Effects of baseline lactate dehydrogenase (LDH), interferon gamma (IFN-g) expression, and tumor mutational burden (TMB) on treatment response to first-line atezolizumab (A) + vemurafenib (V) and cobimetinib (C) in <i>BRAF</i><sup>V600</sup> mutation–positive advanced melanoma.
Bibliographic record
Abstract
9523 Background: The phase 3 IMspire150 study showed that first-line A+V+C improved investigator-assessed PFS vs placebo (P)+V+C in BRAFV600E/K mutation–positive advanced melanoma (hazard ratio 0.78; P=.0249). Prior biomarker analyses showed that IFN-g or TMB > 10 mut/Mb were associated with greater PFS benefits with A+V+C (Lewis et al. J ImmunoTher Cancer 2020;8:A188-A189). We further evaluated the association of these biomarkers with outcomes. Methods: Exploratory recursive partitioning analysis (RPA) was used to model associations between PFS and age ( < 65 vs ≥65 y), Eastern Cooperative Oncology Group performance status (0 vs 1), liver metastases (yes vs no), metastatic sites (≤3 vs > 3), sum of longest tumor diameters ( < 44 mm vs ≥44 mm), baseline LDH (normal [n] vs elevated [e]), TMB ( < 10 vs ≥10 mut/Mb), PD-L1 (negative vs positive), and IFN-g (high [h; > Quartile 3; Q3] vs intermediate [ > Q1 and ≤Q3] vs low [≤Q1]). Time-to-event analyses were summarized using Kaplan-Meier estimates. Results: The RPA analysis included 208/256 (81.3%) patients (pts) from the A+V+C arm of IMspire150 for whom LDH, TMB, IFN-g, and PD-L1 data were available. RPA showed that LDH was associated with PFS. In pts treated with A+V+C and n-LDH, h-IFN-g signature was associated with longer PFS and higher rates of objective response (OR) and complete response (CR) vs low/intermediate (l/i) IFN-g (2-y PFS: 59% vs 38%; ORR: 77% vs 69%; CR: 38% vs 15%, respectively); TMB ≥10 mut/Mb was associated with more favorable outcomes in pts with e-LDH (Table). In contrast, neither IFN-g nor TMB discriminated PFS outcomes in n-LDH or e-LDH pt subgroups receiving P+V+C. Pts with e-LDH and TMB < 10 mut/Mb had poor PFS outcomes, with 2-y PFS rates of 9% and 3% and lower rates of OR (51% and 62%) and CR (5% and 9%) in the A+V+C and P+V+C arms, respectively. Similar trends were observed for duration of response (DOR), and for the subset of pts with BRAFV600E mutation–positive melanoma. A+V+C improved PFS vs P+V+C across all subgroups with the exception of e-LDH and TMB < 10. Conclusions: IFN-g and TMB discriminated PFS benefit in pts receiving A+V+C but not for those receiving P+V+C. Durable responses were observed for pts treated with A+V+C in the n-LDH + h-IFNg subgroups.[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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".