Tumor Microenvironment in Breast Cancer

Q-omics-based exploration of tumor microenvironment remodeling in breast cancer

Query

Tumor microenvironment in breast cancer

Workflow

Literature Discovery → Suggested Task Selection → Survival Analysis → Consensus Analysis → NetCrafter

Analysis

In this tutorial, OmixMind extracts insights from the literature, and Q-omics suggests relevant analysis tasks. We then run a recommended survival analysis to examine prognostic impact and explore consensus data across different conditions and lineages.

Insight

Breast cancer progression is shaped by progressive remodeling of the tumor microenvironment: immune composition predicts prognosis, adipocyte-associated stroma is reprogrammed toward a tumor-supportive state, and the microenvironment shifts functionally toward extracellular-matrix organization and cell–matrix adhesion.

Step 1Query Search

OmixMind

Exploring the dynamic evolution of the tumor microenvironment (TME) Analysis of High-Impact Research

Action — Try the Insights button to generate literature paradigms and the suggested Q-omics analyses.

Step 2Literature Insight Generation

Task 1: Evaluate prognostic impact of infiltrating cells in breast cancerInteractive

Tumor microenvironment profiling identifies immune and stromal cell populations whose infiltration levels significantly influence breast cancer survival outcomes. These prognostic associations are validated through sampling consensus to ensure robustness across diverse clinical and analytical data splits.

Task 2: Compare normal vs tumor infiltrating cell enrichment in BRCAInteractive

Differential enrichment of immune and stromal cells reveals critical microenvironmental alterations driven by breast cancer development. The resulting candidate cell populations are validated by sampling consensus, ensuring high reproducibility across diverse analytical splits within the BRCA cohort.

Task 3: Explore mutation associations with infiltrating cells in breast cancerInteractive

Evaluates statistical associations between somatic mutations and the abundance of immune or stromal cells within the breast tumor microenvironment. The prioritized list highlights mutation-infiltrate relationships validated for technical reproducibility across diverse analytical conditions within the BRCA cohort.

Task 4: Map tumor microenvironment co-infiltration patterns in breast cancerVisualize

Co-infiltration profiling reveals the coordinated presence of diverse immune and stromal cell populations within the breast cancer microenvironment. The resulting heatmap visualizes the complex network of cellular interactions, highlighting synergistic or mutually exclusive infiltration patterns.

Task 5: On-demand correlation of immune checkpoints and TME infiltrationComputed

This task joins RNA expression of checkpoint genes (CD274/PD-L1, PDCD1/PD-1, CTLA4) with immune cell infiltration scores at the sample level to map how immune checkpoint expression correlates with specific microenvironment cell types in breast cancer.

Task 6: Immune cell infiltration changes across breast cancer stagesComputed

Computes on-demand analysis of variance (ANOVA) to identify which immune cell populations significantly change in abundance across TCGA breast cancer stages (Stage I to IV).

Task 7: Cross-analysis evidence of differentially infiltrated and prognostic TME cellsComputed

Intersects precomputed normal-vs-tumor differential infiltration results with clinical survival outcomes in breast cancer, identifying immune cell populations that act as robust biomarkers for both tumor development and patient prognosis.

Task 8: On-demand overall survival stratification by Treg to CD8 ratioComputed

Groups patients based on high (top 25%) vs low (bottom 25%) regulatory T-cell to CD8+ cytotoxic T-cell ratios, and tests if survival times (OS Days) significantly differ using a Welch t-test.

Action — Try Task 2: Compare normal vs tumor infiltrating cell enrichment in BRCA to see the results

Step 3Fold-Change Analysis

Overview & Findings

Significant tumor-versus-normal abundance differences in TCGA-BRCA (p-values < 0.01) – 19 genes RED upregulated in tumor – 16 genes BLUE downregulated in tumor Select a specific cell to perform the following analyses – Box plot – Consensus samplings – Consensus lineages

Tumor infiltration of 35 infiltrating cells significantly different between tumor and normal samples in BRCA — Q-omics fold-change result table

Continued fold-change result table — downregulated infiltrating cells (negative log fold-change) in BRCA with consensus scores

Box Plot

MSC abundance was significantly higher in tumor tissue than in normal tissue (log₂ Fold-Change = 0.298) HSC abundance was significantly lower in tumor tissue than in normal tissue (log₂ Fold-Change = −0.353)

HSC box plot — log2 enrichment score in normal vs tumor BRCA samples; HSC abundance significantly lower in tumor (log2 fold-change -0.353, p < 0.001)

Step 4Consensus Analysis

Consensus Samplings

MSC was significant in 6 of 12 sampling conditions for a tumor-versus-normal difference in TCGA-BRCA (p < 0.05). HSC was significant in 6 of 12 sampling conditions for a tumor-versus-normal difference in TCGA-BRCA (p < 0.05).

MSC list of 6 significant sampling options — MSC significant in 6 of 12 sampling conditions for a tumor-versus-normal difference in TCGA-BRCA (p < 0.05); positive fold-changes around 0.3

HSC list of 6 significant sampling options — HSC significant in 6 of 12 sampling conditions for a tumor-versus-normal difference in TCGA-BRCA (p < 0.05); negative fold-changes around -0.35

Consensus Lineages

MSC was differentially expressed between tumor and normal in 12 of 18 TCGA cancer types (p < 0.05). HSC was differentially expressed between tumor and normal in 20 of 18 TCGA cancer types (p < 0.05).

List of 20 consensus lineages with sampling options for the differential enrichment score of HSC between tumor and normal samples; HSC differentially expressed in 20 of 18 TCGA cancer types (p < 0.05), downregulated in tumor