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Abstract P5-03-07: Prevalence of Pathogenic Variants in Cancer Predisposition Genes in Women with Young Onset Breast Cancer

2023· article· en· W4322775459 on OpenAlexaffabout
Kelly Metcalfe, May Lynn Quan, Steven A. Narod, Ellen Warner, Christine M. Friedenreich, Nancy N. Baxter, Aletta Poll, Mohammad R. Akbari

Bibliographic record

VenueCancer Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsSunnybrook Health Science CentreUniversity of CalgaryWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsCHEK2PALB2Breast cancerMSH6MedicineMSH2CancerMLH1OncologyGenetic predispositionInternal medicineGermline mutationGeneticsBiologyMutationGeneDNA mismatch repairColorectal cancer

Abstract

fetched live from OpenAlex

Abstract Introduction: Approximately 5% of breast cancers are diagnosed in women 40 years of age or younger. Known risk factors for young-onset breast cancer are few and can only account for a very small proportion of cases. In this study, we evaluated the contribution of mutations in 24 breast cancer predisposition genes in unselected Canadian women diagnosed with breast cancer at age 40 or younger. Methods: This study is a sub-study of the larger Reducing the bUrden of Breast cancer in Young women (RUBY) Study. In the RUBY study, women diagnosed with breast cancer at the age of 40 years or younger are recruited at the time of diagnosis from 33 centres across Canada. Participants in RUBY provided detailed demographic and clinical data, in addition to provision of serial biospecimens. Participants could elect to consent into the genetics substudy, and have genetic testing performed for pathogenic variants in 24 breast cancer predisposition genes, including ATM, BARD1, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, EPCAM, FAM175A, MLH1, MRE11, MSH2, MSH6, NBN, PALB2, PMS2, PTEN, RAD50, RAD51C, RAD51D, RECQL, STK11, TP53 and XRCC2. Sequencing was performed and all potentially pathogenic variants were confirmed with conventional Sanger sequencing. Pathogenic and likely pathogenic mutations were reported for all 24 genes. CanRisk scores for likelihood of having a pathogenic variant in 8 cancer predisposition genes (BRCA1 BRCA2, PALB2, CHEK2, ATM, RAD51C, RAD51D, and BRIP1) were generated for each participant. Results: 714 women consented and genetic testing was performed on the blood samples provided as a component of the RUBY study. The mean age of the participants was 35.8 years (range 23-40 years), and the mean CanRisk score was 13.7 (range 2.3-98.0). Overall, 150 pathogenic mutations (21.0%) were detected in 147 women (three participants had mutations in two genes). The most common pathogenic variants detected were in BRCA1 (48), BRCA2 (40), CHEK2 (24), ATM (10), and PALB2 (9), representing 87.3% of all pathogenic variants identified. The mean CanRisk score was 28.8% (range 3.2-98.0%) for those identified with a pathogenic variant compared to 9.6% (range 1.0-88.9%) for those with a negative result (p < 0.0001). The prevalence of pathogenic variants was 32.9% for women age 20-30 years, 27.5% for 31-35 years, and 16.7% for 36-40 years. Conclusions: Twenty-one percent of women with breast cancer at age 40 or younger had a pathogenic variant in a breast-cancer predisposition gene. The great majority of these pathogenic variants were found in genes (BRCA1, BRCA2, CHEK2, PALB2) for which there are validated breast cancer treatment recommendations. All women with young-onset breast cancer should be offered germline genetic testing at the time of breast cancer diagnosis to make informed surgical and medical treatment decisions. Citation Format: Kelly Metcalfe, May Lynn Quan, Steven Narod, Ellen Warner, Christine Friedenreich, Nancy Baxter, Aletta J. Poll, Mohammad Akbari. Prevalence of Pathogenic Variants in Cancer Predisposition Genes in Women with Young Onset Breast Cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-03-07.

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 score0.690
Threshold uncertainty score0.623

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.032
GPT teacher head0.366
Teacher spread0.334 · 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
Published2023
Admission routes2
Has abstractyes

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