Introduction: Multiplexed analysis of limited tissue samples can improve our understanding of tumor biology and tumor microenvironment. Chromogenic and fluorescent multiplexed immunohistochemistry (IHC) approaches are available and offer great insights while conserving limited tissue however these approaches have their limitations. Multiplexed chromogenic IHC methods can at best accommodate up to 2-3 distinct markers. Fluorescence-based approaches can support higher degree of multiplexing however spectral overlap issues and differences in labeling efficiency/photostability complicate experimental procedures and data interpretation. Imaging mass cytometry system (IMC) has recently emerged as a novel technology for tissue imaging that enables multiplexed analysis protein expression (up to 40 markers) in a single tissue sample while circumventing the limitations of chromogenic and fluorescent IHC techniques. Metal-conjugated antibodies are used to perform qualitative and quantitative analysis of expression of multiple proteins of interest on a single formalin fixed paraffin embedded (FFPE) tissue slide. Here, we compare the performance of the IMC method with conventional, established IHC techniques using a small panel of markers. Methods: Serial sections of FFPE tonsil and non-small cell lung carcinoma tissues were assessed by monoplex IHC and multiplex IMC for CD3 (Cell Signaling Technology, D7A6E), CD8 (LS Bio, C8/144B), CD68 (Abcam, KP1), PD-L1 (Spring Bio, SP142) and Histone H3 (D1H2). Digital image analysis using Flagship's image analysis software was used to compare performance characteristics of multiplex IMC platform with standard monoplex chromogenic IHC. Results: The staining patterns of the corresponding biomarkers were similar between IMC and IHC on sequential sections. Digital image analysis demonstrated concordance in the percentage of biomarker positive cells within analyzed matched IHC and IMC areas. It was also demonstrated that cellular image segmentation can be performed on IMC images thus allowing for utilization of various software packages for high dimensional single cell analysis of IMC data. Conclusion: Comparative digital image analysis indicates that on FFPE tissues multiplexed IMC platform generates data comparable to that obtained from the monoplex chromogenic IHC platform. We believe that an IMC platform is a new tool capable of dramatically enhancing our ability to study biology of cancer using highly multiplexed analysis of limited tissue samples. Citation Format: Navi Mehra, Carsten Schnatwinkel, Elliott Ergon, Joseph Krueger, Karl Calara-Nielsen, Brad Foulk, Kirti Sharma, Denis Smirnov, Chandra Rao, Tatiana Perova, Rengasamy Boominathan. Comparison of multiplexed imaging mass cytometry in FFPE tissue to monoplex immunohistochemistry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3037.

In vitro diagnostic (IVD) approvals of qualitative immunohistochemical (IHC) assays offer unique patient selection strategies by equipping pathologists with new tools to assess tumor status including tumor immune landscape. PD-L1/PD-1 checkpoint therapies require accurate diagnostic tools for optimal patient selections, however the interpretation of these tests may be very complicated upon manual assessment. As assessment criteria have increased in complexity, an accurate quantitative approach is needed to objectively and consistently interpret challenging paradigms. Recent IVD tests have multifaceted paradigms which assess both tumor and immune positivity. Although the Ventana PD-L1 SP263 and the Dako PD-L1 22C3 qualitative diagnostics are guiding immunotherapy decisions, interpretation of the results may be subjective and challenging in many cases. This creates a further level of complexity as tumor cell positivity, and immune cell presence and positivity must be assessed in combination. In this study, we have used Flagship Biosciences’ image analysis platform (cTA®) to explore the use of artificial intelligence (AI) and machine learning in the context of complex PD-L1 assays to provide accurate and precise quantification that allows for objective interpretation of the IVD. Non-small cell lung cancer (NSCLC) and urothelial carcinoma (UC) samples were stained with the SP263 and 22C3 PD-L1 assays and image analysis was used to provide an interpretation status based upon the IVD scoring paradigms. A comparison of the cTA results across both tests and tissue indications was also performed to explore how we may further support cross-platform PD-L1 solutions that provide adaptable and objective quantification. The use of Flagship’s cTA platform to identify per-cell biofeatures, using machine learning, may successfully quantify PD-L1 staining in various cell populations. The ability to independently assess these populations allows for a consistent and unbiased method for the assessment tumor status for PD-L1 immunotherapy treatment decisions. Citation Format: Jeni Caldara, Joseph S. Krueger, Elliott Ergon, Staci Kearney, Bharathi Vennapusa. Analysis of companion diagnostic potentials for multifaceted PD-L1 assays [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 4923.

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.

e18031 Background: The programmed death-ligand 1 (PD-L1) (22C3) antibody is a companion diagnostic to the check-point inhibitor drug pembrolizumab for head and neck squamous cell carcinoma (HNSCC). Specifically, PD-L1 expression, as determined by a Combined Positive Score (CPS) ≥ 1, defines the clinical decision point for treatment. However, to calculate the CPS, pathologists must account for PD-L1+ tumor cells (TC) and immune cells (IC, i.e., lymphocytes, macrophages) – a cumbersome and time-consuming task that is prone to intra/inter-pathologist variability. We developed a digital image analysis (IA) based solution as a pathologist support tool that accurately and precisely scores images of PD-L1 stained HNSCC tissues. Here we compared the performance of the digital method with manual pathology. To address the literature on the differential regulation of PD-L1 by IC and TC in treatment response, we also examined how PD-L1+ IC contribute to the CPS as compared to PD-L1+ TC. Methods: 19 HNSCC samples were immunohistochemically stained for PD-L1 (22C3). Slides were assessed for CPS by manual pathology: CPS = (# PD-L1 staining cells [TC, lymphocytes, macrophages]/total # viable TC) x 100; wherein CPS < 1 (no PD-L1 expression; no treatment) and CPS ≥ 1 (PD-L1 expression; treatment). Slides were scanned at 20X and analyzed using proprietary IA solutions that identify and separate TC from IC and quantify their PD-L1 expression. Resulting IA markups of cell detection and PD-L1 expression were reviewed by an MD pathologist for accurate cell recognition and stain classification. Upon pathologist approval of the IA performance, PD-L1 staining was evaluated by digital IA for CPS, % PD-L1+ TC, and % PD-L1+ IC. Pearson’s correlation analyses were conducted to assess the concordance between manual and digital CPS along with the association between CPS and PD-L1+ IC versus PD-L1+ TC. Results: Digital and manual CPS were significantly correlated ( r = 0.76, p = 0.00016) and concordant in treatment binning for 14/19 samples; for the 5 discordant samples, manual CPS binned them for no treatment while digital CPS binned them for treatment. Digital CPS significantly correlated with the % PD-L1+ IC ( r = 0.90, p < 0.00001) along with the % PD-L1+ TC ( r = 0.98, p < 0.00001). All 6 samples binned for no treatment by digital CPS had < 1% PD-L1+ IC, whereas all but 2/13 samples binned for treatment by digital CPS had ≥ 1% PD-L1+ IC. Conclusions: The digital method for assessing PD-L1 expression in HNSCC performs similarly to manual pathology with improved accuracy for detecting and quantifying IC versus TC in the CPS calculation. Moreover, the % of PD-L1+ IC seems to contribute substantially to the CPS, to a similar degree as the % of PD-L1+ TC. Given the concordance with manual pathology, this digital method can support pathologists as a clinical diagnostic tool. As such, a clinical validation study of this digital method in a larger HNSCC cohort is underway.

Quantification of tumor-infiltrating lymphocytes (TILs) in non-small cell lung cancers (NSCLC) is valuable for understanding patient prognosis and survival. TILs comprise a subset of tumor-infiltrating leukocytes that modulate immune evasion and response to therapy. Understanding the composition of TIL subsets, especially relative to the total tumor leukocyte population, may provide additional context for understanding NSCLC pathogenesis and patient response to treatment. However, availability of tissues and use of chromogenic assays can limit the number of TIL and leukocyte subset markers assayed in a tissue section. Therefore, this study evaluated the identification of total leukocyte component in NSCLC using morphometric parameters and routine TIL marker monoplex immunohistochemistry (IHC) assays to further identify the composition of TIL subsets. Computational Tissue Analysis (cTA™) tools were used to determine the morphometric parameters which could identify immune cells in the absence of biomarker stain. The morphometric features which characterized immune infiltrates were used to quantify the total immune cell population frequency in the tumor nests and surrounding stroma in hematoxylin-stained tissues. The leukocyte population identified with morphometric parameters was correlated with CD45+ cell frequencies identified by cTA based on biomarker staining in CD45-stained serial sections. This morphometric ruleset was then applied to CD3- and CD8-stained tissues to evaluate the frequency of CD3+ and CD8+ TILs in the context of total infiltrating leukocytes. The relative populations of CD3+ and CD8+ TILs were consistent with available literature demonstrating that the morphometric ruleset could be utilized to enable evaluation of TIL sub-types relative to total leukocyte population without the need for additional IHC stains. The approach could, therefore, provide an added dimension of analysis for tissues stained by IHC for identifying the total immune cell infiltrating component without requiring additional biomarker staining. Citation Format: Elliott Ergon, Allison S. Harney, Nathan Martin, Will Paces, Famke Aeffner, Kristin Wilson, Janet Patterson-Kane, Karen Ryall, Daniel G. Rudmann, Brooke Hirsch, Joseph Krueger. Quantifying tumor-infiltrating leukocytes in hematoxylin stained NSCLC tissue samples using morphometric features [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1674. doi:10.1158/1538-7445.AM2017-1674