Distinct Tumor-Immune Ecologies in Patients with Lung Cancer Predict Progression and Define a Clinical Biomarker of Therapy Response.

Multiplexed imaging of tissues is an approach that holds promise for improving early detection, diagnosis, and treatment of cancer. In this study, we investigated multiplexed histologic images of paired pretreatment and on-treatment samples from nine patients with immunotherapy-refractory non-small cell lung cancer (NSCLC) treated with an oral histone deacetylase inhibitor (vorinostat) combined with a PD-1 inhibitor (pembrolizumab). Patient responses were comprised of either stable disease (SD) or progressive disease (PD). An extensive multiplexed image analysis pipeline involving both cell segmentation and quadrats, coupled with spatial statistics, machine learning, and deep learning, was built to analyze the spatial and temporal features that predict disease progression and identify potential clinical biomarkers. Distinct spatial immune ecologies existed between SD and PD patients, and tumors from PD patients were already characterized by an immunosuppressive environment prior to treatment. Finally, the learned spatial ecologies predicted disease progression better than PD-L1 status alone, suggesting that these ecologies could be used as potential companion biomarkers with PD-L1 in NSCLC. These findings will be investigated in a larger cohort study generated from an ongoing clinical trial (NCT02638090) that includes a wider range of responses, including complete and partial responders. Together, this study developed a computational infrastructure for analyzing multiplex imaging to predict immunotherapy response in NSCLC, which can potentially be generalized to any type of cancer. SIGNIFICANCE: Integration of multiplexed imaging, spatial statistics, and machine learning identifies distinct tumor-immune ecologies that differentiate immunotherapy responders from nonresponders, improving the prediction of progression to guide precision therapy. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .