Abstract 720: Novel prognostic stromal subtypes in triple-negative breast cancer
Bibliographic record
Abstract
Abstract Breast cancer is a heterogeneous disease in terms of presentation, morphology, molecular profile and response to therapy. Gene expression profiling has identified intrinsic molecular subtypes that are associated with clinical markers (ER, PR, HER2) as well as prognosis and survival. However, it is well established that the intrinsic molecular profiles of breast tumors are not sufficient to perfectly predict disease outcome. Increasing evidence indicates that characteristics of the breast stroma influence tumor progression and response to therapy. Previous work in our lab has demonstrated that gene expression signatures in human stroma can predict outcome of breast cancer patients independently of clinical parameters and molecular subtypes. In this study, we expand our findings by focusing on a previously underrepresented subset of breast tumors that have no detectable ER, PR or HER2 (termed Triple-Negative, TN). TN tumors, which represent approximately 15% of all breast cancers, are typically associated with poor outcome. However, the contribution of the stroma to the underlying heterogeneity of TN breast cancer and its corresponding influence on therapeutic response is not well understood. To address this, we isolated TN tumor epithelial and stromal tissues by laser capture microdissection and subjected them to gene expression profiling. Class discovery revealed distinct gene-clusters (stromal properties) which are associated with prognosis in TNBC whole tumor samples. Analysis of the genes comprising each stromal property suggests that the properties primarily represent the prevalence of distinct cell types, namely T cells, B cells, activated fibroblasts, and myoepithelial cells. Importantly, these properties are not mutually exclusive, i.e. some tumors are associated with multiple stromal properties. While confirming the heterogeneity of TN-associated stroma, this also indicates that a multi-parameter classification better reflects the true nature of the tumor microenvironment. This project provides the first integrated in-depth analysis of the contribution of tumor stromal processes to TN disease heterogeneity, and positions the tumor microenvironment for therapeutic intervention. Citation Format: Crista Thompson, Sadiq M. Saleh, Nicholas Bertos, Mathieu Gigoux, Tina Gruosso, Margarita Souleimanova, Hong Zhao, Michael T. Hallett, Morag Park. Novel prognostic stromal subtypes in triple-negative breast cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 720.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".