Rationale: Examination of multiplexed images of tissues has recently emerged as a routine clinical procedure for cancer diagnosis and prognosis. The simultaneous detection of numerous biomarkers enables the interpretation of cellular states and the characterization of tumor-immune interactions in situ and at the single-cell level. However, image processing and the subsequent interpretive and predictive tools for multiplexed image data remain limited.

Methods: We developed a computational multiplexed-image analysis pipeline using cell-segmentation and quadrat-based approaches to analyze the spatial and temporal features of multiplexed non-small cell lung cancer (NSCLC) images, and predict disease progression and identify clinical biomarkers. Images were obtained from nine patients with advanced/metastatic NSCLC who were treated with the oral HDAC inhibitor vorinostat combined with the PD-1 inhibitor pembrolizumab. Images were collected from all patients both pre- and on-treatment (during the third week).

Results: Both cell-segmentation and quadrat-based approaches confirm that different spatial neighborhoods exist that distinguish progressors (PD) from non-progressors (SD): PD patients have distinct ecologies with higher colocalization of PanCK+PD-1+FoxP3 indicating an immunosuppressive environment, whereas SD patients have a higher colocalization of PanCK+PD-L1 along with T cells suggesting immunoactive tumor regions. These can be considered as potential biomarker candidates for predicting tumor progression. Further, from the single-cell analysis, we note there is a higher abundance of immune cells across the tumor border in PD patients than SD patients. Using the quadrat approach for species distribution modeling, we were able to predict treatment response with 91.4 percent accuracy given each patient’s spatial distribution of cell types from pre-treatment images. Further, we can generate risk maps for each image to identify tumor areas indicating higher probabilities of progression during treatment.

Conclusions: We leveraged both single-cell and quadrat-resolution analysis of multiplexed imaging data and identified fundamentally distinct spatial ecologies between PD and SD patients. The ecology in PD patients appears to be primed for immune resistance even before treatment. This ecological diversity between SD and PD patients acts as a biomarker that enables accurate disease progression prediction.

Citation Format: Sandhya Prabhakaran, Chandler Gatenbee, Mark Robertson-Tessi, Amer A. Beg, Jhanelle Gray, Scott Antonia, Robert A. Gatenby, Alexander R. Anderson. Distinct tumor-immune ecologies in NSCLC patients predict progression and define a clinical biomarker of therapy response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5037.