Abstract IA23: Using PARP inhibitors to target ATM-deficient cancers
Bibliographic record
Abstract
Abstract The Ataxia-Telangiectasia Mutated (ATM) serine/threonine protein kinase plays an important role in orchestrating the cellular response to DNA double strand breaks (DSBs). Germline loss of ATM function results in Ataxia-Telangiectasia (A-T), a devastating childhood syndrome characterized by progressive loss of neuromuscular control, immune deficiency and cancer predisposition. ATM is also deleted in a large proportion of human mantle cell lymphoma (MCL) and other lymphomas and recent studies have revealed that ATM is altered in many sporadic cancers including lung, colorectal, breast and prostate cancer. We previously showed that MCL and gastric cancer cell lines with deletion or mutation of ATM are sensitive to the poly-ADP-ribose polymerase (PARP) inhibitor, olaparib and that loss of TP53 enhances olaparib sensitivity (Williamson et al Molecular Cancer Therapeutics, 2010, 9:347-57; Williamson et al, EMBO Molecular Medicine, 2012, 4, 515-527; Kubota et al, Cell Cycle 2014, 13: 2129-37). Here we show that lung and colorectal cancers with mutation or loss of ATM are similarly sensitive to olaparib. The potential for use of PARP and ATM inhibitors in the treatment of ATM deficient malignancies will be explored. Citation Format: Susan P. Lees-Miller. Using PARP inhibitors to target ATM-deficient cancers [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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".