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Record W2395438462 · doi:10.1158/1557-3125.advbc15-a34

Abstract A34: BRCA1 and BRCA2 mutation spectrum across 5, 509 high-risk individuals identifies pathogenic variants associated with ethnicity, age of diagnosis, and type of cancer

2016· article· en· W2395438462 on OpenAlexaffabout
Andrew H. Girgis, Marina Wang, Alexa Fine, Kathleen-Rose Zakoor, Sam Khalouei, George S. Charames, Jordan Lerner‐Ellis

Bibliographic record

VenueMolecular Cancer Research · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsOntario Institute for Cancer ResearchUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsOvarian cancerBreast cancerOncologyMedicineCancerFamily historyIncidence (geometry)Internal medicineBRCA mutationCohortGermline mutationGynecologyMutationGeneticsBiologyGene

Abstract

fetched live from OpenAlex

Abstract Hereditary Breast and Ovarian Cancer Sydrome (HBOC) accounts for approximately 5-10% of breast and ovarian cancer cases and germline mutations of the BRCA1 and BRCA2 genes confer substantially increased risk. However, the risk of developing cancer, the type of cancer and the associated age at diagnosis vary depending on the type of mutation carried and the individual's ethnic background and gender. Screening of BRCA1 and BRCA2 mutations was conducted on 5, 512 high-risk individuals with a prior probability of carrying a pathogenic mutation (>10% chance) from the Advanced Molecular Diagnostics Laboratory (AMDL) (Mount Sinai Hospital, Toronto). Information on age, type of cancer diagnosed and age at diagnosis, ethnicity and family history were collected. Cumulative incidence competing risk and Fine-Gray proportional hazard regression analyses were generated to determine factors associated with earlier diagnosis of cancer. Among 5,029 women and 480 men who underwent testing, a total of 845 unique variants were identified of which, 289 (34.2%) are pathogenic. There were 603 female mutation carriers, of these, 303 were affected with breast or ovarian cancer (50%), 16 with another cancer (2.25%) and 284 were unaffected (47.1%). We also identified 20 different ethnic groups presenting with at least 3 unique variants. Six ethnic groups carried at least one variant associated with an earlier diagnosis of cancer and differential breast or ovarian cancer incidence. For instance, among the Ashkenazi Jewish cohort, the c.68_69del variant was associated with significantly earlier age at diagnosis for ovarian cancer incidence (Gray's test = 40.6; Fine-Gray HR = 1.12, 95% CI = 1.05 – 1.31). By screening a diverse cohort of high-risk individuals for BRCA1 and BRCA2 mutations, we identified pathogenic variants associated with an earlier age of diagnosis and differential incidence of breast or ovarian cancer among unique ethnic groups. Citation Format: Andrew H. Girgis, Marina Wang, Alexa Fine Kathleen-Rose ZakoorSam Khalouei, George Charames, Jordan Lerner-Ellis. BRCA1 and BRCA2 mutation spectrum across 5, 509 high-risk individuals identifies pathogenic variants associated with ethnicity, age of diagnosis, and type of cancer. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Breast Cancer Research; Oct 17-20, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(2_Suppl):Abstract nr A34.

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)0.001
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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.039
GPT teacher head0.381
Teacher spread0.341 · 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".

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Citations1
Published2016
Admission routes2
Has abstractyes

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