in immune cell composition associated with different breast cancer conditions compared to normal breast tissue.

Visual Summary

The stacked bar plot reveals distinct patterns in the composition of T cell and innate lymphoid cell (ILC) subsets across normal and breast cancer conditions:

Breast Cancer Subtypes (ER+, HER2+, TNBC):

Biological Interpretation

The observed population shifts provide critical insights into the immune landscape of breast cancer:

Clinical or Translational Implications

The findings have several important clinical implications for breast cancer:

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References

  1. Regulatory T cells in cancer:

PubMed search: regulatory T cells cancer immunity

  1. NK cells in cancer immunity:

PubMed search: NK cells breast cancer immunity

  1. Targeting Tregs for cancer immunotherapy:

PubMed search: targeting regulatory T cells immunotherapy cancer

8. T cell Subset Population Differences Across Breast Cancer Conditions

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportional representation of various T cell subsets across different breast cancer conditions (ER+, HER2+, TNBC) compared to normal breast tissue. Box plots illustrate the celltype proportion, and statistically significant differences between conditions are indicated by p-values. The reference condition for comparison is "Normal".

Visual Summary

The box plots reveal distinct patterns of T cell subset proportions across breast cancer subtypes and normal tissue, indicating varied immune microenvironments. Several T cell subsets show statistically significant alterations (p < 0.05) in their relative abundance:

Biological Interpretation

The observed shifts in T cell subset proportions highlight distinct immune microenvironments across breast cancer subtypes, offering crucial insights into their varied immunological landscapes and potential implications for disease progression and treatment response:

Context-Dependent T Helper Responses:

Clinical or Translational Implications

The differential distribution of T cell subsets across breast cancer subtypes suggests distinct immune evasion mechanisms and therapeutic vulnerabilities, which can inform personalized clinical strategies.

The overall analysis underscores the importance of characterizing the immune landscape at a single-cell resolution to develop more personalized and effective therapeutic strategies for breast cancer patients.

9. Cell Population Analysis of Macrophage Subsets and Other Cell Types Across Breast Cancer Subtypes and Normal Tissue

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents a population bar plot illustrating the relative proportions of various cell subsets (derived from celltype_subset) across individual samples, grouped by different breast cancer conditions (ER+, HER2+, TNBC) and Normal tissue. The visualization allows for a qualitative assessment of cellular composition shifts in the tumor microenvironment (TME) and normal breast tissue, with a particular focus on macrophage subsets as requested.

Visual Summary

The plot displays stacked bar charts for each sample, with distinct colors representing different cell subsets, as detailed in the comprehensive legend.

Tumor Conditions (ER+, HER2+, TNBC):

Condition-Specific Patterns:

Biological Interpretation

The observed cell population distributions provide critical insights into the breast cancer microenvironment:

Clinical or Translational Implications

10. Macrophage Subset Population Changes Across Breast Cancer Conditions

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportions of different macrophage subsets (Mac M2B, Mac M1, Mac M2D, and Mac M2C) across various breast tissue conditions: Normal, ER+ (Estrogen Receptor-positive), TNBC (Triple-negative breast cancer), and HER2+ (Human Epidermal Growth Factor Receptor 2-positive) breast cancer. The box plots display the distribution of celltype proportions for each macrophage subset within these conditions, with p-values indicating statistically significant differences (p < 0.1) between specific groups.

Visual Summary

The box plots reveal distinct patterns in macrophage subset proportions across the different breast conditions:

Mac (M2B) Proportions:

Mac (M1) Proportions:

Mac (M2D) Proportions:

Mac (M2C) Proportions:

Biological Interpretation

Macrophages are critical components of the tumor microenvironment (TME) and play diverse roles in cancer progression, largely dependent on their polarization state. Macrophages are broadly categorized into M1 (pro-inflammatory, anti-tumor) and M2 (anti-inflammatory, pro-tumor) phenotypes, though their functions are more nuanced and context-dependent.

  1. Shift from M2B to M1 Polarization in Breast Cancer:
    • The significant decrease in Mac (M2B) proportions across all breast cancer subtypes, coupled with a general increase in Mac (M1) proportions, suggests a notable shift in macrophage polarization within the breast TME. M2B macrophages are a less-defined M2 subset but are generally associated with immunomodulation and wound healing. Their reduction could indicate a disruption of normal tissue homeostasis or a repolarization into other macrophage subsets in the cancerous state.
    • The robust increase in Mac (M1) macrophages in all breast cancer subtypes, particularly in HER2+, is often associated with an anti-tumor immune response. M1 macrophages typically produce pro-inflammatory cytokines, express high levels of MHC class II and co-stimulatory molecules, and have potent phagocytic and cytotoxic capabilities against tumor cells. Their enrichment might represent an active immune effort to control tumor growth, or reflect a chronic inflammatory state that, depending on context, can also contribute to tumor progression.
  2. Subtype-Specific Macrophage Dynamics:
    • HER2+ Breast Cancer: The most pronounced increase in M1 macrophages is observed in HER2+ tumors. This M1 predominance might contribute to the relatively higher immune infiltration often reported in HER2+ cancers and could potentially influence response to therapies, particularly those with immunomodulatory effects. The observed borderline decrease in M2C macrophages in HER2+ further supports a shift away from immunosuppressive M2-like phenotypes.
    • TNBC and M2D Macrophages: The significant reduction in Mac (M2D) macrophages in TNBC is noteworthy. M2D macrophages are often implicated in promoting angiogenesis and immunosuppression within tumors. Their decreased presence in TNBC, a highly aggressive subtype known for significant immune infiltration but also frequent immune evasion, suggests a complex interplay of macrophage populations. It might imply that other M2 subtypes (not specifically M2D) or other immunosuppressive cells are dominant in TNBC, or that the immune response in TNBC is distinct from other subtypes.
  3. Complex Role of Macrophages in the TME:
    • The findings highlight the dynamic and heterogeneous nature of macrophages within the breast cancer microenvironment. While M1 macrophages are generally considered anti-tumoral, and M2 subtypes (like M2C, M2D) often pro-tumoral, the observed shifts indicate a complex immune landscape where different macrophage subsets are differentially regulated across various breast cancer subtypes. The TME can induce plasticity in macrophages, meaning their phenotype and function can change over time.

Clinical or Translational Implications

Understanding the precise distribution and functional shifts of macrophage subsets in different breast cancer types can have significant clinical implications:

Further research, including functional assays and spatial analyses, would be essential to elucidate the precise roles of these macrophage subsets in breast cancer progression and treatment response.

11. Ploidy Status of Epithelial Cells Across Breast Cancer Subtypes and Normal Breast Tissue

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[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the distribution of ploidy populations (Aneuploid, Diploid, and Unclear) specifically within the 'Epithelial cell' compartment across various conditions: Normal, Estrogen Receptor-positive (ER+), Human Epidermal Growth Factor Receptor 2-positive (HER2+), and Triple-Negative Breast Cancer (TNBC). The results are presented as stacked bar plots, with each bar representing an individual sample and showing the percentage of epithelial cells categorized as Aneuploid, Diploid, or Unclear. This provides insight into the genomic stability of epithelial cells, which are the resolved tumor-origin cell type in this dataset, under different disease contexts.

Visual Summary

The bar plots display distinct patterns of ploidy distribution among epithelial cells across the different breast conditions:

In summary, 'Normal' samples are consistently diploid, while breast cancer subtypes (ER+, HER2+, TNBC) are characterized by substantial to predominant aneuploidy within their epithelial cell populations, although with notable heterogeneity across individual patient samples.

Biological Interpretation

The observed ploidy patterns strongly corroborate the inferred biological context of breast cancer.

Clinical or Translational Implications

The findings regarding epithelial cell ploidy have several potential clinical and translational implications in the context of breast cancer:

12. Cell-Cell Interaction Patterns Across Breast Cancer Subtypes and Normal Tissue

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interaction (CCI) patterns using CellPhoneDB, comparing various cell types in Normal breast tissue with three distinct breast cancer subtypes: ER+, HER2+, and Triple-Negative Breast Cancer (TNBC). The focus is on interactions involving key components of the tumor microenvironment (TME), including Epithelial cells (distinguished by ploidy where available), Fibroblasts, Macrophages, T cells (CD4+ and CD8+), Endothelial cells, and Smooth muscle cells. The goal is to identify subtype-specific interaction signatures that may contribute to disease progression and identify potential therapeutic targets. The plots display the top 80 most significant cell-cell interactions per group, showing the mean expression level (log2(m), color intensity) and statistical significance (-log10(p), dot size) of ligand-receptor pairs.

Visual Summary

CCI for ER+

The ER+ breast cancer microenvironment is characterized by prominent interactions involving Macrophages and Epithelial cells (both diploid and aneuploid). Key interactions include APOE_TREM2_receptor and APP_CD74 across Macrophage-Epithelial cell pairs, suggesting active immune modulation and antigen presentation pathways. Strong macrophage-macrophage self-interactions are also observed, featuring LGALS9_P4HB, NAMPT_NOX2_complex, PPIA_BSG, and TYROBP_CD44, indicating robust intrinsic macrophage activity and potential pro-tumorigenic functions. Aneuploid Epithelial cells also show self-interactions and interactions with macrophages through these same pathways, hinting at direct communication contributing to tumor survival and immune evasion.

CCI for HER2+

The HER2+ subtype presents a highly complex and diverse interaction landscape with significant involvement of T cells (CD4+ and CD8+), Macrophages, and Aneuploid Epithelial cells. A broad array of chemokine signaling pathways are activated, such as CCL3_CCR1, CCL3L1_CCR1, CCL4_CCR1, and CCL5_CCR1, facilitating immune cell recruitment and crosstalk. Immune checkpoint interactions like CD86_CTLA4, LGALS9_HAVCR2 (Galectin-9-TIM-3 axis), and VSIR_HLA-F are notably present across various immune cell and tumor cell interactions, suggesting active immune evasion mechanisms. Pro-angiogenic pathways (VEGFA_VEGFR2, VEGFA_NRP1), plasminogen activation (PLAU pathways), and pro-metastatic factors like SPP1_integrin_aVb1_complex are also highly active, underscoring the aggressive nature of this subtype. Interestingly, lipid metabolism interactions (e.g., Cholesterol byDHCR7_RORA) are also observed. Aneuploid Epithelial cells are broadly engaged with T cells and macrophages through multiple pathways, indicating direct tumor-immune interplay.

CCI for Normal

Normal breast tissue exhibits interactions primarily related to tissue homeostasis, structural integrity, and basal physiological processes. The most prominent interactions are associated with various integrin complexes involving CDH1, COL (collagens), and FN1 (fibronectin) interacting with Endothelial cells, Diploid Epithelial cells, and Fibroblasts. These interactions are fundamental for cell-extracellular matrix (ECM) adhesion and maintaining tissue architecture. Growth factor signaling via HBEGF_EGFR, PDGFB_PDGFRA, and basal angiogenic signals like VEGFA_VEGFR2 are also present, reflecting normal tissue maintenance. Immune cell interactions are minimal in this baseline condition, focusing on structural and stromal components.

CCI for TNBC

The TNBC microenvironment is characterized by extensive and highly active interactions involving Fibroblasts, Macrophages, and T cells. There is a strong signature of ECM remodeling, with numerous integrin interactions (e.g., COL and FN1 integrin complexes) prominently displayed between Fibroblasts and other stromal/immune cells. Chemokine signaling is robust, including CCL2-CCR2, CCL3_CCR1, CXCL12-CXCR4, indicating significant immune cell recruitment and modulation. Immune checkpoints such as CD86_CTLA4 are also present. Pro-metastatic and pro-invasive pathways, including SPP1_integrin complexes and PLAUR_ADGRG6 (plasminogen activator, urokinase receptor), are highly active. Angiogenic pathways (VEGFA interactions) and multiple Wnt signaling components (WNT1_FZD8, WNT2_FZD4, WNT5A_FZD4) also show strong activity. The collective signature points to a highly inflammatory, pro-fibrotic, and immunosuppressive TME that supports aggressive tumor growth and metastasis.

Biological Interpretation

The analysis reveals distinct cell-cell interaction landscapes across breast cancer subtypes compared to normal tissue, reflecting their unique biological characteristics and TME compositions.

  1. Shift from Homeostasis to Pathogenesis: Normal tissue interactions are dominated by ECM and growth factor signaling essential for tissue maintenance. In contrast, all cancer subtypes show a dramatic shift towards immune-modulatory, pro-angiogenic, and pro-metastatic interactions.
  2. Macrophage Centrality in ER+ and Immune Evasion: In ER+, macrophages are a central hub, interacting extensively with both themselves and epithelial cells (including aneuploid tumor cells) through pathways like APOE_TREM2_receptor. TREM2, expressed on macrophages, can promote tumor growth and immune suppression by modulating the TME and macrophage polarization [1]. LGALS9_P4HB and TYROBP_CD44 further highlight macrophage involvement in immune regulation and tumor cell survival. This suggests that targeting macrophage function or specific macrophage-tumor cell interactions could be relevant in ER+ breast cancer.
  3. Complex Immune-Tumor Crosstalk in HER2+: HER2+ breast cancer displays a highly inflamed and immunogenic TME. The abundance of chemokine-receptor interactions (e.g., CCL-CCR1 family) indicates robust immune cell trafficking and communication. Critically, several immune checkpoints are active, notably CD86_CTLA4 and LGALS9_HAVCR2 (Galectin-9-TIM-3). These inhibitory pathways represent significant mechanisms by which HER2+ tumors may evade anti-tumor immunity, despite their otherwise immunogenic profile [2, 3]. The active VEGFA and SPP1 signaling, often involving aneuploid epithelial cells, further points to tumor angiogenesis and metastatic potential.
  4. Fibroblast-Driven, Immunosuppressive TME in TNBC: TNBC exhibits a profoundly desmoplastic and immunosuppressive TME, heavily shaped by fibroblasts and macrophages. The overwhelming presence of integrin interactions, particularly involving collagen and fibronectin, signifies extensive ECM remodeling by fibroblasts, a hallmark of TNBC that promotes tumor invasion and metastasis [4]. Chemokine axes, such as CXCL12-CXCR4 and CCL2-CCR2, are highly active, mediating the recruitment of tumor-associated macrophages (TAMs) and myeloid-derived suppressor cells (MDSCs) that contribute to immune suppression and angiogenesis [5]. The sustained activity of TGFB1_TGFbeta_receptorI reinforces the pro-fibrotic and immunosuppressive environment.
  5. Aneuploid Epithelial Cell Interactions as Tumor Drivers: Where explicitly resolved, aneuploid epithelial cells (putative tumor cells) actively participate in these interactions, especially with macrophages and T cells. For example, in HER2+, aneuploid epithelial cells engage with T cells via multiple pro-tumorigenic and immune-modulatory pathways. This underscores the direct role of tumor cells in shaping their surrounding microenvironment and dictating disease behavior.

Clinical or Translational Implications

The distinct CCI patterns offer potential avenues for therapeutic intervention and biomarker discovery:

  1. Targeting Immune Checkpoints: The presence of CD86-CTLA4 and LGALS9-HAVCR2 (Galectin-9-TIM-3) interactions in HER2+ and TNBC highlights opportunities for immune checkpoint blockade. While anti-CTLA4 therapies are established, targeting the Galectin-9/TIM-3 axis could offer novel therapeutic strategies or combination approaches, particularly in HER2+ where multiple immune checkpoints appear active [2, 3].
  2. Modulating Macrophage Function: The central role of macrophages in ER+ disease via APOE_TREM2_receptor and in TNBC/HER2+ via various pro-tumorigenic interactions (e.g., NAMPT_NOX2_complex) suggests that therapies aimed at reprogramming TAMs or blocking their interaction with tumor cells could be beneficial [1, 6].
  3. Disrupting ECM Remodeling and Fibrosis: The extensive integrin-mediated interactions and TGFB1 signaling in TNBC point to fibroblasts as critical drivers of disease. Targeting these interactions or pathways could inhibit ECM stiffening, reduce tumor invasion, and potentially improve drug delivery by normalizing the TME [4].
  4. Inhibiting Angiogenesis and Metastasis: VEGFA signaling (HER2+, TNBC) and SPP1_integrin interactions (HER2+, TNBC) represent established and emerging targets for anti-angiogenic and anti-metastatic therapies, respectively. Inhibiting the CXCL12-CXCR4 axis in TNBC could specifically impede tumor cell migration and immune suppression [5].
  5. Subtype-Specific Therapeutic Strategies: The observed differences underscore the need for subtype-specific approaches. For instance, ER+ might benefit more from macrophage-centric therapies, while HER2+ could respond to broader immune checkpoint inhibitors alongside anti-angiogenic agents. TNBC's complex stromal and immune profile necessitates multi-pronged strategies targeting fibroblasts, macrophages, and specific chemokine/Wnt pathways.

References:

[1] GeneCards - TREM2. Available at: https://www.genecards.org/cgi-bin/carddisp.pl?gene=TREM2

[2] PubMed search for "Galectin-9 TIM-3 breast cancer". Available at: https://pubmed.ncbi.nlm.nih.gov/?term=Galectin-9+TIM-3+breast+cancer

[3] PubMed search for "CTLA4 breast cancer". Available at: https://pubmed.ncbi.nlm.nih.gov/?term=CTLA4+breast+cancer

[4] PubMed search for "fibroblast ECM remodeling breast cancer". Available at: https://pubmed.ncbi.nlm.nih.gov/?term=fibroblast+ECM+remolding+breast+cancer

[5] GeneCards - CXCR4. Available at: https://www.genecards.org/cgi-bin/carddisp.pl?gene=CXCR4

[6] PubMed search for "tumor associated macrophages breast cancer". Available at: https://pubmed.ncbi.nlm.nih.gov/?term=tumor+associated+macrophages+breast+cancer

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

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates condition-specific cell-cell interactions (CCI) involving Epithelial cells, major immune cells, and stromal cells across different breast cancer subtypes (ER+, HER2+, TNBC) and Normal breast tissue. The dot plot visualizes the strength and significance of these interactions, identifying those that differ significantly between conditions. Interactions are ranked by significance (-log10(p-value), dot size) and strength (scaled log strength, color intensity), with a focus on the top 60 interactions per group. The inclusion of aneuploid and diploid cell status in interaction labels provides crucial context, distinguishing interactions potentially involving tumor cells from those involving normal cells.

Visual Summary

The dot plot displays a complex landscape of cell-cell interactions, highlighting distinct patterns across the Normal and breast cancer conditions.

Breast Cancer Subtype Differences:

The visual patterns suggest that while some interactions are broadly present (e.g., certain ECM-integrin interactions), several crucial communications are specifically upregulated or unique to particular cancer conditions, especially in TNBC, indicating condition-specific tumor microenvironment remodeling.

Biological Interpretation

The observed cell-cell interaction patterns provide critical insights into the distinct tumor microenvironments of different breast cancer subtypes.

  1. SPP1-Integrin Axis in TNBC (e.g., SPP1_integrin_avb1_complex -- Mac | Epi (Aneuploid))
    • Signaling/Mechanism: Secreted Phosphoprotein 1 (SPP1), also known as Osteopontin, is an extracellular matrix protein and cytokine that binds to various integrin receptors (e.g., αvβ1, αvβ3, αvβ5) and CD44. This interaction activates intracellular signaling pathways (e.g., FAK, MAPK, PI3K/Akt), promoting cell adhesion, migration, survival, and invasion. In the context of cancer, SPP1 is often secreted by tumor-associated macrophages (TAMs), which are abundant in the TNBC microenvironment. This ligand-receptor pair mediates communication between TAMs and aneuploid epithelial (tumor) cells, creating a pro-tumorigenic niche that supports tumor growth, angiogenesis, and metastasis GeneCards: SPP1.
    • Biological Significance: The strong and specific upregulation of this interaction in TNBC suggests that SPP1-mediated communication between macrophages and tumor cells is a critical driver of the aggressive phenotype characteristic of TNBC. It highlights the significant role of immune cell (macrophage) crosstalk with tumor cells in shaping TNBC progression.
  2. CXCL12-CXCR4 Axis in TNBC (e.g., CXCL12_CXCR4 -- Fib | Epi (Aneuploid))
    • Signaling/Mechanism: CXCL12 (Stromal Cell-Derived Factor 1) is a chemokine secreted by stromal cells, including fibroblasts. Its receptor, CXCR4, is frequently overexpressed on breast cancer cells, particularly in metastatic settings. This interaction triggers G-protein coupled receptor signaling, leading to activation of pathways like PI3K/Akt and MAPK/ERK. It plays a pivotal role in tumor cell proliferation, survival, angiogenesis, and metastatic dissemination by guiding tumor cells to organs rich in CXCL12 PubMed search: CXCL12 CXCR4 cancer.
    • Biological Significance: The prominent CXCL12-CXCR4 interaction between fibroblasts and aneuploid epithelial cells in TNBC indicates that stromal-tumor cell communication via this axis is a key mechanism supporting tumor survival, invasion, and metastasis in this aggressive subtype. It underscores the critical contribution of the tumor microenvironment to TNBC pathology.
  3. Collagen-Integrin Interactions (e.g., COL1A1_integrin_a2b1_complex -- Fib | Epi (Aneuploid) in ER+, HER2+, TNBC)
    • Signaling/Mechanism: Collagens (like COL1A1) are major components of the extracellular matrix (ECM), primarily produced by fibroblasts. Integrin α2β1 is a key collagen-binding integrin expressed on epithelial cells. This interaction mediates cell adhesion to the ECM, transmits mechanical cues, and activates intracellular signaling cascades (e.g., FAK, Rho GTPases) that regulate cell proliferation, migration, and differentiation. In cancer, increased ECM stiffness and collagen deposition (desmoplasia) promote tumor progression and drug resistance by providing structural support and pro-survival signals to tumor cells GeneCards: ITGA2B1.
    • Biological Significance: The presence and upregulation of these interactions in various cancer conditions (ER+, HER2+, TNBC) suggest a general mechanism by which fibroblasts contribute to the tumor microenvironment across breast cancer subtypes. This ECM remodeling and subsequent integrin signaling are fundamental to tumor growth and survival by facilitating mechanotransduction and providing a scaffold for tumor progression.
  4. WNT2-FZD7-LRP6 in Normal Tissue (e.g., WNT2_FZD7_LRP6 -- Epi (Diploid) | Fib)
    • Signaling/Mechanism: WNT2 ligand binding to the Frizzled-7 (FZD7) receptor and LRP6 co-receptor initiates canonical Wnt signaling. This leads to the stabilization and nuclear translocation of β-catenin, which then acts as a transcription factor, activating target genes involved in cell proliferation, differentiation, and tissue development. In normal mammary gland, Wnt signaling is crucial for epithelial stem cell maintenance and development UniProt: WNT2.
    • Biological Significance: The strong presence of this interaction between normal epithelial cells and fibroblasts indicates its importance for maintaining normal breast tissue homeostasis and epithelial-stromal crosstalk. Its relative attenuation or alteration in cancer conditions (compared to the strong presence in Normal) might signify a dysregulation of normal developmental pathways, which can contribute to malignant transformation or loss of normal tissue structure.

Clinical or Translational Implications

The identification of condition-specific cell-cell interactions, particularly those involving aneuploid epithelial cells in cancer subtypes, offers significant clinical and translational opportunities.

  1. Targeting SPP1-Integrin in TNBC:
    • Therapeutic Potential: The strong and specific SPP1-integrin interactions in TNBC make this axis an attractive therapeutic target. Disrupting this communication between TAMs and tumor cells could inhibit tumor growth, invasion, and metastasis, potentially overcoming resistance to conventional therapies.

Therapeutic Agents:

  1. Targeting CXCL12-CXCR4 in TNBC:
    • Therapeutic Potential: Given its well-established role in tumor progression, angiogenesis, and metastasis, blocking the CXCL12-CXCR4 axis could be highly effective in TNBC, potentially preventing tumor recurrence and metastatic spread.

Therapeutic Agents:

  1. Modulating Collagen-Integrin Interactions:
    • Therapeutic Potential: The widespread upregulation of collagen-integrin interactions in breast cancer underscores the importance of the desmoplastic tumor microenvironment. Targeting these interactions could normalize ECM stiffness, reduce pro-tumorigenic mechanosignaling, and improve drug delivery by altering the stromal barrier.

Therapeutic Agents:

These findings highlight specific vulnerabilities in the breast cancer microenvironment that could be exploited for developing novel therapeutic strategies, particularly for aggressive subtypes like TNBC where existing targeted therapies are limited. Further mechanistic studies would be warranted to validate these interactions as bona fide therapeutic targets.

14. Condition-Specific Cell-Cell Interaction Patterns in Breast Cancer Subtypes

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates condition-specific cell-cell interaction (CCI) patterns involving genes related to immune checkpoints and cell cycle pathways, focusing on Epithelial cells, major immune, and stromal cells across different breast cancer subtypes (ER+, HER2+, TNBC) and normal breast tissue. The dot plot visualizes the strength (color intensity) and significance (dot size) of identified ligand-receptor interactions between specified cell types, allowing for a comparative assessment of the cellular communication landscape in different conditions.

Visual Summary

The dot plot displays a complex landscape of cell-cell interactions, with distinct patterns emerging across the four conditions: ER+, HER2+, Normal, and TNBC. Each dot represents a specific ligand-receptor pair interacting between two cell types, as indicated on the x-axis ("CCI Index"). The y-axis lists individual samples grouped by their respective conditions. The color intensity of each dot corresponds to the scaled log strength of the interaction, with darker red indicating stronger interactions. The size of the dot represents the -log10(p-value) of the interaction, where larger dots signify higher statistical significance.

Key visual observations include:

Biological Interpretation

The observed condition-specific CCI patterns provide critical insights into the differential cellular communication networks in healthy breast tissue versus various breast cancer subtypes. The focus on immune checkpoint and cell cycle-related pathways highlights mechanisms relevant to tumor progression and immune modulation.

Differential CCI Landscape and Tumor Microenvironment Remodeling

The distinct CCI profiles across conditions signify profound differences in the tumor microenvironment (TME) composition and intercellular signaling. In normal tissue, the observed interactions likely reflect processes maintaining tissue homeostasis and immune surveillance. In breast cancer, particularly TNBC, the altered CCI landscape suggests a shift towards pro-tumorigenic and immunosuppressive environments. The frequent involvement of aneuploid epithelial cells in these cancer-specific interactions directly links malignant cells to the remodeling of their surroundings.

Key Signaling Pathways and Mechanisms

1. TGF-beta Signaling Pathway

Condition-Specific Patterns

2. EGFR Signaling Pathway

Condition-Specific Patterns

3. Immune Checkpoint Signaling (PD-1/PD-L1 Axis)

Condition-Specific Patterns

4. IFN-gamma Signaling Pathway

Condition-Specific Patterns

Clinical or Translational Implications

The differential CCI patterns highlight critical pathways for therapeutic intervention and biomarker development in breast cancer, particularly emphasizing the distinct biology of TNBC.

Immune Checkpoint Blockade in TNBC

The robust PDCD1LG2_PDCD1 -- Mac | T CD8+ interaction in TNBC samples strongly supports the continued use and investigation of PD-1/PD-L1 inhibitors. This interaction underscores a major mechanism of immune evasion in TNBC, where tumor-associated macrophages suppress cytotoxic T-cell activity. Therapies such as pembrolizumab and atezolizumab directly target this axis, reversing T-cell exhaustion and unleashing anti-tumor immunity. Further research could focus on biomarkers predicting response to these therapies or identifying additional mechanisms of immune evasion in non-responders.

Targeting TGF-beta Signaling

The widespread and strong TGF-beta signaling across breast cancer subtypes, especially in TNBC, indicates its potential as a therapeutic target to modulate the tumor microenvironment. Inhibiting TGF-beta could reduce fibrosis, decrease immunosuppression, and potentially sensitize tumors to other treatments like chemotherapy or immunotherapy. Combination therapies, pairing TGF-beta inhibitors with ICIs or chemotherapy, are a promising avenue to overcome resistance mechanisms and improve patient outcomes.

Modulating EGFR Signaling

While EGFR signaling is a known oncogenic driver, its specific patterns of interaction with stromal and immune cells in breast cancer, especially those involving aneuploid epithelial cells, suggest its continued relevance. For patients whose tumors exhibit significant EGFR activation, particularly in specific paracrine loops within the TME, EGFR inhibitors could be considered, possibly in combination with other agents, especially if current standard-of-care treatments fail.

Overall, this analysis provides a detailed map of crucial cellular communication pathways that are differentially regulated across breast cancer subtypes. These findings offer concrete targets and mechanistic insights for developing and refining personalized therapeutic strategies.

15. Epithelial Cell Surfaceome Markers in Breast Cancer Subtypes

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[Analysis Visualization Results]...

Analysis Overview

This analysis identifies and visualizes surfaceome markers in Epithelial cells across different breast cancer conditions (ER+, HER2+, TNBC) and Normal breast tissue, with a distinction made for aneuploid status. The dot plot displays the mean expression level (color intensity) and the fraction of cells expressing each marker (dot size) for individual samples grouped by ploidy status and condition. Markers were selected based on their differential expression against Normal tissue, specifically looking for upregulation (nz_pct_score and FC cutoffs varied by condition). The primary goal is to identify distinct surface marker profiles for each breast cancer subtype and assess their potential as therapeutic targets, categorizing them into established and less well-characterized candidates.

Visual Summary

The dot plot, titled "Epithelial cell condition-specific markers," effectively illustrates the differential expression of surfaceome genes.

Biological Interpretation

The identified surfaceome markers provide insights into the distinct biological characteristics of different breast cancer subtypes, highlighting key signaling pathways, cellular functions, and potential therapeutic vulnerabilities.

Markers in ER+ Epithelial Cells (Aneuploid)

Established Therapeutic Targets:

EPCAM (Epithelial Cell Adhesion Molecule)

MUC1 (Mucin 1)

Less Well-Characterized Markers with Potential as Therapeutic Targets:

SPP1 (Secreted Phosphoprotein 1, Osteopontin)

PLAUR (Plasminogen Activator, Urokinase Receptor)

Markers in HER2+ Epithelial Cells (Aneuploid)

Established Therapeutic Targets:

ERBB2 (HER2, HER2/neu)

Less Well-Characterized Markers with Potential as Therapeutic Targets:

CD47 (Integrin Associated Protein)

NECTIN4 (Nectin Cell Adhesion Molecule 4)

Markers in TNBC Epithelial Cells (Aneuploid)

Established Therapeutic Targets:

FGFR1 (Fibroblast Growth Factor Receptor 1)

Less Well-Characterized Markers with Potential as Therapeutic Targets:

SLC7A5 (LAT1, Large Neutral Amino Acid Transporter 1)

CDCP1 (CUB Domain Containing Protein 1)

Clinical or Translational Implications

The identification of these surfaceome markers in breast cancer epithelial cells holds significant clinical and translational potential:

  1. Diagnostic and Prognostic Biomarkers: Markers like AGR2, SPP1, CDCP1, MUC1, NECTIN4, and SLC7A5, which show differential expression in specific breast cancer subtypes, could serve as valuable diagnostic or prognostic indicators, helping to stratify patients and guide treatment decisions. For instance, high AGR2 in ER+ cancer might predict endocrine resistance, while SLC7A5 levels could inform on the metabolic aggressiveness of TNBC.
  2. Therapeutic Targets:
    • Established Targets: The strong signal for ERBB2 in HER2+ cancer reinforces the validity of existing anti-HER2 therapies and highlights the potential for further refinement (e.g., novel ADCs). EPCAM and MUC1, while not first-line targets in breast cancer, are recognized targets in other epithelial malignancies and their consistent overexpression suggests potential for repurposed or novel agents.
    • Emerging/Potential Targets: Markers like CD47 and NECTIN4 represent exciting avenues for novel immunotherapies (CD47 blockade to enable phagocytosis) and antibody-drug conjugates (NECTIN4-ADCs) in breast cancer, especially in aggressive subtypes like HER2+ and TNBC where treatment options are more limited. The data provides a strong rationale for further investigation of CD47 and NECTIN4 as druggable targets in breast cancer.
    • Metabolic and Signaling Targets: The upregulation of SLC7A5 in TNBC points to a metabolic vulnerability that could be exploited by amino acid transporter inhibitors. Similarly, FGFR1 in TNBC offers an opportunity for targeted kinase inhibition in patients with relevant genetic alterations. PLAUR and SPP1 highlight mechanisms of invasion and metastasis that could be targeted to prevent disease progression.
  3. Experimental Validation: The identified markers provide a prioritized list for experimental validation in relevant *in vitro* and *in vivo* models. Further studies could confirm their functional roles in tumor progression, assess their suitability for various therapeutic modalities (e.g., antibodies, ADCs, small molecule inhibitors), and explore their utility as companion diagnostics for patient selection.

16. Macrophage Surfaceome Marker Expression Across Breast Cancer Subtypes

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Analysis Overview

This analysis identifies surfaceome markers expressed by Macrophage cells across different breast cancer conditions (ER+, HER2+, TNBC) compared to Normal breast tissue. The results are presented as a dot plot, where dot size reflects the fraction of cells expressing a given marker, and color intensity indicates the mean expression level. The primary goal is to identify condition-specific macrophage markers that could serve as biomarkers or therapeutic targets in breast cancer.

Visual Summary

The dot plot visualizes the expression of selected surfaceome markers in Macrophage cells across various breast cancer samples categorized by condition (ER+, HER2+, Normal, TNBC).

Biological Interpretation

Macrophages are critical components of the tumor microenvironment (TME) and play diverse roles in breast cancer progression, including tumor promotion, angiogenesis, metastasis, and immune suppression. The differential expression of surfaceome markers in macrophages across different breast cancer subtypes suggests distinct functional states and interactions within their respective TMEs.

Key Differentially Expressed Markers and Their Biological Roles:

  1. CD163 (Macrophage Scavenger Receptor):
    • Associated Signaling Pathways: Involved in innate immune responses, hemoglobin-haptoglobin complex scavenging, and induction of anti-inflammatory mediators.
    • Role in Tumor Microenvironment: Highly expressed on M2-like tumor-associated macrophages (TAMs), which promote tumor growth, angiogenesis, and immune suppression. Its expression is often correlated with poor prognosis in various cancers, including breast cancer.
    • Potential as Therapeutic Target: Yes. Targeting CD163+ TAMs could reduce their pro-tumoral functions.
    • Relevant Therapeutic Agents: Strategies focus on depleting CD163+ cells or inhibiting their functions.
    • Other Pertinent Information: A widely accepted marker for M2 macrophages and a potential prognostic biomarker. GeneCards: CD163
  2. GPNMB (Glycoprotein NMB):
    • Associated Signaling Pathways: Associated with various pathways involved in cell adhesion, migration, and immune modulation, often interacting with integrins.
    • Role in Tumor Microenvironment: Expressed by TAMs and tumor cells, GPNMB promotes tumor progression, invasion, and metastasis in several cancers. It can also contribute to immune suppression.
    • Potential as Therapeutic Target: Yes. Given its pro-tumoral role and surface expression, it's an attractive target for antibody-drug conjugates (ADCs).
    • Relevant Therapeutic Agents: Glembatumumab vedotin, an ADC targeting GPNMB, has been investigated in clinical trials for breast cancer and melanoma.
    • Other Pertinent Information: High expression is associated with aggressive disease and poor prognosis, particularly in TNBC and melanoma. UniProt: GPNMB
  3. CSF1R (Colony Stimulating Factor 1 Receptor):
    • Associated Signaling Pathways: Activated by CSF1 and IL-34, leading to downstream signaling pathways (e.g., PI3K/Akt, MAPK) that regulate macrophage proliferation, survival, differentiation, and function.
    • Role in Tumor Microenvironment: CSF1R signaling is crucial for the recruitment, survival, and differentiation of TAMs, which are key drivers of tumor growth and metastasis.
    • Potential as Therapeutic Target: Yes. Inhibition of CSF1R is a prominent strategy to reprogram or deplete TAMs, thereby shifting the TME towards an anti-tumoral state.
    • Relevant Therapeutic Agents: CSF1R inhibitors (e.g., Pexidartinib, Cabiralizumab, Emactuzumab) are in various stages of clinical development for different cancers, including breast cancer. PubMed Search: CSF1R inhibitor cancer
    • Other Pertinent Information: While expressed in normal macrophages, its role in supporting TAMs makes it a critical target in cancer.
  4. CD47 ("Don't Eat Me" Signal):
    • Associated Signaling Pathways: Interacts with SIRPα on phagocytic cells (macrophages, dendritic cells), inhibiting phagocytosis.
    • Role in Tumor Microenvironment: Highly expressed on various cancer cells and some myeloid cells (including TAMs), acting as an immune checkpoint to evade phagocytic clearance by macrophages. Its expression contributes to immune escape.
    • Potential as Therapeutic Target: Yes. Blocking CD47 can unleash macrophage-mediated phagocytosis of cancer cells.
    • Relevant Therapeutic Agents: CD47-blocking antibodies (e.g., Magrolimab, TTI-621/622) are under investigation in clinical trials for various solid tumors and hematological malignancies. GeneCards: CD47
    • Other Pertinent Information: The high expression in TNBC macrophages, known for its aggressive nature and immune evasion, is particularly relevant.
  5. VSIR (VISTA/PD-1H):
    • Associated Signaling Pathways: An immune checkpoint molecule that negatively regulates T-cell activation and proliferation.
    • Role in Tumor Microenvironment: Expressed on myeloid cells (including macrophages) and some tumor cells, VSIR contributes to immunosuppression in the TME, similar to PD-L1/PD-1.
    • Potential as Therapeutic Target: Yes. As an emerging immune checkpoint, blocking VSIR could enhance anti-tumor immunity.
    • Relevant Therapeutic Agents: Antibodies targeting VSIR are in preclinical and early clinical development.
    • Other Pertinent Information: Its upregulation in TNBC macrophages suggests a significant role in the immunosuppressive environment of this aggressive subtype. UniProt: VSIR
  6. CD86 (Costimulatory Molecule):
    • Associated Signaling Pathways: Binds to CD28 (costimulation) and CTLA-4 (coinhibition) on T cells, critically regulating T cell activation and tolerance.
    • Role in Tumor Microenvironment: Macrophages can express CD86, which can have dual roles. In M1-like macrophages, it promotes T cell activation. However, in the context of the immunosuppressive TME, CD86 on TAMs can contribute to T cell exhaustion or anergy, particularly when co-expressed with inhibitory ligands like PD-L1. Its high expression in TNBC macrophages might reflect an activated, yet potentially suppressive, state.
    • Potential as Therapeutic Target: Targeting CD86 directly is complex due to its dual roles; however, modulating its interactions (e.g., with CTLA-4 inhibitors) is a current therapeutic strategy.
    • Relevant Therapeutic Agents: Indirectly targeted by immune checkpoint inhibitors (e.g., CTLA-4 blockers like Ipilimumab).
    • Other Pertinent Information: Its role needs to be interpreted in the context of other co-stimulatory and co-inhibitory molecules. GeneCards: CD86
  7. CCR1 (Chemokine Receptor 1):
    • Associated Signaling Pathways: Ligand binding to CCR1 (e.g., CCL3, CCL5) activates G-protein coupled signaling pathways, leading to cell migration and activation.
    • Role in Tumor Microenvironment: Macrophages express CCR1, which mediates their recruitment to inflammatory sites and tumors. Enhanced CCR1 expression in TAMs can facilitate their infiltration into the TME, supporting tumor progression.
    • Potential as Therapeutic Target: Yes. Inhibiting CCR1 could reduce macrophage recruitment to the tumor.
    • Relevant Therapeutic Agents: CCR1 antagonists have been explored in inflammatory diseases and could be investigated in cancer.
    • Other Pertinent Information: Its high expression in TNBC macrophages indicates active recruitment and potentially a highly inflammatory or pro-tumorigenic migratory phenotype. GeneCards: CCR1

Clinical or Translational Implications

The identification of condition-specific surfaceome markers on macrophages in breast cancer has several important clinical and translational implications:

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

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Analysis Overview

This analysis identifies surfaceome markers differentially expressed in Fibroblasts across various breast cancer conditions (ER+, HER2+, TNBC) compared to Normal breast tissue. The aim is to characterize the distinct phenotypes of fibroblasts in the tumor microenvironment (TME) versus normal tissue and evaluate potential therapeutic targets. Markers were selected based on their differential expression (nz_pct_score and fold change) when comparing each condition against the Normal reference, focusing on up to 40 surfaceome markers per condition.

Visual Summary

The dot plot displays the expression of selected surfaceome markers in Fibroblast populations across individual patient samples, grouped by condition (ER+, HER2+, Normal, TNBC).

Condition-Specific Patterns:

Biological Interpretation

The analysis reveals distinct surfaceome profiles of fibroblasts depending on their microenvironment, specifically differentiating normal tissue fibroblasts from Cancer-Associated Fibroblasts (CAFs) in various breast cancer subtypes.

Normal Fibroblast Signature

Normal breast tissue fibroblasts exhibit a unique set of surface markers, suggesting their role in maintaining tissue homeostasis and structure. Markers highly expressed in normal fibroblasts, but downregulated in CAFs, include:

PLAUR (Urokinase-type Plasminogen Activator Receptor, uPAR):

TGFBR2 (Transforming Growth Factor Beta Receptor Type 2):

PRNP (Cellular Prion Protein):

EDNRB (Endothelin Receptor Type B):

Cancer-Associated Fibroblast (CAF) Signature

Fibroblasts in breast cancer samples (ER+, HER2+, TNBC) show a strong activation signature, with numerous surface markers highly expressed and prevalent, distinguishing them as CAFs. These markers underscore their multifaceted roles in promoting tumor progression.

FAP (Fibroblast Activation Protein alpha):

SDC1 (Syndecan-1):

ADAM12 (ADAM Metallopeptidase Domain 12):

CD44:

FGFR1 (Fibroblast Growth Factor Receptor 1):

ITGB1 (Integrin Beta-1) and ITGB5 (Integrin Beta-5):

HLA-C, HLA-E, HLA-DRA, HLA-F (MHC Molecules):

Clinical or Translational Implications

The identification of distinct surfaceome markers for CAFs across breast cancer subtypes has significant clinical and translational implications:

  1. Diagnostic Biomarkers: Elevated expression of markers like FAP, SDC1, ADAM12, CD44, and FGFR1 on fibroblasts could serve as diagnostic or prognostic biomarkers for breast cancer progression and aggressiveness, especially in TNBC.
  2. Therapeutic Targets: CAFs are increasingly recognized as critical players in treatment resistance and metastasis. The identified surfaceome markers, particularly FAP, SDC1, CD44, and FGFR1, represent promising therapeutic targets. Targeting these molecules on CAFs could:
    • Inhibit tumor growth and invasion by disrupting CAF-mediated ECM remodeling.
    • Sensitize tumors to existing therapies by normalizing the TME.
    • Overcome immune suppression by modulating CAF-immune cell interactions (e.g., through HLA molecules).
  3. Combination Therapies: Targeting CAFs directly (e.g., with FAP inhibitors) or their signaling pathways (e.g., FGFR inhibitors) could be combined with standard chemotherapy, targeted therapy, or immunotherapy to enhance treatment efficacy and overcome resistance in breast cancer patients.
  4. Stratification of Patients: Differential expression patterns of these markers across ER+, HER2+, and TNBC subtypes suggest that CAF-targeted therapies might be more effective in specific patient populations, guiding personalized treatment strategies. For instance, the more pronounced CAF signature in TNBC may indicate a greater susceptibility to CAF-targeted interventions.
  5. Monitoring Treatment Response: Changes in the expression of these CAF markers could serve as indicators of treatment response or recurrence, potentially allowing for non-invasive monitoring.

In summary, this analysis provides a valuable resource for understanding CAF biology in breast cancer, highlighting key surfaceome markers that could be exploited for novel diagnostic and therapeutic strategies aimed at improving patient outcomes.

18. T cell CD4+ Condition-Specific Surfaceome Marker Expression Analysis

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Analysis Overview

This analysis identifies condition-specific surfaceome markers for CD4+ T cells, focusing on differential expression across breast cancer subtypes (ER+, HER2+, TNBC) compared to other conditions or the reference normal condition. The visualization employs a dot plot to represent both the mean expression level (color intensity) and the fraction of cells expressing each marker (dot size) for individual samples grouped by their respective breast cancer conditions. The primary goal is to highlight potential diagnostic or therapeutic targets within the CD4+ T cell compartment, particularly in the context of Triple-Negative Breast Cancer (TNBC), as indicated by the filtering parameters and the highlighted region.

Visual Summary

The dot plot displays the expression of several surfaceome markers in CD4+ T cells across different patient samples. Samples are organized by their clinical condition (ER+, HER2+, TNBC).

  1. Overall Marker Expression: Many classical T cell markers like CD2, CD3D, PTPRC (CD45), CD3G, CD7, and CD53 show widespread expression across most samples, indicating the consistent presence and identity of CD4+ T cells.
  2. Condition-Specific Enrichment in TNBC: A distinct cluster of samples labeled "TNBC" (highlighted by a red box) exhibits notably higher expression levels (darker red dots) and a larger fraction of expressing cells (larger dot sizes) for several markers compared to most ER+ and HER2+ samples.
    • Prominently Upregulated in TNBC: Markers such as CD44, LY6E, IL2RG, CD48, CLEC2D, HLA-DPB1, and HLA-DRA show particularly strong signals within the TNBC samples. This suggests an altered activation state or specific functional subsets of CD4+ T cells in the TNBC tumor microenvironment.
    • Contrast with Other Subtypes: In contrast, many ER+ and HER2+ samples generally show lower expression levels and smaller fractions of expressing cells for these specific markers, indicating a potential difference in the CD4+ T cell phenotype across breast cancer subtypes.
  3. Sample-to-Sample Variability: Even within the same condition, there is some variability in marker expression, which might reflect patient-specific differences in immune response or tumor heterogeneity.

Biological Interpretation

The observed differential expression of surfaceome markers in CD4+ T cells, particularly their enrichment in TNBC samples, provides insights into the unique immunological landscape of this aggressive breast cancer subtype. CD4+ T cells are critical orchestrators of adaptive immunity, encompassing various subsets like helper T cells (Th1, Th2, Th17, Tfh) and regulatory T cells (Tregs), each with distinct roles in anti-tumor immunity or immune suppression. The identified markers suggest specific states of activation, differentiation, or immune evasion within the TNBC tumor microenvironment (TME).

Below, we detail several prominent markers showing upregulation in TNBC, discussing their biological roles and potential implications.

CD44

LY6E

IL2RG (CD132)

HLA-DPB1 and HLA-DRA (MHC Class II)

CLEC2D (LLT1)

Clinical or Translational Implications

The identification of these surfaceome markers, particularly their enrichment in TNBC, offers significant clinical and translational implications:

  1. Biomarker Potential: Markers like CD44, LY6E, IL2RG, HLA-DPB1/DRA, and CLEC2D could serve as biomarkers to characterize the immune status of CD4+ T cells in TNBC patients. High expression of certain markers might correlate with specific clinical outcomes or responses to immunotherapy, warranting further investigation in larger cohorts. For instance, high HLA-DPB1/DRA on CD4+ T cells could indicate strong inflammatory signals, which might be a good prognostic factor if accompanied by functional anti-tumor responses.
  2. Therapeutic Targets: The differentially expressed markers represent potential therapeutic targets for novel immunotherapies.
    • CD44: Given its role in T cell trafficking and activation, therapeutic strategies targeting CD44 could aim to modulate T cell infiltration or phenotype, potentially enhancing anti-tumor immunity in TNBC. This might involve antibodies that block CD44 or small molecules that interfere with its signaling. GeneCards: CD44
    • LY6E: As a modulator of T cell function, LY6E could be explored to either activate anti-tumor T cells or inhibit pro-tumorigenic immune components.
    • IL2RG: Enhancing signaling through the common gamma chain receptor (e.g., using modified IL-2 or other γc cytokines) could boost the expansion and function of anti-tumor T cells in TNBC, a strategy already in use with recombinant IL-2. PubMed: Aldesleukin in cancer immunotherapy
    • CLEC2D: As an immune checkpoint that can suppress NK and T cell activity, blocking CLEC2D or its interaction with NKR-P1A could unleash anti-tumor immune responses, similar to current checkpoint inhibitors (e.g., PD-1/PD-L1, CTLA-4). This represents a promising avenue for TNBC, which often benefits from immunotherapy. UniProt: CLEC2D
  3. Understanding TNBC Immunobiology: The specific marker profile observed in TNBC CD4+ T cells provides critical insights into the unique immunobiology of this aggressive breast cancer subtype. It suggests a highly active, potentially exhausted, or immunosuppressive CD4+ T cell compartment that is distinct from ER+ and HER2+ cancers. This knowledge can inform the development of combination therapies tailored for TNBC.
  4. Experimental Validation: These findings warrant further experimental validation. Functional studies using *in vitro* models and *in vivo* preclinical models could investigate the precise roles of these markers in CD4+ T cell function within the TNBC TME. This could include knockdown/knockout experiments, antibody blockade, and adoptive transfer experiments to assess their impact on tumor growth and immune responses.
  5. Patient Stratification: Future research could explore whether specific combinations or levels of these markers can be used to stratify TNBC patients who are more likely to respond to existing or emerging immunotherapies.

19. Differential Expression of Cell Cycle Pathway Genes in Epithelial Cells Across Breast Cancer Subtypes

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Analysis Overview

This analysis investigates the "expressing cell fraction (sample)" of a selected panel of cell cycle pathway genes within Epithelial cells across different breast cancer conditions (ER+, HER2+, TNBC) and Normal breast tissue. The "expressing cell fraction" represents the proportion of cells within each sample that express a given gene. This metric provides insight into the prevalence and heterogeneity of gene activity within the epithelial cell population of different breast tissue contexts. The goal is to identify genes with statistically significant differences in their expressing cell fraction, which may indicate altered cell cycle regulation in breast cancer. Pairwise comparisons between conditions were performed, and statistically significant differences (p < 0.05) are indicated on the plots.

Visual Summary

The box plots illustrate the distribution of the expressing cell fraction for 24 cell cycle pathway genes across four conditions: ER+ breast cancer, HER2+ breast cancer, Normal breast tissue, and TNBC.

  1. Proliferation Markers are Elevated in TNBC: Genes associated with cell proliferation and DNA replication, such as HDAC1, MCM5, PCNA, PTTG1, and MYC, generally show a significantly higher expressing cell fraction in TNBC epithelial cells compared to Normal cells and often other cancer subtypes (ER+, HER2+). For example, HDAC1 shows a significantly higher fraction in TNBC compared to Normal (p=0.00216), HER2+ (p=0.00298), and ER+ (p=0.000551). Similarly, MCM5 is significantly elevated in TNBC compared to Normal (p=0.00231), HER2+ (p=0.0233), and ER+ (p=0.000966).
  2. Cell Cycle Inhibitors/Growth Arrest Genes are Reduced in Cancer: Conversely, several genes typically involved in cell cycle arrest and growth regulation, including SMAD3, SFN, GADD45B, GADD45G, E2F4, and TFDP1, exhibit a significantly *lower* expressing cell fraction in at least some breast cancer subtypes (particularly ER+ and HER2+) compared to Normal tissue. For instance, SMAD3 has a significantly higher fraction in Normal compared to ER+ (p=3.26e-06), HER2+ (p=0.00145), and TNBC (p=0.000256). SFN shows a similar pattern, being significantly higher in Normal compared to ER+ (p=6.64e-10), HER2+ (p=0.000243), and TNBC (p=0.00306).
  3. Complex Patterns for CDK Inhibitors (CDKNs): Interestingly, several cyclin-dependent kinase inhibitor (CDKI) genes, specifically CDKN2A, CDKN2B, CDKN2C, and CDKN2D, show a *higher* expressing cell fraction in TNBC compared to Normal and other subtypes. For example, CDKN2A is significantly more expressed in TNBC than Normal (p=0.000621), HER2+ (p=0.00602), and ER+ (p=0.000815).
  4. Other Differentially Expressed Genes:
    • CDK7, a cyclin-dependent kinase involved in transcription and cell cycle, shows a significantly higher expressing fraction in TNBC compared to Normal (p=0.00129) and HER2+ (p=0.000131).
    • YWHAE and YWHAQ (14-3-3 family proteins) also display higher expressing fractions in TNBC compared to Normal and/or other subtypes.
    • TGFB2, a growth factor, has a higher expressing cell fraction in Normal compared to ER+ (p=0.000381) and HER2+ (p=0.0165).
    • CDK4 shows a higher expressing cell fraction in Normal compared to ER+ (p=0.000131) and HER2+ (p=0.00113).

Biological Interpretation

The observed differential expressing cell fractions of cell cycle pathway genes in epithelial cells reflect significant alterations in cellular proliferation and growth control in breast cancer, especially distinguishing the highly aggressive TNBC subtype.

  1. Elevated Proliferative Capacity in TNBC: The increased expressing cell fraction of genes like HDAC1, MCM5, PCNA, PTTG1, and MYC in TNBC epithelial cells is a hallmark of highly proliferative cancers.
    • MCM5 (Minichromosome Maintenance Complex Component 5) and PCNA (Proliferating Cell Nuclear Antigen) are essential for DNA replication, indicating a larger proportion of TNBC cells are actively engaged in DNA synthesis and cell division.
    • PTTG1 (Securin) is critical for sister chromatid separation during mitosis. Its upregulation suggests active mitotic progression.
    • MYC is a proto-oncogene that drives cell growth and proliferation.
    • HDAC1 (Histone Deacetylase 1) promotes gene silencing and is often overexpressed in cancers, contributing to cell proliferation and survival [GeneCards]. Its increased expressing fraction in TNBC aligns with its role in tumor growth.
    • CDK7 is involved in the activation of other CDKs and regulates transcription. Its higher expressing fraction in TNBC suggests an intensified cell cycle machinery and transcriptional drive in this subtype.
  2. Loss of Growth Regulation in Cancer: The reduced expressing cell fraction of SMAD3, SFN, GADD45B, GADD45G, E2F4, and TFDP1 in cancer conditions (especially ER+ and HER2+) compared to Normal tissue indicates a failure of normal growth control mechanisms.
    • SMAD3 mediates TGF-beta signaling, which often acts as a tumor suppressor in early-stage epithelial cells by inducing cell cycle arrest [GeneCards]. Its reduction suggests diminished anti-proliferative signaling.
    • SFN (Stratifin, 14-3-3 sigma) and GADD45 family members (Growth Arrest and DNA Damage-inducible proteins) are involved in DNA damage response and cell cycle arrest. Their decreased prevalence in cancer epithelial cells implies compromised DNA repair and unchecked cell cycle progression.
    • E2F4 and TFDP1 are key components of transcription factor complexes that regulate cell cycle genes and are often associated with cell cycle quiescence. Their reduced expressing fraction suggests a shift away from a quiescent state towards active cycling.
    • The higher expressing fraction of TGFB2 in Normal tissue compared to ER+ and HER2+ also points to dysregulated growth factor signaling, as TGF-beta signaling can have complex, context-dependent roles in cancer progression.
  3. Paradoxical Upregulation of CDK Inhibitors in TNBC: The increased expressing cell fraction of CDKN2A, CDKN2B, CDKN2C, and CDKN2D in TNBC is intriguing. While these are typically tumor suppressor genes that inhibit CDK activity and cell cycle progression, their upregulation in a highly proliferative cancer could suggest several scenarios:
    • Compensatory Mechanism: Cancer cells, particularly highly proliferative ones like TNBC, may experience oncogenic stress that triggers a compensatory upregulation of CDKIs as a protective mechanism, even if the pathway is ultimately overridden or rendered ineffective at the protein level.
    • Oncogene-Induced Senescence (OIS): Upregulation of CDKIs, especially CDKN2A (p16), is a common feature of cellular senescence. This could indicate a subpopulation of TNBC cells undergoing OIS, a tumor-suppressive program, which could coexist with proliferating cells.
    • Context-Dependent Roles: The "expressing cell fraction" measures mRNA presence. It does not account for protein levels or functional activity, which can be abrogated by mutations or epigenetic silencing in cancer, even with high mRNA.
    • Alternative Functions: Some CDKIs might have non-canonical roles in specific cancer contexts.

The analysis of "expressing cell fraction" is valuable as it reflects cellular heterogeneity within samples. A higher fraction indicates that a larger proportion of epithelial cells within a given sample are exhibiting the gene's activity, providing insights into the collective cellular state of the tissue.

Clinical or Translational Implications

These findings have several potential clinical and translational implications for breast cancer:

  1. Biomarker Potential: The identified genes with significantly altered expressing cell fractions could serve as diagnostic or prognostic biomarkers. For instance, a high expressing fraction of proliferation markers like HDAC1, MCM5, or MYC in epithelial cells could indicate a more aggressive tumor, particularly TNBC. Conversely, a low expressing fraction of growth arrest genes (e.g., SFN, GADD45B, SMAD3) might indicate a higher risk of progression in ER+ and HER2+ cancers.
  2. Therapeutic Targets: Genes with consistently elevated expressing fractions in cancer, especially TNBC, represent potential therapeutic targets.
    • HDAC1 is a promising target; HDAC inhibitors are an active area of cancer research, with some already approved or in clinical trials for various malignancies [PubMed Search]. Targeting HDAC1 could disrupt proliferation in TNBC.
    • The proto-oncogene MYC is a challenging but highly sought-after therapeutic target in many cancers due to its central role in driving proliferation [GeneCards].
    • The MCM complex proteins (MCM5, MCM7) and PCNA are crucial for DNA replication, making them attractive targets for anti-proliferative drugs.
  3. Understanding TNBC Aggressiveness: The distinctive profile of TNBC, characterized by high prevalence of proliferation markers and specific patterns of CDKI expression, underscores its unique biology. This supports the need for targeted therapies specifically designed for TNBC, which often lacks the common endocrine or HER2 targets.
  4. Investigating Drug Resistance and Senescence: The paradoxical upregulation of CDKIs in TNBC warrants further investigation. This could be indicative of a subpopulation undergoing senescence, which might affect treatment response or contribute to resistance mechanisms [PubMed Search]. Understanding this heterogeneity could lead to combination therapies targeting both proliferating and senescent tumor cells.

20. Gene Ontology (GSA) Analysis of Epithelial Cells in Breast Cancer Subtypes

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Ontology (GO) enrichment results for "Epithelial cell" populations, comparing different breast cancer conditions (Diploid, ER+, HER2+, TNBC) against a normal reference, as well as normal epithelial cells against all other conditions. The enrichment is based on genes found to be significantly up-regulated (up-regulated DEGs) in the test condition compared to the reference. These results highlight key biological pathways and processes differentially activated in various breast cancer contexts.

Visual Summary

The provided bar plots illustrate the top enriched GO terms for epithelial cells under five distinct comparisons:

  1. Diploid_vs_Normal: Epithelial cells identified as diploid (potentially including early transformed cells or normal cells in a tumor environment) compared to normal epithelial cells.
  2. ER+_vs_Normal: Estrogen Receptor-positive (ER+) breast cancer epithelial cells compared to normal epithelial cells.
  3. HER2+_vs_Normal: HER2-positive (HER2+) breast cancer epithelial cells compared to normal epithelial cells.
  4. Normal_vs_others: Normal epithelial cells compared to all other conditions (including cancer subtypes).
  5. TNBC_vs_Normal: Triple-negative breast cancer (TNBC) epithelial cells compared to normal epithelial cells.

Each plot displays GO terms on the y-axis, ranked by their statistical significance as represented by -log(p-val) and -log(q-val) on the x-axis. A higher bar indicates greater statistical enrichment.

Key visual observations include:

Biological Interpretation

The Gene Ontology analysis provides critical insights into the altered biological landscape of epithelial cells in breast cancer:

1. Widespread Metabolic Reprogramming in Cancer Epithelial Cells

A dominant theme across all breast cancer subtypes (ER+, HER2+, TNBC) and even in the 'Diploid' epithelial compartment, when compared to normal, is the profound enrichment of metabolic pathways.

2. Dysregulation of Protein Homeostasis and Cellular Stress Response

Pathways related to protein processing and quality control are also highly enriched in cancer epithelial cells.

3. Subtype-Specific Biological Signatures

While commonalities exist, the analysis also reveals subtype-specific characteristics:

4. Characteristics of Normal Epithelial Cells

The "Normal_vs_others" comparison provides a baseline for healthy epithelial cell function:

Clinical or Translational Implications

The findings from this GSA analysis offer several potential avenues for clinical and translational research in breast cancer:

21. Gene Set Enrichment Analysis Reveals Condition- and Cell Type-Specific Pathway Alterations in Breast Cancer

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results, displayed as a dot plot, comparing various cell types and breast cancer conditions (ER+, HER2+, TNBC, and Diploid cells within cancer samples) against a 'Normal' reference condition. The analysis was performed across multiple celltype_minor populations, including Epithelial cells (the resolved tumor-origin cell type), Endothelial cells, Fibroblasts, ILCs, Macrophages, Smooth muscle cells, and T cells (CD4+ and CD8+), all derived from breast tissue.

Each dot represents a specific gene set (pathway) enriched in a particular cell type under a given condition compared to the Normal reference. The color of the dot indicates the Normalized Enrichment Score (NES), where red signifies positive enrichment (pathway genes generally upregulated) and blue signifies negative enrichment (pathway genes generally downregulated). The size of the dot reflects the statistical significance, with larger dots indicating a smaller p-value (-log(p)). A total of 120 key pathways are displayed.

Visual Summary

The dot plot provides a comprehensive overview of pathway activity across different cell types and cancer subtypes.

Biological Interpretation

  1. Tumor Cell Intrinsic Pathways Dominate in Epithelial Cells: The strong upregulation of "Pathways in cancer", "Cell cycle", "DNA replication", and "mTOR signaling pathway" in malignant epithelial cells (ER+, HER2+, TNBC) against normal controls underscores the uncontrolled proliferation and genomic instability characteristic of breast cancer. The enrichment of the "Hippo signaling pathway" (often dysregulated in cancer) and "p53 signaling pathway" (a critical tumor suppressor pathway, whose alteration drives many cancers) further highlights key oncogenic drivers. The persistence of these cancer hallmarks in "Epithelial cell: Diploid_vs_Normal" suggests that even non-aneuploid epithelial cells within the tumor environment are undergoing significant oncogenic transformations or represent early stages of malignancy. Reference: Cancer Hallmarks Review, e.g., Hanahan and Weinberg, Cell 2011, or via PubMed search for "hallmarks of cancer": [https://pubmed.ncbi.nlm.nih.gov/?term=hallmarks+of+cancer]
  2. Remodeling of the Tumor Microenvironment: The consistent upregulation of "ECM-receptor interaction" and "Focal adhesion" pathways in Fibroblasts, Endothelial cells, and Smooth muscle cells across cancer conditions indicates active stromal remodeling. Cancer-associated fibroblasts (CAFs) and other stromal cells contribute to a stiffer, more permissive microenvironment that facilitates tumor growth, invasion, and metastasis. Endothelial cell enrichment for "VEGF signaling pathway" is a direct signature of active angiogenesis, crucial for supplying nutrients and oxygen to the rapidly growing tumor. Reference: Fibroblast role in cancer, e.g., via PubMed search for "cancer associated fibroblasts ECM": [https://pubmed.ncbi.nlm.nih.gov/?term=cancer+associated+fibroblasts+ECM]
  3. Complex Immune Landscape: Changes in immune cell populations reflect the multifaceted role of the immune system in breast cancer. The "NF-kappa B signaling pathway" is often associated with inflammation and immune cell activation. Its varying enrichment across different immune cell types and cancer subtypes suggests a context-dependent immune response, which can be either anti-tumorigenic or pro-tumorigenic (e.g., through fostering immune suppression). "Leukocyte transendothelial migration" indicates active immune cell trafficking, crucial for both immune surveillance and infiltration.
  4. Metabolic Reprogramming for Cancer Growth: The negative enrichment of some canonical metabolic pathways (e.g., "Amino sugar and nucleotide sugar metabolism") in cancer-associated epithelial cells suggests a metabolic shift. Cancer cells often reprogram their metabolism (e.g., Warburg effect) to support rapid proliferation, diverting resources from normal catabolic processes to anabolic pathways.

Clinical or Translational Implications

  1. Biomarker Discovery and Stratification: The identified differentially enriched pathways and their associated genes could serve as valuable biomarkers for diagnosing breast cancer, predicting prognosis, or stratifying patients for specific therapies. For instance, genes within highly activated "Cell cycle" or "mTOR signaling pathway" in epithelial cells could indicate a more aggressive tumor phenotype.
  2. Targeted Therapeutic Opportunities: Pathways consistently upregulated in specific cell types or cancer subtypes represent potential therapeutic targets. Inhibitors of mTOR, Hippo, or VEGF signaling are already in clinical use or trials for various cancers, and their enrichment here supports their relevance in breast cancer. Targeting ECM-remodeling pathways could inhibit tumor invasion and metastasis, while modulating immune cell pathways could enhance immunotherapy efficacy. Reference: NCI Drug Dictionary for mTOR inhibitors: [https://www.cancer.gov/publications/dictionaries/cancer-drug/def/mtor-inhibitor]
  3. Understanding Treatment Resistance: By understanding the specific pathway alterations in different breast cancer subtypes and cell types, including diploid cells, these results can inform strategies to overcome or prevent resistance to standard treatments by targeting compensatory pathways or distinct vulnerabilities. For example, understanding immune pathway dynamics could lead to more effective immunotherapeutic combinations.

22. Discussion

The comprehensive single-cell analysis of breast tissue reveals a highly intricate and dynamically regulated cellular ecosystem in breast cancer, with significant distinctions across molecular subtypes (ER+, HER2+, TNBC) compared to normal tissue. A foundational finding is the epithelial origin of the malignancy, as evidenced by the co-localization of aneuploid cells with epithelial cell clusters and their extensive copy number variations, a hallmark of carcinogenesis. Notably, HER2+ tumors display classic ERBB2 amplification, while all malignant epithelial cells show profound metabolic reprogramming, protein homeostasis stress, and aberrant cell cycle regulation, particularly aggressive proliferation in TNBC.

The tumor microenvironment (TME) is profoundly remodeled in cancer. Population analysis shows a general reduction in innate lymphocytes (ILCs, NK cells) and an increase in immunosuppressive regulatory T cells (Treg) across all cancer subtypes, pointing towards an overall immunosuppressive milieu. However, subtype-specific nuances exist; TNBC often presents with higher cytotoxic T cell infiltration, suggesting a more "hot" immune environment, yet this is often counteracted by robust immunosuppressive mechanisms. Macrophage populations exhibit dynamic shifts, with a decrease in M2B macrophages and an increase in M1 macrophages in cancer, most pronounced in HER2+ cases, indicating a complex balance of pro- and anti-inflammatory roles. The significant reduction in M2D macrophages in TNBC further suggests subtype-specific macrophage polarization and function.

Cell-cell interaction (CCI) analyses unveil critical crosstalk driving disease progression. In ER+ breast cancer, macrophage-epithelial interactions, notably involving APOE-TREM2, suggest active immune modulation promoting tumor growth. HER2+ exhibits a complex interplay with abundant chemokine signaling and active immune checkpoints like CD86-CTLA4 and LGALS9-HAVCR2 (Galectin-9-TIM-3), indicating sophisticated immune evasion strategies. TNBC, in particular, displays a highly desmoplastic and immunosuppressive TME, characterized by extensive fibroblast-driven ECM remodeling via integrin interactions, robust chemokine signaling (e.g., CXCL12-CXCR4) recruiting pro-tumorigenic immune cells, and strong PDCD1LG2-PDCD1 (PD-L1-PD-1) interactions between macrophages and T cells. TGF-beta and EGFR signaling pathways are also widely active, contributing to tumor progression across subtypes.

The surfaceome marker profiling further differentiates these contexts. Epithelial cells in cancer conditions show upregulation of pan-cancer markers (CD24, CD44, EPCAM, MUC1) alongside subtype-specific targets like ERBB2 in HER2+, and FGFR1/SLC7A5/CDCP1 in TNBC. Macrophages in TNBC uniquely express markers like CD47, VSIR, and CCR1, highlighting their distinct immunosuppressive functions. Fibroblasts transition from a homeostatic state to a highly activated CAF phenotype marked by FAP, SDC1, CD44, and FGFR1, extensively remodeling the ECM and influencing immune evasion. CD4+ T cells in TNBC display increased expression of activation/memory markers like CD44, LY6E, IL2RG, and MHC Class II molecules, alongside immune checkpoint-related CLEC2D, suggesting an active but potentially suppressed immune response.

Overall, the findings underscore the necessity of considering the full cellular and molecular context for effective therapeutic development. The pervasive genomic instability in epithelial cells, coupled with the intricate, subtype-specific TME remodeling and immune evasion mechanisms, present both challenges and opportunities for precision oncology. Targeting key cell-cell interactions, specific surface markers on tumor and stromal/immune cells, and aberrant cell cycle or metabolic pathways holds promise for overcoming resistance and improving patient outcomes in breast cancer.

Hypotheses:

  1. The high prevalence of aneuploid epithelial cells in breast cancer subtypes drives specific metabolic reprogramming and protein homeostasis dysregulation, making these processes vulnerable to targeted therapies.
  2. The increased proportion of regulatory T cells (Treg) and the differential expression of immune checkpoints (e.g., PD-L1/PD-1, Galectin-9/TIM-3, CD47, VSIR) in the breast cancer microenvironment are primary mechanisms of immune evasion that can be overcome by combination immunotherapies.
  3. Cancer-associated fibroblasts (CAFs) actively orchestrate ECM remodeling and secrete pro-tumorigenic factors via markers like FAP, SDC1, ADAM12, and integrins, critically supporting tumor growth, invasion, and immune suppression, particularly in TNBC.
  4. Subtype-specific cell-cell interaction axes, such as SPP1-integrin and CXCL12-CXCR4 in TNBC, represent critical communication nodes that can be therapeutically targeted to disrupt tumor-macrophage and tumor-fibroblast crosstalk.
  5. The unique cell cycle and proliferation signature observed in TNBC epithelial cells, including high HDAC1 and MYC expression, contributes to its aggressive phenotype and suggests susceptibility to therapies targeting these pathways.

Potential therapeutic targets:

  1. PD-1/PD-L1 Immune Checkpoint Axis: Robust PDCD1LG2 (PD-L1) expression on macrophages interacting with PDCD1 (PD-1) on CD8+ T cells is a significant immunosuppressive mechanism, particularly prominent in TNBC. Blocking this axis can unleash anti-tumor T cell responses. Evidence: Strong and significant PDCD1LG2_PDCD1 -- Mac | T CD8+ interaction specifically in TNBC samples (Section 14). Validation: Assess efficacy of anti-PD-1/PD-L1 antibodies in TNBC patient-derived organoids or xenografts. Evaluate PD-L1 expression on tumor-associated macrophages (TAMs) in patient biopsies via IHC and correlate with response to ICIs.
  2. CD47 ("Don't Eat Me" Signal): High expression of CD47 on TNBC macrophages and potentially tumor epithelial cells acts as an immune checkpoint, inhibiting phagocytic clearance by macrophages. Blocking CD47 can re-engage the innate immune system. Evidence: CD47 is a highly expressed surfaceome marker on Macrophages in TNBC samples (Section 16). CD47 also observed as an emerging target on HER2+ Epithelial cells (Section 15). Validation: Test CD47-blocking antibodies in in vitro macrophage-tumor cell phagocytosis assays and in vivo TNBC models. Monitor CD47 expression in patient tumors and correlate with clinical response.
  3. Fibroblast Activation Protein (FAP): FAP is a canonical marker of Cancer-Associated Fibroblasts (CAFs), which are highly active in the breast cancer TME, especially TNBC, driving ECM remodeling, tumor growth, and immune suppression. Targeting FAP can disrupt the pro-tumorigenic stroma. Evidence: FAP is a highly expressed and prevalent surfaceome marker on Fibroblasts in ER+, HER2+, and prominently in TNBC samples, indicating robust CAF activation (Section 17). Validation: Evaluate FAP-targeting agents (e.g., ADCs, small molecule inhibitors) in preclinical models. Analyze the impact of FAP inhibition on ECM stiffness, immune cell infiltration, and tumor growth in in vivo models.
  4. CSF1R (Colony Stimulating Factor 1 Receptor): CSF1R signaling is crucial for the recruitment, survival, and differentiation of tumor-associated macrophages (TAMs), which promote tumor growth, angiogenesis, and immune suppression. Inhibiting CSF1R can reprogram or deplete pro-tumoral macrophages. Evidence: CSF1R is a general macrophage identifying marker, and its signaling is critical for TAM biology, which are prevalent in the TME across subtypes (Section 16). Validation: Test CSF1R inhibitors, alone or in combination, in breast cancer models. Assess changes in macrophage populations and their polarization state within the TME following treatment.
  5. SPP1-Integrin Axis: The SPP1 (Osteopontin)-integrin axis mediates critical communication between tumor-associated macrophages (TAMs) and aneuploid epithelial cells, promoting tumor growth, invasion, and metastasis, particularly in aggressive subtypes like TNBC. Evidence: Strong and specific SPP1_integrin_avb1_complex -- Mac | Epi (Aneuploid) interaction significantly upregulated in TNBC (Section 13). SPP1 is also a less well-characterized potential target in ER+ Epithelial cells (Section 15). Validation: Develop and test SPP1-neutralizing antibodies or integrin inhibitors to block this interaction in TNBC models. Evaluate effects on tumor cell migration, invasion, and angiogenesis.
  6. CXCL12-CXCR4 Axis: The CXCL12-CXCR4 axis, prominently active between fibroblasts and aneuploid epithelial cells in TNBC, is a pivotal pathway for tumor cell proliferation, survival, angiogenesis, and metastatic dissemination. Evidence: Prominent CXCL12_CXCR4 -- Fib | Epi (Aneuploid) interaction in TNBC (Section 13). Validation: Evaluate CXCR4 antagonists (e.g., Plerixafor analogs) in preclinical models of TNBC to assess inhibition of metastasis and enhancement of chemotherapy sensitivity.
  7. ERBB2 (HER2): ERBB2 amplification and overexpression are fundamental oncogenic drivers and defining features of HER2+ breast cancer. Targeting HER2 inhibits downstream pro-proliferative and survival pathways. Evidence: ERBB2 shows very high expression and prevalence in HER2+ Epithelial cells, serving as an established therapeutic target (Section 15). Consistent chromosomal amplification on 17q12 (ERBB2) in HER2+ samples (Section 4). Validation: Clinical use of anti-HER2 therapies (e.g., Trastuzumab, Pertuzumab) is well-established. Future validation could focus on novel HER2-targeting ADCs or combination therapies to overcome resistance.
  8. HDAC1: HDAC1 is highly expressed in proliferating cancer cells, especially in aggressive subtypes like TNBC, contributing to uncontrolled cell growth and survival by regulating gene silencing. Evidence: HDAC1 shows a significantly higher expressing cell fraction in TNBC epithelial cells compared to Normal and other cancer subtypes (Section 19). Validation: Test HDAC inhibitors in TNBC cell lines and organoids to evaluate their impact on cell proliferation and apoptosis. Investigate combination therapies with other anti-proliferative agents.

Follow-up validation ideas:

  1. Validate expression levels and co-expression of surface markers (e.g., CD47, VSIR on macrophages; FAP, SDC1 on fibroblasts; CD44 on T cells) in larger patient cohorts using flow cytometry or multiplex immunofluorescence/IHC on patient biopsies to confirm findings at the protein level and assess prognostic value.
  2. Use spatial analysis techniques (e.g., spatial transcriptomics/proteomics) to map the precise localization and interaction hotspots of identified cell types and ligand-receptor pairs (e.g., SPP1-integrin, CXCL12-CXCR4) within tumor tissue sections to confirm predicted cell-cell interactions and their spatial context.
  3. Conduct in vitro co-culture experiments (e.g., tumor cells with macrophages/fibroblasts) or in vivo patient-derived xenograft (PDX) models to test the functional impact of blocking identified ligand-receptor interactions (e.g., using neutralizing antibodies or small molecule inhibitors against SPP1, CXCR4, CD47, FAP) on tumor growth, invasion, and immune cell function.
  4. Validate the functional role of upregulated genes (e.g., AGR2, SLC7A5, CDCP1 in epithelial cells; specific integrin subunits in CAFs) in tumor cell proliferation, survival, and metastasis using genetic knockdown/knockout in relevant breast cancer cell lines.
  5. Analyze independent bulk RNA-seq or single-cell RNA-seq datasets from breast cancer patients to externally validate the observed gene expression signatures, cell population shifts, and pathway enrichments across different subtypes.
  6. Perform high-throughput drug sensitivity screens on breast cancer cell lines (especially TNBC) or patient-derived organoids with specific genetic alterations (e.g., FGFR1 amplification) or high expression of identified targets (e.g., SLC7A5, HDAC1) to identify effective therapeutic agents.
  7. Investigate the functional consequences of altered T cell subsets (e.g., increased Tregs, activated CD4+ T cells) and macrophage polarization (e.g., M1/M2 shifts) on anti-tumor immunity using ex vivo immune cell functional assays (e.g., cytokine production, cytotoxicity assays) from patient samples or mouse models.
  8. Confirm specific copy number amplifications (e.g., SOX2, EIF3E, INTS8) identified in epithelial cells using fluorescence in situ hybridization (FISH) or digital PCR in patient samples, correlating with clinical outcomes.

Limitations:

The analysis is based on single-cell RNA sequencing data, which provides high-resolution insights into cellular heterogeneity and gene expression but is inherently a snapshot and does not capture dynamic cellular processes or protein-level modifications in real-time. Copy number variation (CNV) estimates derived from scRNA-seq can be less precise than dedicated genomic assays (e.g., WGS, FISH). While cell-cell interactions are inferred computationally, their functional validation requires in vitro and in vivo experimental systems. The observed correlations do not establish causality, and further mechanistic studies are required to confirm the functional roles of identified markers and pathways. The dataset's specific patient cohort and sample sizes may limit the generalizability of some findings to the broader breast cancer population. Lastly, this analysis is largely correlative, and observed changes in cell type proportions, marker expression, or pathway activity need to be functionally validated in experimental models to confirm their therapeutic relevance.

23. Query List

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