David S. F. Young

Loss of homotypic cell adhesion by epithelial-mesenchymal transition or mutation limits sensitivity to epidermal growth factor receptor inhibition.

Overexpression and enhanced activation of the epidermal growth factor receptor (EGFR) is frequently observed in human carcinomas. Inhibitors of EGFR signaling have shown clinical utility; however, understanding response at the molecular level is important to define patient subsets most likely to benefit, as well as to support the rational design of drug combinations. Pancreatic and colorectal tumor cell lines insensitive to EGFR inhibition were those that had lost or mutated the epithelial junction constituents E-cadherin and γ-catenin, had lost homotypic adhesion, and often gained proteins associated with an epithelial to mesenchymal–like transition, such as vimentin, zeb1, or snail. In matched pairs of colorectal tumor cells, the epithelial lines showed an average 7-fold greater sensitivity than mesenchymal-like lines. In human pancreatic and colorectal tumor tissues, gain of mesenchymal characteristics and loss of epithelial characteristics correlated with advancing tumor stage. These data indicate an especially sensitive patient subset as well as a rationale for the combination of EGFR antagonists with agents that affect the epithelial to mesenchymal–like transition process as a mechanism to enhance sensitivity for more advanced mesenchymal-like tumors. [Mol Cancer Ther 2007;6(2):532–41]

Abstract 2225: Image analysis-based PD-L1 companion and complementary diagnostics

The PD-1 pathway, comprised of the immune cell co-receptor Programmed Death 1 (PD-1) and its ligands PD-L1 and PD-L2, mediates local immunosuppression in the tumor microenvironment. Immune checkpoint modulators are designed to block the local immunosuppression caused by this pathway. The FDA approved anti-PD-1 antibody therapies Opdivo® (nivolumab; Bristol-Myers Squibb) and Keytruda® (prembrolizumab; Merck & Co) rely on PD-L1 immunohistochemistry (IHC) in vitro diagnostic (IVD) tests to determine the PD-L1 status in patients in non-small cell lung cancer (NSCLC), in order to predict response to these drugs. The current complementary diagnostic for Opdivo® (Dako 28-8 PharmDx®) relies on a pathologist scoring paradigm which considers any patient with ≥1% positive tumor cells an optimal candidate for Opdivo® treatment. However, overall survival (OS) is further increased when patients have ≥5% or ≥10% PD-L1 positive tumor cells. This scoring approach is vastly different than the PD-L1 scoring approach used in the Keytruda® companion diagnostic (Dako 22C3 PharmDx®), which utilizes a ≥50% positive tumor cells value to predict a positive Overall Response Rate (ORR; OS not yet determined). Thus, the 28-8 test for Opdivo® utilizes a more precise approach than the 22C3 test for Keytruda®, and requires a more calibrated scoring approach. This calibrated approach for Opdivo® requires the difficult challenge of pathologists reliably distinguishing membrane staining to define the fine gradations of 1%, 5% and 10% PD-L1 positive neoplastic cells. To best meet this challenge, we developed a digital Tissue Image Analysis (TIA) solution which enabled accurate, unbiased quantification of PD-L1 on a cell-by-cell basis to classify the percentage positive tumor cells in patients with high granularity. Using Flagship9s proprietary CellMapTM algorithm, we evaluated 40 formalin-fixed paraffin-embedded (FFPE) NS-NSCLC samples which were stained using the Dako 28-8 PharmDx® PD-L1 IHC test. The TIA strategy digitally separated tumor cells from other cell types, and quantified membrane staining intensity according to a consistent threshold. The performance of the resulting IHC-TIA assay was evaluated in the context of a CLIA validation study performed by Flagship. The results demonstrated equivalency to the manually scored IVD reference standard; however, the TIA scoring of this assay provided consistent, unbiased, and more detailed scoring of PD-L1 stained tissues for determining the patients with ≥1, ≥5, and ≥10% PD-L1 positive tumor cells with greater confidence than a manual scoring approach. Moving forward, these TIA tools can be utilized to assess PD-L1 positive cell frequencies with greater reliability and granularity to identify optimal treatment cutpoints for these and other PD-L1 IHC tests used to predict response to PD-L1/PD-1 inhibitors. Citation Format: Joseph S. Krueger, Roberto Gianani, Brooke Hirsch, Stefan Pieterse, Famke Aeffner, David Young. Image analysis-based PD-L1 companion and complementary diagnostics. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2225.

Abstract 3391: Companion diagnostic strategies specific to antibody therapies

Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA One premise of antibody-drug conjugates (ADC) is that the bound mAb-antigen complex on the cell surface will internalize and be metabolized by lysosomal proteases to release the free drug. Thus, the efficacy of an ADC is dependent not only on the presence of cell surface antigens, but also an active system of receptor turnover and receptor-mediated endocytosis. Thus, a predictive assay for patient response would ideally account for both the degree of cell surface expression of the target, as well as cytoplasmic presence of the target to quantify a surrogate for receptor turnover and internalization. Immunohistochemistry based assays (IHC) are best suited to address these questions, as it is the only method which provides the ability to measure both membrane and cytoplasm expression of the target simultaneously within archival FFPE biopsies. However, the biological mechanisms behind receptor internalization and turnover have not been elucidated for novel therapeutic targets. In most cases, an IHC assay is utilized to evaluate these measures, without prior advance knowledge of how these measures are suitable for patient selection. Unanticipated difficulties in tissue interpretation, such as low apparent expression of the target, occlusion of membrane staining by cytoplasmic staining, or heterogeneity in staining often lead to failure in determining a correct patient stratification approach. In order to investigate patient selection strategies for ADCs, we have invented several proprietary approaches for measuring critical properties of the therapeutic target on the cell surface or inside the cell which can be used to understand and predict efficacy to an ADC using FFPE biopsies. These quantitative pathology approaches are based on image analysis approaches which been designed specifically for ADC CDx programs to develop a pathology based scoring system which can be predictive of ADC response: 1) Accurately quantifying low levels of cell surface target expression; 2) Defining cell surface target expression independent of cytoplasmic expression; 3) Overcoming staining heterogeneity; and 4) Determining the correct staining thresholds for quantification. These image analysis based approaches can be used to define and evaluate a scoring approach, train pathologists, assess objective performance, and best determine a cutpoint approach using statistical approaches. These image analysis based tools can be used to create a manual scoring paradigm for an IHC assay or can be incorporated into a medical device directly to support the PMA effort. Incorporation of these novel tools will enable ADC developers to create efficacy and patient stratification paradigms which incorporate the critical biological endpoints unique to ADCs. Citation Format: Joseph S. Krueger, David Young, Holger Lange, Steve Potts. Companion diagnostic strategies specific to antibody therapies. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3391. doi:10.1158/1538-7445.AM2015-3391

Abstract 2548: Using whole slide digital image analysis to quantify leukocyte populations in tumor sections

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA In recent years, the tumor microenvironment (TME) has been identified as an important factor influencing the growth and metastasis of the tumor. Multiple studies have shown that adding the “Immunoscore” assessment to the AJCC/UICC-TNM classification system improves the accuracy of outcome prediction, and in some cases outperforms traditional TNM staging. In many instances these studies have been performed utilizing 2-3 independent readers to manually quantify the cells, performed on selective high-powered fields or TMA cores rather than the entire specimen, resulting in variations in counts when different high powered fields (HPFs) or cores are chosen. A key method to increase the throughput and to decrease the variability is to utilize whole slide imaging and computerized image analysis to provide leukocyte counts. An image analysis algorithm which can automatically differentiate tumor from stroma would allow rapid quantification of endpoints in each tissue compartment across the whole specimen. To address these issues, Flagship Biosciences has designed proprietary CellMapTM image analysis algorithm tools to develop an Immunoscore-like paradigm for colorectal cancers (CRCs) to potentially provide new and more accurate TME information to aid in interpretation. Utilizing whole slide imaging (WSI) approaches, CellMap™ allows the quantitation of leukocyte populations (e.g., CD3+, CD8+, FoxP3+) automatically across whole tissue sections. Using this algorithm, leukocyte populations were quantified in sections that have been either singly or dually labeled for inflammatory markers. Our preliminary studies indicate that Immunoscore-like scoring paradigm should be established both in tumor areas and in adjacent stroma to provide the most complete information on the biology of the tumor. We compared the use of this approach in tissue microarray (TMA) cores which generally sample areas of dense tumor mass, and compared automated WSI to manual HPF approaches. Accuracy of the algorithm was demonstrated by comparing data from manual counts to algorithm derived counts using high-powered fields. These data support using CellMap™ in the prospective or retrospective assessment of leukocyte subpopulations in whole slides of clinical samples. This approach will diminish variability in counting, expand the types of endpoints determined, and improve the statistical value of these determinations, thereby facilitating robust TME measurements with clinical value. Note: This abstract was not presented at the meeting. Citation Format: Joseph S. Krueger, Brian Laffin, Holger Lange, Anthony Milici, Eric Neeley, Mirza Peljto, Mahipal Suraneni, David Young. Using whole slide digital image analysis to quantify leukocyte populations in tumor sections. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2548. doi:10.1158/1538-7445.AM2014-2548

Abstract 2842: Evaluation of immunohistochemistry assays against c-Met and HGF to guide companion diagnostic decisions

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA Establishing reagent specificity during immunohistochemistry (IHC) based biomarker or companion diagnostic (CDx) assays is challenging. Antibody specificity is dictated in part by recognition of 3D confirmation of the target binding, target activity, and and/or epitopes post-translation modifications. Fixation effects pose additional challenges to epitope recognition during IHC assay. For these reasons, different antibodies against the same target biomarker may demonstrate diversity in prevalence, range, and staining patterns over identical specimens. Thus, determining reagent specificity is a critical part of IHC and CDx assay development. Interpretation is further complicated by the pattern of biomarker expression in specific cell types (e.g. tumor v. stroma) or cell compartments (e.g. membrane v. cytosol). These factors may be critical to associate the drugs mechanism of action with efficacy. In the companion diagnostic (CDx) setting, the mechanism of the drug, epitope recognition, and staining features used to interpret and quantify the biomarker to predict patient response requires an evidence-based approach, where all features of an IHC assay are considered and tied empirically to patient response to the drug. In this study, we demonstrate these complexities in gastric cancer specimens, by comparing IHC assays using two antibodies that recognize either intracellular or extracellular domains of c-Met receptor (SP44/ C-term and EP154Y/ N-term) in the context of the ligand for c-Met, Hepatocyte Growth Factor (HGF). Therapeutic antibodies targeting c-Met [such as MetMab® (OA-5D5/ Roche)]; or HGF directly [such as Rilotumumab (AMG 102/ Amgen)], have directly linked c-Met protein expression as revealed by IHC to patient response. Thus, we hypothesized that a link between c-Met protein expression and HGF should be discernible. To test this hypothesis, we used image analysis approaches to determine the staining features of each IHC assay in comparison to each other. Surprisingly, we found little concordance between the two c-Met antibodies in evaluating c-Met and HGF expression. We found distinct populations of c-Met expressing vs HGF expressing specimens, whose HGF-c-Met association differed with the c-Met assay used. We examined the tissue and cell compartmentalization, and identified staining features of each reagent which would aid or impede in interpretation strategies for understanding the relationship between c-Met and HGF. These results suggest that the epitope-specific features of each c-Met antibody determines the relationship with HGF expression, and how quantitative image analysis endpoints can be used to make critical decisions during the development of an IHC companion diagnostic. Note: This abstract was not presented at the meeting. Citation Format: Joseph S. Krueger, Brian Laffin, Holger Lange, Eric Neeley, Mirza Peljto, Mohamed Salama, Mahipal Suraneni, David Young. Evaluation of immunohistochemistry assays against c-Met and HGF to guide companion diagnostic decisions. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2842. doi:10.1158/1538-7445.AM2014-2842

Abstract 4981: Evaluating the contribution of heterogeneity and the tumor microenvironment in companion diagnostic approaches

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA As our understanding of the factors which affect efficacy of a targeted therapy increases, the reliance on histopathological analysis of a biomarker has also increased. This is often due to the necessity to weigh the critical factors of a target or biomarker protein in tissue and cellular context. Currently, histopathologic assessment of tumors which aims to project patient clinical outcome utilize drug target response factors, such as relative expression of the drug target of the drug target or a resistance mechanism in the target (tumor) cells or the tumor microenvironment (TME ). Multiple studies have also shown that TME factors such as inflammatory cell content can be prognostic and predictive. For example, adding the Immunoscore assessment to the TNM classification systems improves the accuracy of disease prognosis. This correlation has led to the concept of predictive “immunoprofiling”, which uses an individuals immune system profile to predict that patients response to immunomodulating antibody therapy. For these reasons, it is critical to evaluate the drug target or biomarker in conjunction with TME features when defining a patient selection strategy. Critical factors such as tissue and/or cellular compartmentalization and tumor heterogeneity direct the interpretation of these measures and their predictive value. To address the need for careful, contextual interpretation of histopathological evaluations, Flagship has built CellMap™ image analysis algorithms to directly measure heterogeneity and the TME components in a whole tissue section. These approaches allow biomarker interpretation in the complex context of spatial, architectural, and morphological information to aid histological definition and quantification. In this study, we utilized a cohort of specimens from 20 colorectal cancer (CRC) patients and immunologically stained them in order to visualize c-Met, as a characteristic and biologically relevant therapy target; and CD3+ and CD8+ to visualize the inflammatory cell environment. Using these immunohistochemical markers as a prototype for a simultaneous evaluation of a molecular target and the TME, we characterized the biomarker and inflammatory content in both the tumor and stroma using our CellMap™ image analysis algorithms. This provided detailed accounting of the molecular target profile (tumor vs stroma; membrane vs cytoplasm vs nucleus), and the “immunoprofile”, which allowed us to quantitatively describe and associate these features relative to each other in the context of heterogeneity. This data demonstrated discrete patterns of association between c-met and the TME, serving as a potentially critical measure which reflects a biological process relevant to disease outcome. These studies demonstrate novel tools which can assess both the prognostic and predictive value of key measurements which reflect complex tumor biology. Citation Format: Joseph S. Krueger, Brian Laffin, Holger Lange, Anthony Milici, Mirza Peljto, Eric Neeley, Mahipal Suraneni, David Young. Evaluating the contribution of heterogeneity and the tumor microenvironment in companion diagnostic approaches. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4981. doi:10.1158/1538-7445.AM2014-4981

Abstract 5426: Assessing factors predictive of response to ADCs for companion diagnostic strategies

One premise of antibody-drug conjugates (ADC) is that the bound mAb-antigen complex on the cell surface will internalize and be metabolized by lysosomal proteases to release the free drug. Thus, the efficacy of an ADC is dependent not only on the presence of cell surface antigens, but also in-tact delivery of the conjugated drug, and an active pathway of receptor-mediated endocytosis. On a cellular level, the biological mechanisms behind receptor internalization and turnover have not been elucidated for novel therapeutic targets. On a tissue level, vascularization and hypoxic profile will affect delivery of the antibody into the tissue. Varying expression of the antibody target within the tissue (antigen density) will also affect the uptake of the ADC. Furthermore, the extracellular stability of the ADC may be affected by the activity of the various proteases in the tumor microenvironment (TME), outside of the lysozome. These concepts are critical for efficacy, and thus are especially important for companion diagnostic approaches (CDx) meant to predict response to ADCs. Thus, a predictive assay which would account not only for the degree of cell surface expression of the target, but also receptor internalization and potential effects of tumor microenvironment is required. To answer this need, Flagship Biosciences has invented several proprietary approaches for measuring critical properties of the therapeutic target on the cell surface or inside the cell, as well as properties of the TME which could be used to understand and predict efficacy to an ADC using FFPE biopsies. These quantitative pathology approaches are based on image analysis approaches which been designed specifically for ADC CDx programs to answer these critical questions: 1) Defining cell surface target expression independent of cytoplasmic expression; 2) Estimating receptor flux (turnover, internalization) based on staining profile; 3) Assessing critical factors in the TME which may affect delivery of the in-tact drug to the intracellular target; 4) Assaying vascular properties which may affect delivery of the drug; and 5) Heterogeneity of the target within a tumor. We are able to provide these as discrete evaluations or multiplex these evaluations for an integrative answer which can be derived from a typical clinical biopsy. Incorporation of these novel tools will enable ADC developers to create efficacy and patient stratification paradigms which incorporate the critical biological endpoints unique to ADCs. Citation Format: Joseph S. Krueger, Holger Lange, Steve Potts, David Young. Assessing factors predictive of response to ADCs for companion diagnostic strategies. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5426. doi:10.1158/1538-7445.AM2014-5426

Abstract C34: Using quantitative image analysis of a putative immunohistochemistry assay against C-met to guide companion diagnostic decisions.

When developing an immunohistochemistry (IHC) based biomarker or companion diagnostic assay, establishing reagent specificity is very challenging. Because antibodies recognize three dimensional epitopes, epitope recognition may be based on a specific confirmation (activated, receptor occupied, etc) or biological state (glycosylation, cell surface vs cytoplasmic) of the target protein. Furthermore, recognition of the epitope in Formalin-Fixed, Paraffin Embedded (FFPE) specimens imposes additional challenges due to the fixation effects. For these reasons, different antibodies against the same target biomarker may demonstrate different prevalence, range, and staining patterns in the same specimens. Determining reagent specificity is a critical part of IHC assay development; but the typical approaches utilized (such as Western Blotting, etc) do not directly prove specificity in the FFPE setting. Additionally, the presence or absence of the protein in a particular tissue (tumor vs stroma) or cell (membrane/ cytosol/ nuclear) compartment may be critical to associate the drugs mechanism of action with efficacy. In the companion diagnostic (CDx) setting, the mechanism of the drug, epitope recognition, and staining features used to interpret and quantify the biomarker to predict patient response requires an evidence-based approach, where all features of an IHC assay are considered and tied empirically to patient response to the drug. In this study, we demonstrate these complexities in gastric cancer specimens, by comparing IHC assays using two antibodies against the intracellular vs extracellular domains of c-met (SP44/ C-term and EP154Y/ N-term) in the context of the ligand for c-met, Hepatocyte Growth Factor (HGF). Therapeutic antibodies targeting c-met [such as MetMab® (OA-5D5/ Roche)]; or HGF directly [such as Rilotumumab (AMG 102/ Amgen)], have linked c-met protein expression by IHC directly to patient response. Thus, we hypothesized that a link between c-met protein expression and HGF should be discernible. To test this hypothesis, we used image analysis approaches to determine the staining features of each IHC assay in relation to each other. Surprisingly, we found little concordance between the two c-met antibodies in evaluating c-met and HGF expression. We found distinct populations of c-met expressing vs HGF expressing specimens, whose HGF-c-met association differed with the c-met assay used. We examined the tissue and cell compartmentalization, and identified staining features of each reagent which would aid or impede in interpretation strategies for understanding the relationship between c-met and HGF. These results suggest that the epitope-specific features of each c-met antibody determines the relationship with HGF expression, and how quantitative image analysis endpoints can be used to make critical decisions during the development of an IHC companion diagnostic. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):C34. Citation Format: Joseph S. Krueger, Brian Laffin, Mirza Peljto, Mahipal Suraneni, Holger Lange, David Young. Using quantitative image analysis of a putative immunohistochemistry assay against C-met to guide companion diagnostic decisions. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr C34.

Abstract B44: Assessing factors predictive of response to ADCs for companion diagnostic strategies.

One premise of antibody-drug conjugates (ADC) is that the bound mAb-antigen complex on the cell surface will internalize and be metabolized by lysosomal proteases to release the free drug. Thus, the efficacy of an ADC is dependent not only on the presence of cell surface antigens, but also in-tact delivery of the conjugated drug, and an active pathway of receptor-mediated endocytosis. On a cellular level, the biological mechanisms behind receptor internalization and turnover have not been elucidated for novel therapeutic targets. On a tissue level, vascularization and hypoxic profile will affect delivery of the antibody into the tissue. Varying expression of the antibody target within the tissue (antigen density) will also affect the uptake of the ADC. Furthermore, the extracellular stability of the ADC may be affected by the activity of the various proteases in the tumor microenvironment (TME), outside of the lysozome. These concepts are critical for efficacy, and thus are especially important for companion diagnostic approaches (CDx) meant to predict response to ADCs. Thus, a predictive assay which would account not only for the degree of cell surface expression of the target, but also receptor internalization and potential effects of tumor microenvironment is required. To answer this need, Flagship Biosciences has invented several proprietary approaches for measuring critical properties of the therapeutic target on the cell surface or inside the cell, as well as properties of the TME which could be used to understand and predict efficacy to an ADC using FFPE biopsies. These quantitative pathology approaches are based on image analysis approaches which been designed specifically for ADC CDx programs to answer these critical questions: 1) Defining cell surface target expression independent of cytoplasmic expression; 2)Estimating receptor flux (turnover, internalization) based on staining profile; 3) Assessing critical factors in the TME which may affect delivery of the in-tact drug to the intracellular target; 4) Assaying vascular properties which may affect delivery of the drug; and 5)Heterogeneity of the target within a tumor. We are able to provide these as discrete evaluations or multiplex these evaluations for an integrative answer which can be derived from a typical clinical biopsy. Incorporation of these novel tools will enable ADC developers to create efficacy and patient stratification paradigms which incorporate the critical biological endpoints unique to ADCs. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):B44. Citation Format: Joseph S. Krueger, Holger Lange, Steve Potts, David Young. Assessing factors predictive of response to ADCs for companion diagnostic strategies. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr B44.

Validation of an image analysis algorithm to quantify leukocyte populations using whole slide image analysis

In recent years, the tumor microenvironment (TME) has been identified as an important factor influencing the growth and metastasis of the tumor. In the TME, different classes of inflammatory cells have been found to exert either a pro- or anti-tumor effect. This has resulted in a growing need to utilize immunohistochemistry to label these leukocyte populations, thereby allowing for the cells to be quantified. In many instances these studies have been performed utilizing 2-3 independent readers to manually quantify the cells requiring significant time both for the actual counting as well as the training needed to minimize the variation from reader to reader. In addition, manual counting is usually done on selective high-powered fields rather than the entire specimen, resulting in variations in counts when different fields are chosen. A key method to increase the throughput and to decrease the variability is to utilize whole slide imaging and computerized image analysis to provide leukocyte counts. An image analysis algorithm which can automatically differentiate tumor from stroma would allow rapid quantification of endpoints in each compartment, such as: tumor burden; number of inflammatory cells/area; or percent of inflammatory cells/total cells in each tissue compartment. In this poster, the validation and utilization of an algorithm to quantify immunolabeled leukocytes in both tumor sections and tissue microarrays is described. Utilizing whole slide imaging approaches, an image analysis algorithm (CellMap™) that allows the quantitation of leukocyte populations (e.g., CD3+, CD8+, FoxP3+) automatically across whole tissue sections has been developed. This approach has been used to evaluate samples of colorectal cancer and non-small cell lung carcinoma. Using this algorithm, leukocyte populations were quantified in sections that have been either singly or dually labeled for inflammatory markers. Accuracy of the algorithm was demonstrated by comparing data from manual counts to algorithm derived counts using high-powered fields. The results of the high-powered field analysis were compared to an analysis across the whole tissue section, demonstrating the effect of variability when user defined fields are chosen. These data support using CellMap™ in the prospective or retrospective assessment of leukocyte subpopulations in clinical samples. This approach will diminish variability in counting, expand the types of endpoints determined, and improve the statistical value of these determinations, thereby facilitating robust TME measurements with clinical value.