Biomarkers of treatment benefit with atezolizumab plus vemurafenib plus cobimetinib in BRAFV600 mutation–positive melanoma
Bibliographic record
Abstract
Background The phase III IMspire150 study (NCT02908672) demonstrated significantly improved progression-free survival (PFS) with atezolizumab, vemurafenib, and cobimetinib (atezolizumab group) versus placebo, vemurafenib, and cobimetinib (control group) in patients with BRAF V600 -mutated advanced melanoma. We report exploratory biomarker analyses to optimize targeting of patients who are more likely to benefit from triplet combination therapy. Patients and methods Five hundred fourteen patients were randomized to atezolizumab ( n = 256) or control ( n = 258). Outcomes were evaluated in subgroups defined by key biomarkers, including programmed death-ligand 1 (PD-L1) expression, lactate dehydrogenase (LDH) level, tumor mutational burden (TMB), and interferon-γ (IFN-γ) gene signature. Exploratory recursive partitioning analysis was then used to model associations between PFS and baseline covariates, including key biomarkers. Results PFS benefit for atezolizumab versus control was greater in patients with high TMB [≥10 mutations/Mb; hazard ratio (HR) 0.73; 95% confidence interval (CI) 0.52-1.02; P = 0.067] versus low TMB (<10 mutations/Mb; HR 0.92; 95% CI 0.65-1.30; P = 0.64) and similar between patients with strong IFN-γ (≥median; HR 0.76; 95% CI 0.54-1.06) versus weak IFN-γ ( P = 0.032) than in the PD-L1+ subgroup (HR 1.16; 95% CI 0.75-1.80; P = 0.51). Recursive partitioning analysis showed that IFN-γ discriminated PFS outcomes in patients with normal LDH, whereas TMB discriminated outcomes in patients with elevated LDH in the atezolizumab group. Neither IFN-γ nor TMB discriminated PFS outcomes in the control group. Conclusions Treatment benefits in the atezolizumab group seemed to be most evident in patients with elevated LDH and PD-L1– tumors. LDH remains the primary predictor of outcomes regardless of treatment. IFN-γ and TMB further differentiate outcomes for patients treated with atezolizumab, vemurafenib, and cobimetinib.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".