Abstract B021: CFLAR targeting selectively exploits extrinsic apoptosis signaling in triple-negative breast cancer
Bibliographic record
Abstract
Abstract The extrinsic apoptotic pathway activates programmed cell death through the binding of extracellular death ligands, such as TNFα, to their cognate receptors. Although some death ligands are often upregulated in the tumor microenvironment, this pathway is subverted in cancer cells to instead favor growth and survival rather than the induction of apoptosis. Reflecting the strict control cancer cells exert over this pathway, functional genomics screens, such as DepMap, have identified several of the extrinsic apoptotic components as essential targets, with selective dependency in a subset of cancer cell lines. We have genetically validated that downregulation of CFLAR, a negative regulator of the extrinsic apoptosis pathway, causes apoptosis and cell growth inhibition alone, but specially in combination with TNFα signaling, across multiple cancer types, with increase prevalence in Triple-negative breast cancer. Furthermore, we have shown that in order to have maximal activity most cell lines require targeting of CFLAR short isoform splicing variant. Importantly, CFLAR downregulation in non-tumoral models presented much less activity than the observed in the sensitive cancer models supporting the selective activity of CFLAR in a subset of cancer cells. Citation Format: Victor Quereda, Shane W. O'Brien, Tony C. Della Pietra, James J. Foley, Andy Fedoriw. CFLAR targeting selectively exploits extrinsic apoptosis signaling in triple-negative breast cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr B021.
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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) | 0.002 | 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".