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Transcriptional profiling of matched biopsies reveals molecular determinants of enzalutamide resistance.

2022· article· en· W4286298199 on OpenAlexaff
Thomas C. Westbrook, Xiangnan Guan, Aaron M. Udager, Michael C. Haffner, Tomasz M. Beer, Rahul Aggarwal, Charles J. Ryan, Martin Gleave, Jiaoti Huang, Christopher P. Evans, Robert E. Reiter, Owen N. Witte, Matthew B. Rettig, Joshua M. Stuart, George Thomas, Felix Y. Feng, Eric J. Small, Joel A. Yates, Zheng Xia, Joshi J. Alumkal

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEnzalutamideProstate cancerMedicineDiseaseAndrogen receptorTranscriptomeCancerOncologyBioinformaticsCancer researchInternal medicineGeneticsBiologyGeneGene expression

Abstract

fetched live from OpenAlex

5058 Background: Castration-resistant prostate cancer (CRPC) is the lethal form of the disease. One of the principal therapies in CRPC is the potent androgen receptor (AR) signaling inhibitor enzalutamide (enza). Most patients benefit from enza, but disease progression is nearly universal. A variety of resistance mechanisms have been described by comparing enza-naïve and enza-resistant tumors. However, these results are largely from different groups of patients and do not provide information on the changes induced by enza within a given patient. Lineage plasticity—most commonly-exemplified by loss of AR signaling and switch from a luminal to an alternate differentiation program—is a particularly aggressive resistance mechanism. Importantly, lineage plasticity appears to be increasing in incidence since more widespread use of potent AR signaling inhibitors such as enza. To improve our understanding of resistance mechanisms induced by enza treatment, we analyzed the transcriptomes of matched metastatic CRPC patient biopsies obtained prior to treatment and at the time of disease progression. Methods: All biopsies were obtained as part of the Stand Up 2 Cancer/Prostate Cancer Foundation-funded West Coast Dream Team, a prospective, IRB-approved protocol focused on understanding the biology of metastatic CRPC. We identified 21 patients for whom matched tumor biopsies with RNA-seq were available prior to starting treatment with enza and at the time of progression while still taking enza. Results: Our RNA-seq analysis demonstrates that the majority of progression tumors cluster with their baseline pair, suggesting that enza does not markedly change the tumor transcriptome in most cases. Three of 21 patients showed evidence of lineage plasticity at progression by gene expression analysis. By analyzing the RNA-seq data, we identified pathways linked to stemness that were more activated in baseline tumors from patients whose progression tumors underwent lineage plasticity. Furthermore, we identified a gene signature enriched in these baseline tumors that was associated with risk of lineage plasticity after enza treatment. We determined that high expression of this signature was strongly associated with poor survival from the time of AR signaling inhibitor treatment in independent patient samples, suggesting this signature is linked to poor patient outcome. Conclusions: Enza-resistant tumors are heterogeneous. Most tumors do not undergo significant transcriptional changes at progression vs. baseline. Matching recent reports, approximately 15% of tumors underwent lineage plasticity upon progression. Our work implicates a gene program that may predispose tumors to enza-induced lineage plasticity. Finally, the gene signature we identified may be a marker of lineage plasticity risk and tumor aggressiveness in CRPC prior to the initiation of AR signaling inhibitor therapy.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

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)0.0020.001

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 head0.148
GPT teacher head0.488
Teacher spread0.340 · 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
Published2022
Admission routes1
Has abstractyes

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