G. David Young

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.

Inducible expression of TGFβ, snail and Zeb1 recapitulates EMT in vitro and in vivo in a NSCLC model.

The progression of cancer from non-metastatic to metastatic is the critical transition in the course of the disease. The epithelial to mesenchymal transition (EMT) is a mechanism by which tumor cells acquire characteristics that improve metastatic efficiency. Targeting EMT processes in patients is therefore a potential strategy to block the transition to metastatic cancer and improve patient outcome. To develop models of EMT applicable to in vitro and in vivo settings, we engineered NCI-H358 non-small cell lung carcinoma cells to inducibly express three well-established drivers of EMT: activated transforming growth factor β (aTGFβ), Snail or Zeb1. We characterized the morphological, molecular and phenotypic changes induced by each of the drivers and compared the different end-states of EMT between the models. Both in vitro and in vivo, induction of the transgenes Snail and Zeb1 resulted in downregulation of epithelial markers and upregulation of mesenchymal markers, and reduced the ability of the cells to proliferate. Induced autocrine expression of aTGFβ caused marker and phenotypic changes consistent with EMT, a modest effect on growth rate, and a shift to a more invasive phenotype. In vivo, this manifested as tumor cell infiltration of the surrounding mouse stromal tissue. Overall, Snail and Zeb1 were sufficient to induce EMT in the cells, but aTGFβ induced a more complex EMT, in which changes in extracellular matrix remodeling components were pronounced.

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.

Quantitative assessment of pancreatic cancer precursor lesions in IHC-stained tissue with a tissue image analysis platform

Tissue image analysis (tIA) is emerging as a powerful tool for quantifying biomarker expression and distribution in complex diseases and tissues. Pancreatic ductal adenocarcinoma (PDAC) develops in a highly complex and heterogeneous tissue environment and, generally, has a very poor prognosis. Early detection of PDAC is confounded by limited knowledge of the pre-neoplastic disease stages and limited methods to quantitatively assess disease heterogeneity. We sought to develop a tIA approach to assess the most common PDAC precursor lesions, pancreatic intraepithelial neoplasia (PanIN), in tissues from KrasLSL-G12D/+; Trp53LSL-R172H/+; Pdx-Cre (KPC) mice, a validated model of PDAC development. tIA profiling of training regions of PanIN and tumor microenvironment (TME) cells was utilized to guide identification of PanIN/TME tissue compartment stratification criteria. A custom CellMap algorithm implementing these criteria was applied to whole-slide images of KPC mice pancreata sections to quantify p53 and Ki-67 biomarker staining in each tissue compartment as a proof-of-concept for the algorithm platform. The algorithm robustly identified a higher percentage of p53-positive cells in PanIN lesions relative to the TME, whereas no difference was observed for Ki-67. Ki-67 expression was also quantified in a human pancreatic tissue sample available to demonstrate the translatability of the CellMap algorithm to human samples. Together, our data demonstrated the utility of CellMap to enable objective and quantitative assessments, across entire tissue sections, of PDAC precursor lesions in preclinical and clinical models of this disease to support efforts leading to novel insights into disease progression, diagnostic markers, and potential therapeutic targets.

Abstract 2683: Cellmap: Tumor cell subpopulation analysis in immunohistochemistry stained tumor tissue

Proceedings: AACR 103rd Annual Meeting 2012‐‐ Mar 31‐Apr 4, 2012; Chicago, IL All potential factors within individual patients which contribute to a lack of response to a given therapy are not known, but cancer biologists have long hypothesized that distinct and disparate populations within the tumor can be selected for by therapy to outgrow and emerge as a resistant tumor. As more targeted therapies are being developed, the understanding of these subpopulations of cells within a tumor has become very important to clinical strategy. Thus, there is a need to effectively distinguish and evaluate different populations of cells in a tumor within formalin fixed tissue. These contextual evaluations are important for understanding the biology of a target, evaluating pharmacodynamic or surrogate efficacy markers, or evaluating biomarkers for a companion diagnostic approach. Immunohistochemistry (IHC) remains the most direct approach to evaluating biomarkers within tissue context, but requires a pathologist to subjectively separate the complex components of tumor tissue and the compartments of the tumor cells themselves to deliver a numerical score that is based on the staining intensity of a cell and the percentage of cells which stain. This output is considered qualitative, due to pathologist subjectivity in scoring sample regions, the inability to effectively discriminate minor differences in staining intensities for a biomarker, and the inability to deliver a dataset with sufficient sample size to overcome bias deficiencies. Furthermore, a significant amount of information content is lost in this score, eliminating the potential to identify and analyze discrete cell populations within a tumor that may be leading to refractory to therapy. In contrast, modern image analysis (IA) approaches can deliver a far more quantitative IHC score by objectively distinguishing tumor components and cellular compartments, detecting minor differences in staining intensity, and by performing this function across the whole tumor section. However, current IA approaches are designed only to report an average or thresholded intensity across the analyzed region, without reporting the cell-by-cell statistics required to identify discrete cell populations within a tumor. To answer this, we have designed Cellmap, which can analyze an IHC stained tumor tissue section which has been digitally imaged, and make multiparametric measurements about cell morphology and biomarker staining in every cell individually. This information can be reported tumor-wide, or within a specific component of the tumor, and/or within a compartment of the cell simultaneously. Cellmap can be used to make quantitative measurements which identify specific cells with specific signaling processes, and determine their location within a tumor section. This information can be used to identify and quantify discrete cell populations relevant to a disease hypothesis which are associated with a specific tumor microenvironment. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 2683. doi:1538-7445.AM2012-2683

Abstract 4160: Evaluating epithelial-to-mesenchymal transition (EMT) in the EL1-luc/EL1-SV40 T-antigen transgenic mouse model of pancreatic cancer

Epithelial-to-mesenchymal transition (EMT) is a biological function important in normal cellular processes such as embryonic development and wound healing. In cancer it is thought that the tumor cell machinery can re-activate these normal pathways resulting in more aggressive and invasive tumors. The loss of E-cadherin and the gain of vimentin are hallmarks which identify the process of EMT and have been shown to correlate with poor prognosis in multiple solid tumor types. While many preclinical models are utilized to evaluate mechanisms of tumorigenesis few in vivo models evaluating parameters of EMT have been described. The EL1-luc/EL1-SV40 T-antigen transgenic mouse represents a model of pancreatic cancer whereby mice develop tissue specific, spontaneous and bioluminescent pancreatic tumors. To evaluate whether EMT occurs in the EL1-luc/EL1-SV40 T-antigen model in vivo, we collected primary pancreatic tissue from male mice between 10 and 21 weeks of age. The tissue was formalin fixed, paraffin embedded and then utilized for histopathological endpoints such as Hemotoxylin and Eosin staining as well as immunohistochemistry for markers known to be involved in EMT such as E-cadherin and vimentin. We found the tumors to express both markers and become very heterogeneous over time. In early tumors E-cadherin expression is membrane localized and very high. Over time there are areas of the tumors that have reduced or lost E-cadherin expression. Vimentin expression was highly variable but when present tended to be highly expressed. In many of the later stage tumors there was substantial heterogeneity reflected by the appearance of multiple cell types within a tumor. We utilized the Aperio (Aperio Technologies, Vista, CA) slide scanner and software system to evaluate serial sections of tumor samples and found that in some sections of the tumor E-cadherin is present and vimentin is absent, whereas in other areas of the tumor vimentin is present in the absence of E-cadherin. Additionally, we identified areas of the tumor that seem to be expressing both markers which suggests that the EL1-luc/EL1-SV40 T-antigen transgenic mouse may recapitulate many aspects of EMT observed in vivo, thus offering a model system to study the signaling and molecular changes necessary for this process during cancer progression. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4160.