Quantitative image profiling of the tumor microenvironment on double stained immunohistochemistry images using deep learning.

person Mate Levente Nagy NeoGenomics, Aliso Viejo, CA info_outline Mate Levente Nagy, Anna Juncker-Jensen, Ben Ovadia, Robert Smale, Karen Yamamoto, Josette William, Nicholas Hoe, Raghav Padmanabhan

Authors person Mate Levente Nagy NeoGenomics, Aliso Viejo, CA info_outline Mate Levente Nagy, Anna Juncker-Jensen, Ben Ovadia, Robert Smale, Karen Yamamoto, Josette William, Nicholas Hoe, Raghav Padmanabhan Organizations NeoGenomics, Aliso Viejo, CA Abstract Disclosures Research Funding Other Background: Spatial locations of immune cells in the tumor microenvironment (TME) correlate with clinical outcome in cancers. A worse patient outcome has been reported in oral squamous cancer for individuals with an increased number of regulatory T cells within 30µm of CD8+ cells [1]. The spatial relationship between CD8+ and PD-L1+ cells has become an area of interest for understanding PD-L1 inhibition. The combined assessment of CD8 and PD-L1 in NSCLC tumors was shown to outperform CD8 or PD-L1 alone as prognostic markers for predicting treatment with immune checkpoint inhibitors [2]. Methods: As reported in these cases, quantifying spatial relationships between two biomarkers is valuable in providing clinical insights. Spatial analytics of the TME requires accurate cell segmentation and classification. To that end, NeoGenomics has developed a deep learning pipeline to automatically segment and classify cells from whole slide double stained IHC images. Results: In this study, we performed IHC double staining and cell classification analysis on two sets of assays (CD8+ PD-L1+ and CD8+ FoxP3+) in NSCLC tumors by modifying our MultiOmyx analysis pipeline [3]. In addition to cell segmentation and classification image outputs, we also generate tables with cell morphological information, phenotype counts and densities, and biomarker intensities that can be used to define H-score-like measures. Advanced spatial analytics is performed to identify clustering patterns of various phenotypes. These analyses enable investigation of complex cell interactions in TMEs. Conclusions: NeoGenomics quantitative double stained IHC assay is compatible with any two biomarkers of interest even if they are expressed on the same cell as long as the sub-cellular localization of the markers is different (membrane CD8, nuclear FoxP3). The combination of double staining for CD8+ PD-L1+ or CD8+ FoxP3+ and quantifying spatial relationships between the two biomarkers is capable of providing the needed context to guide treatment decisions. References: Feng Z et al., JCI Insight. 2017 Jul 20:e93652 Steele KE et al., J Immunother Cancer 2018;6:20 Nagy ML et al., AACR Annual Meeting 2018; April 14-18, 2018; Chicago, IL