Circulating tumor DNA levels and related kinetics as prognostic biomarkers for clinical outcomes in mCRPC: A post hoc analysis of CM 7DX.
Bibliographic record
Abstract
252 Background: Liquid biopsy assays in mCRPC are utilized for treatment eligibility of PARP inhibitors (HRR) and Pembrolizumab (MSI High). These ctDNA targeted gene panels provide translational data beyond gene mutations. Tumor Fraction (TF), the percentage of cfDNA from tumor, is a promising prognostic efficacy biomarker in pre-chemo mCRPC, and a potential predictive biomarker to post-chemo PSMA radioligand therapy. Methods: We analyzed ctDNA run with Illumina-TSO500 from the CA209-7DX Ph3 mCRPC trial (Nivo+chemo vs. Chemo in chemo-naïve mCRPC) to determine: TF at baseline or changes at Cycle6 Day1 (C6D1). TF at each timepoint was determined through the maximum somatic allelic frequency. Results: We present a combined analysis of treatment arms as both showed similar association between clinical outcomes and TF levels as well as clearance. Out of 1030 ITT patients TF data was available for 527 (51%) patients. Patients in the highest TF tertile had TF of ≥4.6% and TF<0.3% for the lowest tertile. Patients in the lowest TF tertile had 10 months longer overall survival (OS) to the highest TF tertile (p<0.001). Patients in the highest TF tertile demonstrated higher hazard ratio for OS (HR 3.42,[2.34-4.98] p<0.001) and rPFS (HR 1.95 [1.47-2.59], p<0001) over lowest tertile. This differential was maintained across arms and indicates that TF is a strong prognostic biomarker. High TF outperformed common stratification factors (prior exposure to chemo in CSPC or novel ARPIs) for poor prognostication in a multivariate analysis. TF high patients have higher rates of AR-Amp/AR-LBD mutations (72.6%) than TF low (28.8%). Patients with TF clearance, defined as TF < 1% at C6D1 had 8 months mOS advantage over patients with a TF >1% at baseline and on-treatment. TF clearance strongly correlated with OS (HR=0.42 [0.28-0.62], p<0.001), rPFS benefit (HR=0.24 [0.17-0.34], p<0.001) and PSA response (28% vs. 62%) regardless of treatment arm. Conclusions: This data advocates for plasma TF as a robust prognostic biomarker and as a potential stratification opportunity in mCRPC clinical trials. TF clearance may act as an early indicator of treatment response and hence can be an important monitoring tool for mCRPC patient management. Clinical trial information: NCT04100018 .
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".