Saad Maliazurina B, Al-Tashi Qasem, Hong Lingzhi, Verma Vivek, Li Wentao, Boiarsky Daniel, Li Shenduo, Petranovic Milena, Wu Carol C, Carter Brett W, Shroff Girish S, Cascone Tina, Le Xiuning, Elamin Yasir Y, Altan Mehmet, Heeke Simon, Sheshadri Ajay, Chang Joe Y, Lee Percy P, Liao Zhongxing, Gibbons Don L, Vaporciyan Ara A, Lee J Jack, Wistuba Ignacio I, Haymaker Cara, Mirjalili Seyedali, Jaffray David, Gainor Justin F, Lou Yanyan, Di Federico Alessandro, Pecci Federica, Awad Mark, Ricciuti Biagio, Heymach John V, Vokes Natalie I, Zhang Jianjun, Wu Jia
Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA.
Nat Commun. 2025 Jul 24;16(1):6828. doi: 10.1038/s41467-025-61823-w.
Immune checkpoint inhibitors (ICIs), either as monotherapy (ICI-Mono) or combined with chemotherapy (ICI-Chemo), improves survival in advanced non-small cell lung cancer (NSCLC). However, prospective guidance for choosing between these options remains limited, and single-feature biomarkers like PD-L1 prove inadequate. We develop a machine learning model using clinicogenomic data from four cohorts (MD Anderson n = 750; Mayo Clinic n = 80; Dana-Farber n = 1077; Stand Up To Cancer n = 393) to predict individual benefit from adding chemotherapy. Benefit scores are calculated using five distinct functions derived from 28 genomic and 6 clinical features. Our integrated model, A-STEP (Attention-based Scoring for Treatment Effect Prediction), estimates heterogeneous treatment effects and achieves the largest reduction in 3-month progression risk, improving weighted risk reduction by 13-23% over stand-alone models. A-STEP recommends treatment changes for over 50% of patients, most often favoring ICI-Chemo. In simulation on external cohort, patients treated in accordance with A-STEP recommendations show improved 2-year progression-free survival (HR = 0.60 for ICI-Mono treatment arm; HR = 0.58 for ICI-Chemo treatment arm). Predictive features include FBXW7, APC, and PD-L1. In this study, we demonstrate how machine learning can fill critical gaps in immunotherapy selection for NSCLC, by modeling treatment heterogeneity with real-world clinicogenomic data, driving precision medicine beyond conventional biomarker boundaries.
免疫检查点抑制剂（ICIs），无论是作为单一疗法（ICI-Mono）还是与化疗联合使用（ICI-Chemo），都能提高晚期非小细胞肺癌（NSCLC）患者的生存率。然而，在这两种治疗方案之间进行选择的前瞻性指导仍然有限，像PD-L1这样的单一特征生物标志物也被证明是不够的。我们利用来自四个队列（MD安德森癌症中心n = 750；梅奥诊所n = 80；达纳-法伯癌症研究所n = 1077；抗癌站起来组织n = 393）的临床基因组数据开发了一种机器学习模型，以预测添加化疗的个体获益情况。获益分数是使用从28个基因组特征和6个临床特征得出的5种不同函数计算得出的。我们的综合模型A-STEP（基于注意力的治疗效果预测评分）估计了异质性治疗效果，并在3个月的疾病进展风险方面实现了最大程度的降低，与独立模型相比，加权风险降低提高了13%-23%。A-STEP为超过50%的患者推荐了治疗方案的改变，大多数情况下倾向于ICI-Chemo。在外部队列的模拟中，按照A-STEP建议接受治疗的患者显示出2年无进展生存率提高（ICI-Mono治疗组HR = 0.60；ICI-Chemo治疗组HR = 0.58）。预测特征包括FBXW7、APC和PD-L1。在本研究中，我们展示了机器学习如何通过利用真实世界的临床基因组数据对治疗异质性进行建模，从而填补NSCLC免疫治疗选择中的关键空白，推动精准医学超越传统生物标志物的界限。
