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Circulating tumor DNA levels and related kinetics as prognostic biomarkers for clinical outcomes in mCRPC: A post hoc analysis of CM 7DX.

2025· article· en· W4407699740 on OpenAlexaff
Karim Fizazi, Fred Saad, Teresa Alonso‐Gordoa, Philippe Barthélémy, Jeffrey C. Goh, Tomáš Büchler, Daniel Castellano, Chung‐Wei Lee, Ding Jiang, Pradipta Ray, David J. Paulucci, Ana Lako, Saurabh Gupta, Sumit K. Subudhi, Maximiliano A.G. Van Kooten Losio, Hernán Cutuli

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicinePost-hoc analysisPost hocOncologyInternal medicineCancer researchPathology

Abstract

fetched live from OpenAlex

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 .

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.069
GPT teacher head0.456
Teacher spread0.387 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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