Abstract B010: Drug tolerant persister cancer cells escape therapy-induced senescence
Bibliographic record
Abstract
Abstract Triple-negative breast cancer (TNBC) accounts for 15% of breast cancers and is the most aggressive subtype lacking precision oncology therapeutic strategies. Standard-of-care is predominantly chemotherapy in the neoadjuvant setting (NACT). Despite good responses, ∼40% of TNBC patients develop resistance and present residual disease at surgery. Overcoming resistance and implementing better therapeutic options is critical. Our objectives are to develop targeting opportunities for drug tolerant persister (DTP) cells which underlie residual disease. Using our unique biobank of patient derived xenografts from treatment naive as well as NACT resistant TNBC, we developed longitudinal in vivo models of residual and relapse tumors to standard-of-care therapy. Transcriptomic analysis revealed transient metabolic changes such as oxidative phosphorylation, starvation and autophagy, associated with hallmarks of senescence at residual disease. We demonstrated that therapy-induced senescent cells in vitro can escape cell cycle arrest and resume proliferation through autophagy. Interfering with autophagy impairs redox balance, promotes ferroptosis and delays tumor relapse. These results support that autophagy is a promising targetable vulnerability in TNBC residual disease. Citation Format: Anne-Marie Fortier, Jason Topolski, Gabriel Alzial, Anie Monast, Hellen Kuasne, Dongmei Zuo, Alain Pacis, Genevieve Deblois, Morag Park. Drug tolerant persister cancer cells escape therapy-induced senescence [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 B010.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".