Novel predictive markers of PSA response to abiraterone acetate in men with metastatic castration-resistant-prostate-cancer (mCRPC).
Bibliographic record
Abstract
5058 Background: Abiraterone acetate (AA) prolongs survival in men with mCRPC pre- and post- chemotherapy. To date, clinical predictive biomarkers of response remain poorly characterized. The aim of this retrospective study was to identify and analyze predictors of response to AA in men with mCRPC. Methods: All men receiving AA at the Princess Margaret Cancer Centre between November 2009 and December 2012 were reviewed. PSA response rate (RR) was defined according to PCWG2 criteria and assessed 12 weeks after AA start. Potential predictive factors were analyzed using uni- and multivariable logistic regression models. Results: In total, 70 patients were evaluable for response: 34 men were chemotherapy naive and had a PSA RR of 44% (95% CI 27-62%); 36 men had prior chemotherapy and had a PSA RR of 33% (95% CI 17-50%). In univariable analysis, pre-treatment lactate dehydrogenase (LDH) level >220 U/L (ULN) and a neutrophil-to-lymphocyte ratio (NLR) >5 were both significantly associated with a lack of PSA response. On multivariable analysis, NLR>5 remained significantly associated with lack of a PSA response (Table 1A). Men were then stratified into three groups according to these two variables. These groups were significantly associated with RRs (Table 1B). PSA RR was not found to be associated with the Gleason score, initial stage, time from initial diagnosis to mCRPC or to AA initiation, prior ketoconazole or docetaxel treatment, pre-treatment alkaline phosphate or PSA doubling time. Conclusions: A pretreatment NLR>5 and an LDH>ULN were both strongly associated with a lack of PSA response to AA. These factors may be key in stratifying men into different response groups to AA. [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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".