Single-cell multimodal analysis reveals tumor microenvironment predictive of treatment response in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) constitutes over 80% of lung cancer cases and remains a leading cause of cancer-related mortality worldwide. Despite the advent of immune checkpoint inhibitors, their efficacy is limited to 27 to 45% of patients. Identifying likely treatment responders is essential for optimizing healthcare and improving quality of life. We generated multiplex immunofluorescence (mIF) images, histopathology, and RNA sequencing data from human NSCLC tissues. Through the analysis of mIF images, we characterized the spatial organization of 1.5 million cells based on the expression levels for 33 biomarkers. To enable large-scale characterization of tumor microenvironments, we developed NucSegAI, a deep learning model for automated nuclear segmentation and cellular classification in histology images. With this model, we analyzed the morphological, textural, and topological phenotypes of 45.6 million cells across 119 whole-slide images. Through unsupervised phenotype discovery, we identified specific lymphocyte phenotypes predictive of immunotherapy response. Our findings can improve patient stratification and guide selection of effective therapeutic regimens.