Predicting patient outcomes after treatment with immune checkpoint blockade: A review of biomarkers derived from diverse data modalities

Summary

Immune checkpoint blockade (ICB) therapy targeting cytotoxic T-lymphocyte-associated protein 4, programmed death 1, and programmed death ligand 1 has shown durable remission and clinical success across different cancer types. However, patient outcomes vary among disease indications. Studies have identified prognostic biomarkers associated with immunotherapy response and patient outcomes derived from diverse data types, including next-generation bulk and single-cell DNA, RNA, T cell and B cell receptor sequencing data, liquid biopsies, and clinical imaging. Owing to inter- and intra-tumor heterogeneity and the immune system’s complexity, these biomarkers have diverse efficacy in clinical trials of ICB. Here, we review the genetic and genomic signatures and image features of ICB studies for pan-cancer applications and specific indications. We discuss the advantages and disadvantages of computational approaches for predicting immunotherapy effectiveness and patient outcomes. We also elucidate the challenges of immunotherapy prognostication and the discovery of novel immunotherapy targets.

Graphical abstract

Abstract

Liu et al. reviewed recently developed biomarkers derived from multi-omics and multi-modality data resources for predicting cancer patient outcomes treated with immune checkpoint blockade therapy. These biomarkers play an important role in patient selection for clinical decision-making, offer insights into mechanisms driving tumor-cell-intrinsic and -extrinsic heterogeneity, and contribute to discovering novel therapeutic targets. As immunotherapy advances, the integration of advanced computational approaches with ever-expanding data resources promises continued progress and substantial benefits for cancer patients.