Neoadjuvant Durvalumab Alone or Combined with Novel Immuno-Oncology Agents in Resectable Lung Cancer: The Phase II NeoCOAST Platform Trial
Bibliographic record
Abstract
Neoadjuvant chemoimmunotherapy improves pathologic complete response rate and event-free survival in patients with resectable non-small cell lung cancer (NSCLC) versus chemotherapy alone. NeoCOAST was the first randomized, multidrug platform trial to examine novel neoadjuvant immuno-oncology combinations for patients with resectable NSCLC, using major pathologic response (MPR) rate as the primary endpoint. Eighty-three patients received a single cycle of treatment: 26 received durvalumab (anti-PD-L1) monotherapy, 21 received durvalumab plus oleclumab (anti-CD73), 20 received durvalumab plus monalizumab (anti-NKG2A), and 16 received durvalumab plus danvatirsen (anti-STAT3 antisense oligonucleotide). MPR rates were higher for patients in the combination arms versus durvalumab alone. Safety profiles for the combinations were similar to those of durvalumab alone. Multiplatform immune profiling suggested that improved MPR rates in the durvalumab plus oleclumab and durvalumab plus monalizumab arms were associated with enhanced effector immune infiltration of tumors, interferon responses and markers of tertiary lymphoid structure formation, and systemic functional immune cell activation. SIGNIFICANCE: A neoadjuvant platform trial can rapidly generate clinical and translational data using candidate surrogate endpoints like MPR. In NeoCOAST, patients with resectable NSCLC had improved MPR rates after durvalumab plus oleclumab or monalizumab versus durvalumab alone and tumoral transcriptomic signatures indicative of augmented immune cell activation and function. See related commentary by Cooper and Yu, p. 2306. This article is featured in Selected Articles from This Issue, p. 2293.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".