Network-based machine learning approach to predict immunotherapy response in cancer patients

TLDR

Immune checkpoint inhibitors have improved cancer survival, yet only about 30 % of solid‑tumor patients respond and existing biomarkers poorly predict response. The study introduces NetBio, a network‑based machine‑learning framework designed to select immunotherapy‑response biomarkers for robust precision oncology predictions. NetBio was trained on over 700 ICI‑treated patients with transcriptomic data and clinical outcomes, accurately predicting responses across melanoma, gastric, and bladder cancers. NetBio accurately predicted ICI responses in these cancers and outperformed conventional biomarkers such as ICI targets and tumor‑microenvironment markers.

Abstract

Immune checkpoint inhibitors (ICIs) have substantially improved the survival of cancer patients over the past several years. However, only a minority of patients respond to ICI treatment (~30% in solid tumors), and current ICI-response-associated biomarkers often fail to predict the ICI treatment response. Here, we present a machine learning (ML) framework that leverages network-based analyses to identify ICI treatment biomarkers (NetBio) that can make robust predictions. We curate more than 700 ICI-treated patient samples with clinical outcomes and transcriptomic data, and observe that NetBio-based predictions accurately predict ICI treatment responses in three different cancer types-melanoma, gastric cancer, and bladder cancer. Moreover, the NetBio-based prediction is superior to predictions based on other conventional ICI treatment biomarkers, such as ICI targets or tumor microenvironment-associated markers. This work presents a network-based method to effectively select immunotherapy-response-associated biomarkers that can make robust ML-based predictions for precision oncology.