Genomic and transcriptomic features of androgen receptor signaling inhibitor resistance in metastatic castration-resistant prostate cancer
Bibliographic record
Abstract
BACKGROUNDAndrogen receptor signaling inhibitors (ARSIs) have improved outcomes for patients with metastatic castration-resistant prostate cancer (mCRPC), but their clinical benefit is limited by treatment resistance.METHODSTo investigate the mechanisms of ARSI resistance, we analyzed the whole-genome (n = 45) and transcriptome (n = 31) sequencing data generated from paired metastatic biopsies obtained before initiation of first-line ARSI therapy for mCRPC and after radiographic disease progression. We investigated the effects of genetic and pharmacologic modulation of SSTR1 in 22Rv1 cells, a representative mCRPC cell line.RESULTSWe confirmed the predominant role of tumor genetic alterations converging on augmenting androgen receptor (AR) signaling and the increased transcriptional heterogeneity and lineage plasticity during the emergence of ARSI resistance. We further identified amplifications involving a putative enhancer downstream of the AR and transcriptional downregulation of SSTR1, encoding somatostatin receptor 1, in ARSI-resistant tumors. We found that patients with SSTR1-low mCRPC tumors derived less benefit from subsequent ARSI therapy in a retrospective cohort. We showed that SSTR1 was antiproliferative in 22Rv1 cells and that the FDA-approved drug pasireotide suppressed 22Rv1 cell proliferation.CONCLUSIONOur findings expand the knowledge of ARSI resistance and point out actionable next steps, exemplified by potentially targeting SSTR1, to improve patient outcomes.FUNDINGNational Cancer Institute (NCI), NIH; Prostate Cancer Foundation; Conquer Cancer, American Society of Clinical Oncology Foundation; UCSF Benioff Initiative for Prostate Cancer Research; Netherlands Cancer Institute.
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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 | 0.001 | 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".