Informing immunotherapy with multi-omics driven machine learning

Yawei Li, Xin Wu, Deyu Fang, Yuan Luo*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

11 Scopus citations

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.

Original languageEnglish (US)
Article number67
Journalnpj Digital Medicine
Volume7
Issue number1
DOIs
StatePublished - Dec 2024

Funding

This study is supported in part by National Institutes of Health of USA (Grant No. U01TR003528 and 1R01LM013337) awarded to Y.Luo and (Grant No. R01CA257520 and R01CA232347) to D.F.

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Health Informatics
  • Computer Science Applications
  • Health Information Management

Fingerprint

Dive into the research topics of 'Informing immunotherapy with multi-omics driven machine learning'. Together they form a unique fingerprint.

Cite this