Bibliographic record
Abstract
Abstract Poly(ADP-ribose) polymerase inhibitors (PARPIs) kill cancer cells by trapping PARP1 and PARP2. Talazoparib, the most potent PARP inhibitor (PARPI), exhibits remarkable selectivity among the NCI-60 cancer cell lines beyond BRCA inactivation. Our genomic analyses reveal high correlation between response to talazoparib and Schlafen 11 (SLFN11) expression. Causality was established in four isogenic SLFN11-positive and -negative cell lines and extended to olaparib. Response to the talazoparib-temozolomide combination was also driven by SLFN11 and validated in 36 small cell lung cancer cell lines, and in xenograft models. Resistance in SLFN11-deficient cells was caused neither by impaired drug penetration nor by activation of homologous recombination. Rather, SLFN11 induced irreversible and lethal replication inhibition, which was independent of ATR-mediated S-phase checkpoint. The resistance to PARPIs by SLFN11 inactivation was overcome by ATR inhibition, mechanistically because SLFN11-deficient cells solely rely on ATR activation for their survival under PARPI treatment. Our study reveals that SLFN11 inactivation, which is common (~45%) in cancer cells, is a novel and dominant resistance determinant to PARPIs. Citation Format: Yves Pommier, Junko Murai. PARP trapping and Schlafen 11 [abstract]. In: Proceedings of the AACR Special Conference on DNA Repair: Tumor Development and Therapeutic Response; 2016 Nov 2-5; Montreal, QC, Canada. Philadelphia (PA): AACR; Mol Cancer Res 2017;15(4_Suppl):Abstract nr IA19.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".