Nivolumab (NIVO) + low-dose ipilimumab (IPI) in previously treated patients (pts) with microsatellite instability-high/mismatch repair-deficient (MSI-H/dMMR) metastatic colorectal cancer (mCRC): Long-term follow-up.
Bibliographic record
Abstract
635 Background: In the phase II CheckMate-142 trial, NIVO + low-dose IPI (1 mg/kg) provided meaningful clinical benefit in previously treated MSI-H/dMMR mCRC pts after a median follow-up of 13.4 mo. Here, we present long-term follow-up (median 25.4 mo) of these pts. Methods: Pts received NIVO 3 mg/kg + low-dose IPI Q3W (4 doses) followed by NIVO 3 mg/kg Q2W until disease progression. Primary endpoint was investigator (INV)-assessed objective response rate (ORR; RECIST v1.1). Results: Of 119 treated pts, 76% had ≥ 2 prior lines of therapy. ORR and disease control rates (DCR) were 58 and 81%, respectively (Table). Complete response (CR) rate increased with long-term follow-up from 3 (13.4 mo) to 6% (25.4 mo). Median duration of response (DOR) was not reached, with 68% of responses ongoing at data cutoff. At 24 mo, progression-free survival (PFS) and overall survival (OS) rates were 60 and 74%, respectively; OS rates were 96, 56, and 29% in pts with CR or partial response (PR), stable disease (SD), and progressive disease (PD), respectively. Grade 3–4 treatment-related adverse events (TRAEs) occurred in 31% of pts; 10% (grade 3–4) and 13% (any grade) of pts had TRAEs leading to discontinuation. Conclusions: Long-term follow-up with NIVO + low-dose IPI provides durable clinical benefit with deepening of response and a manageable safety profile with no new safety signals, demonstrating long-term benefit of NIVO + low-dose IPI for previously treated pts with MSI-H/dMMR mCRC. Clinical trial information: NCT02060188. [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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".