Bespoke circulating tumor DNA (ctDNA) analysis as a predictive biomarker in solid tumor patients (pts) treated with single-agent pembrolizumab (P).
Bibliographic record
Abstract
2542 Background: Limited data exist in the clonal dynamics of serial ctDNA as a predictive biomarker in advanced solid tumor pts receiving immune checkpoint blockade. Methods: Pts with mixed solid tumors received single agent P (anti-PD-1) 200 mg IV Q3wks in the investigator-initiated phase II INSPIRE trial (NCT02644369). ctDNA was assayed at baseline (B) and start of cycle 3 (C3) using a pt-specific amplicon-based NGS assay (Signatera™). Samples were considered ctDNA positive if ≥2 of 16 pt-specific targets met the qualifying confidence score threshold. Results: Results of 70 pts are presented. Demographics: male 46%; median age=60 yrs (range 21–82); head and neck (20%), triple negative breast (14%) and ovarian (14%) cancers comprised the major malignancies. Median no. of P cycles=4 (range 2–35); follow up was 14m (range 2–29); RECIST responses: CR 2.9% (n=2), PR 17% (n=12), CBR (CR+PR+SD≥6 cycles) 31% (n=22), RECIST/clinical PD (n=43/10; 65%/15%). Median PFS=3.3m and median OS=17.8m. 68/70 pts had ctDNA detected at baseline (median=16/16 variants) demonstrating 97% sensitivity. Table shows correlation of ΔctDNA (ctDNAB compared to ctDNAC3) with clinical efficacy parameters, whereas ctDNAB values did not reach statistical significance. Conclusions: A strong correlation exists between ΔctDNA with OS, PFS, CBR and ORR with P, suggesting it is a potential predictive biomarker in pts with mixed solid tumors. Clinical trial information: NCT02644369. [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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".