Perioperative durvalumab plus chemotherapy plus new agents for resectable non-small-cell lung cancer: the platform phase 2 NeoCOAST-2 trial
Bibliographic record
Abstract
In the phase II NeoCOAST-2 platform study, 202 patients with untreated, resectable stage IIA-IIIB non-small-cell lung cancer (NSCLC) were randomized to receive neoadjuvant durvalumab plus platinum-doublet chemotherapy with oleclumab, a CD73 inhibitor (Arm 1), or with monalizumab, a NKG2A inhibitor (Arm 2), or neoadjuvant durvalumab plus single-agent platinum chemotherapy with the TROP-2 antibody-drug conjugate (ADC) datopotamab deruxtecan (Arm 4), followed by surgical resection and adjuvant durvalumab with oleclumab or monalizumab (Arms 1 and 2) or durvalumab alone (Arm 4). Primary endpoints were pathological complete response (pCR) rate and safety; secondary endpoints included feasibility of surgery and major pathological response (mPR) rate. In the modified intention-to-treat population (n = 198; Arm 1, n = 74; Arm 2, n = 70; Arm 4, n = 54), pCR rates were 20.3% (15/74; 95% CI, 11.8-31.2), 25.7% (18/70; 95% CI, 16.0-37.6) and 35.2% (19/54; 95% CI, 22.7-49.4), and mPR rates were 41.9% (31/74; 95% CI, 30.5-53.9), 50.0% (35/70; 95% CI, 37.8-62.2) and 63.0% (34/54; 95% CI, 48.7-75.7) in arms 1, 2, and 4, respectively. In the safety population, 69/74 (93.2%), 66/71 (93.0%), and 51/54 (94.4%) patients underwent surgery, respectively. Overall, grade ≥3 treatment-related adverse events occurred in 27/74 (36.5%), 29/71 (40.8%) and 11/54 (20.4%) patients, respectively. In NeoCOAST-2, the first neoadjuvant trial examining an ADC plus chemo-immunotherapy in resectable NSCLC, pCR rates were highest in the datopotamab-deruxtecan-containing arm, warranting further investigation in larger trials of ADCs and checkpoint inhibition in the neoadjuvant setting. ClinicalTrials.gov identifier: NCT05061550 .
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".