Dickkopf-1 (DKK1) is a secreted modulator of Wnt signaling that is frequently overexpressed in tumors and associated with poor clinical outcomes. DKN-01 is a humanized monoclonal therapeutic antibody that binds DKK1 with high affinity and has demonstrated clinical activity in gastric/gastroesophageal junction (G/GEJ) patients with elevated tumoral expression of DKK1. Here we report on the validation of a DKK1 RNAscope chromogenic in situ hybridization assay to assess DKK1 expression in G/GEJ tumor tissue. To reduce pathologist time, potential pathologist variability from manual scoring and support pathologist decision making, a digital image analysis algorithm that identifies tumor cells and quantifies the DKK1 signal was developed. Following CLIA guidelines the DKK1 RNAscope chromogenic in situ hybridization assay and digital image analysis algorithm were successfully validated for sensitivity, specificity, accuracy, and precision. The DKK1 RNAscope assay in conjunction with the digital image analysis solution is acceptable for prospective screening of G/GEJ adenocarcinoma patients. The work described here will further advance the companion diagnostic development of our DKK1 RNAscope assay and could generally be used as a guide for the validation of RNAscope assays with digital image quantification.

BackgroundDetermination of programmed death-ligand 1 (PD-L1) level in tumor by immunohistochemistry (IHC) is widely used to predict response to check point inhibitor therapy. In particular, the Dako PD-L1 (22C3) antibody is a common companion diagnostic to the monoclonal antibody drug Keytruda® (pembrolizumab) in non-small cell lung cancer (NSCLC).1 However, for the practicing pathologist, interpretation of the PD-L1 (22C3) assay is cumbersome and time consuming. Manual pathologist scoring also suffers from poor intra- and inter-pathologist precision, particularly around the cut-off point.2 In this clinical validation study, we developed an image analysis (IA) based solution to accurately and precisely score digital images obtained from PD-L1 stained NSCLC tissues for making clinical enrollment decisions.Methods10 NSCLC tissue samples were purchased from a qualified vendor and IHC stained for PD-L1; 4 of these samples had serial sections stained on two separate days. Stained slides were scanned at 20X magnification and analyzed using Flagship Biosciences’ IA solutions that quantify PD-L1 expression and separate tumor and stromal compartments. Resulting image markups of cell detection and PD-L1 expression were reviewed by an MD pathologist for acceptance. PD-L1 staining was evaluated by digital IA in the sample’s tumor compartment for Total Proportion Score (TPS,%). Assay specificity was defined by ≥ 90% of the tissue cohort exhibiting appropriate cell recognition (≥ 90% cells correctly recognized as determined by the pathologist), with ≤ 10% false positive rate for staining classification. Sensitivity was defined by ≥ 90% of the cohort exhibiting appropriate cell identification (≥ 90% cells correctly identified), with ≤ 10% false negative rate for staining classification. Accuracy was defined by the combination of sensitivity and specificity and precision was defined by concordance of the binned TPS (<1%, ≥ 1%, ≥ 50%) in ≥ 80% of the samples stained on multiple days.ResultsThe preliminary results show that IA can yield high analytical sensitivity, specificity, accuracy, and precision in the determination of the PD-L1 score. 100% of the tissue cohort met criteria for analytical specificity, sensitivity, and accuracy and 100% of the samples stained on multiple days met the precision criteria.ConclusionsThis data demonstrates the feasibility of an IA approach as applied to PD-L1 (22C3) scoring. Ongoing experiments include application of the developed 22C3 algorithm on a separate cohort of 20 NSCLC samples to determine the correlation of digital scoring and scoring obtained by three pathologists. Additionally, we will evaluate the precision obtained by digital scoring in relation to the intra- and inter-pathologist concordance.ReferencesIncorvaia L, Fanale D, Badalamenti G, et al. Programmed death ligand 1 (PD-L1) as a predictive biomarker for pembrolizumab therapy in patients with advanced non-small-cell lung cancer (NSCLC). Adv Ther 2019;36:2600–2617.Rimm DL, Han G, Taube JM, et al. A prospective, multi-institutional, pathologist-based assessment of 4 immunohistochemistry assays for PD-L1 expression in non–small cell lung cancer. JAMA Oncol 2017;3:1051–1058.

The anti-PD-L1 antibody (22C3) is used as a companion diagnostic for successful checkpoint inhibitor therapy in head and neck squamous cell carcinoma. The positive predictive value of this assay, however, is less than ideal as a high PD-L1 score does not necessarily associate with a good response, and a good response may be seen with a low or negative PD-L1 score. In principle, this could be due to lack of accuracy and/or precision of the assay, as well as biological factors, such as variable expression across tumor cells and interacting immune cells. In this study, we describe a digital image analysis algorithm to address assay-related problems and improve both accuracy and precision, as well as improve identification of PD-L1 expression in differing cell populations of the tumor microenvironment. In addition, we propose a panel of assays that in aggregate might reveal inherent heterogeneity among cases with similar PD-L1 score and thus provide vital context underlying the efficacy of checkpoint inhibitors in the clinic. Here we show that applying novel machine-learning based digital image analysis to multiplex assays together with digital scoring of PD-L1 provides an accurate and precise assessment of the real-world tumor milieu that we hypothesize will help establish the immunophenotypes that inform therapeutic efficacy of checkpoint inhibitors. We have additionally interrogated whether expression of PD-L1 in these groups are consistent with known molecular patterns assayed using novel methodologies, such as high-plex digital spatial transcriptomics through the nanoString GeoMx Digital Spatial Profiler (DSP) platform and assessed the ability of the different digital platforms to provide concordant data relating to real-world expression patterns of PD-L1 and associated biomarkers. Thus, our results point to the importance of robust methodologies, used in combination, to evaluate complex tumor immune landscapes and the advantages of digital analyses to provide accurate and precise clinical contexts for better patient outcomes. Citation Format: Clara Troccoli, Lauren Matelski, Adam Beharry, Vanessa Ly, Morgan Wambaugh, Melanie Amen, Will Paces, Geoffrey Metcalf, Roberto Gianani, Tom Turi. Digital and spacial characterization of PD-L1 expression and IVD assay performance in the immune landscape of head and neck squamous cell carcinoma: A multimodal approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6634.

Several studies have shown that the location and expression of infiltrating immune cells in patient tumors can better identify which patients are more likely to respond to anti- PD-1/PD-L1 therapy. In particular, immunohistochemistry-based studies have shown that the spatial location of PD-L1 expression has particular biological relevance, as PD-L1 expression in the tumor cells or immune cells in the tumor syncytium, tumor microenvionment (TME), or tumor-stroma boundary all describe differential PD-L1 biology. Here, we use Flagship’s digital pathology platform (cTA®) to investigate IHC based PD-L1 and CD8 staining patterns in Non-Small Cell Lung (NSCLC) and Urothelial Carcinoma (UC) tissue biopsies. The cTA platform creates thousands of per-cell Biofeatures™ derived from the digital pathology images of the IHC stained tissue, and applies Artificial Intelligence (AI) to the data to summary score endpoints for patient and cohort classification. In this approach, each tissue’s IO landscape is represented using an “IO Scorecard“, which summarizes the IHC biomarker data in a summary score which captures a comprehensive analysis of the tissue sample. The AI-determined scorecard models can be used to monitor changes before and after drug treatment and/or create predictive models for patient response outcomes. In this study, NSCLC and Urothelial Carcinoma samples were sectioned and stained using either the FDA-approved Dako 22C3 or SP263 PD-L1 IHC assays. Serial sections of each tissue specimen were also stained for CD8 expression. The cTA process detected all cells, assigned them to the tumor or TME compartments, and recorded the Biofeatures™ data which characterized PD-L1 or CD8 staining in the Tumor, Tumor/TME margin, or TME compartments. The method was validated by its ability to reproduce pathologist scoring for PD-L1 and CD8. Using the accepted data, we used the AI system to create novel Scorecard based patient classifications which describe differential PD-L1 status, CD8 status, and PD-L1/CD8 status combined. The AI Scorecard approach demonstrated that certain PD-L1 staining Biofeatures™ may also predict the CD8 status of a tumor, suggesting that additional CD8 staining may not be necessary to understand important expression patterns pertaining to cytotoxic T-cells. In summary, we demonstrated how the “IO Scorecards” are able to classify patients into differential immune status cohorts using a novel AI based scoring system, which relies only on PD-L1 IHC staining, by creating a comprehensive, contextual profile of PD-L1 staining that does not require additional CD8 IHC staining to characterize the impact of cytotoxic T-cells in a tissue sample. Citation Format: Charles Caldwell, Will Paces, Jeni Caldara, Bharathi Vennapusa, Joseph S. Krueger. Using digital pathology based “IO Scorecards” to describe relationships between PD-L1 expression and CD8 positive immune cell infiltration [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3130.

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 &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.