Multi-gene risk score for prediction of clinical outcomes in treatment-naïve metastatic castrate-resistant prostate cancer
Bibliographic record
Abstract
BACKGROUND: To determine the performance of a multi-gene copy number variation (MG-CNV) risk score in metastatic tissue and plasma biospecimens from treatment-naïve metastatic castration-resistant prostate cancer (mCRPC) patients for prediction of clinical outcomes. METHODS: The mCRPC tissue and plasma cell-free DNA (cfDNA) biospecimen sequencing results obtained from publicly accessed cohorts in dbGaP, cBioPortal, and an institutional mCRPC cohort were used to develop a MG-CNV risk score derived from gains in AR, MYC, COL22A1, PIK3CA, PIK3CB, NOTCH1 and losses in TMPRSS2, NCOR1, ZBTB16, TP53, NKX3-1 in independent cohorts for determining overall survival (OS), progression-free survival (PFS) to first-line androgen receptor pathway inhibitors (ARPIs). The range of the risk scores for each cohort was dichotomized into "high-risk" and "low-risk" groups and association with OS/PFS determined. Univariate and multivariable Cox proportional hazards regressions were applied for survival analyses (P < .05 for statistical significance). RESULTS: Of 1137 metastatic tissue-plasma biospecimens across all cohorts, 699/1137 were treatment-naive mCRPC (235/699 metastatic tissue; 464/699 plasma-cfDNA), and 311/1137 were matched tissue-cfDNA pairs. In multivariable analysis, the MG-CNV risk score derived from metastatic tissue or in cfDNA was statistically significantly associated with OS with high score associated with short survival (hazard ratio = 2.65, confidence interval = 1.99 to 3.51; P = 1.35-11) and shorter PFS to ARPIs (median PFS of 7.8 months) compared with 14 months in patients with low-risk score. CONCLUSIONS: A molecular risk score in treatment-naïve mCRPC state obtained either in metastatic tissue or cfDNA predicts clinical survival outcomes and offers a tumor biology-based tool to design biomarker-based enrichment clinical trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".