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Record W2080009731 · doi:10.1158/1538-7445.am2012-3116

Abstract 3116: Patterns of genomic loss of heterozygosity predict homologous recombination repair defects in ovarian cancer

2012· article· en· W2080009731 on OpenAlexaff
Kirsten M. Timms, Victor Abkevich, Bryan T. Hennessy, Jennifer Potter, Mark Carey, Larissa A. Meyer, Russell R. Broaddus, Karen H. Lu, Srikanth Jammulapati, Lisa M. Fitzgerald, Thomas C. Krivak, Julie A. DeLoia, Alexander Gutin, Gordon B. Mills, Jerry S. Lanchbury

Bibliographic record

VenueCancer Research · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLoss of heterozygosityOvarian cancerBiologyHomologous recombinationCancer researchDNA repairBreast cancerGeneGeneticsCancerAllele

Abstract

fetched live from OpenAlex

Abstract Introduction: BRCA1 and BRCA2 are key members of the homologous recombination (HR) pathway. Mutations in these genes and other HR pathway defects have potential therapeutic relevance when used to support agents that introduce or exploit double-strand DNA breaks. This study examines the association between HR deficiency and genomic patterns of loss of heterozygosity (LOH). Methods: Ovarian tumors from two independent datasets were characterized for germline and somatic defects in BRCA1 and BRCA2. Whole genome LOH profiles were generated using Affymetrix SNP arrays. Publically available data was downloaded from the TCGA website for a third independent ovarian cancer dataset. RAD51C promoter methylation was assayed in two of the datasets. Comprehensive profiling of BRCA1 and BRCA2 defects, and genome wide LOH was also performed on approximately 70 breast, ovarian, colon and pancreatic cell lines. Results: Examination of the pattern of LOH within ovarian tumors with BRCA1, BRCA2, or RAD51C defects compared to tumors without defects in these genes has resulted in the development of a homologous recombination deficiency (HRD) score that has highly significant association with HRD (p=9*10-11). An intermediate class of LOH sizes (>15 Mb but less than a whole chromosome) is highly positive correlated with defective HR, suggesting this class of LOH exists due to double strand DNA break formation and requires repair by HR. The HRD score was validated in two independent ovarian cancer datasets (p=2*10-7 and 9*10-29), and successfully identified breast and pancreatic cell lines with BRCA defects, suggesting it will be effective across multiple tumor types. Conclusions: BRCA1 or BRCA2 mutation carriers have improved outcomes following treatment with DNA damaging agents such as platinum salts, and preclinical studies have demonstrated PARP inhibitor efficacy in BRCA1 or BRCA2 deficient cells. HR deficiency in ovarian cancer is not solely due to germline BRCA1 and BRCA2 mutations, and HR deficiency is not unique to ovarian tumors. Each type of cancer is likely to have a unique spectrum of genetic variants resulting in HRD. The HRD score appears capable of detecting HRD regardless of etiology or mechanism. This score could have clinical utility in breast and ovarian cancer, and could be used to target the use of PARP inhibitors and platinum salts in other cancers. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3116. doi:1538-7445.AM2012-3116

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.029
GPT teacher head0.376
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2012
Admission routes1
Has abstractyes

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