Abstract PD6-05: Distinct tumor microenvironments stratify triple negative breast cancer into immune subtypes
Bibliographic record
Abstract
Abstract Background: Triple negative breast cancer (TNBC) are especially difficult to treat effectively. While only 20-30% of TNBC patients respond to chemotherapy in the neoadjuvant setting, overall outcome remains poor for non-responding patients. Engaging the immune system promises optimal personalized cancer therapy as mounting evidence suggests that immune-checkpoint inhibitor immunotherapies may become a therapeutic option for TNBC patients. The presence of CD8+ T cells, a crucial component of the cytotoxic arm of the adaptive immune response, is associated with good clinical outcome in TNBC patients. Specifically, it is the efficient CD8+ T cell invasion and infiltration in the tumor that is associated with good outcome. On the other hand, some tumors accumulate CD8+ T cells in the tumor-associated stroma with poor infiltration in the tumor epithelium. These patients show poor outcome. As CD8+ T cell infiltration in the tumor is a crucial step to mount an efficient anti-tumor response, we thus wondered how the tumor microenvironment affects CD8+ T cell invasion into the tumor epithelial compartment of the TNBC tumors. Methods: To identify potential stroma-dependent mechanisms that potentiate or inhibit CD8+ T cells invasion into the tumor epithelium, we coupled analysis of spatial patterns of CD8+ T cell localization by Immunohistochemistry (IHC) andperformed gene expression profiling of laser-capture microdissected tumor-associated stroma (as well as matched epithelium and bulk tumor) from 38 TNBC chemotherapy-naive primary cases. GSEA-based Metasignatures were derived from bulk tumor gene expression data from our cohort. To investigate the compartment of origin of the pathways identified via the Metasignatures, the (LCM)-derived tumor stromal and epithelial gene expression were analyzed. Results: CD8+ T cell quantification in different compartments of the tumor identify 3 main subgroups of TNBC based on CD8+ T cell localization. Importantly we developed a 2-step classification scheme based on CD8+ T cell localization. We developed metasignatures following our 2 steps classification and identified key bulk tumor metasignatures that showed prognostic value in an independent cohort. In addition the matched LCM gene expression from the tumor epithelium and stromal compartments allowed us to identify the compartment of origin. Importantly, while 1 group of TNBC tumor was showing a significant anti-tumor response, the 2 other groups showed absence of such environment. The 2 non inflamed immune subtypes showed distinct phenotypes and biologies associated with poor anti-tumor response that we validated by immunohistochemistry and fluorescence. These results highlight different potential mecanisms that lead to immune evasion and allow us to stratify TNBC into immune subgroups. Citation Format: Gruosso T, Gigoux M, Bertos N, Manem VS, Zuo D, Saleg SM, Souleimanova M, Zhao H, Johnson RM, Monette A, Muñoz Ramos V, Hallett MT, Stagg J, Lapointe R, Omeroglu A, Meterissian S, Buisseret L, Van den Eyden G, Salgado R, Guiot M-C, Haibe-Kains B, Park M. Distinct tumor microenvironments stratify triple negative breast cancer into immune subtypes [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr PD6-05.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".