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Record W3152837137 · doi:10.1101/2021.04.20.440154

<i>SPOP</i> mutation confers sensitivity to AR-targeted therapy in prostate cancer by reshaping the androgen-driven chromatin landscape

2021· preprint· en· W3152837137 on OpenAlexaff
Ivana Grbeša, Michael A. Augello, Deli Liu, Dylan McNally, Christopher Gaffney, Dennis Huang, Kevin Lin, Ramy Goueli, Brian D. Robinson, Francesca Khani, Lesa D. Deonarine, Mirjam Blattner, Olivier Elemento, Elai Davicioni, Andrea Sboner, Christopher E. Barbieri

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsGenome British Columbia
FundersWeill Cornell Medical CollegeMetLife FoundationNational Cancer InstituteDamon Runyon Cancer Research FoundationProstate Cancer Foundation
KeywordsProstate cancerChromatinAndrogen receptorCancer researchBiologyCarcinogenesisMutantMutationChromoplexyPhenotypeProstateReprogrammingCancerGeneticsDNAGenePCA3

Abstract

fetched live from OpenAlex

Abstract The normal androgen receptor (AR) cistrome and transcriptional program are fundamentally altered in prostate cancer (PCa). Here, we show that SPOP mutations, an early event in prostate tumorigenesis, reshape the chromatin landscape and AR-directed transcriptional program in normal prostate cells. Induction of SPOP mutation results in DNA accessibility and AR binding patterns found in human PCa. Consistent with dependency on this AR reprogramming, castration of SPOP mutant mouse models results in the loss of neoplastic phenotypes. Finally, human SPOP mutant PCa show improved response to AR-targeted therapies. Together, these results show that a single genomic alteration may be sufficient to reprogram the chromatin of normal prostate cells toward oncogenic phenotypes and that SPOP mutant tumors may be preferentially dependent on AR signaling through this mechanism.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

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)0.0030.001

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 head0.019
GPT teacher head0.270
Teacher spread0.251 · 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 designBench or experimental
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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicProstate Cancer Treatment and Research→French-language works237,207→