Determining biomarkers of response to docetaxel for patients with metastatic castration-resistant prostate cancer (mCRPC) using circulating cell-free tumor DNA (ctDNA).
Bibliographic record
Abstract
260 Background: Docetaxel is associated with improved outcomes for mCRPC. There are currently no predictive molecular biomarkers for docetaxel to assist with patient selection. Methods: Patients commencing docetaxel for mCRPC between November 2015 and August 2017 were enrolled on a prospective cohort study evaluating circulating biomarkers. A plasma sample for ctDNA analysis was collected before initiation of docetaxel. Targeted sequencing of 73 prostate cancer-relevant genes was performed on leukocyte DNA (germline) and plasma cell-free DNA. Patient records were reviewed for baseline clinical characteristics, PSA response (≥ 50% decline from baseline), and time to PSA progression (TTPP) (PCWG3 criteria). Results: There were 33 patients enrolled; all patients had received prior abiraterone or enzalutamide and none had received prior taxanes. At baseline, the median age was 69.9 years, 30.3% had ECOG performance status 2, 66.7% had bone metastasis and 9.1% had liver metastases. The PSA response rate (RR) was 39.4% and 33.3% had a TTPP less than 3 months. 82% of patients had a ctDNA fraction >5%. Deleterious BRCA2 or ATM defects were present in 12.1% (4/33) of patients (3 somatic and 1 germline). TP53 alterations were identified in 39.4% (13/33), RB1 loss in 21.2% (7/33), PTEN loss in 21.2% (7/33), and Androgen Receptor (AR) amplification in 42.2% (14/33). PTEN deletion was associated with a trend toward inferior RR and TP53 had no discernible effect on PSA RR (Table). BRCA2/ATM defects were associated with a trend toward a lower rate of PSA progression within 3 months (Table). Conclusions: In this preliminary analysis, TP53 alterations had no discernible effect on efficacy of docetaxel chemotherapy. Accrual is ongoing in order to further clarify whether there is an association between PTEN, RB1, AR and BRCA2/ATM alterations and benefit from docetaxel. [Table: see text]
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".