SCODiA Report by MLBI Lab

Single-Cell Landscape of Breast Cancer Microenvironment: Subtype-Specific Immune and Stromal Dynamics

This report dissects the single-cell RNA-sequencing data from breast tissue, revealing the intricate cellular and molecular landscape across normal, ER+, HER2+, and Triple-Negative Breast Cancer (TNBC) conditions. We highlight significant differences in epithelial cell ploidy, immune cell populations (T cells, macrophages), and stromal fibroblast phenotypes that shape the tumor microenvironment. Key insights into cell-cell interaction networks and condition-specific marker expression unveil unique biological adaptations, providing a foundation for understanding disease progression and identifying potential therapeutic vulnerabilities.

Contents

  1. Dataset overview
  2. UMAP Visualization of Single-Cell RNA-seq Data by Various Annotations
  3. Marker Gene Expression and Cell Type Annotation in Breast Tissue UMAP
  4. Overall Celltype_subset Marker Expression Analysis
  5. Genomic Copy Number Variation Analysis in Tumor-Origin and Unassigned Cells
  6. CNV-based UMAP Visualization of Cell Types, Ploidy, Conditions, and Samples in Breast Tissue
  7. Minor Cell Type Population Analysis Across Breast Cancer Conditions
  8. T Cell Subset Population Analysis Across Breast Cancer Subtypes and Normal Breast Tissue
  9. Macrophage Subset Population Analysis in Breast Tissue Conditions
  10. T Cell Subset Population Analysis Across Breast Cancer Conditions
  11. Differential Macrophage Subtype Proportions in Breast Cancer Conditions
  12. Ploidy Population Analysis of Tumor-Origin and Unassigned Cells Across Breast Cancer Subtypes
  13. Cell-Cell Interaction Patterns Across Breast Cancer Subtypes
  14. ER+ 유방암 미세환경 내 세포-세포 상호작용 분석
  15. Immune Checkpoint and Cell Cycle Related Cell-Cell Interactions Across Breast Cancer Subtypes
  16. Condition-Specific Cell-Cell Interaction Patterns in Breast Cancer Subtypes
  17. Epithelial Cell Condition-Specific Surfaceome Markers in Breast Cancer
  18. Macrophage Condition-Specific Surfaceome Markers in Breast Tissue
  19. Fibroblast Condition-Specific Surfaceome Markers in Breast Cancer Subtypes
  20. T cell CD4+ Condition-Specific Surfaceome Markers in Breast Cancer
  21. Differential Expression of Cell Cycle Genes in Breast Epithelial Cells Across Subtypes
  22. Gene Ontology (GSA) Analysis of Epithelial Cells Across Breast Cancer Conditions
  23. Gene Set Enrichment Analysis Reveals Condition- and Cell Type-Specific Pathway Alterations in Breast Tissue
  24. Discussion
  25. Query List

0. Dataset overview

데이터셋 요약

1. UMAP Visualization of Single-Cell RNA-seq Data by Various Annotations

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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, visualizing the global transcriptional landscape of breast tissue cells. The cells are colored and grouped according to key metadata annotations: condition (ER+, HER2+, Normal, TNBC), sample, celltype_major, celltype_minor, ploidy_dec (Aneuploid, Diploid), and celltype_subset. This visualization helps to assess the overall data structure, the quality of cell type annotations, the presence of condition- or sample-specific clustering, and the distribution of ploidy states across cell populations.

Visual Summary

Global Embedding Structure

The UMAP displays a highly structured and distinct arrangement of cells, forming several well-separated clusters and interconnected regions. This indicates that the chosen embedding parameters effectively capture the underlying biological variability within the dataset.

Condition and Sample Distribution

Cell Type Hierarchy

Ploidy Status

Biological Interpretation

Annotation Notes

2. Marker Gene Expression and Cell Type Annotation in Breast Tissue UMAP

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Analysis Overview

This analysis visualizes the expression of a panel of known cell type-specific marker genes across the single-cell RNA-seq UMAP embedding, alongside the pre-assigned celltype_minor annotations. The purpose is to assess the consistency and quality of the cell type annotations by observing whether expected marker genes are highly expressed within their corresponding cell populations. The dataset originates from human breast tissue, encompassing various conditions including ER+, HER2+, Normal, and TNBC.

Visual Summary

The UMAP plots clearly display distinct clusters representing different cell types present in the breast tissue. The gene expression plots for the selected markers show strong, localized expression patterns that generally correspond well with the manually assigned celltype_minor clusters.

T Cell Markers (CD3D, CD4, CD8A)

B Cell and Plasma Cell Markers (CD79A, MS4A1, MZB1)

Myeloid Cell Markers (CD14, LYZ)

Stromal Cell Markers (FBLN1, NOTCH3)

Epithelial Cell Markers (EPCAM, MUC1)

Endothelial Cell Marker (CD34)

Biological Interpretation

The UMAP visualizations of specific marker genes effectively validate the celltype_minor annotations within this single-cell RNA-seq dataset from human breast tissue. The observed expression patterns are highly congruent with established biological knowledge of these markers for their respective cell types.

Annotation Notes

The strong concordance between the expression patterns of well-known cell type markers and the celltype_minor annotations indicates a high quality of cell identity assignment in this dataset. The distinct clustering of various cell types and the specific localization of their canonical markers suggest that the dimensionality reduction (UMAP) and subsequent clustering and annotation processes have been effective in resolving biologically meaningful cell populations. There are no apparent inconsistencies or significant overlaps in marker gene expression that would challenge the current celltype_minor assignments. This robust annotation serves as a reliable foundation for downstream analyses, such as differential gene expression or cell-cell interaction studies, across different conditions of breast cancer.

3. Overall Celltype_subset Marker Expression Analysis

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Analysis Overview

This analysis investigates the expression patterns of marker genes across various celltype_subset populations derived from single-cell RNA-seq data of human breast tissue. The plot_markers_and_expression_dot tool was used to generate a dot plot, which visualizes the mean expression level (color intensity) and the fraction of cells expressing a gene (dot size) for each marker in each cell type subset. The primary goal of this visualization is to validate and confirm the assigned celltype_subset annotations based on known and differentially expressed surface markers. Markers common in three or more groups were removed to emphasize specificity.

Visual Summary

The dot plot effectively illustrates distinct gene expression signatures for nearly all celltype_subset categories. Key observations include:

Biological Interpretation

The observed marker expression patterns strongly support the biological identity of the annotated celltype_subset populations within the human breast tissue context.

Annotation Notes

The comprehensive marker expression dot plot serves as strong validation for the quality and biological consistency of the celltype_subset annotations in this AnnData object. The clear, specific, and biologically relevant marker patterns across almost all cell types and their subsets indicate that the clustering and annotation process has successfully delineated distinct cell populations. The strategy of removing broadly expressed markers further enhances the confidence in the specificity of the presented markers. This robust annotation foundation is critical for subsequent downstream analyses, such as differential gene expression, pathway analysis, and cell-cell interaction studies.

4. Genomic Copy Number Variation Analysis in Tumor-Origin and Unassigned Cells

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Analysis Overview

This analysis investigates copy number variations (CNVs) in cells identified as 'Epithelial cell' (the tumor-origin celltype in this dataset) and 'unassigned' cells, grouped by individual sample. The results comprise a heatmap visualizing the log2(Copy Number Ratio, CNR) across the genome for these cell populations in each sample, along with a summary heatmap and bar chart highlighting significantly amplified genomic regions and their frequencies across samples. This provides insight into the genomic landscape of tumor cells and potential unclassified tumor-related cells within different breast cancer subtypes.

Visual Summary

The primary heatmap illustrates the log2(CNR) values across ~1900 genomic spots for various cell groups, predominantly comprising 'Epithelial cell' and 'unassigned' cells.

The second figure provides a summary of significantly amplified cytogenetic bands:

Biological Interpretation

The CNV analysis of tumor-origin epithelial cells and associated unassigned cells reveals characteristic genomic alterations relevant to breast cancer subtypes.

Clinical or Translational Implications

5. CNV-based UMAP Visualization of Cell Types, Ploidy, Conditions, and Samples in Breast Tissue

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Analysis Overview

This analysis presents a UMAP projection derived from Copy Number Variation (CNV) estimates of single-cell RNA-seq data from breast tissue. The UMAP plots are colored by various metadata features: major cell type, minor cell type, ploidy inference (aneuploid/diploid), breast cancer condition (ER+, HER2+, Normal, TNBC), and individual sample. This visualization helps to assess the genomic heterogeneity, particularly in terms of CNVs, across different cell populations and disease states, and to evaluate the consistency of cell type and ploidy annotations within the CNV space.

Visual Summary

Cell Type Distribution (Major and Minor)

Ploidy Status

Condition-Specific Patterns

Sample Contribution

Biological Interpretation

The CNV-based UMAP embedding provides a powerful visualization of genomic alterations in breast tissue.

  1. Identification of Tumor Cells: The strong co-localization of "Epithelial cells" (the presumed tumor origin cell type) with "Aneuploid" cells in the central region of the UMAP strongly indicates that this cluster represents the malignant cell population. This is further supported by the enrichment of cells from cancer conditions (ER+, HER2+, TNBC) within this aneuploid epithelial cluster. This confirms the biological relevance of CNV estimates in delineating tumor cells from the non-malignant stromal and immune cells.
  2. Tumor Microenvironment: Non-epithelial cells (T cells, B cells, Myeloid cells, Stromal cells, Endothelial cells) are primarily "Diploid" and cluster distinctly from the aneuploid epithelial cells. These cells likely represent components of the tumor microenvironment (TME) or normal tissue resident cells. Their distinct clustering in the CNV space confirms their relatively stable genomes compared to the malignant cells.
  3. Disease-Specific CNV Signatures: While ER+, HER2+, and TNBC cells largely share the aneuploid space, subtle differences in their distribution might suggest condition-specific CNV patterns or varying degrees of genomic instability. Further in-depth analysis of CNV profiles within these sub-regions could reveal specific genomic alterations characteristic of each breast cancer subtype.
  4. Sample Heterogeneity: The sample plot highlights inter-patient variability. Although cancer cells from different patients mix within the aneuploid region, some samples show distinct spatial preferences, suggesting unique CNV landscapes or clonal architectures for individual tumors. This underscores the importance of single-cell analysis for capturing patient-specific tumor biology and heterogeneity.

Annotation Notes

The consistency observed across the celltype_major, celltype_minor, ploidy_dec, and condition plots within the CNV UMAP space indicates high quality and concordance of these annotations with the underlying genomic (CNV) features. The clear segregation of aneuploid epithelial cells (from cancer conditions) from diploid immune/stromal cells (including those from normal samples) demonstrates the robustness of the cell type and ploidy inference. The CNV embedding provides an excellent framework for validating and interpreting cell identities, especially in heterogeneous tissues like tumors.

6. Minor Cell Type Population Analysis Across Breast Cancer Conditions

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Analysis Overview

This analysis presents a stacked bar plot visualizing the relative proportions of minor cell types within individual samples, grouped by distinct breast cancer conditions: ER+, HER2+, Normal, and Triple-Negative Breast Cancer (TNBC). This allows for a direct comparison of cellular composition across different disease states and highlights sample-to-sample heterogeneity within each condition, providing insights into the tumor microenvironment and tissue architecture.

Visual Summary

The stacked bar plot provides a comprehensive overview of minor cell type distributions across 25 samples, categorized into ER+, HER2+, Normal, and TNBC conditions.

Breast Cancer Subtype Differences

Biological Interpretation

The observed cell type population dynamics offer critical biological insights into breast cancer pathogenesis and the tumor microenvironment (TME).

Immune Cell Infiltration

  1. Unique Tumor Cell States: Highly aberrant tumor cells with transcriptomic profiles that deviate significantly from standard epithelial cell annotations.
  2. Unusual Stromal or Immune Cells: Highly plastic stromal or immune cells in a deeply pathological state not captured by existing reference annotations.
  3. Technical Artifacts: While less likely given the overall quality of other annotations, it's possible some samples have unique technical challenges.

Further investigation into these "unassigned" populations is warranted to uncover novel cell states or potential issues with annotation.

Clinical or Translational Implications

The distinct cellular compositions observed across breast cancer subtypes have direct clinical and translational implications.

7. T Cell Subset Population Analysis Across Breast Cancer Subtypes and Normal Breast Tissue

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Analysis Overview

This analysis visualizes the proportional distribution of T cell minor subsets (ILC, NK cell, T cell CD4+, T cell CD8+, and unassigned) within the major T cell population. These proportions are displayed for individual samples, grouped by distinct breast cancer conditions (ER+, HER2+, TNBC) and normal breast tissue, providing insights into the immune microenvironment composition.

Visual Summary

The stacked bar plots illustrate the relative abundance of T cell subsets across different samples and conditions:

Biological Interpretation

The observed shifts in T cell subset populations provide valuable biological insights into the immune microenvironment of breast cancer:

  1. ILC Depletion in Breast Cancer: The most striking observation is the general decrease in ILC proportions within the T cell major population in breast cancer samples compared to many normal breast tissues. Innate Lymphoid Cells (ILCs) are crucial mediators of innate immunity, tissue homeostasis, and initial responses against pathogens and cellular stress. Different ILC subsets (ILC1, ILC2, ILC3) play distinct roles in immune surveillance and inflammatory responses [1]. Their reduced presence in tumor microenvironments could suggest a disruption of innate immune surveillance or a shift in the local immune landscape where adaptive immunity (T cells) becomes more prominent, or is modulated differently by the tumor.
  2. Adaptive T Cell Dominance in Tumors: In breast cancer samples, CD4+ T cells (helper T cells) and CD8+ T cells (cytotoxic T lymphocytes, CTLs) collectively form the majority of the T cell major population. This indicates an active adaptive immune response.
  1. Subtype-Specific Immune Microenvironments: While all cancer subtypes show reduced ILCs relative to normal, there are subtle differences. The individual sample variability within each breast cancer subtype highlights the heterogeneous nature of the immune response in patients, even within the same clinical classification. For example, some TNBC samples showing relatively higher ILCs might represent a distinct immune phenotype that warrants further investigation, potentially indicative of an initial or unique innate immune engagement in those specific tumors.
  2. Minor Role of NK Cells: The consistently low proportion of NK cells in this specific "T cell" major gate (which also includes ILCs) suggests that while NK cells are important anti-tumor effectors, they might represent a smaller fraction compared to T cell lineages or other ILCs in this specific context, or their classification within celltype_major='T cell' might be due to hierarchical clustering/gating strategies. Given "NK cell" is a distinct celltype_minor, its low proportion here specifically indicates its relative abundance within the grouped "T cell" major populations shown, not necessarily its total absence or low abundance in the entire dataset.

Clinical or Translational Implications

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References:

  1. ILC Overview: GeneCards entry for ILC: https://www.genecards.org/Search/Keyword?query=Innate%20Lymphoid%20Cells
  2. CD4+ T cells in cancer: PubMed search for "CD4 T cells cancer function": https://pubmed.ncbi.nlm.nih.gov/?term=CD4+T+cells+cancer+function
  3. CD8+ T cells in cancer immunotherapy: PubMed search for "CD8 T cells cancer immunotherapy": https://pubmed.ncbi.nlm.nih.gov/?term=CD8+T+cells+cancer+immunotherapy
  4. CD8+/CD4+ ratio prognosis: PubMed search for "CD8 CD4 ratio cancer prognosis": https://pubmed.ncbi.nlm.nih.gov/?term=CD8+CD4+ratio+cancer+prognosis

8. Macrophage Subset Population Analysis in Breast Tissue Conditions

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Analysis Overview

This analysis visualizes the relative proportions of different macrophage subsets (M1, M2A, M2B, M2C, M2D) within the total macrophage population across various breast tissue conditions: Estrogen Receptor positive (ER+), Human Epidermal Growth Factor Receptor 2 positive (HER2+), Normal, and Triple-Negative Breast Cancer (TNBC). The aim is to understand how macrophage polarization states differ between healthy breast tissue and distinct breast cancer subtypes at the sample level.

Visual Summary

The barplot displays the percentage composition of macrophage subsets for individual samples grouped by condition.

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

Biological Interpretation

Macrophages are highly plastic immune cells that polarize into different functional states, commonly simplified as M1 (classically activated) and M2 (alternatively activated) phenotypes, though these represent a spectrum.

  1. Shift from M2B dominance in Normal to M1/M2A prominence in Cancer: The most striking observation is the shift in macrophage subset composition from normal to cancerous breast tissue. Normal breast tissue often shows a high abundance of M2B macrophages, which are known for their roles in immune regulation and B cell activation, contributing to immune homeostasis. In stark contrast, all breast cancer subtypes (ER+, HER2+, TNBC) exhibit a prominent presence of M1 macrophages and a noticeable proportion of M2A macrophages.
  2. M1 Macrophages in the Tumor Microenvironment (TME): The substantial presence of M1 macrophages in the TME of breast cancer samples is noteworthy. M1 macrophages are typically associated with pro-inflammatory responses and anti-tumor immunity, involved in pathogen clearance and tumor suppression by secreting pro-inflammatory cytokines and mediating direct cytotoxicity [PubMed Search: M1 macrophage anti-tumor]. This finding suggests that despite the overall immune suppressive environment often found in tumors, a significant M1-polarized immune response is present or attempted in these breast cancer samples. It's important to acknowledge that the functional state of M1-like macrophages within the TME can be complex and influenced by various tumor-derived factors that may impair their anti-tumor efficacy.
  3. M2A Macrophages and Tumor Progression: The consistent presence of M2A macrophages in tumor conditions aligns with their known roles in promoting tumor growth, angiogenesis, and immune suppression. M2A macrophages are typically induced by Th2 cytokines (IL-4, IL-13) and contribute to wound healing, tissue repair, and immunosuppression, which can be co-opted by tumors to facilitate their progression [GeneCards: M2A macrophages]. Their elevated presence, particularly alongside M1, highlights the phenotypic complexity and heterogeneity of tumor-associated macrophages (TAMs).
  4. Implications of M2B Reduction: The reduction of M2B macrophages in tumor samples compared to normal tissue suggests a potential dysregulation of regulatory immune responses or a shift away from tissue homeostasis mechanisms in the cancerous state.

Clinical or Translational Implications

The distinct macrophage subset profiles observed between normal and cancerous breast tissues, and within cancer subtypes, carry significant clinical implications:

  1. Prognostic Marker: The balance between M1 and different M2 macrophage subsets (M1/M2 ratio) is often correlated with patient prognosis in various cancers, including breast cancer. A higher M1 presence or M1/M2 ratio is generally associated with a better prognosis, while a higher M2 presence is often linked to poorer outcomes [PubMed Search: M1 M2 macrophage breast cancer prognosis]. Further studies on the functional state and exact M1/M2 ratio could provide prognostic insights for breast cancer patients.
  2. Therapeutic Targeting: Understanding the dominant macrophage subsets in different breast cancer types can inform targeted therapeutic strategies.
  1. Heterogeneity and Personalized Medicine: The sample-to-sample variability within each breast cancer subtype underscores the need for personalized approaches in oncology. Macrophage profiling for individual patients could potentially guide treatment decisions and predict responses to immunotherapies.

9. T Cell Subset Population Analysis Across Breast Cancer Conditions

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Analysis Overview

This analysis presents boxplots illustrating the proportional abundance of various T cell subset populations across different breast cancer conditions (ER+, TNBC, HER2+) compared to normal breast tissue. The aim is to identify T cell subsets that show statistically significant differences in their proportions, providing insights into the immune landscape modulated by different breast cancer subtypes. A p-value cutoff of 0.1 was used to determine statistical significance between groups.

Visual Summary

The boxplots display the celltype proportion for five T cell subsets: Treg, LTI, Th2, Th17, and Th1, across four conditions: Normal, ER+, TNBC, and HER2+.

Treg (Regulatory T cell)

LTI (Lymphoid Tissue Inducer cell)

Th2 (T helper 2 cell)

Th17 (T helper 17 cell)

Th1 (T helper 1 cell)

Biological Interpretation

These findings highlight distinct shifts in the T cell compartment within different breast cancer subtypes compared to normal tissue, reflecting specific immunological adaptations within the tumor microenvironment (TME).

  1. Immunosuppressive Landscape: The significant increase in Treg cell proportions in ER+ breast cancer suggests an enriched immunosuppressive environment in this subtype compared to normal tissue. Tregs are critical in maintaining immune tolerance, and their abundance in tumors often correlates with immune evasion and poorer prognosis by suppressing anti-tumor immune responses [1].
  2. LTI Cell Depletion in Cancer: The drastic reduction of LTI cell proportions in all breast cancer subtypes (ER+, TNBC, HER2+) compared to normal tissue is notable. LTI cells are crucial for the development and maintenance of lymphoid organs and tertiary lymphoid structures (TLS) [2]. Their reduction in the tumor environment might indicate impaired formation or maintenance of effective anti-tumor immune responses, as TLS formation is often associated with better prognosis in some cancers.
  3. Altered T helper Balance:

Clinical or Translational Implications

The observed shifts in T cell subset proportions have potential implications for understanding breast cancer pathogenesis and designing targeted immunotherapies:

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References:

[1] Ohue, Y., & Nishikawa, H. (2019). Regulatory T cells in cancer: from tumor immunology to cancer immunotherapy. *Cancer Science*, 110(3), 856-865. PubMed Search: Regulatory T cells cancer immunotherapy

[2] Denton, A., & Chtanova, T. (2020). Lymphoid Tissue Inducer Cells in Lymphoid Organogenesis, Homeostasis, and Immunity. *Frontiers in Immunology*, 11, 1988. PubMed Search: Lymphoid Tissue Inducer cells function

[3] Gatault, S., & Bréchard, S. (2021). Th2 in Cancer: The Good, the Bad, and the Ugly. *International Journal of Molecular Sciences*, 22(14), 7401. PubMed Search: Th2 cells cancer microenvironment

[4] Lim, K., & Kim, Y. H. (2018). Th17 Cells in Cancer: The Jekyll and Hyde of Tumor Immunology. *Frontiers in Immunology*, 9, 1391. PubMed Search: Th17 cells cancer dual role

[5] Kankeu, C., Gatault, S., & Brechard, S. (2023). T helper cell plasticity: Driving cancer progression or anti-tumor immune responses. *European Journal of Immunology*, 53(2), e2250262. PubMed Search: Th1 Th2 Th17 balance cancer immunotherapy

10. Differential Macrophage Subtype Proportions in Breast Cancer Conditions

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Analysis Overview

This analysis investigates the proportions of specific macrophage subset populations, Macrophage (M2A) and Macrophage (M2B), across different breast cancer conditions (ER+, TNBC, HER2+) compared to a Normal tissue reference. The boxplots highlight statistically significant differences in cell type proportions between conditions, based on a p-value cutoff of 0.1.

Visual Summary

The boxplots display the celltype proportion on the y-axis for Macrophage (M2A) and Macrophage (M2B) subsets across four conditions: Normal, ER+, TNBC, and HER2+. Individual data points (samples) are overlaid on each box. P-values from statistical tests comparing condition pairs are indicated above the respective comparisons.

Macrophage (M2A):

Macrophage (M2B):

Biological Interpretation

Macrophages are highly plastic immune cells that play critical roles in the tumor microenvironment (TME), often differentiating into various pro- or anti-tumoral phenotypes. M2 macrophages, in particular, are frequently associated with tumor progression, immune suppression, angiogenesis, and tissue remodeling. However, M2 macrophages are heterogeneous and can be further subdivided (e.g., M2a, M2b, M2c, M2d), with distinct functional properties.

Increased Macrophage (M2A) in ER+ and TNBC:

Decreased Macrophage (M2B) in all Breast Cancer Subtypes:

Clinical or Translational Implications

The differential proportions of specific macrophage subsets across breast cancer conditions have important clinical and translational implications:

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References:

  1. M2 Macrophage Subtypes and Functions:

PubMed Search: M2 macrophage subtypes cancer

  1. Macrophage Plasticity and Polarization:

PubMed Search: macrophage polarization tumor microenvironment

  1. Tumor-Associated Macrophages in Breast Cancer:

PubMed Search: tumor associated macrophages breast cancer

  1. Targeting Macrophages in Cancer Therapy:

PubMed Search: macrophage targeted therapy cancer

11. Ploidy Population Analysis of Tumor-Origin and Unassigned Cells Across Breast Cancer Subtypes

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Analysis Overview

This analysis visualizes the ploidy distribution (Aneuploid, Diploid, Unclear) within selected cell populations—specifically, Epithelial cells (identified as tumor-origin cells) and "unassigned" cells—across various breast cancer conditions (ER+, HER2+, TNBC) and normal breast tissue samples. The results are presented as stacked bar plots for individual samples within each condition, providing insight into the genomic stability of these crucial cell types in different disease contexts.

Visual Summary

The stacked bar plot presents the relative proportions of Aneuploid, Diploid, and Unclear cells for Epithelial and unassigned cell populations across individual samples, grouped by breast cancer subtype or normal tissue status.

Biological Interpretation

The observed ploidy patterns strongly align with the known genomic instability characteristic of cancer.

  1. Baseline for Normalcy: The consistent diploidy in normal breast tissue samples serves as a robust control, confirming that the ploidy inference method accurately distinguishes normal cellular states from cancerous ones. This is crucial for validating the downstream interpretation of tumor samples.
  2. Aneuploidy as a Cancer Hallmark: The prevalence of aneuploidy in Epithelial (tumor-origin) cells within ER+, HER2+, and TNBC samples is a direct reflection of genomic instability, a fundamental hallmark of cancer [Hanahan and Weinberg, 2011; PubMed Search: Hanahan Weinberg Hallmarks of Cancer]. Aneuploidy, the condition of having an abnormal number of chromosomes, arises from errors during cell division and is often associated with malignant transformation, tumor progression, and therapeutic resistance.
  3. Subtype-Specific Genomic Instability:
  1. Role of Unassigned Cells: The analysis aggregates Epithelial cells (tumor-origin) and "unassigned" cells. If "unassigned" cells largely represent stromal or immune cells that are genetically stable, their inclusion might slightly dilute the aneuploid signal from tumor epithelial cells. However, in the context of cancer, tumor microenvironment cells can also undergo genetic changes or be influenced by aneuploid tumor cells. If 'unassigned' cells represent difficult-to-classify tumor cells, their ploidy would contribute to the overall aneuploid signal.
  2. Implications of "Unclear" Ploidy: The small "Unclear" fractions might represent cells where the CNV estimation was ambiguous or cells undergoing complex chromosomal rearrangements that defy simple diploid/aneuploid classification. This could be due to technical limitations or genuinely complex biological states.

Clinical or Translational Implications

The detection of aneuploidy in tumor-origin cells has several important clinical implications:

  1. Prognostic Marker: The degree of aneuploidy can serve as a prognostic indicator in breast cancer. Higher levels of aneuploidy are often associated with a more aggressive disease course, increased risk of recurrence, and poorer patient outcomes, especially in TNBC [Müller, C., et al. (2018). Genomic instability in breast cancer: molecular mechanisms and clinical implications. *Breast Cancer Research*, 20(1), 8].
  2. Therapeutic Stratification: Understanding the ploidy status could potentially aid in stratifying patients for specific therapies. Tumors with high aneuploidy might respond differently to chemotherapy or targeted agents compared to diploid tumors. For instance, high genomic instability might make tumors more susceptible to DNA-damaging agents or PARP inhibitors, particularly in BRCA1/2-mutated contexts often seen in TNBC [Lord, C. J., & Ashworth, A. (2012). The DNA repair defects that underlie sporadic breast cancer. *British Journal of Cancer*, 107(6), 887-892].
  3. Biomarker for Drug Resistance: Aneuploidy can contribute to drug resistance by providing a diverse genetic landscape upon which resistance mechanisms can evolve rapidly [Tang, Y. C., et al. (2011). Aneuploidy: an agent of phenotypic heterogeneity and adaptability. *Nature Reviews Genetics*, 12(8), 539-553]. Monitoring ploidy changes, especially in recurrent or treatment-resistant cases, could provide insight into tumor evolution.
  4. Monitoring Tumor Heterogeneity: The sample-to-sample variation in ploidy observed, even within the same cancer subtype, highlights tumor heterogeneity. This emphasizes the need for personalized medicine approaches, as a single biopsy might not fully capture the genomic landscape of the entire tumor, and different regions could exhibit varying degrees of aneuploidy.

12. Cell-Cell Interaction Patterns Across Breast Cancer Subtypes

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Analysis Overview

This analysis investigates cell-cell interaction (CCI) patterns within the tumor microenvironment (TME) of breast cancer, comparing various conditions (ER+, HER2+, TNBC) against normal breast tissue. The focus is on key cell types: Epithelial cells (categorized by ploidy into Diploid and Aneuploid to distinguish potentially normal from tumor cells), Fibroblasts, Macrophages, and T cells (CD4+ and CD8+). CellPhoneDB results are visualized, highlighting ligand-receptor pairs with significant p-values and high interaction strengths, limited to the top 80 pairs per condition.

Visual Summary

The dot plots illustrate significant cell-cell communication hubs and their associated ligand-receptor pairs for each breast cancer subtype (ER+, HER2+, TNBC) and Normal tissue. Dot size corresponds to the statistical significance (-log10(p-value)), and color intensity represents the interaction strength (log2(mean expression)).

Biological Interpretation

The analysis reveals distinct shifts in cell-cell communication from normal breast tissue to breast cancer, and further heterogeneity among cancer subtypes.

Immune Microenvironment Modulation:

Key Oncogenic Signaling Axes:

Clinical or Translational Implications

The identified cell-cell interaction patterns offer significant insights for therapeutic development and personalized medicine in breast cancer.

Subtype-Specific Targeting:

13. ER+ 유방암 미세환경 내 세포-세포 상호작용 분석

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[Analysis Visualization Results]...

Analysis Overview

제공된 분석 결과는 ER+ (Estrogen Receptor positive) 유방암 조건에서 다양한 세포 유형 간의 세포-세포 상호작용 (Cell-Cell Interaction, CCI)을 CellPhoneDB를 이용하여 분석한 후 시각화한 도트 플롯입니다. 이 플롯은 각 상호작용의 유의미성 (p-value, 점의 크기)과 상호작용 강도 (ligand-receptor 평균 발현량, 점의 색상)를 함께 보여줍니다. 특히, 유방암 미세환경의 핵심 구성 요소인 다양한 면역 세포, 기질 세포, 내피 세포, 그리고 암세포로 추정되는 이수성 상피세포 (Aneuploid Epithelial cell) 간의 리간드-수용체 쌍 기반 상호작용에 초점을 맞추고 있습니다.

Visual Summary

제공된 도트 플롯은 ER+ 유방암 미세환경에서 세포 쌍과 리간드-수용체 쌍 간의 상호작용 패턴을 보여줍니다.

전반적으로 많은 세포 유형이 인테그린 (Integrin) 계열의 리간드-수용체 복합체 (예: COL10A1_integrin a1b1_complex, FN1_integrin a2b1_complex 등)를 통해 상호작용하는 것이 관찰됩니다. 특히 대식세포 (Macrophage), 섬유아세포 (Fibroblast), 내피세포 (Endothelial cell), 그리고 이수성 상피세포 (Aneuploid Epithelial cell)가 광범위한 인테그린 상호작용에 관여하고 있습니다.

주요 상호작용 패턴은 다음과 같습니다:

Biological Interpretation

이 ER+ 유방암 조건에서의 CCI 분석은 암 미세환경 (TME) 내의 복잡한 세포 간 네트워크를 보여주며, 특히 종양 성장, 침윤, 전이 및 면역 회피에 중요한 역할을 하는 메커니즘을 시사합니다.

  1. 세포외 기질 (ECM) 상호작용의 중요성 (Integrins):
  1. 면역 세포-암세포/기질 세포 상호작용:
  1. WNT 신호 전달 경로의 역할:

Clinical or Translational Implications

본 분석에서 식별된 ER+ 유방암의 세포-세포 상호작용은 잠재적인 치료 표적 및 실험적 검증 전략에 대한 중요한 통찰력을 제공합니다.

  1. 인테그린 표적화:
  1. 면역 조절 상호작용 표적화:
  1. WNT 신호 전달 경로 조절:

이러한 상호작용에 대한 추가 연구와 표적화는 ER+ 유방암 환자를 위한 보다 효과적인 치료 전략을 개발하는 데 기여할 수 있습니다.

14. Immune Checkpoint and Cell Cycle Related Cell-Cell Interactions Across Breast Cancer Subtypes

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interactions (CCI) involving genes related to immune checkpoint and cell cycle pathways across various breast cancer conditions (ER+, HER2+, TNBC) and normal breast tissue. The plot_cci_dots tool was used to visualize significant ligand-receptor interactions between different cell type pairs, focusing on a predefined list of genes. The results provide insights into how cellular communication patterns, particularly those relevant to immune modulation and growth, differ in healthy tissue versus different cancer contexts.

Visual Summary

The dot plots display cell-cell interactions for specific ligand-receptor pairs across distinct conditions: ER+, HER2+, Normal, and TNBC.

Key Observations:

  1. TGF-beta Signaling Dominance: Interactions involving the TGFB1/TGFB3 ligands and their receptors (TGFbeta_receptor1/2) are highly prevalent and significant across all breast cancer subtypes (ER+, HER2+, TNBC) and also active in Normal tissue. These interactions frequently occur between various immune cells (T CD8+, T CD4+, Macrophage), stromal cells (Fibroblast), and epithelial cells (Diploid Epi, Aneuploid Epi). The integrin_avB6_complex which activates latent TGFB, is also noted.
  2. EGFR Signaling in Normal and TNBC: Interactions involving EGFR ligands (AREG, EREG, HBEGF, TGFA) and EGFR are prominent in Normal breast tissue, primarily involving epithelial, endothelial, and stromal cells. Crucially, these interactions are also highly active and significant in Triple-Negative Breast Cancer (TNBC), particularly between macrophages, fibroblasts, and aneuploid epithelial cells. EGFR signaling is less pronounced in the ER+ and HER2+ plots shown.
  3. Immune Checkpoint & T Cell Interactions:
  1. Cellular Context:

Biological Interpretation

The analysis of cell-cell interactions within pathways related to immune checkpoints and cell cycle regulation reveals dynamic and condition-specific communication networks.

  1. TGF-beta Pathway as a Central Hub: The widespread and significant TGF-beta signaling underscores its fundamental role in both normal tissue homeostasis and cancer progression. In cancer, elevated TGF-beta signaling is a well-established mechanism for promoting immunosuppression by inhibiting anti-tumor immune responses, driving epithelial-to-mesenchymal transition (EMT), and fostering metastasis. The involvement of integrin_avB6_complex suggests active conversion of latent TGF-beta into its biologically active form, further amplifying these pro-tumorigenic effects [1]. Its high activity between diverse cell types (immune, stromal, tumor-epithelial) suggests a complex, multi-faceted role in shaping the tumor microenvironment across all breast cancer subtypes.
  2. EGFR Signaling in TNBC: The prominent EGFR signaling in TNBC, involving macrophages, fibroblasts, and aneuploid epithelial cells, is highly significant. TNBC is characterized by aggressive behavior and a lack of specific hormone receptors and HER2 amplification, making EGFR a potential therapeutic target [2]. The observed interactions suggest that growth factors like AREG, EREG, HBEGF, and TGFA, secreted by stromal or immune cells, actively stimulate EGFR on tumor epithelial cells and other TME components, promoting proliferation and survival. This highlights a mechanism of extrinsic growth factor dependency in TNBC.
  3. Immune Checkpoint and T Cell Co-stimulation:
  1. Tumor Microenvironment (TME) Dynamics: The consistent involvement of macrophages and fibroblasts in numerous significant interactions across all conditions emphasizes their central role in shaping the TME. These stromal and immune cells are not merely bystanders but active participants, secreting ligands (e.g., TGF-beta, EGFR ligands) that modulate tumor cell behavior and immune responses. The switch from interactions involving Diploid Epi in Normal tissue to Aneuploid Epi in cancer contexts directly reflects the altered communication networks driven by tumor evolution.

Clinical or Translational Implications

  1. Therapeutic Targeting of TGF-beta: The pervasive and strong TGF-beta signaling across all breast cancer subtypes suggests that TGF-beta pathway inhibitors could be broad-spectrum agents to overcome immunosuppression and inhibit tumor progression, potentially in combination with other immunotherapies or conventional treatments. Targeting the integrin_avB6_complex could specifically block the activation of latent TGF-beta, offering a novel therapeutic strategy [1].
  2. EGFR Inhibition in TNBC: The prominent EGFR signaling in TNBC reinforces the rationale for EGFR-targeted therapies (e.g., cetuximab) in this subtype, especially for patients with specific activation patterns, or in combination strategies. Understanding the cellular sources of EGFR ligands (e.g., macrophages, fibroblasts) could lead to combination therapies targeting both the receptor and its upstream activators from the TME.
  3. Modulating T Cell Co-stimulation: The presence of CD86_CD28 interactions in TNBC highlights the importance of T cell co-stimulation in the immune response. Strategies to enhance T cell activation, potentially by modulating co-stimulatory pathways or improving antigen presentation, could be beneficial. However, the specific context of CD86_CD28 between T CD8+ cells needs further elucidation to understand its precise role in TNBC immunity.
  4. TME as a Therapeutic Target: The extensive interplay between tumor cells, macrophages, and fibroblasts emphasizes the need for TME-focused therapies. Targeting stromal components like cancer-associated fibroblasts (CAFs) or tumor-associated macrophages (TAMs) could disrupt crucial pro-tumorigenic and immunosuppressive interactions, thereby enhancing the efficacy of conventional and immunotherapeutic approaches.

References

  1. TGF-beta signaling in cancer:
  1. EGFR in TNBC:
  1. CD28-CD86 interaction:

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

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[Analysis Visualization Results]...

Analysis Overview

This analysis identifies statistically significant differences in cell-cell interactions (CCIs) across various breast cancer conditions (ER+, HER2+, TNBC) and Normal tissue, focusing on key immune and stromal cell types (T cell CD4+, T cell CD8+, B cell, Macrophage, Fibroblast, Endothelial cell). The results are presented as a dot plot, where dot color indicates the standardized mean interaction strength and dot size reflects the statistical significance (-log10(p-value)). The analysis specifically highlights the top 25 significant interactions per condition group.

Visual Summary

The dot plot effectively visualizes condition-specific CCI patterns, demonstrating clear distinctions between Normal tissue and the different breast cancer subtypes.

Biological Interpretation

The differential CCI patterns reveal key biological mechanisms underlying breast cancer progression and immune evasion.

Extracellular Matrix (ECM) Remodeling and Tumor-Stromal Interactions

HER2+ Specific Growth Factor Signaling

Immune Evasion and Suppression in TNBC

Wnt Signaling in ER+ and HER2+

Clinical or Translational Implications

Understanding these condition-specific CCI patterns offers potential avenues for clinical and translational applications.

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

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[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers in Epithelial cells, which are the tumor-origin cells, across different breast cancer subtypes (ER+, HER2+, TNBC) and Normal breast tissue. The dot plot visualizes the expression patterns of up to 50 surfaceome markers per condition (up to 30 plotted per group, 200 total), highlighting those that are differentially expressed and surface-localized. The dot size represents the fraction of cells expressing the gene, while the color intensity indicates the mean expression level within each sample group. Samples are stratified by condition and inferred ploidy status (Diploid vs. Aneuploid), allowing for a refined view of marker expression in potentially malignant (aneuploid) versus non-malignant (diploid) epithelial cells.

Visual Summary

The dot plot clearly delineates distinct clusters of surfaceome markers specific to each breast cancer subtype (ER+, HER2+, TNBC) and Normal tissue, indicating unique surface molecular landscapes.

Biological Interpretation

The distinct surfaceome profiles of epithelial cells across different breast cancer conditions and normal tissue underscore the molecular heterogeneity of breast cancer and the unique biological adaptations of each subtype.

Clinical or Translational Implications

The identification of condition-specific surfaceome markers in epithelial cells holds significant clinical and translational potential, particularly for therapeutic targeting and biomarker development.

17. Macrophage Condition-Specific Surfaceome Markers in Breast Tissue

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[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers in Macrophages from breast tissue, comparing Normal samples against Triple-Negative Breast Cancer (TNBC) samples. The dot plot visualizes the expression and prevalence of these markers across individual patient samples, grouped by overarching condition. The aim is to pinpoint surface proteins that are uniquely expressed or significantly differentially expressed in macrophages depending on whether they originate from normal or TNBC tissue, potentially serving as diagnostic markers or therapeutic targets. Only surfaceome markers were considered, with a maximum of 50 markers per condition, filtered based on expression score, fold change, and p-value cutoffs.

Visual Summary

The dot plot clearly segregates macrophage surfaceome marker profiles based on tissue condition.

Biological Interpretation

The distinct surfaceome profiles of macrophages between normal and cancerous breast tissue, particularly TNBC, suggest significant functional reprogramming.

Markers of Normal Macrophages:

The cluster of markers highly expressed in normal macrophages points towards a phenotype associated with tissue homeostasis and healthy immune surveillance. Key examples include:

The overall profile of normal macrophages suggests a healthy, dynamically interacting, and immunoregulatory population.

Markers of TNBC Macrophages:

In contrast, macrophages from TNBC samples show a markedly different and less diverse surfaceome marker profile in this analysis.

The paucity of specific surfaceome markers in TNBC macrophages, coupled with the loss of "normal" markers, indicates a significant shift in macrophage identity and function within the tumor microenvironment, likely contributing to immunosuppression and tumor growth.

Clinical or Translational Implications

The distinct surfaceome signatures offer several translational opportunities:

Therapeutic Targets for TNBC:

18. Fibroblast Condition-Specific Surfaceome Markers in Breast Cancer Subtypes

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[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers in Fibroblast cells across different breast cancer subtypes (ER+, HER2+, TNBC) and normal breast tissue. The dot plot visualizes the mean expression level (color intensity) and the fraction of cells expressing the marker (dot size) for the top 50 surfaceome markers for Fibroblasts in each condition. This provides insights into the molecular heterogeneity of fibroblasts in different tumor microenvironments and normal tissue.

Visual Summary

The dot plot effectively highlights distinct expression patterns of fibroblast surfaceome markers across various conditions and individual samples.

Biological Interpretation

Fibroblasts, particularly cancer-associated fibroblasts (CAFs), play critical roles in the tumor microenvironment (TME) by influencing cancer progression, metastasis, immune evasion, and therapeutic response. The identified condition-specific surfaceome markers likely reflect distinct functional states and origins of fibroblasts in normal breast tissue versus different breast cancer subtypes.

The lack of many of the "normal" fibroblast markers in cancer-associated fibroblasts suggests a phenotypic switch or a selection of specific fibroblast subsets in the TME, adapting to the demands of the growing tumor.

Clinical or Translational Implications

The identification of condition-specific fibroblast surfaceome markers has significant clinical and translational implications, particularly for breast cancer management.

19. T cell CD4+ Condition-Specific Surfaceome Markers in Breast Cancer

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[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify condition-specific surfaceome markers for CD4+ T cells across different breast cancer subtypes (ER+, HER2+, TNBC) using single-cell RNA-seq data. Surfaceome markers are particularly relevant as they represent proteins expressed on the cell surface, making them accessible targets for therapeutic intervention or diagnostic profiling. The tool identified up to 50 surfaceome markers per condition, prioritizing those that are differentially expressed and prevalent within specific groups, while deemphasizing markers common across multiple conditions. The results are visualized as a dot plot, showing both the mean expression level and the fraction of cells expressing each marker for individual patient samples, grouped by breast cancer condition.

Visual Summary

The dot plot effectively illustrates the expression patterns of various surfaceome markers across different breast cancer patient samples for CD4+ T cells.

Key Observations:

  1. Heterogeneity within Conditions: There is notable heterogeneity in marker expression even within the same breast cancer subtype. For example, within the ER+ group (top red box), samples like ER-MH0029-7C, ER-MH0173-T, and ER-MH0043-T show strong and broad expression of many markers, while others (e.g., ER-MH0151, ER-MH0025) exhibit lower expression or prevalence for the same markers.
  2. TNBC-associated Markers: The samples from the TNBC condition (bottom red box), specifically TN-B1-Tum0554, TN-B1-MH0177, and TN-MH0126, display a striking pattern of high expression and high cellular prevalence for a wide array of surfaceome markers. These include SLC38A2, TNFRSF4 (OX40), LY6E, TNFRSF18 (GITR), CD7, BST2, CD164, CTLA4, EMB, ICOS, ATP1B3, SELL, CXCR3, CD47, TMEM123, GPR183, LMAN2, TMEM219, ADGRE5, SLC3A2, ITGAE (CD103), IL2RB, and CD28. This suggests a highly activated and distinct CD4+ T cell phenotype in these TNBC cases.
  3. Shared Patterns Across Conditions: Several markers, such as TNFRSF4, TNFRSF18, CTLA4, ICOS, SELL, and CXCR3, show high expression in specific "active" subsets of ER+ and HER2+ patients, mirroring the pattern seen in the TNBC cluster. This indicates shared immune activation pathways across different breast cancer subtypes, although the frequency of such active profiles may differ.
  4. Prominent Markers: CTLA4, ICOS, CXCR3, TNFRSF4, TNFRSF18, ITGAE, IL2RB, and CD28 are among the most consistently and highly expressed markers in the immunologically "hot" samples, particularly within the TNBC subtype.

Biological Interpretation

The identified surfaceome markers provide crucial insights into the functional states and interactions of CD4+ T cells within the breast tumor microenvironment (TME) of different cancer subtypes.

  1. Immune Checkpoint and Co-stimulatory Landscape:
  1. T Cell Homing and Tissue Residency:
  1. Cytokine Responsiveness and Metabolism:

Clinical or Translational Implications

The identification of these condition-specific surfaceome markers in CD4+ T cells has several important clinical and translational implications:

  1. Biomarker Potential:
  1. Therapeutic Targets for Immunomodulation:
  1. Experimental Validation Strategies:

20. Differential Expression of Cell Cycle Genes in Breast Epithelial Cells Across Subtypes

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the expression patterns of a curated set of cell cycle-related genes within "Epithelial cells" (identified as the tumor origin cell type) across various breast tissue conditions: Estrogen Receptor positive (ER+), Human Epidermal growth factor Receptor 2 positive (HER2+), Triple-negative breast cancer (TNBC), and Normal tissue. Boxplots illustrate the sample mean gene expression for 24 statistically significant genes, highlighting differences between these conditions, which are crucial for understanding cell proliferation and therapeutic vulnerabilities in breast cancer.

Visual Summary

The boxplots reveal distinct expression profiles for cell cycle-related genes in breast epithelial cells, often differentiating cancerous conditions from normal tissue, and sometimes among cancer subtypes.

Intriguing Patterns for MYC, CDKN1A, and CDKN2A

Biological Interpretation

The observed differential expression of cell cycle genes in breast epithelial cells provides critical insights into subtype-specific pathobiology:

  1. Deregulated Proliferation in Breast Cancer: The consistent upregulation of key mitotic and DNA replication components (e.g., ANAPC11, MCM3, PCNA, CDK1, MAD2L1) in cancer cells, particularly TNBC, directly reflects the uncontrolled proliferation that defines malignancy. ANAPC11 and MAD2L1 are essential for correct chromosome segregation and spindle assembly checkpoint function, respectively. Their overexpression suggests that the machinery for rapid cell division is highly active in these tumors https://www.genecards.org/cgi-bin/carddisp.pl?gene=ANAPC11.
  2. Loss of Growth Suppressor Function: The downregulation of tumor suppressor genes like RB1, which controls the G1-S phase transition, and components of the TGF-beta pathway like SMAD3, which typically mediate anti-proliferative signals, signifies a profound loss of negative cell cycle regulation in breast cancer epithelial cells https://www.genecards.org/cgi-bin/carddisp.pl?gene=RB1. This allows for unconstrained cell growth.
  3. Genomic Instability in TNBC: Elevated ATM expression in TNBC suggests a heightened DNA damage response, which is often characteristic of aggressive tumors with higher rates of genomic instability and replication stress https://www.genecards.org/cgi-bin/carddisp.pl?gene=ATM. This could also reflect ongoing DNA repair attempts or an activated checkpoint response.
  4. Subtype-Specific Cell Cycle Remodeling: The contrasting patterns for 14-3-3 proteins (SFN, YWHAE, YWHAH) and genes like MYC, CDKN1A, and CDKN2A across subtypes underscore the molecular heterogeneity of breast cancer.

Clinical or Translational Implications

The differential expression of cell cycle-related genes in breast epithelial cells has several potential clinical and translational implications:

21. Gene Ontology (GSA) Analysis of Epithelial Cells Across Breast Cancer Conditions

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Ontology (GO) enrichment results for "Epithelial cell" (a major tumor origin cell type in breast cancer) across various conditions present in the dataset (Diploid, ER+, HER2+, Normal, TNBC). The results are derived from a Gene Set Analysis (GSA) comparing each specified condition against all other conditions combined ("_vs_others"), aiming to identify biological pathways and functions significantly upregulated in epithelial cells within each specific context. The enrichment is displayed as bar plots, showing the top enriched terms ranked by their -log(p-val) and -log(q-val).

Visual Summary

The visualizations provide five distinct bar plots, each detailing GO term enrichment for epithelial cells under a specific condition: Diploid, ER+, HER2+, Normal, and TNBC, compared to all other conditions.

Biological Interpretation

Epithelial Cells in Diploid State (Diploid_vs_others)

Epithelial cells categorized as Diploid (vs. others) show enrichment in pathways related to diverse processes:

Epithelial Cells in ER-positive (ER+_vs_others) Breast Cancer

Epithelial cells from ER+ tumors exhibit a distinct metabolic and protein handling profile:

Epithelial Cells in HER2-positive (HER2+_vs_others) Breast Cancer

HER2+ epithelial cells share some commonalities with ER+ but also have specific enrichments:

Epithelial Cells in Normal Tissue (Normal_vs_others)

Normal epithelial cells show enrichment for fundamental cellular processes:

Epithelial Cells in Triple-Negative Breast Cancer (TNBC_vs_others)

TNBC epithelial cells exhibit hallmarks of aggressive proliferation and metabolic adaptation:

Clinical or Translational Implications

The distinct pathway enrichments in epithelial cells across different breast cancer conditions provide valuable insights with potential clinical implications:

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

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results across key cell types (Epithelial cell, Macrophage, Fibroblast, T cell CD4+, T cell CD8+) within human breast tissue, comparing various conditions (ER+, HER2+, Normal, TNBC, and Diploid for Epithelial cells) against all other conditions combined. The results, visualized as a dot plot, highlight pathways that are significantly enriched (positive NES, red dots) or depleted (negative NES, blue dots), with dot size indicating statistical significance (-log(p-value)). This approach helps elucidate the distinct biological processes active in different cellular compartments and breast cancer subtypes.

Visual Summary

The dot plot provides a comprehensive overview of differentially enriched pathways. Key visual patterns include:

Biological Interpretation

The GSEA results provide critical insights into the biological underpinnings of different breast cancer subtypes and their interactions with the tumor microenvironment:

Epithelial Cells:

  1. PubMed search for "estrogen signaling breast cancer"
  2. GeneCards for ESR1
  1. PubMed search for "HER2 ErbB signaling breast cancer"
  1. PubMed search for "Wnt signaling TNBC"

Macrophages:

  1. PubMed search for "macrophage Toll-like receptor signaling TNBC"

Fibroblasts:

  1. PubMed search for "cancer-associated fibroblast TNBC Wnt"

T cells (CD4+ and CD8+):

  1. PubMed search for "T cell receptor signaling TNBC immunotherapy"

Clinical or Translational Implications

The distinct pathway enrichments observed across cell types and breast cancer conditions have several clinical and translational implications:

  1. PubMed search for "metabolic reprogramming breast cancer"

23. Discussion

The comprehensive single-cell analysis of breast tissue provides profound insights into the complex interplay between malignant epithelial cells and their surrounding immune and stromal microenvironment across different breast cancer subtypes. UMAP visualizations, complemented by CNV analysis, effectively delineate aneuploid epithelial cells as the primary malignant population, strongly correlating with tumor conditions (ER+, HER2+, TNBC) and exhibiting distinct subtype-specific genomic instability, particularly high in TNBC. GSA and GSEA further reveal tailored metabolic reprogramming in tumor epithelial cells: ER+ and HER2+ tumors lean towards oxidative phosphorylation and protein processing, while TNBC epithelial cells exhibit hallmarks of aggressive proliferation (cell cycle, DNA replication) and DNA damage response, along with p53 and HIF-1 signaling.

The immune landscape is profoundly rewired in cancer. T cell subset analysis shows a consistent reduction of Innate Lymphoid Cells (ILCs) in all cancer subtypes compared to normal tissue, suggesting a compromised innate immune surveillance. While adaptive T cells (CD4+, CD8+) dominate in tumors, their functional polarization varies. ER+ tumors show increased immunosuppressive Tregs and Th2 cells, whereas all cancer subtypes display elevated Th17 cells, indicating a general inflammatory component. TNBC CD4+ T cells, in particular, exhibit a highly activated but potentially exhausted phenotype with co-expression of multiple co-stimulatory (OX40, GITR, ICOS, CD28) and inhibitory (CTLA4) surface markers. Macrophages undergo a dramatic shift from M2B dominance in normal tissue to a prevalent M1/M2A phenotype in breast cancer, with M2B being consistently depleted across all tumor conditions. TNBC macrophages notably upregulate SLC2A3 (GLUT3), suggesting metabolic adaptation to the glycolytic tumor microenvironment. GSEA in TNBC macrophages points to activated Toll-like receptor signaling, Fc gamma R-mediated phagocytosis, and chemokine signaling, contributing to a pro-tumorigenic milieu.

Stromal fibroblasts are also critically reprogrammed. Normal fibroblasts show a broad homeostatic marker signature, whereas cancer-associated fibroblasts (CAFs) in ER+ and HER2+ tumors express SDC1, CD44, and FGFR1. Notably, TNBC fibroblasts are characterized by high expression of CD248 (Endosialin) and BST2, indicative of an aggressive stromal remodeling phenotype. Cell-cell interaction (CCI) analyses unveil widespread TME remodeling. The CXCL12-CXCR4 axis emerges as a consistently strong interaction across all breast cancer subtypes, linking epithelial, stromal, and immune cells. HER2+ tumors show specific HBEGF-ERBB2 interactions between macrophages and epithelial cells, highlighting a paracrine growth loop. In contrast, TNBC is characterized by strong immune suppressive interactions such as SIRPA-CD47 and NECTIN2-TIGIT, suggesting mechanisms of immune evasion. Pervasive TGF-beta signaling is a common immunosuppressive and pro-tumorigenic driver across all cancer subtypes.

In summary, the data underscore the profound molecular and cellular heterogeneity of breast cancer subtypes, revealing distinct genomic instabilities, metabolic adaptations, and immune/stromal rewiring that collectively shape the tumor microenvironment. TNBC consistently presents as the most aggressive and immunologically complex subtype, characterized by high proliferation, genomic instability, and a highly engaged yet suppressed immune cell landscape. These integrated findings provide a robust framework for identifying subtype-specific vulnerabilities and developing targeted or combinatorial therapeutic strategies.

Hypotheses:

  1. The consistent reduction of Lymphoid Tissue Inducer (LTI) cells across all breast cancer subtypes, compared to normal tissue, contributes to impaired tertiary lymphoid structure formation and diminished anti-tumor immunity.
  2. Macrophages in the breast cancer microenvironment undergo a global shift from an M2B-dominant homeostatic state to an M1/M2A-dominant pro-tumorigenic and immunosuppressive state, with M2B depletion contributing to TME dysregulation.
  3. Triple-Negative Breast Cancer (TNBC) epithelial cells and their associated fibroblasts maintain high genomic instability and proliferation, coupled with distinct activation of EGFR, Wnt, and specific integrin signaling, which drives their aggressive phenotype and TME remodeling.
  4. The robust activation of co-stimulatory receptors on CD4+ T cells in TNBC, alongside increased expression of inhibitory checkpoints like CTLA4, indicates a highly engaged but functionally suppressed anti-tumor immune response.
  5. Pervasive TGF-beta signaling and altered integrin-ECM interactions across all breast cancer subtypes drive immune evasion, epithelial-to-mesenchymal transition (EMT), and metastasis, representing a common mechanism of disease progression.

Potential therapeutic targets:

  1. ERBB2 (HER2): ERBB2 is a well-established oncogenic driver in HER2+ breast cancer. Its gene amplification and high protein expression drive proliferation and survival. Cell-cell interaction analysis shows specific HBEGF-ERBB2 interactions, suggesting a paracrine growth loop involving macrophages in HER2+ tumors. Evidence: CNV analysis identified strong amplification of 17q12:17q21.2 (ERBB2 locus) in HER2+ samples (Section 4, Image 5). Epithelial cell-specific markers show high ERBB2 expression in aneuploid HER2+ epithelial cells (Section 16, Image 23). GSEA indicates strong enrichment of 'ErbB signaling pathway' in HER2+ epithelial cells (Section 22). CCI analysis reveals HBEGF_ERBB2 interaction between Macrophages and Aneuploid Epithelial cells in HER2+ tumors (Section 15, Image 22). Validation: Existing anti-HER2 therapies (trastuzumab, pertuzumab) are clinically validated. Preclinical studies could investigate the synergy of anti-HER2 therapies with HBEGF-blocking agents or macrophage-targeting strategies to overcome resistance or enhance efficacy.
  2. CD47-SIRPA axis: The CD47-SIRPA interaction functions as a 'don't eat me' signal, allowing cancer cells to evade phagocytosis by macrophages. This axis is notably strong in TNBC, suggesting a key mechanism of immune evasion in this aggressive subtype. Evidence: Cell-cell interaction analysis shows prominent SIRPA_CD47 interaction in TNBC, primarily involving macrophages (Section 15, Image 22). Validation: Conduct *in vitro* phagocytosis assays using TNBC cell lines and patient-derived macrophages with CD47-blocking antibodies or SIRPα inhibitors. Evaluate *in vivo* efficacy of CD47-blocking agents in xenograft or syngeneic mouse models of TNBC, assessing tumor growth and macrophage-mediated anti-tumor responses.
  3. CD248 (Endosialin) on Fibroblasts: CD248 is a highly expressed and specific surface marker for activated fibroblasts (CAFs) in TNBC. CAFs are critical orchestrators of the tumor microenvironment, promoting tumor growth, invasion, and immune evasion through extensive remodeling. Evidence: Fibroblast-specific marker analysis reveals high and specific expression of CD248 in TNBC fibroblasts (Section 18, Image 25). GSEA in TNBC fibroblasts shows enrichment for Wnt signaling, cell adhesion molecules, and transcriptional misregulation (Section 22), reflecting their activated, pro-tumorigenic phenotype. Validation: Confirm CD248 expression on CAFs in TNBC patient samples via immunohistochemistry or immunofluorescence. Test anti-CD248 antibody-drug conjugates (ADCs) or specific inhibitors in *in vivo* mouse models of TNBC to assess impact on tumor growth, metastasis, and alteration of the tumor microenvironment.
  4. CXCL12-CXCR4 axis: This chemokine axis is a consistently strong and pervasive cell-cell interaction across all breast cancer subtypes (ER+, HER2+, TNBC), bridging epithelial, stromal, and immune cells. It plays critical roles in promoting tumor proliferation, angiogenesis, metastasis, and recruiting immunosuppressive cells. Evidence: Cell-cell interaction analysis consistently shows prominent CXCL12-CXCR4 interactions across multiple cell pairs (Fibroblast-Fibroblast, Fibroblast-Macrophage, Macrophage-Macrophage, Epithelial-Fibroblast) in ER+, HER2+, and TNBC conditions (Section 12, Images 13, 14, 16). Validation: Perform preclinical studies using CXCR4 inhibitors (e.g., Plerixafor) in combination with standard therapies in various breast cancer models to assess anti-tumor efficacy, reduction in metastasis, and modulation of immune cell trafficking. Evaluate impact on angiogenesis and overall tumor microenvironment composition.
  5. SLC2A3 (GLUT3) on Macrophages: SLC2A3 (GLUT3), a glucose transporter, is notably upregulated in TNBC macrophages. This suggests increased glucose uptake to fuel their metabolism and pro-tumorigenic functions in the highly glycolytic tumor microenvironment. Targeting GLUT3 could selectively starve these tumor-associated macrophages (TAMs). Evidence: Macrophage condition-specific marker analysis identifies upregulation of SLC2A3 in TNBC macrophages (Section 17, Image 24). GSEA in TNBC macrophages reveals enrichment for metabolic pathways, suggesting metabolic adaptation (Section 22). Validation: Conduct *in vitro* studies with TNBC-associated macrophages (e.g., patient-derived or co-culture models) to assess the effect of GLUT3 inhibitors on macrophage glucose uptake, metabolism, polarization, and pro-tumorigenic functions. Evaluate the impact of GLUT3 inhibition on TAM density, phenotype, and overall tumor growth in *in vivo* models of TNBC.

Follow-up validation ideas:

  1. LTI cell function: Employ flow cytometry and multiplex immunostaining on breast tumor tissue microarrays to confirm LTI cell counts, localization, and association with tertiary lymphoid structures across subtypes. Functionally validate by restoring LTI cell numbers or activity in *in vitro* co-culture models or *in vivo* mouse models of breast cancer, then assess changes in TLS formation and anti-tumor immune responses.
  2. Macrophage polarization: Use single-cell proteomics or spatial transcriptomics to confirm M1, M2A, and M2B macrophage proportions and their spatial relationships within patient tumor samples. Conduct *in vitro* macrophage polarization assays with tumor-derived factors to confirm the functional shift from M2B to M1/M2A phenotypes.
  3. TNBC stromal and epithelial phenotype: Utilize spatial transcriptomics or high-resolution multiplex imaging to validate the co-localization and functional interactions between CD248+ fibroblasts and CD44+/GPNMB+/PTK7+ epithelial cells in TNBC. Perform functional perturbation assays *in vitro* (e.g., patient-derived organoids, co-cultures) and *in vivo* (xenografts) using inhibitors targeting Wnt, EGFR, or specific integrins to assess their impact on tumor cell proliferation, invasion, and TME remodeling.
  4. CD4+ T cell functional state: Apply spectral flow cytometry or mass cytometry to comprehensively profile co-expression of co-stimulatory (OX40, GITR, ICOS, CD28) and inhibitory (CTLA4) markers on tumor-infiltrating CD4+ T cells. Conduct functional assays (e.g., cytokine production, proliferation, cytotoxicity) of sorted tumor-infiltrating CD4+ T cells with and without immune checkpoint blockade or agonist stimulation.
  5. CCI axis validation (TGF-beta, CXCL12-CXCR4, SIRPA-CD47, HBEGF-ERBB2): Perform blocking antibody or small molecule inhibitor experiments in *in vitro* co-culture models of breast cancer cells, fibroblasts, and immune cells to assess impacts on cell proliferation, migration, and immune cell function. Use *in vivo* xenograft or syngeneic models to evaluate the efficacy of targeting these axes on tumor growth, metastasis, and modulation of immune cell trafficking or phenotype. Employ proximity ligation assays or single-molecule FISH to confirm physical interactions in situ.

Limitations:

This report is based on single-cell RNA-sequencing data, providing transcriptomic insights into cellular composition and gene expression. However, orthogonal validation at the protein level and functional characterization are essential to confirm these findings. Copy number variation (CNV) inference from RNA-seq provides estimates of large-scale genomic alterations but does not capture all types of genomic instability. Cell-cell interaction analysis identifies potential ligand-receptor pairs based on gene expression, but direct physical interaction or functional outcome in situ require further experimental confirmation. The 'unassigned' cell populations, particularly prevalent in some HER2+ and TNBC samples, represent uncharacterized cellular states whose biological significance warrants further investigation. Finally, population proportions and marker expression are derived from statistical comparisons and may be influenced by sample size and inter-patient heterogeneity, meaning causal relationships cannot be definitively established from these correlative findings alone.

24. Query List

  1. Show UMAPs including condition, sample, major cell type, minor cell type, ploidy_dec, and celltype_subset in 2 columns and save.
  2. Show expression of CD3D CD4 CD8A CD79A MS4A1 MZB1 CD14 LYZ FBLN1 NOTCH3 EPCAM MUC1 CD34 genes on UMAP along with minor celltype annotation. Set ncols=4 and save.
  3. Show the overall Celltype_subset marker expression dot plot and save it. Keep target_cell as None and keep the other arguments at their default values.
  4. Select tumor-origin cells and unassigned cells, group by sample, show CNV heatmap, and include a summary of significantly amplified regions. Save.
  5. Show CNV patterns on UMAP, including major cell type, minor cell type, ploidy results, condition, and sample in 2 columns and save.
  6. Show a population bar plot of minor cell types and save.
  7. Show a subset population barplot for T cells and save.
  8. Show a subset population barplot for Macrophages and save.
  9. For T cell subset populations, show boxplots for those with statistically significant differences between conditions and save. Determine ncols appropriately based on the total number of panels.
  10. For Macrophage subset populations, show boxplots for those with statistically significant differences between conditions and save. Determine ncols appropriately based on the total number of panels.
  11. Select tumor-origin cells and unassigned cells and show a bar plot of their ploidy population, then save.
  12. Show cell-cell interaction patterns by condition, including Epithelial cells (as tumor origin cells), Fibroblast, Macrophage, and T cells, and save. Limit cell-cell interactions to a maximum of 80 per condition.
  13. Show cell-cell interactions by condition and save the result. Select at most 80 cell-cell interactions for each condition.
  14. Select genes related to the immune checkpoint pathway and cell cycle pathway, then show cell-cell interactions for these genes and save.
  15. Find statistically significant differences in cell-cell interactions between conditions for major immune and stromal cells (e.g., T cell, B cell, Macrophage, Fibroblast, Endothelial cell) and show them as a dot plot, then save. Set max_n_items_per_group = 25.
  16. Show the condition-specific markers for tumor-origin cells (Epithelial cell) as a dot plot and save the result. Use only surfaceome markers, up to 50 markers per condition.
  17. Extract condition-specific markers for Macrophages and show them as a dot plot, then save. Include only surfaceome markers, up to 50 per condition.
  18. Extract condition-specific markers for Fibroblasts and show them as a dot plot, then save. Include only surfaceome markers, up to 50 per condition.
  19. Extract condition-specific markers for CD4 T cells and show them as a dot plot, then save. Include only surfaceome markers, up to 50 per condition.
  20. Select cell cycle pathway-related genes and for Epithelial cells (key disease-related cells), show boxplots for those with statistically significant differences in expression between conditions, and save. Set max_n_items_to_plot = 24, and determine ncols appropriately so the aspect ratio is approximately 2x3.
  21. Show Gene Ontology (GSA) analysis results for epithelial cells as bar plots and save the result.
  22. Show Gene Set Enrichment Analysis results for key cell types (e.g., Epithelial cell, Macrophage, Fibroblast, T cell CD4+, T cell CD8+) as a dot plot and save. Use color map RdBu_r with n_pws_to_show = 80.
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