Anthony J. Milici

Duchenne and Becker Muscular Dystrophies: A Review of Animal Models, Clinical End Points, and Biomarker Quantification:

Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD) are neuromuscular disorders that primarily affect boys due to an X-linked mutation in the DMD gene, resulting in reduced to near absence of dystrophin or expression of truncated forms of dystrophin. Some newer therapeutic interventions aim to increase sarcolemmal dystrophin expression, and accurate dystrophin quantification is critical for demonstrating pharmacodynamic relationships in preclinical studies and clinical trials. Current challenges with measuring dystrophin include the variation in protein expression within individual muscle fibers and across whole muscle samples, the presence of preexisting dystrophin-positive revertant fibers, and trace amounts of residual dystrophin. Immunofluorescence quantification of dystrophin can overcome many of these challenges, but manual quantification of protein expression may be complicated by variations in the collection of images, reproducible scoring of fluorescent intensity, and bias introduced by manual scoring of typically only a few high-power fields. This review highlights the pathology of DMD and BMD, discusses animal models of DMD and BMD, and describes dystrophin biomarker quantitation in DMD and BMD, with several image analysis approaches, including a new automated method that evaluates protein expression of individual muscle fibers.

Eteplirsen treatment for Duchenne muscular dystrophy: Exon skipping and dystrophin production

Objective To describe the quantification of novel dystrophin production in patients with Duchenne muscular dystrophy (DMD) after long-term treatment with eteplirsen. Methods Clinical study 202 was an observational, open-label extension of the randomized, controlled study 201 assessing the safety and efficacy of eteplirsen in patients with DMD with a confirmed mutation in the DMD gene amenable to correction by skipping of exon 51. Patients received once-weekly IV doses of eteplirsen 30 or 50 mg/kg. Upper extremity muscle biopsy samples were collected at combined study week 180, blinded, and assessed for dystrophin-related content by Western blot, Bioquant software measurement of dystrophin-associated immunofluorescence intensity, and percent dystrophin-positive fibers (PDPF). Results were contrasted with matched untreated biopsies from patients with DMD. Reverse transcription PCR followed by Sanger sequencing of newly formed slice junctions was used to confirm the mechanism of action of eteplirsen. Results Reverse transcription PCR analysis and sequencing of the newly formed splice junction confirmed that 100% of treated patients displayed the expected skipped exon 51 sequence. In treated patients vs untreated controls, Western blot analysis of dystrophin content demonstrated an 11.6-fold increase (p = 0.007), and PDPF analysis demonstrated a 7.4-fold increase (p < 0.001). The PDPF findings were confirmed in a re-examination of the sample (15.5-fold increase, p < 0.001). Dystrophin immunofluorescence intensity was 2.4-fold greater in treated patients than in untreated controls (p < 0.001). Conclusion Taken together, the 4 assays, each based on unique evaluation mechanisms, provided evidence of eteplirsen muscle cell penetration, exon skipping, and induction of novel dystrophin expression. Classification of evidence This study provides Class II evidence of the muscle cell penetration, exon skipping, and induction of novel dystrophin expression by eteplirsen, as confirmed by 4 assays.

Abstract 4042: Quantifying immune oncology markers across multiple tissue sections with digital image analysis

Immuno-oncology (IO) approaches requires an understanding of the types of immune cells present in the tumor microenvironment (TME) as well as the precise localization of these immune cells. However, the acquisition of spatial IO information is technically challenging, due to the requirement for multiplex labeling of immune cells and the need to categorize their location and biomarker content simultaneously. Additionally, the multiplex biomarker panel must be engineered in advance based on a priori assumptions about the correct marker combinations and their location (such as the tumor epithelial nests, TME, or specifically the tumor-stroma interface). This limits the ability to implement novel, scientifically driven assessments into an existing clinical trial which already has defined immunohistochemistry endpoints. To meet the needs of IO clinical trials, Flagship Biosciences has developed a proprietary image analysis platform, FACTS (Feature Analysis on Complementary Tissue Sections), to deduce both the necessary multiplex staining information and the spatial context of IO markers based on the utilization of existing monochrome or multiplex stained slides from a clinical trial. In this study, we demonstrate the utility of this approach to deliver biologically relevant endpoints important for IO clinical trials. First, we performed single IO biomarker staining for CD4, CD8 and FoxP3 in colorectal cancer patient samples and developed a novel image analysis approach that allowed for the accurate quantification of multiple IO markers within specific margins of the tumor microenvironment across multiple tissue sections. For each biomarker the positive cell populations were binned based on distance from the tumor-TME boundary (i.e.% positive cells within 100μM, 200μM or 500μM of the tumor-TME boundary). Next, we used computational alignment for evaluating the co-localization of multiple immune cell biomarkers from serial sections with single stains for CD4, CD8 or FoxP3. We demonstrate that the information extracted from multiple single stained serial sections can be used to generate co-localization information for multiple IO markers in a given patient sample. Lastly, we developed multiplex chromogenic assays for these same markers, analyzed the multiplex-stained slides with image analysis, and derived the same analysis endpoints for the multiplex slides to the digitally aligned individually labeled sections. The study found that similar interpretation of the inflammatory landscape was possible with multiplex-stained slides and digital alignment of monoplex-stained slides. In summary, this study demonstrated a novel image analysis approach that allows for quantification of IO markers utilizing clinically relevant wet assay technologies and existing IHC stained slides derived from an immune-oncology clinical trial. Citation Format: Ciara A. Martin, Joshua Black, Nathan T. Martin, Logan Cerkovnik, Jasmeet Bajwa, Kristin Wilson, Daniel Rudmann, Carsten Schnatwinkel, AJ Milici. Quantifying immune oncology markers across multiple tissue sections with digital image analysis. [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 4042.

Abstract 4162: Identifying T lymphocytes in IHC-stained tissues independently of CD3+ staining using morphometric features extracted by image analysis

Assessments of leukocyte populations in the context of cancer tissues are typically determined by staining for leukocyte subtype markers in formalin fixed tissues. This requires the identification, categorization, and localization of multiple leukocytes within tissue context utilizing multiple markers. Meeting these demands are challenging due to technical constraints on the number of individual markers which can be visualized or scored in a single slide, and the complexity of staining observed. The use of multispectral imaging with fluorescent multiplexed markers has better enabled the assessment of multiple markers in a single tissue section. However, these wet chemistry and image capture technologies are very complex, and can only be successfully implemented with significant laboratory infrastructure, specialty equipment, and experienced resources. For these reasons, this approach is not widely implementable in the clinical pathology laboratory setting, where such tests are oriented to a companion diagnostic utility. Approaches which rely on widely adopted chromogenic immunohistochemistry (IHC) staining are preferred in the clinical laboratory setting, but the number of assayable markers is limited to 1-3 unique markers in a single tissue. In order to create a useful approach that could be implemented in existing clinical laboratory workflow, Flagship Biosciences has developed an approach for deriving the complex endpoints often necessary in immuno-oncology studies which rely on 1-3 chromagenic stains and computer interpretation of the tissue using only hematoxylin counterstain to identify T-lymphocytes. In a proof-of-concept study, we utilized our Tissue Image Analysis (TIA) tools to identify morphometric parameters which could identify T-lymphocytes, independent of staining for the T-lymphocyte marker CD3. A cohort of non-small cell lung cancer (NSCLC) tissues was stained by CD3 IHC, and both CD3 and isotype-stained tissues were analyzed with Flagship9s CellMap™ software to capture the morphometric and staining features of cells in the tissues. The morphometric features which characterized CD3+ cells were used to approximate the T-lymphocyte population frequency in the isotype-stained tissues. This T-lymphocyte classification scheme was defined based on hematoxylin staining alone, and accuracy of T-lymphocyte classification was verified by CD3 staining. Based on this study, the method described herein could be utilized to reasonably estimate the frequency of T-lymphocyte subsets (e.g. CD4+, CD8+, etc.) or different marker-positive leukocyte (e.g. macrophages) subsets relative to the total T-lymphocyte population without an additional T-lymphocyte marker such as CD3. The approach could, therefore, provide an added dimension of analysis for tissues stained by IHC without adding complexity to the wet assay by necessitating a marker for T-lymphocytes. Citation Format: Nathan T. Martin, Joshua Black, Famke Aeffner, Logan Cerkovnik, Jasmeet Bajwa, Staci J. Kearney, Crystal Pulliam, A.J. Milici, Joseph Krueger. Identifying T lymphocytes in IHC-stained tissues independently of CD3+ staining using morphometric features extracted by image analysis. [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 4162.

Abstract C108: 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 relationship 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, Famke Aeffner. Quantifying PD-L1 spatial distribution signatures for patient selection approaches. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr C108.

Abstract C109: Quantitative analysis of multiple subtypes of immune system cells in cancer tissues

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 HE 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr C109.

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 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