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1269 Pretreatment prediction of anti-PD-1 immunotherapy response using machine learning in patients with metastatic melanomas using targeted transcriptomics in circulating CD8 cells

Background The aim of this study is to access the role of machine learning in identifying patients that will respond to anti-PD1 from expression data from circulating CD8 T-cells in patients with metastatic melanoma. Methods Normalized RNA expression data was obtained from gene expression omnibus (GEO) a public functional genomics data repository. 1 All project files for GSE166181 were down-loaded in clinical, RNA sequencing data, and metadata for each of these patients. Pretreatment data were extracted for each patient labeled “ T0 ” which was used in the prediction. All normalized expression data that had no values were deleted and a final profile of the expression profiles was obtained. The expression of these genes was assigned as high or low. The patients were divided into 70/30 for training and test set. The overall accuracy of each cell and total accuracy for individual patients.