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Identification, incidence and clinical outcomes of patients (pts) with hypermutated prostate cancer (PC).

2019· article· en· W4248908788 on OpenAlexaff
Simon Fu, Elie Ritch, Steven Yip, Daniel Khalaf, Sinja Taavitsainen, Matti Annala, Gillian Vandekerkhove, Igal Kushnir, Sebastién J. Hotte, Cristiano Ferrario, Alexander W. Wyatt, Kim N.

Bibliographic record

VenueJournal of Clinical Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsMcGill UniversityJuravinski Cancer CentreBC Cancer AgencyOttawa HospitalUniversity of British ColumbiaJewish General Hospital
Fundersnot available
KeywordsMedicineProstate cancerInternal medicineEnzalutamideOncologyAndrogen deprivation therapyMicrosatellite instabilityKRASCancerIncidence (geometry)GastroenterologyGeneticsAndrogen receptorBiologyAlleleMicrosatellite

Abstract

fetched live from OpenAlex

195 Background: A small proportion of metastatic PC exhibit outlier somatic mutation rates. The incidence, clinical course and treatment response of pts with hypermutation (HM) is poorly characterised. Methods: We performed targeted sequencing of 1047 plasma cell-free DNA samples and calculated somatic mutation burden. HM samples and available matched archival tissue were additionally subjected to whole exome sequencing. Trinucleotide mutational signatures and microsatellite instability (MSI) were determined via nonnegative matrix factorization and mSINGS, respectively. We evaluated PSA decline ≥50% from baseline (PSA50), time from androgen deprivation therapy (ADT) to castration-resistant prostate cancer (CRPC), median duration of 1st line CRPC therapy (1L CRPCT) and median OS (time from CRPC to death). The control cohort consisted of 199 CRPC pts treated with 1L abiraterone + prednisone (ABI+P) or enzalutamide (ENZ). Results: 671 samples from 434 pts had ctDNA% > 2 and were evaluable. The median mutation rate was 2.59/Mb (range, 0.9 – 155.6/Mb). 32 samples from 24 pts had > 11/Mb and fell above the 95th percentile for mutational burden. 10/24 pts had biallelic loss of mismatch repair (MMR) genes MSH2/6, and a further 5 pts without confirmed MMR defects had enrichment of trinucleotide signatures associated with MMR and/or were MSI high by mSINGS. The remaining 9 pts had either BRCA2 mutations or Kataegis (localized hypermutation). Clinical data was available for 10/15 MMR defective pts. Median age was 73.6 y. At diagnosis, 70% had Gleason score ≥8, 50% with M1 disease, median PSA was 22.8 (6.8 – 820). PSA50 with ADT (n = 8) or ADT + docetaxel (n = 2) was 100% in the castration sensitive setting. 5 pts had ENZ, 4 ABI + P, and 1 cabazitaxel in 1L CRPCT. Comparing the MMR defective with the control cohort, median time from ADT to CRPC was 9.1 m (95% CI 6.9 – 11.4) vs. 18.2 m (95% CI 15.1 – 21.3), p = 0.001; 1L CRPCT duration was 3.9 m (95% CI 1.3 – 6.5) vs. 8.4 m (95% CI 7.2 – 9.6), p = < 0.001; median OS was 13.1 m (95% CI 0.33 – 25.9) vs. 40.1 m (95% CI 32.4 – 47.8), p < 0.001. Conclusions: HM and MMR defects can be identified in a liquid biopsy. Although these pts can have poor outcomes with standard therapy, ctDNA may help selection for immunotherapy.

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 score0.002
Threshold uncertainty score0.005

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 studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.085
GPT teacher head0.503
Teacher spread0.418 · 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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Citations0
Published2019
Admission routes1
Has abstractyes

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