肺癌患者における distinct な腫瘍免疫生態系が進行を予測し、治療応答の臨床バイオマーカーを定義する

Multiplexed imaging of tissues is an approach that holds promise for improving early detection, diagnosis, and treatment of cancer. Here, we investigated multiplexed histological images of paired pre- and on-treatment samples from nine patients with immunotherapy-refractory non-small cell lung cancer (NSCLC) treated with an oral HDAC inhibitor (vorinostat) combined with a PD-1 inhibitor (pembrolizumab). Patient responses 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 immune-suppressive environment prior to treatment. Finally, the learned spatial ecologies predicted disease progression better than PD-L1 status alone, suggesting 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.