Informing immunotherapy with multi-omics driven machine learning

Abstract

Progress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid and hematologic malignancies. However, the benefits of immunotherapy are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict patient response is crucial. Machine learning (ML) play a pivotal role in harnessing multi-omic cancer datasets and unlocking new insights into immunotherapy. This review provides an overview of cutting-edge ML models applied in omics data for immunotherapy analysis, including immunotherapy response prediction and immunotherapy-relevant tumor microenvironment identification. We elucidate how ML leverages diverse data types to identify significant biomarkers, enhance our understanding of immunotherapy mechanisms, and optimize decision-making process. Additionally, we discuss current limitations and challenges of ML in this rapidly evolving field. Finally, we outline future directions aimed at overcoming these barriers and improving the efficiency of ML in immunotherapy research.

Deciphering the tumor immune microenvironment from a multidimensional omics perspective: insight into next-generation CAR-T cell immunotherapy and beyond

“…Providing information related to spatial-level cellular distribution, co-organization, and cell–cell interaction in the TIME, machine learning (ML) methods could advance our understanding of spatio-temporal heterogeneity and complex molecular structures of TIME [ 20 , 179 , 180 ]. ML and artificial intelligence-driven analyses of pathology images enables histological image-level spatial analysis and spatial TIME analysis at the single cell level.…”

“…The emergence of state-of-the-art ML algorithms enabled the identification of T-cell neoantigens through MHC class I/II presentations. The newly developed pipeline utilizes genomics data of tumor samples, usually derived from whole-genome sequencing (WGS) or WES, to infer the mutated peptides based on the somatic non-synonymous single-nucleotide variants (SNVs) [ 180 ]. Some other studies employ ML models to predict neoantigens by estimating the binding affinity between a given mutated peptide and an MHC class I molecule [ 185 , 186 ].…”

Simplifying Data Analysis in Biomedical Research: An Automated, User-Friendly Tool

“…However, the field of omics also faces challenges, like high costs, complexity, and variability in human studies. To counteract this, there has been a focus on omics sciences, bolstered by advancements in machine learning [ 22 , 23 ] and large language models (LLMs) [ 24 , 25 , 26 ]. These technologies have demonstrated significant potential for processing and interpreting the extensive datasets produced by omics techniques [ 27 ].…”