Authors: Rami S. Vanguri, Jia Luo, Andrew T. Aukerman, Jacklynn V. Egger, Christopher J. Fong, Natally Horvat, Andrew Pagano, Jose de Arimateia Batista Araujo-Filho, Luke Geneslaw, Hira Rizvi, Ramon Sosa, Kevin M. Boehm, Soo-Ryum Yang, Francis M. Bodd, Katia Ventura, Travis J. Hollmann, Michelle S. Ginsberg, Jianjiong Gao, , Rami Vanguri, Matthew D. Hellmann, Jennifer L. Sauter, Sohrab P. Shah
Published: 2022-08-29
DOI: 10.1038/s43018-022-00416-8
Source: Full article
AbstractImmunotherapy is used to treat almost all patients with advanced non-small cell lung cancer (NSCLC); however, identifying robust predictive biomarkers remains challenging. Here we show the predictive capacity of integrating medical imaging, histopathologic and genomic features to predict immunotherapy response using a cohort of 247 patients with advanced NSCLC with multimodal baseline data obtained during diagnostic clinical workup, including computed tomography scan images, digitized programmed death ligand-1 immunohistochemistry slides and known outcomes to immunotherapy. Using domain expert annotations, we developed a computational workflow to extract patient-level features and used a machine-learning approach to integrate multimodal features into a risk prediction model. Our multimodal model (area under the curve (AUC) = 0.80, 95% confidence interval (CI) 0.74–0.86) outperformed unimodal measures, including tumor mutational burden (AUC = 0.61, 95% CI 0.52–0.70) and programmed death ligand-1 immunohistochemistry score (AUC = 0.73, 95% CI 0.65–0.81). Our study therefore provides a quantitative rationale for using multimodal features to improve prediction of immunotherapy response in patients with NSCLC using expert-guided machine learning.