Abstract 3883: Evolution of genomic and epigenomic heterogeneity in prostate cancer from tissue and liquid biopsies
Bibliographic record
Abstract
Abstract Castration resistant prostate cancer (CRPC) is an aggressive, highly plastic, late-stage disease. We previously showed that the two histological subtypes, adenocarcinoma (CRPC-Adeno) and neuroendocrine (CRPC-NE), show four epigenetic and transcriptomic subtypes: CRPC-AR depends on the androgen receptor pathway, CRPC-SCL is stem-cell like, CRPC-WNT is dependent on the WNT pathway, and CRPC-NE has high expression of neuroendocrine markers. Here, by analyzing data from 500 patients with tissue and/or liquid biopsies, we uncover the landscape of molecular heterogeneity in patient tumors. Analysis of whole-genome sequencing revealed genomic variants associated with CRPC-SCL and allowed development of a computational classifier which can predict presence of CRPC-SCL in patient tumors solely using genomic alterations with 81% accuracy. In particular, analysis of matched chromatin conformation data (Hi-C) showed a complex rearrangement on chromosome 4 disrupts enhancer - promoter contacts leading to downregulation of MOB1B, which can lead to upregulation of the YAP/TAZ pathway that is characteristic of CRPC-SCL. Joint computational inference of epigenomic state and genomic variants from cell-free DNA collected at multiple points during the evolution of resistance to AR signaling inhibitors allowed investigation of genomic and epigenomic co-evolution at an unprecedented resolution. We discuss the current limits of detection for tumoral epigenomic and genomic states using cell-free DNA for clinical application. Importantly, our study demonstrates the utility of liquid biopsies for discovery of basic biological mechanisms leading to treatment resistance, beyond their use for biomarkers. Citation Format: Marjorie Roskes, Alexander Martinez-Fundichely, Weiling Li, Sandra Cohen, Hao Xu, Shahd ElNaggar, Anisha Tehim, Metin Balabin, Chen Khuan Wong, Yu Chen, Ben Raphael, Ekta Khurana. Evolution of genomic and epigenomic heterogeneity in prostate cancer from tissue and liquid biopsies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3883.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".