Examination of the additive value of CTC biomarkers of heterogeneity (Het) and chromosomal instability to nuclear-localized (nl) AR-V7+ CTCs in prediction of poor outcomes to androgen receptor signaling inhibitor (ARSi) in metastatic castration resistant prostate cancer (mCRPC).
Bibliographic record
Abstract
5075 Background: Prediction of ARSi benefit in mCRPC is an unmet medical need. Recently, the Epic Sciences CTC based nl AR-V7 test validated as a predictive biomarker in two multi-center validation studies and has received Medicare coverage for use in mCRPC. While the nl AR-V7 biomarker is highly specific to resistance and predictive of improved response with taxane Rx, it is a measure of just one mechanism of resistance to ARSis. CTC Het measured by the Shannon Index and CTC chromosomal instability measured by predicted number of Large Scale Transitions (pLST) have both been associated with poor OS to ARSis in previous analysis. Here we investigate the relationship of Het and pLST to nlAR-V7 in order to assess multi-clonal resistance and determine if these biomarkers can provide added sensitivity in the nlAR-V7 negative patient population. Methods: 275 blood samples from 2nd+ line mCRPC patients prior to treatment with ARSi (n=148) or taxanes (n=137) were obtained between 2012 and 2017 from 3 clinical centers. Detectable CTCs in each blood sample were assayed for nlAR-V7, Het, and pLST using the Epic Sciences platform. Biomarkers were analyzed in context of each other and outcomes including clinical co-variates. Results: 94% of samples had detectable CTCs, 84% were evaluable for Het analysis (> 2 CTCs), and 76% were evaluable for pLST (> 3 CTCs). Conclusions: Addition of CTC Het (Shannon Index) and CTC chromosomal instability (pLST) biomarkers to nlAR-V7 identifies an additional 15% of mCRPC pts (38% of total) that are predicted to have poor survival to AR signaling inhibitors. [Table: see text][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 | 0.001 |
| 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".