Abstract IA02: Defects in DNA repair genes revealed by clinical sequencing of advanced cancer patients
Bibliographic record
Abstract
Abstract In 2011, we introduced the Mi-Oncoseq program, an IRB-approved protocol to carry out integrative sequencing of advanced cancer patients. It was one of the first comprehensive DNA and RNA sequencing programs offered for cancer patients. The purpose of this program was to determine the utility of genomic sequencing of tumors and germline coupled with a multi-disciplinary Precision Medicine Tumor Board (PMTB) in the management of advanced cancer patients. Collectively across all patient cohorts we have enrolled over 1500 patients since inception of Mi-Oncoseq where molecular results for both somatic and germline aberrations are returned to physicians. Both common and rare mutations were identified in numerous cellular pathways that are involved in carcinogenesis including those in the DNA repair pathway. We identified homozygous deletions, deleterious mutations and/or LOH in genes including BRCA1/2, ATM, Fanconi's anemia genes, and CHEK2 that are potentially actionable. For example, a multi-institutional, collaborative SU2C-PCF study of men with metastatic castrate-resistant prostate cancer (mCRPC) found that treatment with the PARP inhibitor olaparib in patients whose prostate cancers were no longer responding to standard treatments and who had defects in DNA-repair genes led to a high response rate (Mateo et al. NEJM, 2015). Additionally, a follow-up study found 11.8% of mCRPC patients had a germline mutation in a DNA-repair gene, including moderate risk genes (ATM, CHEK2, PALB2, RAD51D) for which clinical testing criteria do not exist (Pritchard et al, NEJM, 2016). Most of these patients would not qualify for germline genetic testing under current clinical care models. Our studies demonstrate that DNA repair mutations are common in mCRPC patients who may benefit from therapies such as PARP inhibitors and when identified in the germline, could have a significant impact on their family members where early cancer screening could be implemented as appropriate. Citation Format: Arul Chinnaiyan. Defects in DNA repair genes revealed by clinical sequencing of advanced cancer patients [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 IA02.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".