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DNA repair mutations and treatment-emergent small cell neuroendocrine prostate cancer (t-SCNC) as hallmarks of distinct subgroups of metastatic castration resistant prostate cancer (mCRPC): Data from the West Coast Prostate Cancer Dream Team.

2018· article· en· W2890415736 on OpenAlexaff
Rahul Aggarwal, Paul Lloyd, Jiaoti Huang, Tomasz M. Beer, Li Zhang, George Thomas, Lawrence D. True, Joshi J. Alumkal, Verena Friedl, Alana S. Weinstein, Robert E. Reiter, Matthew B. Rettig, Primo N. Lara, Martin Gleave, Adam Foye, Denise Playdle, Felix Y. Feng, Kim N., Joshua M. Stuart, Eric J. Small

Bibliographic record

VenueJournal of Clinical Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsBC Cancer AgencyUniversity of British Columbia
Fundersnot available
KeywordsProstate cancerMSH6MSH2MLH1MedicineCancerCHEK2Cancer researchPathologyBiologyOncologyInternal medicineDNA mismatch repairGeneGeneticsMutationGermline mutationColorectal cancer

Abstract

fetched live from OpenAlex

5039 Background: Genomic alterations in DNA repair genes are present in approximately 20-30% of patients with mCRPC. t-SCNC may be increasing in prevalence and is associated with adverse clinical outcomes. Potential overlap between these two subsets of mCRPC was assessed. Methods: Eligible patients (pts) underwent a metastatic tumor core needle biopsy at one of 5 centers. Tumor tissue was sent for consensus pathology call (JH, GT, LT) and targeted next-generation DNA sequencing. Chi-square test was used to compare the frequency of genomic alterations in select DNA repair genes (BRCA1, BRCA2, ATM, CDK12, RAD51, PALB2, FANCA, CHEK2, MLH1, MSH2, MLH3, and MSH6) between tumors with versus those without small cell histology. Frozen tissue from the same metastatic tumor underwent RNA sequencing (RNA-seq). Pts were prospectively followed for overall survival (OS). Results: 119 consecutive biopsies with sufficient tumor to permit histologic assessment and targeted NGS were included, including 14 biopsies (13%) with t-SCNC. In the overall cohort, DNA repair mutations were present in 41 biopsies (34%). By histologic subtype, mutations were present in 40/105 (38%) of adenocarcinoma tumors vs. only 1 of 14 (7%) biopsies with t-SCNC histology (p = 0.047). In contrast, TP53 and/or RB1 loss were enriched in the t-SCNC cohort (83% vs. 34%, p = 0.0015). Unsupervised hierarchical analysis of the transcriptome identified small cell-enriched cluster of biopsies (N = 12) that were mutually exclusive of DNA repair mutations and enriched for E2F transcriptional targets on RNA-seq. The presence of t-SCNC histologic differentiation was associated with worse OS from date of mCRPC (median OS 44.5 vs. 36.6 months; log-rank p = 0.027); the presence of DNA repair mutations was not (p = 0.734). Conclusions: Tumors harboring DNA repair pathway mutations or t-SCNC differentiation may represent distinct disease subsets of mCRPC with differing clinical outcomes. Added together, the two subsets account for approximately 40% of mCRPC pts. Independent prospective validation of these findings is warranted. Clinical trial information: NCT02432001.

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 score0.011
Threshold uncertainty score0.022

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.156
GPT teacher head0.462
Teacher spread0.306 · 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
Published2018
Admission routes1
Has abstractyes

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