Atezolizumab versus chemotherapy in advanced or metastatic NSCLC with high blood-based tumor mutational burden: primary analysis of BFAST cohort C randomized phase 3 trial
Bibliographic record
Abstract
Tumor mutational burden (TMB) is being explored as a predictive biomarker for cancer immunotherapy outcomes in non-small cell lung cancer. BFAST (NCT03178552)-an open-label, global, multicohort trial-evaluated the safety and efficacy of first-line targeted therapies or immunotherapy in patients with unresectable Stage IIIB or IV advanced or metastatic non-small cell lung cancer who were selected for biomarker status using blood-based targeted next-generation sequencing. In the Phase 3 cohort C evaluating blood-based (b)TMB as a biomarker of atezolizumab efficacy, patients with bTMB of ≥10 (N = 471) were randomized 1:1 to receive atezolizumab or platinum-based chemotherapy per local standard of care. Cohort C did not meet its primary endpoint of investigator-assessed progression-free survival in the population with bTMB of ≥16 (hazard ratio, 0.77; 95% confidence interval: 0.59, 1.00; P = 0.053). Adverse events leading to treatment withdrawal occurred in 10% of patients in the atezolizumab arm and 20% in the chemotherapy arm. Adverse events of special interest occurred in 42% of patients in the atezolizumab arm and 26% in the chemotherapy arm. A prespecified exploratory analysis compared the bTMB clinical trial assay with the FoundationOne Liquid Companion Diagnostic assay and showed high concordance between assays. Additional exploration of bTMB to identify optimal cutoffs, confounding factors, assay improvements or cooperative biomarkers is warranted.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| 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".