Abstract 3879: Image analysis-based identification and quantification of macrophages in PD-L1 (22C3) stained NSCLC tissue sections without additional macrophage-specific staining
Authors: Will Paces, Roberto Gianani, Huong Nguyen, Bradley Long, Michael Argyres, Cris Luengo, Rebecca Kim, Charles Caldwell
Journal: Cancer Research (2020)

Abstract PD-L1 positivity in tumor-associated macrophages has been related to a favorable response to anti-PD-1 and PD-L1 targeted therapies. This concept has driven interest in specifically identifying macrophages in PD-L1 stained tissues, but pathologists often have difficulty in performing this task reliably. Thus, there is a clear need for tools capable of detecting and classifying tumor-associated macrophages in the context of a standardized PDL1 assay. One such method relies on the utilization of image analysis (IA) which measures hundreds of defining cellular features. This is necessary because macrophages identification based on few features would be difficult due to their varying morphologies and similarity with tumor cells. In order to isolate macrophages using multiple aspects of their cellular characteristics, AI and machine learning algorithms were leveraged to provide robust models for their identification. In this study we compare two artificial intelligence (AI) assisted macrophage classification methods for their accuracy in specifically identifying CD68 positive macrophages. Both approaches are based on interrogating a cohort of 20 NSCLC sections with two tumor-associated macrophage recognition algorithms, the first based on a neural network (NN), and the second on a decision tree (DT) model. These classifiers were developed by training a learning model on the morphometric and chromogenic PD-L1 staining features measured on detected cells, using their fluorescent CD68 expression profile as an indicator of their macrophage lineage. These classification algorithms were trained on images developed using procedures wherein tissue sections were stained by immunofluorescence with antibody to the macrophage associated CD68 marker and subsequently scanned to obtain a digital image. After removal of the coverslip and stripping of the CD68 antibodies, the sections were then stained with the anti-PDL1 22C3 antibody and rescanned to obtain a second digital image. Coregistration of the two digital images allowed for the identification of CD68 positive cells in the context of only the 22C3 PDL1 assay. The accuracy of each classification algorithm was determined by comparing the positivity for macrophage lineage among algorithm predicted macrophage to CD68 positivity. For both the NN and the DT classifier, we demonstrated a &amp;gt; 90% accuracy classification of macrophages identification. The decision tree method achieved similarly accurate predictions These data indicate that both NN and DT methods allow for independent identification of macrophages using only a 22C3 IHC PD-L1 assay, making it possible to isolate the contribution of PD-L1 macrophage positivity to successful checkpoint therapy response. Citation Format: Will Paces, Roberto Gianani, Huong Nguyen, Bradley Long, Michael Argyres, Cris Luengo, Rebecca Kim, Charles Caldwell. Image analysis-based identification and quantification of macrophages in PD-L1 (22C3) stained NSCLC tissue sections without additional macrophage-specific staining [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3879.