Distinct tumor-immune ecologies in NSCLC patients predict progression and define a clinical biomarker of therapy response

1. Lung cancer is the leading cause of cancer death worldwide with 1.76 million people die as a result of the disease yearly. Analyzing high-throughput multiplexed images of tissues has recently emerged as a routine clinical procedure for early detection, clinical cancer diagnosis, treatment planning and prognosis for many cancer types including non-small cell lung cancer (NSCLC). Multiplexed images enable the precise interpretation of spatial distribution of cells and cellular states and the characterization of tumor-immune interactions in situ and at the single-cell level. Through two cornerstone image processing techniques - cell segmentation and image tiling generating quadrats - we perform an in-depth analysis of multiplexed images based on the frequency, phenotype, and spatial distribution of immune and tumor cells within the immune landscape of NSCLC. Multiplexed images were obtained from nine patients with advanced/metastatic NSCLC with progression who were treated with an oral HDAC inhibitor (vorinostat) combined with a PD-1 inhibitor (pembrolizumab). Images were collected from all patients both pre- and on-treatment (days 15-21). We profile ∼100K cells and ∼10K quadrats from tumor and adjacent tissues to characterize cellular composition and spatial neighborhoods and elucidate that distinct spatial cellular ecologies exist across progressive disease (PD) and stable disease (SD) patients where tumors of PD patients are characterized by a highly suppressed immune environment prior to treatment enabling higher chances of disease progression during treatment. These fundamentally distinct architectures across PD and SD patients enable disease progression prediction and clinical biomarker identification. Our results also provide a potential companion biomarker for PD-L1 in NSCLC. Validating the biomarker and its feasibility will require further in-depth investigation.