Strategies for Improving the Performance of Prediction Models for Response to Immune Checkpoint Blockade Therapy in Cancer.

ABSTRACT: Immune checkpoint blockade (ICB) therapy holds promise for bringing long-lasting clinical gains for the treatment of cancer. However, studies show that only a fraction of patients respond to the treatment. In this regard, it is valuable to develop gene expression signatures based on RNA sequencing (RNAseq) data and machine learning methods to predict patients' response to the ICB therapy, which contributes to more personalized treatment strategy and better management of cancer patients. However, due to the limited sample size of ICB trials with RNAseq data available and the vast number of candidate gene expression features, it is challenging to develop well-performed gene expression signatures. In this study, we used several published melanoma datasets and investigated approaches that can

SUBMITTER: Zeng T 

PROVIDER: S-EPMC10370229 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature