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Characteristics of probands with mutations predisposing to gynecologic cancers enrolled in a facilitated cascade testing protocol.

2020· article· en· W3032414505 on OpenAlexaff
Kristina Hwang, Katherine Baumann, Allison L. Brodsky, Kathleen Lutz, Deanna Gerber, Jessica Martineau, Ophira Ginsburg, Julia Smith, Douglas A. Levine, Bhavana Pothuri

Bibliographic record

VenueJournal of Clinical Oncology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineEndometrial cancerFamily historyOvarian cancerCancerLynch syndromeProbandGenetic testingInternal medicineOncologyBreast cancerGermline mutationCHEK2Genetic counselingMSH6GynecologyMLH1MutationGeneticsColorectal cancer

Abstract

fetched live from OpenAlex

e13682 Background: We sought to evaluate the feasibility of obtaining genetic testing (GT) for first- or second-degree (1°, 2°) family members of a proband known to have actionable germline mutation associated with endometrial and/or ovarian cancer through a coordinated referral system. Here we characterize the initial probands and discuss the time frame of their enrollment in facilitated cascade testing (CT). Methods: Patients with pathogenic mutations associated with gynecologic cancers were determined from cancer genetics and gynecologic oncology clinics. Consenting patients completed a RedCap survey on personal cancer history and history of GT. They were then asked to contact 1° and/or 2° relatives regarding their GT results. Relatives were advised to contact our team. Relatives who consented to the study were referred for GT and contacted for follow up to ascertain whether they received GT and/or took action to reduce cancer risk. Results: From 3/2019- 1/2020, this study has accrued 39 probands. The median age was 39 years (range: 25-68). The most commonly expressed gene mutations were BRCA1 or BRCA2 (87.18%, n=34). Other mutations included BRIP1, MLH1, MSH2, MSH6, and PMS2. The majority (62%) of probands had no personal history of cancer. Among those who had a history of cancer (n=15), 80% had breast and/or ovarian cancer, and 80% reported their genetic mutation was discovered at or after the time of their cancer diagnosis. Median age at time of enrollment in CT was 49 years (range: 29-66) for patients with a history of cancer and 32 years (range: 25- 68) for those without, (p=0.009). Among all probands, the median time between mutation identification and enrollment in CT was 2 years (range=0 to 11). There was no significant difference in time between mutation identification and enrollment in CT when comparing those with and without cancer histories (p=0.5). Conclusions: These results suggest that patients with pathogenic mutations predisposing to gynecologic cancer are willing to undergo CT even if they have no personal history of cancer. Patients with a history of cancer tend to be older at time of enrollment in CT, likely because most discover their genetic mutation at or after the time of cancer diagnosis. With an average of 2 years elapsed between time of mutation identification and enrollment in CT, there is need for expansion of CT accessibility. Increased education and awareness among patients and providers in identifying those who may benefit from CT, screening, and risk reducing surgery to prevent cancer is needed.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.002

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 head0.106
GPT teacher head0.429
Teacher spread0.322 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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