Integration of clinical, pathological, radiological, and transcriptomic data improves the prediction of first-line immunotherapy outcome in metastatic non-small cell lung cancer

Abstract The survival of patients with metastatic non-small cell lung cancer (NSCLC) has been increasing with immunotherapy, yet efficient biomarkers are still needed to optimize patient care. In this study, we explored the benefits of multimodal approaches to predict immunotherapy outcome using multiple machine learning algorithms and integration strategies. We leveraged a novel multimodal cohort of 317 metastatic NSCLC patients treated with first-line immunotherapy, collecting at baseline positron emission tomography images, digitized pathological slides, bulk transcriptomic profiles, and clinical information. Most integration strategies investigated yielded multimodal models surpassing both the best unimodal models and established univariate biomarkers, such as PD-L1 expression. Additionally, several multimodal combinations demonstrated improved patient risk stratification compared to models built with routine clinical features only. Our study thus provided new evidence of the superiority of multimodal over unimodal approaches, advocating for the collection of large multimodal NSCLC cohorts to develop and validate robust and powerful immunotherapy biomarkers.