Phase II COLET study: Atezolizumab (A) + cobimetinib (C) + paclitaxel (P)/nab-paclitaxel (nP) as first-line (1L) treatment (tx) for patients (pts) with locally advanced or metastatic triple-negative breast cancer (mTNBC).
Bibliographic record
Abstract
1013 Background: COLET showed that the addition of C (MEK1/2 inhibitor) to P resulted in an increased ORR (38%; Brufsky, SABCS 2017); IMpassion130 demonstrated clinical benefit with the combination of PD-L1 inhibitor A and nP as 1L tx for pts with mTNBC (Schmid, N Engl J Med, 2018). We investigated the efficacy and safety of A + C + P/nP in pts with mTNBC, as this combination may target multiple cancer immune escape mechanisms simultaneously. Methods: In the multi-stage, multi-cohort Phase II COLET study, pts with histologically confirmed mTNBC were randomized 1:1 to receive 1L tx with A 840 mg IV (d1, d15) + C 60 mg qd (d3-d23) + P 80 mg/m2 IV (d1, d8, d15; cohort 2) or + nP 100 mg/m2 (d1, d8, d15; cohort 3) in 28-day cycles until progression or toxicity. The primary endpoint (EP) was confirmed ORR per investigator-assessed RECIST 1.1. Additional EPs were DOR, PFS, OS, safety and exploratory efficacy by PD-L1 status. Results: As of 10 Aug 2018 (6.5-mo median follow-up), 63 and 62 pts were evaluable for efficacy and safety, respectively. In cohorts 2 and 3, 21 pts (66%) and 20 pts (65%) had received neo/adjuvant taxane tx, 9 pts (28%) and 6 pts (19%) had a disease-free interval of ≤12 mo, respectively. All pts had ≥1 AE; 69% and 70% had Gr 3-5 AEs and 47% and 43% had serious AEs in cohorts 2 and 3, respectively. Efficacy data for all pts and by PD-L1 expression on tumor-infiltrating immune cells (IC ≥1%; PD-L1+) are summarized in the Table. Conclusions: ORRs were similar between the A + C + P arm and A + C + nP arm. Numerically higher ORR and PFS were observed in pts with PD-L1+ disease. The combination’s safety profile was consistent with the known individual safety profiles, and A did not increase toxicity. Clinical trial information: NCT02322814. [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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".