Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma.
Andrew PattersonNoam AuslanderPublished in: Nature communications (2022)
Immune Checkpoint Inhibitor (ICI) therapy has revolutionized treatment for advanced melanoma; however, only a subset of patients benefit from this treatment. Despite considerable efforts, the Tumor Mutation Burden (TMB) is the only FDA-approved biomarker in melanoma. However, the mechanisms underlying TMB association with prolonged ICI survival are not entirely understood and may depend on numerous confounding factors. To identify more interpretable ICI response biomarkers based on tumor mutations, we train classifiers using mutations within distinct biological processes. We evaluate a variety of feature selection and classification methods and identify key mutated biological processes that provide improved predictive capability compared to the TMB. The top mutated processes we identify are leukocyte and T-cell proliferation regulation, which demonstrate stable predictive performance across different data cohorts of melanoma patients treated with ICI. This study provides biologically interpretable genomic predictors of ICI response with substantially improved predictive performance over the TMB.
Keyphrases
- cell proliferation
- machine learning
- end stage renal disease
- deep learning
- newly diagnosed
- chronic kidney disease
- stem cells
- ejection fraction
- prognostic factors
- gene expression
- combination therapy
- risk factors
- quality improvement
- dna methylation
- wild type
- big data
- electronic health record
- copy number
- bone marrow
- mass spectrometry
- high speed
- genome wide
- data analysis