美国阿斯利康公司Etai Jacob研究小组近日取得一项新成果。经过不懈努力，他们的最新研究提出了人工智能驱动的预测性生物标志物发现与对比学习，以改善临床试验结果。2025年4月17日，国际知名学术期刊《癌细胞》发表了这一成果。

为了解决这个问题，该团队提出了一个基于对比学习的神经网络框架-预测性生物标志物建模框架(PBMF) -以自动化，系统和无偏的方式探索潜在的预测性生物标志物。回顾性地应用于真实的临床基因组数据集，特别是免疫肿瘤学（IO）试验，他们的算法确定了接受IO治疗的个体的生物标志物，这些个体比接受其他治疗的个体存活时间更长。研究人员通过揭示仅基于早期研究数据的预测性、可解释的生物标志物，展示了他们的框架如何回顾性地为3期临床试验做出贡献。与最初的试验相比，识别出这种预测性生物标志物的患者的生存风险提高了15%。PBMF提供了一种通用、快速、自动化的方法，为生物标志物策略提供信息，为临床决策提供可操作的结果。

据介绍，现代临床试验可以捕获每个个体的数十种临床基因组学测量值。发现预测性生物标志物，而不是预后标志物，仍然具有挑战性。

附：英文原文

Title: AI-driven predictive biomarker discovery with contrastive learning to improve clinical trial outcomes

Author: Gustavo Arango-Argoty, Damian E. Bikiel, Gerald J. Sun, Elly Kipkogei, Kaitlin M. Smith, Sebastian Carrasco Pro, Elizabeth Y. Choe, Etai Jacob

Issue&Volume: 2025-04-17

Abstract: Modern clinical trials can capture tens of thousands of clinicogenomic measurements per individual. Discovering predictive biomarkers, as opposed to prognostic markers, remains challenging. To address this, we present a neural network framework based on contrastive learning—the Predictive Biomarker Modeling Framework (PBMF)—that explores potential predictive biomarkers in an automated, systematic, and unbiased manner. Applied retrospectively to real clinicogenomic datasets, particularly for immuno-oncology (IO) trials, our algorithm identifies biomarkers of IO-treated individuals who survive longer than those treated with other therapies. We demonstrate how our framework retrospectively contributes to a phase 3 clinical trial by uncovering a predictive, interpretable biomarker based solely on early study data. Patients identified with this predictive biomarker show a 15% improvement in survival risk compared to those in the original trial. The PBMF offers a general-purpose, rapid, and robust approach to inform biomarker strategy, providing actionable outcomes for clinical decision-making.

DOI: 10.1016/j.ccell.2025.03.029

Source: https://www.cell.com/cancer-cell/abstract/S1535-6108(25)00130-8