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The comprehensive methylation landscape of metastatic castration-resistant prostate cancer (mCRPC) identifies new phenotypic subtypes: Results from the West Coast Prostate Cancer Dream Team (WCDT).

2020· article· en· W3029442987 on OpenAlexaff
Eric J. Small, Shuang Zhao, William S. Chen, Haolong Li, Adam Foye, Martin Sjöström, Jun Hua, Rahul Aggarwal, Joshi J. Alumkal, Tomasz M. Beer, Martin Gleave, Matthew B. Rettig, Owen N. Witte, Primo Lara, Arul M. Chinnaiyan, Christopher Maher, David A. Quigley, Felix Y. Feng

Bibliographic record

VenueJournal of Clinical Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDNA methylationEpigenomicsProstate cancerMethylationEpigeneticsTranscriptomeBiologyCancerMedicineEpigenomeOncologyGeneticsGeneGene expression

Abstract

fetched live from OpenAlex

5507 Background: While recent studies have delineated the genomic landscape of mCRPC, its epigenomic landscape has not been as well characterized. The goal of this study was to define the comprehensive methylation landscape of mCRPC. Methods: mCRPC patients (pts) underwent a metastasis biopsy as part of a multi-institutional study (NCT02432001). Deep whole-genome bisulfite sequencing (mean depth 46x) was performed on fresh frozen tissue from 100 mCRPC patients; data was paired with deep whole-genome and transcriptome sequencing from the same samples. Unbiased hierarchical clustering of the mCRPC methylome was undertaken, and the survival of patients in each cluster was calculated using the Kaplan Meier method. Results: Unbiased hierarchical clustering revealed several distinct subtypes. 22% of mCRPC samples exhibited a novel epigenomic subtype associated with hyper-methylation. This hypermethylated (HM) cluster was significantly associated with somatic mutations in genes known to be involved in methylation, eg TET2 and DNMT3B, as well as in genes in which mutations have been associated with hyper-methylation in other cancer types ( IDH1 in glioblastoma and BRAF in colon cancer). mCRPC survival was 56.1 mos in pts with HM cancers compared to 35.6 mos in non-HM (p = .055). Methylome clustering also identified a unique cluster comprised of all patients with treatment-induced small cell/neuroendocrine cancer, a subtype previously associated with poor survival. Conclusions: This integrated study of whole-genome, whole methylome and whole-transcriptome sequencing provides the first comprehensive overview of the important regulatory role of methylation in metastatic castration-resistant prostate cancer, and has identified at least two distinct subtypes. The clinical and therapeutic implications of methylation subtypes should be explored in future studies. 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)0.000
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 score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.153
GPT teacher head0.454
Teacher spread0.301 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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