Abstract IA23: Deconvolution of the triple-negative breast cancer microenvironment
Bibliographic record
Abstract
Abstract Breast cancer heterogeneity is one of the principal obstacles both to predicting outcome and to determining an effective course of treatment for this disease. Although genomic technologies have been used to gain a better understanding, by identifying gene expression signatures associated with clinical outcome and breast cancer subtypes, relatively little is known about heterogeneity in the tumor microenvironment. It is now accepted that changes in the normal cells that constitute the tumor microenvironment (TME) play important roles in determining cancer progression and ultimate outcome. We and others have established that an immune gene expression signature correlates with good outcome in triple negative breast cancer (TNBC), yet fails to accurately predict outcome in all patients. Examining stromal heterogeneity in TNBC has identified four distinct stromal clusters, three of which are prognostic, contain distinct immune signatures and a signature of fibrosis. Lymphocytic infiltration and access to tumor parenchyma is not well understood due to high levels of spatial heterogeneity within tumors. We show that location of CD8+T cells is strongly influenced by TME subtypes and identify gene expression signatures predictive of distinct CD8+T cell localization and patient outcome. Since mounting evidence suggests that immune-checkpoint inhibitor immunotherapies may be promising for only a subset of TNBC patients, highlights the importance of understanding how the TME influences CD8+T cell location. Citation Format: Sadiq Saleh, Tina Gruosso, Mathieu Gigoux, Nicholas Bertos, Atilla Omeroglu, Dongmei Zuo, Sarkis Meterissian, Michael Hallett, Morag Park. Deconvolution of the triple-negative breast cancer microenvironment. [abstract]. In: Proceedings of the AACR Special Conference: Function of Tumor Microenvironment in Cancer Progression; 2016 Jan 7–10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2016;76(15 Suppl):Abstract nr IA23.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".