SCODiA Report by MLBI Lab

Single-Cell Landscape of Breast Cancer: Cellular Heterogeneity, Dysregulated Interactions, and Therapeutic Opportunities

This single-cell RNA sequencing analysis of breast tissue identifies significant cellular and molecular reprogramming in primary tumors compared to normal tissue. Aneuploid epithelial cells, representing the malignant population, are clearly distinguished by genomic instability and aberrant gene expression. The tumor microenvironment exhibits profound shifts in immune and stromal cell populations, driven by altered cell-cell interactions and pathway enrichments crucial for tumor progression and immune evasion.

Contents

  1. Dataset overview
  2. UMAP Embedding of Single-Cell RNA-Seq Data Colored by Metadata
  3. Major Cell Type Score and Annotation Mapping on UMAP
  4. Overall Celltype_subset Marker Expression Dot Plot Analysis
  5. Analysis of Copy Number Variations in Tumor-Origin and Unassigned Cells
  6. CNV-based UMAP Visualization of Breast Tissue Single-cell RNA-seq Data
  7. Minor Cell Type Population Analysis in Breast Tissue
  8. T Cell Subpopulation Analysis in Normal vs. Primary Breast Tumor Tissue
  9. Differential Proportions of T Cell and ILC Subsets in Breast Primary Tumors
  10. Macrophage Population Distribution Across Normal and Primary Breast Tumor Samples
  11. Ploidy Population Analysis in Tumor-Origin and Unassigned Cells of Breast Tissue
  12. Primary Tumor vs. Normal Cell-Cell Interaction Patterns in Breast Tissue
  13. Normal vs. Tumor Microenvironment: Comparative Analysis of Cell-Cell Interactions in Breast Tissue
  14. Immune Checkpoint and Cell Cycle Related Cell-Cell Interactions in Breast Tissue
  15. Condition-Specific Cell-Cell Interaction Patterns in Breast Tissue
  16. 유방암 상피세포의 조건별 표면 마커 분석
  17. Macrophage Condition-Specific Surfaceome Markers in Breast Tissue
  18. Fibroblast Condition-Specific Surfaceome Markers in Breast Tissue
  19. Cell Subtype-Specific Surfaceome Marker Identification
  20. Epithelial Cell Cycle Gene YWHAZ Upregulation in Primary Breast Tumors
  21. Epithelial Cell Gene Ontology Analysis: Differential Pathway Enrichment Across Ploidy and Tumor Status in Breast Tissue
  22. Gene Set Enrichment Analysis (GSEA) in Breast Cancer Endothelial and Epithelial Cells
  23. Discussion
  24. Query List

0. Dataset overview

데이터셋 요약:

세포 타입

uns['CCI']: 조건별 세포-세포 상호작용 (CellPhoneDB) 결과

uns['CCI_sample']: 샘플별 세포-세포 상호작용 (CellPhoneDB) 결과

uns['DEG']: 각 celltype_minor 내 조건 간 차등 발현 유전자 (DEG) 결과

uns['GSEA']: 각 celltype_minor 내 조건 간 유전자 세트 농축 분석 (GSEA) 결과

uns['GSA_up']: 각 celltype_minor 내 조건 간 GO/GSA 결과

obsm['X_cnv']: CNV (Copy Number Variation) 추정치

1. UMAP Embedding of Single-Cell RNA-Seq Data Colored by Metadata

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents Uniform Manifold Approximation and Projection (UMAP) visualizations of single-cell RNA sequencing data from breast tissue, colored by various metadata features: condition (normal vs. primary tumor), sample, celltype_major, celltype_minor, ploidy_dec, and celltype_subset. These plots provide an overview of the dataset structure, cell type annotation quality, and the distribution of cells across different biological and technical covariates.

Visual Summary

Condition

The UMAP colored by condition shows a clear separation between normal (maroon) and primary_tumor (purple) cells. While some regions of the UMAP are mixed, indicating shared cell types or cell states between conditions, distinct clusters dominated by either normal or primary_tumor cells are evident. Notably, a large, well-defined cluster in the lower-right area of the UMAP is almost exclusively composed of primary_tumor cells, suggesting a population highly specific to the tumor microenvironment.

Sample

The sample plot displays a diverse distribution of individual samples across the UMAP. Most major cell clusters appear to be composed of cells from multiple patients, indicating good integration of data across different samples and suggesting that the observed clustering is driven by biological variation rather than strong batch effects. Some smaller, peripheral clusters might show enrichment for one or a few samples, which is common for rare cell populations or specific sample characteristics.

Celltype_major

This plot reveals distinct clustering according to broad cell types. Epithelial cell (orange) forms a large, somewhat diffuse cluster, often situated centrally or in regions with other structural cells. Stromal cell (light green) and Endothelial cell (red-orange) also form substantial clusters. Immune cell populations like T cell (cyan), Myeloid cell (light yellow), and B cell (dark red) form separate, well-defined clusters, indicating robust differentiation of these major lineages. A relatively small proportion of cells are labeled as unassigned (purple), scattered throughout the embedding.

Celltype_minor

The celltype_minor plot provides a more granular view of cell populations. Within the Epithelial cell major cluster, specific minor types are not explicitly broken out but are represented by a continued orange coloration, indicating the overall epithelial compartment. Fibroblast (light orange), Endothelial cell (red-orange), Macrophage (yellow), T cell CD8+ (dark blue), and T cell CD4+ (light blue) show more defined boundaries and separation within their respective major cell type compartments. Plasma cell (green) appears as a distinct, smaller cluster.

Ploidy_dec

The ploidy_dec plot highlights cells inferred as Aneuploid (maroon) and Diploid (yellow). A significant proportion of Aneuploid cells are concentrated within the large primary_tumor-specific cluster observed in the condition plot, and also in other smaller, specific regions. The majority of cells are Diploid and are broadly distributed across the embedding. A very small number of cells are labeled Unclear (purple) and are sparsely distributed. This strong spatial correlation between Aneuploid cells and primary_tumor condition suggests these aneuploid cells are likely the malignant epithelial cells.

Celltype_subset

This plot offers the finest resolution of cell types, showing numerous distinct subsets. For instance, different T cell subsets (e.g., T cell (Cytotoxic), T cell (Tfh), T cell (Treg)) are visible as sub-clusters within the broader T cell region. Macrophage subsets (Macrophage (M1), Macrophage (M2A), M2B, M2C, M2D) also show differential localization. Luminal epithelial cell and Mammary epithelial cell differentiate within the epithelial compartment. This level of annotation confirms the intricate cellular heterogeneity within the dataset.

Biological Interpretation

The UMAP visualizations provide critical insights into the cellular landscape of breast tissue and primary tumors:

  1. Tumor vs. Normal Segregation: The pronounced separation of primary_tumor and normal cells on the UMAP reflects significant global transcriptomic changes driven by the cancerous state. This indicates robust differential gene expression patterns exist between these conditions, making downstream analyses like differential expression and pathway enrichment highly informative for identifying tumor-specific biology.
  2. Malignant Cell Identification: Given that the Tumor origin celltype is Epithelial cell and aneuploidy is a hallmark of cancer, the strong co-localization of Aneuploid cells with primary_tumor regions, particularly within the large Epithelial cell cluster, provides strong evidence for the identification of the malignant epithelial cell population. This is a crucial finding for understanding tumor biology and progression.
  3. Heterogeneity of the Tumor Microenvironment: The presence and distinct clustering of various immune (T cells, B cells, Myeloid cells, ILCs, Mast cells, NK cells) and stromal (Fibroblasts, Endothelial cells, Smooth muscle cells) cell types in both normal and tumor contexts highlight the complex cellular composition of the breast tissue and the tumor microenvironment. The ability to resolve these cell types down to celltype_subset level allows for detailed investigation into their specific roles and interactions within the tumor.
  4. Annotation Quality and Data Structure: The clear and consistent clustering of cells by celltype_major, celltype_minor, and celltype_subset across the UMAP indicates high-quality cell type annotation. The hierarchical refinement from major to minor to subset annotations effectively captures the biological hierarchy of cell identity. The good mixing of sample origins within cell type clusters suggests successful integration of data from multiple patients, minimizing potential batch effects and ensuring that biological signals dominate the embedding structure.

Annotation Notes

The UMAP plots demonstrate generally high-quality annotations. Cells labeled as unassigned are minimal and scattered, suggesting that most cells have been confidently assigned to a known cell type. The clear separation of cell types at multiple granularities, and the meaningful co-localization of Aneuploid cells with the primary_tumor condition, reinforce the reliability of the cell type and ploidy annotations. This robust foundation is essential for subsequent in-depth analyses, such as cell-cell interaction studies or pathway analyses, by ensuring that comparisons are made between well-defined and biologically relevant cell populations.

2. Major Cell Type Score and Annotation Mapping on UMAP

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[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes single-cell RNA sequencing data on a UMAP (Uniform Manifold Approximation and Projection) embedding. The plots display various major cell type scores, inferred by HiCAT, along with the inferred ploidy status and the pre-existing major cell type annotations. This allows for an assessment of cell type identification, the consistency between computational scoring and annotations, and the distribution of aneuploidy across the cellular landscape.

Visual Summary

The provided UMAP plots offer a comprehensive view of cell type distribution and characteristics within the dataset:

Biological Interpretation

  1. Robust Cell Type Identification: The strong spatial segregation of different HiCAT major cell type scores into distinct UMAP clusters, which are highly concordant with the celltype_major annotations, suggests a robust and consistent cell type identification process. This indicates that the major cell populations within the breast tissue single-cell dataset are well-defined by their transcriptomic profiles.
  2. Identification of Tumor Cells via Aneuploidy: A striking observation is the clear co-localization of the Epithelial cell cluster (as shown by both HiCAT score and celltype_major annotation) with the Aneuploid cell population. Given that the Tumor origin celltype is specified as 'Epithelial cell' and the tissue is 'Breast' with 'primary_tumor' and 'normal' conditions, this strong overlap strongly indicates that the Aneuploid cells represent the malignant epithelial cells of the breast tumor. Aneuploidy, the presence of an abnormal number of chromosomes, is a hallmark of cancer cells and is frequently observed in breast carcinoma [1].

[1] PubMed search for "aneuploidy breast cancer"

  1. Tumor Microenvironment Composition: The remaining clusters, predominantly composed of Diploid cells, represent the tumor microenvironment (TME) or normal breast tissue components. These include:
  1. Annotation Quality: The high degree of concordance between the HiCAT cell type scores and the celltype_major annotations validates the quality of the existing cell type assignments. This consistency strengthens confidence in downstream analyses that rely on these cell type labels.

Annotation Notes

The UMAP plots demonstrate excellent segregation of major cell types, with both the computational HiCAT scores and the celltype_major annotations defining clear, distinct clusters. The crucial finding of aneuploid cells coinciding perfectly with the epithelial cell population in the tumor context provides strong evidence for accurate identification of malignant cells. The overall embedding structure is well-defined, and the cell identity annotations appear robust.

3. Overall Celltype_subset Marker Expression Dot Plot Analysis

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Analysis Overview

This analysis presents a dot plot illustrating the expression patterns of marker genes across various celltype_subset populations identified in the single-cell RNA sequencing (scRNA-seq) dataset. The primary goal of this visualization is to validate the assigned cell type identities by assessing whether each celltype_subset expresses known canonical marker genes. The plot displays both the mean expression level (color intensity) and the fraction of cells expressing each gene (dot size) within each cell group. The red boxes highlight marker genes most enriched within specific cell type subsets, aiding in identity confirmation.

Visual Summary

The dot plot is structured with celltype_subset annotations on the y-axis and identified marker genes on the x-axis.

Overall, a clear diagonal pattern of strong red, large dots within the red boxes is observed, indicating distinct and specific marker gene expression for most celltype_subset populations. This pattern suggests that the current celltype_subset annotations are well-supported by their respective gene expression profiles.

Biological Interpretation

The marker gene expression patterns strongly support the assigned celltype_subset identities, validating the annotation quality of the dataset.

Annotation Notes

The dot plot demonstrates excellent concordance between the assigned celltype_subset labels and their respective marker gene expression profiles. The specificity and enrichment of markers within their expected cell types are high, with minimal off-target expression observed for most key markers. The number of cells per group is generally robust, providing confidence in the observed expression patterns. This analysis provides strong evidence for the high quality and accuracy of the cell type annotations in this AnnData object. The clear segregation of markers confirms that the clustering and annotation workflow successfully captured biologically distinct cell populations.

4. Analysis of Copy Number Variations 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 cell type in breast tissue) and 'unassigned' cells. The CNV estimates, obtained from single-cell RNA-seq data, are visualized as a heatmap grouped by individual samples, distinguishing between diploid and aneuploid cell populations. A summary of significantly amplified cytogenetic regions across samples is also provided to highlight recurrent genomic alterations.

Visual Summary

The CNV heatmap displays log2(CNR) values across genomic spots for the targeted cell populations. Amplifications are shown in red, and deletions in blue.

1q21.3:1q23.2

1q32.1:1q32.2 (containing the *NFASC* gene)

1q41:1q42.3

8q22.1:8q23.1 (containing the *EIF3E* gene)

Other frequently amplified regions (frequency > 50%) include various segments on chromosomes 5, 7, 10, 15, and 17.

Biological Interpretation

The striking difference in CNV landscapes between the "Diploid" and "Aneuploid" patient groups among the 'Epithelial cell' and 'unassigned' populations underscores the hallmark of genomic instability in tumor cells.

Clinical or Translational Implications

5. CNV-based UMAP Visualization of Breast Tissue Single-cell RNA-seq Data

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Analysis Overview

This analysis presents a Uniform Manifold Approximation and Projection (UMAP) visualization generated specifically from Copy Number Variation (CNV) estimates for single cells from breast tissue. This approach allows for the unsupervised clustering of cells based on their genomic alterations, rather than gene expression patterns. The UMAP plots are colored by major cell type, minor cell type, ploidy declaration (ploidy_dec), condition (primary tumor vs. normal), and individual sample to reveal underlying structural relationships and heterogeneity driven by CNV profiles.

Visual Summary

Embedding Structure and Ploidy

The UMAP clearly segregates cells into distinct clusters based on their CNV profiles. A prominent observation is the robust separation based on ploidy:

Cell Type Distribution

Condition and Sample Distribution

Condition:

Biological Interpretation

The CNV-driven UMAP provides compelling evidence for genomic instability as a primary driver of cellular heterogeneity in breast cancer.

  1. Malignant Cell Identification: The strong co-localization of Aneuploid cells with Epithelial cells from primary_tumor samples strongly identifies these cells as the malignant tumor cells. This separation from the predominantly Diploid stromal and immune cells reinforces the genomic basis of tumor identity.
  2. Tumor Microenvironment (TME) Composition: The presence of a substantial Diploid cell population (comprising stromal, immune, and endothelial cells) within the primary_tumor samples, co-clustering with normal cells, accurately reflects the complex cellular ecosystem of the TME, which consists of non-malignant host cells.
  3. Genomic Heterogeneity within Tumors: The observation that Aneuploid cells from different samples form distinct clusters on the CNV-UMAP suggests significant inter-patient genomic heterogeneity in breast cancer. This implies that while all tumor cells may share a general aneuploid status, the specific chromosomal gains and losses can vary widely between individuals, potentially influencing tumor biology and treatment response.
  4. Robustness of Ploidy Inference: The clear separation of Aneuploid from Diploid cells, especially given that the UMAP embedding itself is based on CNV estimates, validates the accuracy and biological relevance of the ploidy_dec annotation. Cells labeled as Unclear often reside in transitional or ambiguous genomic states, which might represent cells with low levels of aneuploidy, partial CNVs, or technical limitations in their CNV estimation.

Annotation Notes

6. Minor Cell Type Population Analysis in Breast Tissue

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Analysis Overview

This analysis provides a population bar plot illustrating the relative proportions of different minor cell types across individual samples from both normal breast tissue and primary breast tumors. This visualization is crucial for understanding the cellular composition of the breast tissue microenvironment in health and disease, highlighting shifts associated with tumor development.

Visual Summary

The stacked bar plots display the percentage composition of various minor cell types for each sample, grouped by condition (normal vs. primary_tumor).

Normal Tissue Composition:

Primary Tumor Tissue Composition:

Biological Interpretation

The observed shifts in minor cell type populations between normal and primary tumor breast tissue provide critical insights into the tumor microenvironment (TME) dynamics:

  1. Dominance of Epithelial and Stromal Cells: The consistent high proportion of Epithelial cells in both normal and tumor tissue aligns with the epithelial origin of most breast cancers. The sustained high presence of Fibroblasts (often referred to as Cancer-Associated Fibroblasts, CAFs, in tumors) highlights their crucial role in tumor progression, extracellular matrix remodeling, and creation of a pro-tumorigenic niche https://pubmed.ncbi.nlm.nih.gov/30678683/.
  2. Immune Cell Infiltration and Heterogeneity:
  1. Significant "Unassigned" Population in Tumors: The notable increase in "unassigned" cells in several primary tumor samples is a key observation. This could represent:

Further investigation into these "unassigned" cells is warranted to characterize their identity and potential functional roles in tumor biology.

  1. Tissue Specificity: This analysis is highly relevant to breast cancer, where the interplay between epithelial tumor cells, fibroblasts, and infiltrating immune cells is well-documented in driving disease progression and therapeutic resistance.

Clinical or Translational Implications

7. T Cell Subpopulation Analysis in Normal vs. Primary Breast Tumor Tissue

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Analysis Overview

This analysis visualizes the proportional distribution of minor cell types within the major cell type category "T cell" across normal and primary breast tumor samples. The plot_celltype_population tool was used to break down the 'T cell' major population into its celltype_minor constituents, which include T cell CD4+, T cell CD8+, ILC, NK cell, and unassigned populations. This comparison aims to highlight shifts in immune cell composition between healthy and cancerous breast tissue microenvironments.

Visual Summary

The stacked bar plot displays the relative proportions of T cell subsets and related lymphoid cells per sample, grouped by condition (normal vs. primary_tumor).

Biological Interpretation

The analysis reveals distinct lymphoid cell compositions in normal breast tissue versus primary breast tumors, with significant heterogeneity within the tumor microenvironment.

  1. Broad Definition of "T cell" Major Category: It's important to note that within this dataset, the celltype_major category "T cell" appears to encompass a broader lymphoid compartment, as its celltype_minor breakdown includes not only conventional T cells (CD4+, CD8+) but also Innate Lymphoid Cells (ILCs) and Natural Killer (NK) cells. This implies a hierarchical classification where these distinct lymphocyte populations are initially grouped under a common "T cell" major label before more granular annotation.
  2. ILC Dominance in Normal Breast Homeostasis: The consistent dominance of ILCs in normal breast tissue suggests their crucial role in maintaining immune surveillance and tissue homeostasis in healthy mammary glands. ILCs are key regulators of inflammation, tissue repair, and immune responses at mucosal and barrier surfaces, which would include the breast. Specific ILC subsets (ILC1, ILC2, ILC3) have diverse functions, and their overall abundance indicates a highly active innate lymphoid immune component.
  3. T Cell Infiltration and Immunosuppression in Tumors: The observed increase in CD4+ and CD8+ T cell proportions in a subset of primary tumor samples indicates active immune cell infiltration into the tumor microenvironment (TME).
  1. Heterogeneity of the Breast Cancer Immune Microenvironment: The marked variability in lymphoid cell composition among primary tumor samples underscores the profound heterogeneity of the breast cancer immune microenvironment. This suggests that different tumors might engage with the immune system in diverse ways, impacting their progression and responsiveness to immunotherapies.

Clinical or Translational Implications

The findings have several clinical and translational implications for breast cancer:

8. Differential Proportions of T Cell and ILC Subsets in Breast Primary Tumors

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Analysis Overview

This analysis investigates the proportional differences of specific T cell and innate lymphoid cell (ILC) subsets within breast tissue, comparing primary tumor samples against normal tissue samples. The aim is to identify immune cell populations that are significantly altered in the tumor microenvironment, which can provide insights into tumor immunology and potential therapeutic targets. The plot_box_for_celltype_population_with_signif_difference tool was utilized to visualize these differences, specifically focusing on subsets within the 'T cell' major category and related ILCs, and highlighting those with a p-value less than or equal to 0.1.

Visual Summary

The box plots display the cell type proportions for five immune cell subsets: Th17, Treg, Tfh, LTI, and ILC1, comparing primary_tumor (blue boxes) and normal (orange boxes) conditions.

Biological Interpretation

The observed alterations in the proportions of these specific T cell and ILC subsets in the breast primary tumor microenvironment provide critical insights into the immune landscape of breast cancer.

  1. Increased Immunosuppressive and Pro-inflammatory T cells (Treg, Th17, Tfh):
  1. Decreased Innate Lymphoid Cells (LTI, ILC1):

These shifts collectively paint a picture of an altered immune microenvironment in primary breast tumors, characterized by increased populations that can contribute to immunosuppression (Tregs) or complex inflammatory responses (Th17, Tfh), alongside a potential reduction in innate immune cells involved in lymphoid organization (LTI) and anti-tumor responses (ILC1).

Clinical or Translational Implications

The differential proportions of these immune cell subsets hold significant clinical and translational implications for breast cancer:

9. Macrophage Population Distribution Across Normal and Primary Breast Tumor Samples

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Analysis Overview

The plot_celltype_population tool was utilized to visualize the distribution of cells annotated as 'Macrophage' (from the celltype_minor category) across individual samples derived from both 'normal' and 'primary_tumor' breast tissue conditions. The objective of this analysis was to examine the population composition specifically within the macrophage cell type.

Visual Summary

The generated bar plot presents two distinct panels, segmenting the data by 'normal' and 'primary_tumor' conditions. Each panel displays multiple bars, with the x-axis representing individual patient samples (e.g., "Patient_1_49758L_RNA" for normal tissue, "Patient_10_3C7D1L_RNA" for primary tumor tissue), and the y-axis indicating a percentage from 0 to 100.

Biological Interpretation

Clinical or Translational Implications

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References:

[1] PubMed search for "macrophage tumor breast cancer M1 M2" (PubMed Search)

[2] PubMed search for "macrophage polarization cancer prognosis" (PubMed Search)

10. Ploidy Population Analysis in Tumor-Origin and Unassigned Cells of Breast Tissue

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Analysis Overview

This analysis investigates the ploidy status (Aneuploid, Diploid, Unclear) of cells identified as "Epithelial cell" (the designated tumor-origin cell type for breast tissue) or "unassigned" cells within various patient samples from both normal breast tissue and primary breast tumors. The visualization provides a stacked bar plot, with each bar representing a single patient sample and showing the proportional distribution of ploidy states for the target cell populations within that sample. This helps to identify overall trends in chromosomal stability across disease conditions and patient samples.

Visual Summary

The bar plot clearly delineates the ploidy distribution in target cells (Epithelial and unassigned cells) between normal and primary tumor conditions.

Biological Interpretation

The observed shift in ploidy from predominantly diploid in normal samples to significantly aneuploid in primary tumor samples, particularly within the Epithelial cell population (tumor-origin cells), is a well-established hallmark of cancer.

Clinical or Translational Implications

11. Primary Tumor vs. Normal Cell-Cell Interaction Patterns in Breast Tissue

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Analysis Overview

This analysis investigates cell-cell interaction (CCI) patterns in breast tissue, comparing 'normal' and 'primary_tumor' conditions. The plot_cci_dots tool was used to visualize significant ligand-receptor interactions between key cell types: Epithelial cells (distinguished by ploidy into Diploid and Aneuploid), Fibroblasts, Macrophages, and T cells (CD4+ and CD8+). Up to 80 significant interactions (p-value < 0.05, mean expression > 0.01) were selected for visualization per condition, revealing distinct cellular communication landscapes in healthy and cancerous breast microenvironments.

Visual Summary

Normal Condition:

Primary Tumor Condition:

Biological Interpretation

The comparison highlights a dramatic shift in cellular communication from a homeostatic state in normal breast tissue to a pro-tumorigenic microenvironment.

Dysregulated Growth Factor and Developmental Signaling:

Clinical or Translational Implications

The identified specific ligand-receptor interactions provide critical insights for potential therapeutic interventions and biomarker development in breast cancer:

Therapeutic Target Prioritization:

12. Normal vs. Tumor Microenvironment: Comparative Analysis of Cell-Cell Interactions in Breast Tissue

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Analysis Overview

This analysis investigates cell-cell interactions (CCIs) in breast tissue, comparing normal and primary tumor conditions using single-cell RNA sequencing data. The plot_cci_dots tool, based on CellPhoneDB, was utilized to identify and visualize significant ligand-receptor interactions between different cell types. The plots display the top 80 interacting gene pairs for each condition, with dot size representing the statistical significance (-log10(p-value)) and dot color indicating the interaction strength (log2(mean expression)). The analysis specifically highlights interactions involving normal (Diploid) and malignant (Aneuploid) epithelial cells, along with various stromal and immune cell types.

Visual Summary

  1. CCI for Normal Breast Tissue:
  1. CCI for Primary Tumor Breast Tissue:

Altered Ligand-Receptor Landscape:

Biological Interpretation

The comparison between normal and primary tumor conditions reveals significant shifts in cell-cell communication, reflecting the hallmarks of cancer development and progression.

Clinical or Translational Implications

The identified cell-cell interactions represent a rich source of potential therapeutic targets and biomarkers for breast cancer.

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References:

  1. Collagen-integrin interactions in cancer: For a general overview of integrins in cancer:

PubMed Search: "integrin cancer" review

  1. SPP1 (Osteopontin) in cancer: For its role in ECM remodeling, immunity, and cancer progression:

GeneCards: SPP1

PubMed Search: "SPP1 cancer" review

  1. VEGF-VEGFR signaling in angiogenesis: For details on angiogenesis in cancer:

PubMed Search: "VEGF angiogenesis cancer" review

  1. Tumor-associated macrophages: For their role in the tumor microenvironment:

PubMed Search: "tumor associated macrophages" review

  1. CXCL12-CXCR4 axis in cancer: For its role in cell migration and metastasis:

GeneCards: CXCL12

GeneCards: CXCR4

  1. WNT5A in cancer: For its role in non-canonical Wnt signaling and metastasis:

GeneCards: WNT5A

PubMed Search: "WNT5A cancer" review

  1. Anti-angiogenic therapies in cancer: For clinical implications of VEGF targeting:

PubMed Search: "anti-VEGF therapy cancer" review

  1. Integrin inhibitors in cancer therapy: For therapeutic potential of targeting integrins:

PubMed Search: "integrin inhibitor cancer therapy" review

  1. Wnt pathway inhibitors in cancer: For therapeutic approaches targeting Wnt signaling:

PubMed Search: "Wnt pathway inhibitor cancer" review

13. Immune Checkpoint and Cell Cycle Related Cell-Cell Interactions in Breast Tissue

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Analysis Overview

This analysis investigates cell-cell interactions (CCI) within breast tissue, specifically focusing on a predefined set of genes related to immune checkpoints and cell cycle pathways. The plot_cci_dots tool was used to visualize significant ligand-receptor interactions between various cell types in both normal and primary tumor conditions. While the initial query included a broad list of immune checkpoint and cell cycle genes, the resulting plots highlight specific interactions involving EGFR signaling (AREG-EGFR, HBEGF-EGFR, EREG-EGFR), IFN-gamma signaling (CD93-IFNGR1), and TGF-beta signaling (TGFB1/2-TGFBR1/2/3, TGFB1-integrin_avb6_complex) that met the significance and mean expression cutoffs. The analysis distinguishes between Diploid Epithelial cells and Aneuploid Epithelial cells, reflecting the ploidy inference for the tumor origin cell type.

Visual Summary

CCI for Normal Condition

CCI for Primary Tumor Condition

Key Differences Between Normal and Tumor

Biological Interpretation

The observed cell-cell interactions underscore critical aspects of breast cancer biology and its microenvironment.

Role of Tumor Microenvironment (TME) Cells

Clinical or Translational Implications

The findings from this CCI analysis provide several potential clinical and translational avenues:

14. Condition-Specific Cell-Cell Interaction Patterns in Breast Tissue

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Analysis Overview

This analysis investigates statistically significant differences in cell-cell interactions (CCIs) between normal breast tissue and primary_tumor conditions, focusing on major immune and stromal cells, as well as epithelial cells. The results are visualized as a dot plot, where the size of each dot represents the statistical significance (-log10(p-value)) of the interaction, and the color intensity indicates the standardized mean interaction strength across samples. The plot highlights the top 25 most significant CCI pairs in each condition group, allowing for a comparative assessment of the tumor microenvironment versus normal tissue.

Visual Summary

The dot plot, "Condition-specific CCI pattern," effectively illustrates distinct CCI landscapes in normal versus primary tumor breast tissue.

Normal Tissue Interactions

Primary Tumor Interactions

Biological Interpretation

The observed condition-specific CCI patterns provide critical insights into the dynamic interplay within the breast tumor microenvironment compared to normal tissue homeostasis.

Clinical or Translational Implications

The identified condition-specific CCI patterns have significant clinical and translational implications for breast cancer:

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References:

[1] Prostaglandin E2 in cancer: A PubMed search for "prostaglandin E2 cancer immune" can provide context. E.g., PubMed Search

[2] Chemokine receptors ACKR1 and immune cell trafficking: A PubMed search for "ACKR1 chemokine immune trafficking". E.g., PubMed Search

[3] CD47-SIRPA "don't eat me" signal: GeneCards entry for CD47 or SIRPA provides a good overview. E.g., GeneCards

[4] TRAIL/TNFSF10 in cancer: A PubMed search for "TNFSF10 TRAIL apoptosis cancer". E.g., PubMed Search

[5] Integrins in cancer: GeneCards entry for Integrin. E.g., GeneCards or GeneCards

[6] Collagen VI in tumor microenvironment: A PubMed search for "collagen VI cancer microenvironment". E.g., PubMed Search

[7] Fibronectin in tumor microenvironment: A PubMed search for "fibronectin cancer microenvironment". E.g., PubMed Search

[8] CXCL14-CXCR4 axis in cancer: A PubMed search for "CXCL14 CXCR4 cancer angiogenesis". E.g., PubMed Search

[9] Integrin inhibitors in cancer therapy: A PubMed search for "integrin inhibitors cancer therapy". E.g., PubMed Search

[10] CXCR4 antagonists in cancer therapy: A PubMed search for "CXCR4 antagonists cancer therapy". E.g., PubMed Search

15. 유방암 상피세포의 조건별 표면 마커 분석

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[Analysis Visualization Results]...

Analysis Overview

본 분석은 단일 세포 RNA 시퀀싱 데이터를 활용하여 유방 조직 내 상피세포(tumor-origin cells)에서 정상 조건과 원발성 종양(primary_tumor) 조건 간의 차등 발현되는 표면 마커를 식별하고 시각화합니다. 특히, 핵형(ploidy) 상태(이배체(Diploid) 또는 이수체(Aneuploid))에 따른 종양 샘플의 구분과 마커 발현 패턴을 확인합니다. 'plot_markers_and_expression_dot' 도구를 사용하여 조건별 최대 50개의 표면 마커가 선택되었으며, 이들의 발현 수준과 발현 세포 비율이 점도표로 표시되었습니다.

Visual Summary

점도표는 상피세포에서 정상 및 종양 조건에 따라 유의하게 차등 발현되는 표면 마커 유전자들의 평균 발현량(점의 색깔)과 발현 세포 비율(점의 크기)을 보여줍니다.

정상 특이 마커:

종양 특이 마커:

Biological Interpretation

이 분석은 유방암의 종양 발생 및 진행과 관련된 상피세포의 표면 단백질 변화를 밝혀냅니다.

종양 상피세포 마커 (종양 촉진 역할):

Clinical or Translational Implications

이 분석 결과는 유방암 진단 및 치료를 위한 중요한 임상적, 번역적 함의를 가집니다.

16. Macrophage Condition-Specific Surfaceome Markers in Breast Tissue

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[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify surfaceome markers that are distinctly expressed in Macrophage populations when comparing normal breast tissue macrophages to those found in primary breast tumors. By focusing on surfaceome markers, we identify proteins that are accessible on the cell surface, making them potential candidates for cell-surface-based assays (e.g., flow cytometry, immunohistochemistry) or therapeutic targeting. The plot_markers_and_expression_dot tool was used to visualize these condition-specific markers, presenting both the fraction of cells expressing each marker and its mean expression level across individual patients within each condition.

Visual Summary

The dot plot clearly differentiates Macrophage populations based on their surfaceome marker expression profiles across normal and primary tumor conditions.

The patient-specific bars on the right indicate the number of Macrophage cells analyzed per patient, showing adequate cell numbers for robust marker identification. The clustering of patients by condition (normal vs. primary tumor) visually confirms the differential expression patterns.

Biological Interpretation

The observed differential expression of surfaceome markers highlights a profound phenotypic shift in macrophages infiltrating primary breast tumors compared to those in normal breast tissue.

This comprehensive signature for TAMs indicates their multifaceted roles in supporting tumor progression, angiogenesis, immune evasion, and metastasis in breast cancer.

Clinical or Translational Implications

The identification of distinct surfaceome markers for normal versus tumor-associated macrophages carries significant clinical and translational potential.

By selectively targeting these surface markers, it may be possible to specifically modulate TAMs in breast cancer, thereby improving therapeutic outcomes while minimizing off-target effects on normal tissue macrophages.

17. Fibroblast Condition-Specific Surfaceome Markers in Breast Tissue

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[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers in Fibroblast cells from breast tissue, comparing "normal" and "primary_tumor" conditions. The results are presented as a dot plot, where each row represents a patient sample, and each column represents a gene. The size of the dot indicates the fraction of cells expressing the gene, and the color intensity reflects the mean expression level within that patient's fibroblast population. Only surfaceome markers were considered, up to 50 per condition.

Visual Summary

The dot plot clearly differentiates two groups of patient samples: five "normal" samples and eleven "primary_tumor" samples, as indicated by the bar above the heatmap.

Key upregulated markers in primary tumor fibroblasts include:

The intensity and breadth of expression for these markers are notably higher in tumor samples compared to normal, highlighting their condition-specific nature.

Biological Interpretation

The observed upregulation of numerous surfaceome markers in fibroblasts from primary breast tumors points to a significant phenotypic shift, indicative of their transformation into Cancer-Associated Fibroblasts (CAFs). These findings align with the established role of CAFs as critical components of the tumor microenvironment (TME) in breast cancer.

Other Pro-tumorigenic Roles:

This collective shift in surface protein expression underscores the multifaceted contributions of CAFs to breast cancer progression, from matrix remodeling to immune suppression and growth support.

Clinical or Translational Implications

The distinct expression of these surfaceome markers in breast cancer-associated fibroblasts offers significant clinical and translational opportunities:

18. Cell Subtype-Specific Surfaceome Marker Identification

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[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify and visualize cell-type-specific surfaceome markers across various cell subsets present in the breast tissue single-cell RNA-seq dataset. The generated dot plot displays the mean expression level (color intensity) and the fraction of cells expressing a gene (dot size) for candidate markers within each celltype_subset.

It's important to note a discrepancy between the user query and the visualization. The user specifically requested "condition-specific markers for T cell CD4+". However, the provided plot shows general cell-type markers for *many different celltype_subset categories* (not just T cell CD4+), and it does not differentiate between conditions (e.g., primary_tumor vs. normal). Therefore, this interpretation will focus on the observed cell-type specificity across the displayed cell subsets, with particular attention to the CD4+ T cell subsets requested in the query, rather than condition-specific differences. While the find_cfg parameter specified surfaceome_only: True, some key intracellular transcription factors and cytokines, known to be highly specific for these cell types, are also prominently featured, indicating the strong association of these master regulators with cell identity.

Visual Summary

The dot plot effectively visualizes the expression patterns of numerous genes across 25 distinct celltype_subset categories. Each row represents a cell subset, and each column represents a gene. The red boxes highlight groups of genes that are highly specific and highly expressed within particular cell subsets.

Key observations include:

Biological Interpretation

The identified markers provide valuable insights into the identity and functional characteristics of various immune and stromal cell populations within the breast tissue. For T cell CD4+ subsets, these markers are critical for their differentiation, function, and interaction with other cells in the tumor microenvironment.

The identification of these cell-type-specific markers, including both surface proteins and master transcription factors, provides a comprehensive molecular signature for each cell subset. The inclusion of intracellular markers despite the surfaceome_only setting highlights their critical role in defining cell identity, suggesting that for certain cell types, intracellular master regulators are indispensable for accurate identification and functional understanding.

Clinical or Translational Implications

Although this analysis did not provide *condition-specific* markers for T cell CD4+ as initially requested, the identified cell-type-specific surfaceome markers are highly valuable for several translational applications:

  1. Cell Isolation and Characterization: Surface markers like SELL, PDCD1, CTLA4, and TNFRSF4 can be used for flow cytometry-based immunophenotyping or cell sorting to isolate specific T cell CD4+ subsets from complex breast tissue samples. This enables further functional studies of these populations.
  2. Biomarker Discovery: These markers can serve as potential diagnostic or prognostic biomarkers. For instance, the infiltration levels of specific T cell CD4+ subsets (e.g., Treg or Tfh) identified by their unique surface markers could correlate with disease progression or response to therapy in breast cancer.
  3. Therapeutic Targets: Surface receptors such as PDCD1 (PD-1) on Tfh cells and CTLA4 or TNFRSF18 (GITR) on Treg cells are established or emerging targets for immunomodulatory therapies, particularly in cancer.

Further analysis to identify *condition-specific* markers within T cell CD4+ subsets would directly inform how these populations are altered in primary tumors compared to normal tissue, leading to more targeted therapeutic strategies or prognostic indicators.

19. Epithelial Cell Cycle Gene YWHAZ Upregulation in Primary Breast Tumors

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the differential expression of genes related to the Cell Cycle pathway in Epithelial cells (the tumor-origin cell type) from breast tissue. Specifically, a box plot displays the expression levels of the gene YWHAZ, comparing primary tumor samples to normal samples, highlighting statistically significant differences. The gene expression values represent the mean expression per sample.

Visual Summary

The box plot shows the expression of YWHAZ in Epithelial cells across two conditions: 'primary_tumor' and 'normal'.

Biological Interpretation

YWHAZ (Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta polypeptide), also known as 14-3-3 zeta, is a crucial member of the 14-3-3 protein family. These proteins are highly conserved regulatory molecules that modulate the function of various client proteins involved in signal transduction, cell cycle progression, apoptosis, and cellular metabolism through protein-protein interactions [1].

The observed significant upregulation of YWHAZ in Epithelial cells from primary breast tumors, compared to normal breast tissue, is biologically relevant given its established role in cell cycle regulation and cancer biology.

Clinical or Translational Implications

The differential expression of YWHAZ in breast cancer Epithelial cells suggests several potential clinical and translational implications:

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References:

  1. General 14-3-3 function: Yaffe, M. B. (2002). 14-3-3 proteins: universal regulators of signalling. *Current Opinion in Cell Biology*, *14*(2), 209-216. PubMed search: 14-3-3 proteins cell cycle regulation
  2. 14-3-3 proteins in cell cycle: Obsil, T., & Obsilova, V. (2008). 14-3-3 proteins: a family of versatile regulators of cell cycle progression. *Vitamins and Hormones*, *78*, 27-57. PubMed search: 14-3-3 proteins cell cycle
  3. YWHAZ in cancer: Wang, W., et al. (2018). The role of 14-3-3ζ in human cancer. *Molecular Cancer*, *17*(1), 1-13. PubMed search: YWHAZ oncogene breast cancer
  4. YWHAZ as biomarker in breast cancer: GeneCards entry for YWHAZ, often includes links to disease associations and biomarker studies. GeneCards: YWHAZ

20. Epithelial Cell Gene Ontology Analysis: Differential Pathway Enrichment Across Ploidy and Tumor Status in Breast Tissue

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[Analysis Visualization Results]...

Analysis Overview

This analysis utilizes Gene Ontology (GSA) to identify enriched biological pathways in epithelial cells from breast tissue. The results are presented as bar plots, showing the negative logarithm of p-values (-log(p-val)) and q-values (-log(q-val)) for pathway enrichment across three key comparisons:

  1. Diploid epithelial cells vs. Aneuploid epithelial cells: Highlighting pathways associated with ploidy status within epithelial cells.
  2. Normal epithelial cells vs. Primary tumor epithelial cells: Identifying pathways characteristic of the healthy epithelial state.
  3. Primary tumor epithelial cells vs. Normal epithelial cells: Revealing pathways enriched in cancerous epithelial cells.

The goal is to provide biological insights into the functional shifts occurring in epithelial cells in the context of breast cancer progression and ploidy changes.

Visual Summary

The bar plots display the top 60 enriched GO terms, ranked by -log(p-val), for each comparison. Higher bars indicate more significant enrichment.

Biological Interpretation

Functional Signatures of Diploid vs. Aneuploid Epithelial Cells

In the breast tissue epithelial compartment, diploid cells show upregulation of pathways associated with early carcinogenesis, cell-matrix interactions, and tumor suppression when compared to aneuploid cells. The enrichment of "Proteoglycans in cancer" and "Pathways in cancer" suggests an active role within the tumor microenvironment, possibly reflecting either less transformed cells or those responding to oncogenic stimuli in a structured manner. The prominence of "Focal adhesion" and "ECM-receptor interaction" indicates intact or heightened communication with the extracellular matrix, critical for maintaining tissue architecture or initiating remodeling. "Cellular senescence," a known tumor-suppressive mechanism that limits the proliferative capacity of damaged cells, is also enriched, suggesting that diploid cells may retain a greater capacity for cell cycle arrest in response to stress. The enrichment of viral infection pathways (e.g., HPV, EBV) might reflect a general activation of innate immune or stress response pathways that are not necessarily indicative of direct viral infection in breast cancer, but rather common cellular responses to perturbation.

Homeostatic Processes in Normal Epithelial Cells

Normal breast epithelial cells, when compared to their malignant counterparts, exhibit a robust enrichment in pathways central to cellular anabolism, catabolism, energy production, and programmed cell death. High enrichment of "Ribosome," "Proteasome," and "Protein processing in endoplasmic reticulum" underscores their active role in protein synthesis, folding, and degradation, essential for maintaining cellular function and integrity. "Oxidative phosphorylation" highlights efficient energy metabolism. The upregulation of "Apoptosis" and "Cellular senescence" pathways emphasizes the tightly regulated cell turnover and damage control mechanisms inherent to healthy tissue, crucial for preventing aberrant cell accumulation. "Focal adhesion" and "Regulation of actin cytoskeleton" reflect the organized structural and mechanical properties of healthy epithelial layers.

Oncogenic Reprogramming in Primary Tumor Epithelial Cells

Primary tumor epithelial cells display a clear reprogramming of metabolism, altered cell cycle control, and changes in cell junction dynamics when compared to normal epithelial cells. The enrichment of "Cell cycle" pathways is a direct hallmark of uncontrolled proliferation, a defining feature of cancer. Metabolic shifts are evident with increased "Oxidative phosphorylation," "Autophagy," "Ubiquitin mediated proteolysis," and activation of the "Citrate cycle" (TCA cycle), indicating altered energy demands and nutrient utilization often associated with the Warburg effect and other cancer-specific metabolic adaptations. Dysregulation of cell-cell adhesion, as suggested by changes in "Tight junction" and "Adherens junction" pathways, is critical for tumor progression, enabling cells to detach from the primary tumor and potentially invade surrounding tissues. Activation of "AMPK signaling pathway" and "MAPK signaling pathway" further points to dysregulated growth and survival signals. The consistent appearance of pathways related to neurodegenerative diseases (e.g., Huntington, Parkinson) across different comparisons might indicate shared underlying mechanisms of cellular stress, protein misfolding, or metabolic dysfunction that become perturbed in various pathological states, including cancer, rather than a direct etiological link to neurodegeneration in breast cancer.

Clinical or Translational Implications

The distinct pathway enrichments in diploid vs. aneuploid and normal vs. primary tumor epithelial cells offer potential avenues for clinical translation:

Understanding these functional shifts at the pathway level provides a comprehensive view of how breast epithelial cells transform from a healthy state to a malignant one, with distinct features also observed based on their ploidy status. This knowledge can help in identifying novel therapeutic strategies and biomarkers for breast cancer.

21. Gene Set Enrichment Analysis (GSEA) in Breast Cancer Endothelial and Epithelial Cells

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results, visualized as a dot plot, for two key cell types: Endothelial cells and Epithelial cells. GSEA identifies biological pathways or gene sets that are coordinately up- or down-regulated in a given condition compared to a reference or 'other' conditions. Here, we investigate pathway enrichment in Endothelial cells from normal tissue versus other conditions, and from primary tumor tissue versus other conditions. For Epithelial cells, we compare diploid cells versus other epithelial cells, and primary tumor epithelial cells versus other epithelial cells. The dot size represents the statistical significance (-log(p-value)), and the color indicates the Normalized Enrichment Score (NES), where red signifies positive enrichment (pathway genes generally upregulated) and blue signifies negative enrichment (pathway genes generally downregulated).

Visual Summary

The dot plot effectively illustrates the differential enrichment of a wide range of biological pathways across the analyzed cell types and conditions.

Biological Interpretation

The GSEA results reveal profound shifts in cellular biology associated with breast cancer progression in both the cancerous epithelial cells and the surrounding tumor microenvironment (specifically, endothelial cells).

Tumor-Associated Proliferation and Metabolism:

Quiescence in Normal/Diploid Cells:

Signaling and Stress Responses:

Clinical or Translational Implications

The distinct pathway enrichments observed in primary tumor cells, both epithelial and endothelial, hold significant clinical and translational relevance for breast cancer:

22. Discussion

The comprehensive single-cell analysis of breast tissue reveals a dramatic reprogramming of the cellular landscape and communication networks in primary tumors, fundamentally distinct from normal tissue homeostasis. The identification of aneuploid epithelial cells, which strongly co-localize with primary tumor regions and exhibit extensive copy number variations, unequivocally marks them as the malignant population. These tumor-origin cells display upregulated surface markers such as EGFR, MUC1, ERBB3, and CA12, along with significant activation of cell cycle pathways, notably YWHAZ. Gene Ontology and GSEA further highlight their metabolic reprogramming, favoring glycolysis and altered oxidative phosphorylation, alongside activated proliferative pathways like PI3K-Akt, MAPK, and Wnt signaling, all crucial for uncontrolled tumor growth.

The tumor microenvironment (TME) undergoes extensive remodeling. Fibroblasts transform into Cancer-Associated Fibroblasts (CAFs), characterized by high expression of FAP, MMP14, NRP1/2, CD276, and CDH11. These CAFs are central to pathological extracellular matrix (ECM) remodeling, mediating extensive integrin-collagen and fibronectin-integrin interactions that contribute to tissue stiffness and support tumor invasion. Macrophages, or Tumor-Associated Macrophages (TAMs), adopt a distinct phenotype in tumors, upregulating surface markers like CCR7, CXCR4, EREG, IL6R, and ITGAV. Their engagement in cell-cell interactions, particularly via the MIF-CD74/CXCR4 axis and SPP1-CD44/integrin signaling, underscores their pro-inflammatory, immunosuppressive, and pro-angiogenic roles. The immune cell compartment also shifts, with primary tumors showing increased proportions of immunosuppressive Tregs, pro-inflammatory Th17 cells, and T follicular helper (Tfh) cells, while innate lymphoid cells like LTI and ILC1 are often diminished, indicative of an altered and often suppressive anti-tumor immune response. Endothelial cells within tumors exhibit a highly proliferative and pro-angiogenic signature, with strong VEGFA-VEGFR signaling, essential for tumor vascularization. Cross-talk involving EGFR and TGF-beta pathways is intensified and broadened across multiple cell types in the TME, highlighting pervasive growth factor and immunomodulatory signaling that fuels tumor progression. The specific activation of TGFB1-integrin_avb6_complex interactions in tumor suggests a unique mechanism of TGF-beta activation driving pro-tumorigenic effects.

Hypotheses:

  1. Aneuploid epithelial cells, marked by specific surfaceome and cell cycle gene upregulation, are the primary drivers of tumor growth and actively orchestrate the remodeling of the surrounding microenvironment in breast cancer.
  2. The phenotypic shift of fibroblasts to CAFs and macrophages to TAMs in the breast tumor microenvironment is critical for establishing an immunosuppressive and pro-invasive niche, directly contributing to disease progression.
  3. Dysregulated cell-cell communication, particularly through specific ligand-receptor axes like MIF-CD74, SPP1-integrin, and enhanced growth factor signaling (EGFR, TGF-beta), dictates the malignant behavior of tumor cells and their interactions with stromal and immune components.
  4. The observed metabolic reprogramming in primary tumor epithelial cells represents a key vulnerability that can be exploited for targeted therapeutic interventions.

Potential therapeutic targets:

  1. EGFR/ERBB3 (HER3): Overexpressed on tumor-origin epithelial cells and activated in tumor-specific cell-cell interactions, driving cell proliferation, survival, and migration. Evidence: Upregulated in primary tumor epithelial cells (surfaceome markers, Image 19). EGFR ligands (AREG, HBEGF, EREG) show enhanced interaction in tumor CCIs (Image 13, Image 16). GSEA shows positive enrichment of MAPK and PI3K-Akt signaling pathways in tumor epithelial cells (Image 27). Validation: Test existing EGFR/HER3 inhibitors (e.g., gefitinib, erlotinib, antibody therapies) in breast cancer patient-derived organoids or xenograft models. Evaluate treatment response via tumor growth inhibition, cell viability, and changes in downstream signaling pathways.
  2. FAP (Fibroblast Activation Protein alpha): Highly specific marker for Cancer-Associated Fibroblasts (CAFs), involved in ECM remodeling and creating a pro-tumorigenic, immunosuppressive microenvironment. Evidence: Robustly and consistently upregulated in primary tumor fibroblasts (surfaceome markers, Image 21). Involved in extensive collagen-integrin and fibronectin-integrin interactions (Image 14, Image 18). Validation: Assess the efficacy of FAP-targeting agents (e.g., FAP-specific antibodies, FAP-targeting ADCs) in preclinical breast cancer models. Monitor changes in tumor stiffness, ECM composition, and immune cell infiltration.
  3. CXCR4 / CXCL12 axis: Upregulated in tumor-associated macrophages (TAMs) and involved in fibroblast-endothelial cell interactions, promoting TAM recruitment, angiogenesis, and tumor metastasis. Evidence: CXCR4 is a highly upregulated surface marker in primary tumor macrophages (Image 20). CXCL14-CXCR4 interactions are prominent between fibroblasts and endothelial cells in primary tumors (Image 18). Validation: Utilize CXCR4 antagonists (e.g., plerixafor) in combination therapies in vivo to assess impact on TAM infiltration, angiogenesis, tumor growth, and metastatic potential. Conduct in vitro migration assays for TAMs and endothelial cells.
  4. MIF (Macrophage Migration Inhibitory Factor) / CD74 axis: Highly active in macrophage-driven inflammation, immunosuppression, and tumor progression within the tumor microenvironment. Evidence: MIF-CD74/CXCR4 interactions are highly active across multiple macrophage-involved cell pairs (Mac/Mac, Mac/Fib, Mac/Diploid Epi, Mac/Aneuploid Epi) in primary tumors (Image 13). Validation: Test small molecule inhibitors or blocking antibodies targeting MIF or CD74 in preclinical models. Evaluate their impact on TAM polarization, immune evasion, and tumor growth, potentially in combination with immunotherapies.
  5. TGF-beta pathway (specifically TGFB1-integrin_avb6_complex): Intensified and dysregulated signaling in the tumor microenvironment, promoting fibrosis, immune suppression, EMT, and activating latent TGF-beta. Evidence: TGF-beta signaling is broadly active in tumor CCIs (Image 13, Image 16). The TGFB1-integrin_avb6_complex interaction is specifically observed in the primary tumor context, involving aneuploid epithelial cells and fibroblasts (Image 16). Validation: Target integrin αvβ6 specifically with blocking antibodies or use pan-TGF-beta inhibitors. Assess their effect on ECM deposition, tumor cell invasion, and immune cell function in vitro and in vivo. Measure activation of TGF-beta downstream targets.

Follow-up validation ideas:

  1. Perform spatial transcriptomics or multiplex immunofluorescence to confirm the localization and spatial interactions of aneuploid epithelial cells, CAFs (FAP+, MMP14+), and TAMs (CXCR4+, EREG+) within the tumor microenvironment.
  2. Conduct in vitro co-culture experiments using isolated aneuploid epithelial cells, CAFs, and TAMs to validate the functional impact of specific ligand-receptor interactions (e.g., MIF-CD74, SPP1-CD44) on cell proliferation, migration, and immune modulation using blocking antibodies or gene editing.
  3. Utilize patient-derived organoid or xenograft models to test the efficacy of inhibitors targeting key dysregulated pathways (e.g., EGFR, TGF-beta, CXCR4, FAP) or their combination on tumor growth, metastasis, and TME composition.
  4. Validate the prognostic and predictive value of identified surface markers (e.g., EGFR, MUC1, FAP, CXCR4) and altered immune cell proportions (Tregs, Th17) in larger independent breast cancer cohorts using immunohistochemistry and flow cytometry.
  5. Employ targeted metabolomics to confirm the metabolic shifts (e.g., glycolysis, oxidative phosphorylation) in isolated primary tumor epithelial cells and assess the impact of metabolic inhibitors on their viability and proliferation.

Limitations:

This report is based on single-cell RNA sequencing data, which provides high-resolution cellular insights but has inherent limitations. Observed gene expression patterns and inferred cell-cell interactions are correlative and require functional validation to establish causality. Copy number variation inference from RNA-seq is an approximation and may not capture all genomic alterations. While the UMAP visualization provides a projection of cellular states, it does not directly represent the spatial organization of cells, which is crucial for contextualizing cell-cell interactions. The findings are derived from a specific cohort of breast cancer patients, and results may vary in broader populations or different breast cancer subtypes. Further in vitro and in vivo experimental validation is necessary to confirm the functional relevance of the identified markers and pathways.

23. Query List

  1. Show UMAP colored by condition, sample, celltype_major, celltype_minor, ploidy_dec, and celltype_subset in 2 columns, and save it.
  2. Show major celltype scores on UMAP and save them.
  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. For tumor-origin cells and unassigned cells, show a CNV heatmap grouped by sample, along with a summary of significantly amplified copy number regions, and save it.
  5. Show CNV patterns on UMAP, including major celltype, minor celltype, ploidy_dec, condition, and sample in 2 columns, and save it.
  6. Show a population bar plot for minor cell types and save it.
  7. Show a subset population bar plot for T cells and save it.
  8. For T cell subsets, show a box plot for populations that show significant differences between conditions and save it. Set ncols appropriately based on the total number of panels.
  9. Show a subset population bar plot for macrophages and save it.
  10. For tumor-origin cells and unassigned cells, show a ploidy population bar plot and save it.
  11. Show cell-cell interaction patterns for tumor-origin cells, fibroblasts, macrophages, and T cells, by condition and save them. Select up to 80 cell-cell interactions per condition.
  12. Show cell-cell interactions by condition and save the result. Select at most 80 cell-cell interactions for each condition.
  13. For genes related to immune checkpoint and cell cycle pathways, show cell-cell interactions and save them.
  14. For major immune and stromal cells, find statistically significant differences in cell-cell interactions between conditions and show them as a dot plot, and save it. Set max_n_items_per_group to 25.
  15. 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.
  16. For Macrophage, extract condition-specific markers and show them as a dot plot, and save it. Include only surfaceome markers, up to 50 per condition.
  17. For Fibroblast, extract condition-specific markers and show them as a dot plot, and save it. Include only surfaceome markers, up to 50 per condition.
  18. For T cell CD4+, extract condition-specific markers and show them as a dot plot, and save it. Include only surfaceome markers, up to 50 per condition.
  19. For Epithelial cell (the tumor-origin cell type), show a box plot of Cell Cycle pathway-related genes with statistically significant expression differences between conditions, and save it. Set max_n_items_to_plot to 24, and ncols such that the panel ratio is approximately 2x3.
  20. Show Gene Ontology (GSA) analysis results for epithelial cells as bar plots and save the result.
  21. Show Gene Set Enrichment Analysis (GSEA) results for major cell types as a dot plot and save it. Use 'RdBu_r' for the colormap and set n_pws_to_show to 80.
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