Holger Lange

Evaluating tumor heterogeneity in immunohistochemistry-stained breast cancer tissue

Quantitative clinical measurement of heterogeneity in immunohistochemistry staining would be useful in evaluating patient therapeutic response and in identifying underlying issues in histopathology laboratory quality control. A heterogeneity scoring approach (HetMap) was designed to visualize a individual patients immunohistochemistry heterogeneity in the context of a patient population. HER2 semiquantitative analysis was combined with ecology diversity statistics to evaluate cell-level heterogeneity (consistency of protein expression within neighboring cells in a tumor nest) and tumor-level heterogeneity (differences of protein expression across a tumor as represented by a tissue section). This approach was evaluated on HER2 immunohistochemistry-stained breast cancer samples using 200 specimens across two different laboratories with three pathologists per laboratory, each outlining regions of tumor for scoring by automatic cell-based image analysis. HetMap was evaluated using three different scoring schemes: HER2 scoring according to American Society of Clinical Oncology and College of American Pathologists (ASCO/CAP) guidelines, H-score, and a new continuous HER2 score (HER2cont). Two definitions of heterogeneity, cell-level and tumor-level, provided useful independent measures of heterogeneity. Cases where pathologists had disagreement over reads in the area of clinical importance (+1 and +2) had statistically significantly higher levels of tumor-level heterogeneity. Cell-level heterogeneity, reported either as an average or the maximum area of heterogeneity across a slide, had low levels of dependency on the pathologist choice of region, while tumor-level heterogeneity measurements had more dependence on the pathologist choice of regions. HetMap is a measure of heterogeneity, by which pathologists, oncologists, and drug development organizations can view cell-level and tumor-level heterogeneity for a patient for a given marker in the context of an entire patient cohort. Heterogeneity analysis can be used to identify tumors with differing degrees of heterogeneity, or to highlight slides that should be rechecked for QC issues. Tumor heterogeneity plays a significant role in disconcordant reads between pathologists.

Multiplexed measurement of proteins in tissue in a clinical environment.

There is an emerging need for more effective approaches to accurately quantitate protein expression in tissue samples. In many clinical studies and particularly in pharmaceutical clinical trials, access to adequate tissue samples is a major bottleneck, and thus techniques to measure protein expression in these valuable tissue specimens is important. This study will review current approaches in multiplexing of protein expression in tissue, and discusses new approaches using a novel image registration technique across multiple tissue sections.

Tissue pattern recognition error rates and tumor heterogeneity in gastric cancer.

The anatomic pathology discipline is slowly moving toward a digital workflow, where pathologists will evaluate whole-slide images on a computer monitor rather than glass slides through a microscope. One of the driving factors in this workflow is computer-assisted scoring, which depends on appropriate selection of regions of interest. With advances in tissue pattern recognition techniques, a more precise region of the tissue can be evaluated, no longer bound by the pathologist’s patience in manually outlining target tissue areas. Pathologists use entire tissues from which to determine a score in a region of interest when making manual immunohistochemistry assessments. Tissue pattern recognition theoretically offers this same advantage; however, error rates exist in any tissue pattern recognition program, and these error rates contribute to errors in the overall score. To provide a real-world example of tissue pattern recognition, 11 HER2-stained upper gastrointestinal malignancies with high heterogeneity were evaluated. HER2 scoring of gastric cancer was chosen due to its increasing importance in gastrointestinal disease. A method is introduced for quantifying the error rates of tissue pattern recognition. The trade-off between fully sampling tumor with a given tissue pattern recognition error rate versus randomly sampling a limited number of fields of view with higher target accuracy was modeled with a Monte-Carlo simulation. Under most scenarios, stereological methods of sampling-limited fields of view outperformed whole-slide tissue pattern recognition approaches for accurate immunohistochemistry analysis. The importance of educating pathologists in the use of statistical sampling is discussed, along with the emerging role of hybrid whole-tissue imaging and stereological approaches.

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 2360: Quantitative analysis of multiple subtypes of immune system cells in cancer tissues

Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA Current cancer biology acknowledges the key role of the immune system in tumor biology, and promise for the modulation of immune system in cancer treatment. The composition of the inflammatory cell populations in tissues is reflective of the overall state of the Tumor Micro-Environment (TME), and the identification of distinct inflammatory cell types may hold prognostic or predictive value. Immunohistochemistry allows for reliable identification of the cell constituents to facilitate analysis of the TME while remaining in the tissue context. Establishing a quantitative paradigm for inflammatory cell types and subtype profiling requires unbiased and automated whole-tissue based quantitation methods, which are capable of spatial integration of multiple inflammatory cell markers across the whole tissue. While single slide fluorescent multiplex approaches can address this need, the use of difficult-to-implement wet assay strategies involving multiplexing 6-8 fluorescent markers on the same tissue section are difficult to implement in a global clinical diagnostic lab setting. To answer this need, we combined novel advents in Tissue Image Analysis (TIA) to integrate spatial expression of serial-section stained whole tissue clinical lung cancer specimens. In this proof-of-principle study,we were able to superimpose specific locations of individual cell types onto 6 serial sections and evaluate different inflammatory cell types. We used serial sections of clinical lung specimens stained for six immune phenotypic markers (CD68, CD4, CD8, CD33, FoxP3, and CD11b) to illustrate a repertoire of inflammatory cell types. Our proprietary CellMap algorithm was utilized to identify, enumerate, and determine the precise location of individual inflammatory cells in tissues on cell-by-cell basis in the tumor microenvironment (TME). Our proprietary FACTS (Feature Analysis on Consecutive Tissue Sections) approach was used to integrate the spatial expression of individual markers onto a reference H&E slide, and/or adjacent slides. Using the aligned FACTS data and our proprietary MultivariateMap approach, we integrated the patterns of each marker based on immune cell type function and their location relative to each other and the tumor epithelial cells. In this study, we demonstrated how spatial integration of immune cell markers in the context of whole tissues can be applied to the diagnostic setting. By creating a comprehensive landscape of the immune system state in the tissue biopsies, we were able to identify crucial patterns which represent function and role in immune system biology. These approaches provide a robust platform for immuno-oncology applications by providing information on the state of the immune system in cancer using approaches implementable in the clinic. The use of these approaches will benefit further understanding of cancer pathology, and can directly lead to the development of diagnostic tests with clinical utility. Citation Format: Mirza Peltjo, Carsten Schnatwinkel, Nathan Martin, Holger Lange, Joseph S. Krueger. Quantitative analysis of multiple subtypes of immune system cells in cancer tissues. [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 2360. doi:10.1158/1538-7445.AM2015-2360

Abstract 2358: Quantifying PD-L1 spatial distribution signatures for patient selection approaches

Inhibitors of inflammatory checkpoints, such as PD-L1 inhibitors, have demonstrated great promise in preclinical and clinical studies. This therapeutic paradigm focuses on controlling natural inflammatory checkpoints to stimulate an elevated inflammatory response against the tumor to increase anti-tumor inflammatory cell infiltrates in the tumor microenvironment or decrease inflammatory suppressor infiltrates. The proteins which control these processes can be found in the tumor cells, cells in the tumor micro-environment (TME), or in both locales. Positive cells are often assessed in a qualitative or semi-quantitative manner using immunohistochemistry and evaluation of a limited number of representative microscopy fields across a particular tissue compartment (tumor vs stroma) or the whole tissue area. However, the locale of the inflammatory suppressors such as PD-L1 may be more revealing than estimating the tumor-wide dispersion of an inflammatory cell type. Unfortunately, the intricate spatial relationships and the often complex distribution of inflammatory cells in tissues pose significant challenges for a meaningful evaluation. We have developed an approach which can quantify these spatial relationships in a contextual, biologically meaningful score. Immunohistochemistry staining for PD-L1 in whole lung cancer tissue sections was performed, and our CellMap software was used to assess inflammatory cell distribution in the whole tissue sections. PD-L1 positive cells were quantified relative to: 1) the total number of cells in the tumor and stromal tissue compartments, and 2) the number of cells within a distance from the tumor/stroma interface. Interestingly, several unique PD-L1 distribution patterns relative to the tumor/stroma interface were observed in the sample cohort analyzed. Quantifying the distribution of PD-L1 positive cells as a function of distance from the tumor/stroma interface revealed distribution signatures, which could be used to differentiate between samples. In contrast, this differentiation of the same samples was not possible when PD-L1 cells were assessed relative to the total number of cells. This study provided a novel method for assessing inflammatory cell type spatial distribution relative to a tissue feature, the tumor/stroma interface. The data suggested that unique spatial patterns of inflammatory cell type distribution could be used to uniquely stratify patients compared to existing quantitative methods. Taken together, this proof-of-concept study demonstrates a unique quantitative assessment of inflammatory cell infiltrates in tumors that could be used to gain new insights into inflammatory cell type distributions and interactions in tumors, inflammatory cell spatial responses to oncology therapies, and novel patient selection criteria for traditional and immuno-oncology therapeutics. Citation Format: Joseph S. Krueger, Nathan Martin, Anthony Milici, Holger Lange. Quantifying PD-L1 spatial distribution signatures for patient selection approaches. [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 2358. doi:10.1158/1538-7445.AM2015-2358

Quantitative paradigm for analysis of multiple subtypes of immune system cells in lung cancer tissues

The state of the immune system is reflected, in part, by the cell populations present in individual tissues which reflect the tumor microenvironment (TME). Several studies have suggested that understanding the TME constituents is useful for predicting drug response outcomes. Despite the biological significance of various inflammatory cell types in the TME, a widely accepted quantitative paradigm that allows the comparison of multiple subtypes of inflammatory cells in tissues is lacking. This is largely due to our inability to integrate key spatial information for multiple biomarkers across whole tissue sections, understand the concordance of individual biomarkers/cell types, account for heterogeneity in the region of analysis, and the absence of validated correlative scoring paradigms to measure against drug response. Finally, if these hurdles of understanding are met, a system needs to be created which can efficiently and reproducibly asses these readouts in a clinical-use environment. In this study, we revealed by immunohistochemistry distinct populations of immune system cells on serial sections of clinical lung cancer tissues. The immune cell markers evaluated included CD68, CD33, CD11b, FoxP3, CD4, and CD8. Whole tissue image analysis (tIA) was performed using Flagships CellMap™ algorithm tool for each biomarker. Biomarker expression was analyzed on cell by cell basis for the tumor microenvironment and tabulated across the whole tissues. In order to integrate spatial information of CD68, CD33, CD11b, FoxP3, CD4, and CD8 biomarker expression from each slide onto a single tissue section, we utilized Flagships patented FACTS™ tIA tool. This tool allows for an overlay of biomarker content information on to a single reference slide (H&E), and thus can provide key spatial information on biomarker expression relationships and presence of distinct cell populations in relation to each other. These studies directly demonstrate an effective utilization of combination of IHC with a set of Flagship tIA-based tools to score and analyze immune cell biomarker content in whole clinical tissue samples. Importantly, these tools can spatially correlate multiple biomarkers across serial sections of a single tissue. These approaches will lead to the development of standardized quantitative paradigms with predictive power in evaluating patient drug response.

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