Tumor PD-L1 expression and gene panel mutational profile as outcome predictors of PD-1-based checkpoint inhibition therapy in metastatic melanoma: A prospective multicenter DeCOG study.
Bibliographic record
Abstract
9568 Background: PD-1 checkpoint inhibition (CPI) has recently advanced to one of the most effective treatment strategies in melanoma. However, since a considerable proportion of patients shows upfront therapy resistance, baseline predictive biomarkers of therapy outcome are needed. Methods: This prospective multicenter study included metastatic melanoma patients whose formalin-fixed paraffin-embedded tumor tissue samples taken prior to the start of a systemic non-adjuvant therapy of any line were analyzed for PD-L1 expression on tumor cells by immunohistochemistry (clone 28-8, DAKO) and for COSMIC-annotated oncogenic mutations by 29-gene panel sequencing (MiSeq, Illumina). Clinical baseline and follow-up data were collected within the DeCOG multicenter skin cancer registry ADOREG. Results: From 09/2015 until 10/2020, 706 enrolled patients from 15 centers were evaluable for the endpoints best overall response (BOR), progression-free (PFS) and overall survival (OS). Thereof, 540 patients received PD-1-based CPI as first systemic treatment after tumor tissue analysis. 197/540 patients tested positive for PD-L1 (cut-off = 5%) in pre-treatment tumors, and revealed a favourable BOR (objective response 34.4% versus 19.1%; p < 0.0001), PFS (median 10.4 versus 4.2 months; p < 0.0001) and OS (median 45.1 versus 18.8 months; p = 0.001) compared to patients with PD-L1 negative tumors. 47/540 patients presented oncogenic mutations of three or more genes in pre-treatment tumors, and revealed a favourable BOR (objective response 46.8% versus 32.1%; p = 0.041), PFS (median 15.1 versus 6.1 months; p = 0.008) and OS (median not reached versus 25.2 months; p = 0.027) compared to patients whose tumors showed mutations in two or less genes. Multivariable Cox regression including sex, primary site, non-adjuvant systemic pre-treatment, serum LDH, and ECOG performance state demonstrated tumor PD-L1 expression and gene panel mutational profile as independent predictors of survival upon treatment with PD-1-based CPI. In contrast, in 106/706 patients treated with BRAF/MEK inhibitors as first systemic treatment after tumor tissue analysis, no association was found between tumor PD-L1 expression or gene panel mutational profile and therapy outcome. Conclusions: PD-L1 expression quantification and gene panel mutational profiling provide useful outcome predictors of PD-1-based CPI therapy in metastatic melanoma patients.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".