Abstract 3064: Retinoic acid: An effective therapy for basal-like breast cancer
Bibliographic record
Abstract
Abstract We have identified a novel strategy to identify breast cancer patients who will benefit from an existing anti-cancer agent, retinoic acid (RA). While RA has not yet achieved success in the treatment of breast cancers, we hypothesized that it can be an effective therapy for a subset of triple-negative breast cancer (TNBC) patients. TNBC is among the most aggressive breast cancers, and lacks targeted therapies. TNBCs can be further subtyped into basal-like and claudin-low, which differ in gene expression and drug sensitivities. Understanding the molecular basis of these subtypes will lead to the development of more effective treatment options for TNBC. To test this hypothesis, we performed tumor growth assays on TNBC cell lines and patient-derived xenografts (PDXs). We found that RA treatment decreased the tumor growth of four basal-like TNBC cell lines (MDA-MB-468, HCC70, SUM149, HCC1937). In contrast, RA increased the tumor growth of two claudin-low TNBC cell lines (MDA-MB-231, MDA-MB-436). Gene expression and methylation analysis of these affected cell lines revealed subtype-specific expression of RA-inducible genes due to silencing by DNA methylation, e.g. of the RA-inducible tumor-suppressor gene RARRES1. RARRES1 is silenced by methylation in claudin-low cell lines, but is hypomethylated and expressed in basal-like cells. Use of the subtype-specific expression and methylation profiles allowed us to accurately predict the response of 4 PDXs to RA treatment. Continued classification of TNBCs into these two subtypes will enable clinical use of RA, in part due to the subtype-specific hypomethylation of RA-inducible tumor suppressor genes including RARRES1. We have identified additional subtype-specific biomarkers which can predict the response of patient tumors to RA treatment, thus identifying a novel targeted therapy strategy for TNBCs. Citation Format: Krysta Mila Coyle, Cheryl A. Dean, Dejan Vidovic, Ian C. Weaver, Carman A. Giacomantonio, Paola Marcato. Retinoic acid: An effective therapy for basal-like breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3064. doi:10.1158/1538-7445.AM2017-3064
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".