Lung-MAP (SWOG S1400): Design, implementation, and lessons learned from a biomarker-driven master protocol (BDMP) for previously-treated squamous lung cancer (sqNSCLC).
Bibliographic record
Abstract
9576 Background: S1400, a BDMP, was designed to address an unmet need in sqNSCLC, run within the National Clinical Trials Network of the National Cancer Institute using a public-private partnership (PPP). The goal of was to establish an infrastructure for biomarker-screening and rapid evaluation of targeted therapies in biomarker-defined groups leading to regulatory approval. Methods: S1400 included a screening part using the FoundationOne assay and a clinical trial part with biomarker-driven studies (BDS) and “non-match” studies (NMS) for patients not eligible for any BDS. Patients could be screened (SaP) at progression or pre-screened (PreS). Results: Between June 2014 and January 2019, 1864 patients enrolled (711 PreS, 1079 SaP), 1674 with biomarker results, and 653 registered to a study with 217 to BDS and 436 to NMS. Six BDS and 3 NMS were initiated in small subsets with all BDS and 2 NMS completed within 2-3 years (see Table). Completed BDS have not demonstrated activity with 0-2 responses. On S1400I, Nivolumab and ipilimumab did not improve survival. Response with durvalumab (S1400A) was 16%. Conclusions: Lung-MAP met its goal to quickly answer targeted and other novel therapy questions in rare sqNSCLC subpopulations, answering questions that likely would not have been otherwise feasible, thereby demonstrating value. Activated just prior to the success of PD-(L)1 therapies in sqNSCLC, the trial had to undergo major design changes. Lessons learned include the need to update based on new science and that the PPP collaboration was essential to success. Lung-MAP continues now with new BDS and NMS in all NSCLC as of January 2019. Clinical trial information: NCT02154490 . [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 | 0.105 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".