Abstract B046: Analyzing heterogeneity of tumor microenvironment of TNBC patients by meta-analysis of 7 breast cancer scRNAseq studies
Bibliographic record
Abstract
Abstract Breast cancer can be classified into several types. Among them, triple-negative breast cancer (TNBC) is breast cancer that does not express estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Since targeted therapy for breast cancer relies on the three receptors, treatment outcomes have been worst in TNBCs. However, the clinical benefit of immune-checkpoint inhibitors (ICIs) with chemotherapy over chemotherapy alone was demonstrated in several trials, opening a new avenue for patients with metastatic PD-L1+ TNBC. After several studies with ICIs, researchers found that the tumor microenvironment affects the response rate of immunotherapies. Therefore, to unravel the heterogeneity of the tumor microenvironment of TNBC patients, we collected 7 public single-cell RNA sequencing breast cancer studies. From 141,068 cells populating tumors and tumor-microenvironment (TMEs), we first identified epithelial cells separately. Using whole cells including epithelial and immune-stromal cells, we identified the Baylor-proposed TNBC molecular subtype of each patient. This approach with epithelial cells revealed that 19 basal-like immune-activated (BLIA), 5 basal-like immune-suppressed (BLIS), 2 luminal-androgen receptor (LAR), 3 mesenchymal (MES), and 3 unclassified subtypes. By comparing cell number and cell-cell interactions, the heterogeneity of TME-consisting cell populations between the TNBC subtypes was analyzed. Analysis of tumor-infiltrating lymphocytes revealed their difference in activation, expansion, and exhaustion programs across patients. Among the subtypes, the BLIA subtype had a greater amount of exhausted CD8+ T cells and regulatory T cells. This finding was consistent with cell-cell interaction analysis. In the case of the BLIA subtype, the interaction between regulatory T cells and CD8 T cells was more active than in other subtypes. Also, the interaction between cytotoxic cells (T cells and NK cells) and regulatory T cells were more active in the BLIA subtype. Additionally, tumor cells in the BLIA subtype seem to inhibit cytotoxic cells to a greater extent. Among the interactions between epithelial cells and exhausted CD8+ T cells, MDK interaction was higher in the BLIA subtype. Additionally, the expression of the MDK gene in endothelial cells and macrophages leads to upregulation in immune infiltration which causes more T cells in TME. Also, we found that the progression-free survival of patients differs due to the expression of the MDK gene. In conclusion, the MDK gene causes a T cell-enriched environment in TME, which leads to different progression-free survival. Citation Format: Kyungsoo Kim, Jee Hung Kim, Soong June Bae, Sung Gwe Ahn, Joon Jeong. Analyzing heterogeneity of tumor microenvironment of TNBC patients by meta-analysis of 7 breast cancer scRNAseq studies [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr B046.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".