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Record W3092552341 · doi:10.1002/1878-0261.12817

Tumor and germline next generation sequencing in high grade serous cancer: experience from a large population‐based testing program

2020· article· en· W3092552341 on OpenAlexafffund
Melanie Care, Jeanna McCuaig, Blaise Clarke, Sylvie Grenier, Raymond H. Kim, Marjan Rouzbahman, Natalie Stickle, Marcus Q. Bernardini, Tracy Stockley

Bibliographic record

VenueMolecular Oncology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
FundersPrincess Margaret Cancer FoundationAstraZeneca Canada
KeywordsGermlineConcordanceGenetic testingPopulationCancerOncologyMedicineSerous fluidGermline mutationSomatic cellInternal medicineBiologyGeneticsMutationGene

Abstract

fetched live from OpenAlex

The aim of this study was to determine the prevalence of somatic and germline pathogenic variants (PVs) in high-grade serous cancer (HGSC) and to demonstrate the technical feasibility and effectiveness of a large-scale, population-based tumor testing program. It involved a retrospective review of genetic test results in 600 consecutive HGSC tumor samples and a subsequent comparison of germline and tumor results in a subset of 200 individuals. Tumor testing was successful in 95% of samples (570/600) with at least one BRCA1/2 PV identified in 16% (93/570) of cases. Among the 200 paired cases, BRCA1/2 PVs were detected in 38 tumors (19%); 58% were somatic (22/38); and 42% were germline (16/38). There was 100% concordance between germline and tumor test results. This is the largest series of BRCA1/2 testing in HGSC (tumor-only and paired cohorts), reported to date, and our data show that an effectively designed and validated population-based tumor testing program can be used to determine both treatment eligibility and hereditary cancer risk.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.262
Threshold uncertainty score0.787

Codex and Gemma teacher scores by category

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

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 head0.071
GPT teacher head0.337
Teacher spread0.266 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations18
Published2020
Admission routes2
Has abstractyes

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