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PROMISE Registry: A prostate cancer registry of outcomes and germline mutations for improved survival and treatment effectiveness.

2022· article· en· W4213332982 on OpenAlexaff
Channing J. Paller, Justin Lorentz, Tiffani A. DeMarco, Walter M. Stadler, Andrew J. Armstrong, Mary‐Ellen Taplin, Maha Hussain, Роберто Пили, Shifeng Mao, Jo Ann Elrod, Alexandra Sokolova, Elisabeth I. Heath, Rana R. McKay, Jake Vinson, Rebecca Green, Christina Tran, Natalie Macario, Audrey Cook, Jenny Chiang, Heather H. Cheng

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineCHEK2PALB2MSH2Prostate cancerMSH6Genetic testingPTENOncologyGermlineMLH1Internal medicineCancerGermline mutationGeneticsDNA mismatch repairMutationGeneColorectal cancerBiology

Abstract

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TPS191 Background: Recent updates to genetic testing recommendations and approved treatment options for prostate cancer (PCa) patients (pts) have clarified the need for comprehensive genetic registries. Germline DNA damage repair (DDR) defects are present in over 10% of pts who develop metastatic castration-resistant prostate cancer (mCRPC) while 5-10% of pts with localized PCa have germline pathogenic variants in DDR genes. NCCN guidelines have recently expanded to address genetic testing to include high risk localized, node positive and metastatic disease, in addition to family cancer history criteria. In May 2020, the FDA approved 2 PARP inhibitors for mCRPC treatment. Genetic registries can address the critical need to identify pts for recently approved targeted treatments, understand real-world effects of targeted therapies, and expand clinical trials examining less common mutations. PROMISE is a prospective genetic registry equipped to meet these needs. Methods: 5,000 PCa pts will be screened via the online study portal and at-home germline testing to identify and enroll 500 eligible pts with germline pathogenic variants, likely pathogenic variants, and variants of uncertain significance (VUS) in the genes of interest: ATM, ATR, BRCA1, BRCA2, BRIP1, CHEK2, FAM175A, GEN1, HOXB13, MRE11A, MLH1, MSH2, MSH6, NBN, PALB2, PMS2, PTEN, RAD51C, RAD51D, TP53 and XRCC2. Additional genes may be added as evidence emerges. Eligible pts must be assigned male at birth and have documented PCa through tissue biopsy, and/or PSA >100ng/dL, and/or radiographic evidence of disease. Pts with or without prior genetic testing, including those with known pathogenic variants, are encouraged to enroll. Exclusion criteria are: inability or unwillingness to provide information for eligibility and incomplete inclusion criteria. Following germline testing, all pts will be offered genetic counseling and periodic newsletters with updates on treatments and clinical trials. Every 6 months, eligible pts will complete a patient-reported outcome (PRO) survey (EORTC QLQ-C30) and updated medical records will be obtained for clinical data abstraction. Eligible pts will enter long-term follow-up. The primary endpoint is the creation of a prospective genetic registry of PCa pts. Additional endpoints include: frequency of pathogenic or likely pathogenic germline variants of interest, recruitment of a control group with a VUS in the genes of interest, association between disease characteristics and germline testing results, comparison of PROs between disease subpopulations, longitudinal outcomes, and overall survival. Study duration will be 20 years (active recruitment: 5 years, follow-up: 15 years). PROMISE is recruiting at 10 US sites and has 282 subjects enrolled in the screening phase to date. PROMISE is sponsored and managed by the Prostate Cancer Clinical Trials Consortium. Clinical trial information: NCT04995198.

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)0.006
metaresearch head score (Gemma)0.018
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.008
Science and technology studies0.0010.000
Scholarly communication0.0020.003
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0280.012

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 head0.133
GPT teacher head0.508
Teacher spread0.375 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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