Abstract B012: Genomic and epigenomic drivers of double-negative metastatic prostate cancer
Bibliographic record
Abstract
Abstract Systemic targeted therapy in prostate cancer is primarily focused on ablating androgen receptor (AR) signaling. Androgen deprivation therapy and second-generation AR-targeted therapy selectively favor the development of treatment-resistant subtypes of metastatic castration-resistant prostate cancer (mCRPC), defined by whether the tumor expresses either AR or neuroendocrine (NE) markers. Among the subtypes of mCRPC, the molecular drivers of double-negative (AR-/NE-) mCRPC are poorly defined. In this study, we comprehensively characterize genomic and epigenomic features of treatment-emergent mCRPC subtypes in 210 tumors by integrating matched RNA sequencing, whole-genome sequencing, and whole-genome bisulfite sequencing. We show that AR-/NE- tumors exhibit a clinically and molecularly distinct phenotype. Patients with AR-/NE- mCRPC tumors have the shortest survival, and these tumors preferentially harbor amplification of the chromatin remodeler CHD7 and loss of PTEN. We demonstrate that methylation changes in CHD7 candidate enhancers are linked to elevated CHD7 expression in AR-/NE+ tumors. Moreover, we use genome-wide methylation analysis to nominate the Krüppel-like factor gene KLF5 as a driver of the AR-/NE- phenotype and link its activity to loss of the tumor suppressor RB1. These observations reveal the aggressiveness of the AR-/NE- tumors and elucidate genomic and epigenomic drivers of mCRPC subtypes, which may facilitate the identification of novel therapeutic targets in this highly aggressive disease. Citation Format: Arian Lundberg, Meng Zhang, Rahul R. Aggarwal, Haolong Li, Li Zhang, Adam Foye, Martin Sjöström, Jonathan Chou, Kevin Chang, Thaidy Moreno-Rodriguez, Raunak Shrestha, Avi Baskin, Xiaolin Zhu, Alana S. Weinstein, Noah Younger, Joshi J. Alumkal, Tomasz M. Beer, Kim N. Chi, Christopher P. Evans, Martin Gleave, Primo N. Lara, Robert E. Reiter, Matthew B. Rettig, Owen N. Witte, Alexander W. Wyatt, Felix Y. Feng, Eric J. Small, David A. Quigley. Genomic and epigenomic drivers of double-negative metastatic prostate cancer [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr B012.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".