Validation of a Muscle-Specific Tissue Image Analysis Tool for Quantitative Assessment of Dystrophin Staining in Frozen Muscle Biopsies.

CONTEXT.­: Duchenne muscular dystrophy is a rare, progressive, and fatal neuromuscular disease caused by dystrophin protein loss. Common investigational treatment approaches aim at increasing dystrophin expression in diseased muscle. Some clinical trials include assessments of novel dystrophin production as a surrogate biomarker of efficacy, which may predict a clinical benefit from treatment. OBJECTIVES.­: To establish an immunofluorescent scanning and digital image analysis workflow that provides an objective approach for staining intensity assessment of the immunofluorescence dystrophin labeling and determination of the percentage of biomarker-positive fibers in muscle cryosections. DESIGN.­: Optimal and repeatable digital image capture was achieved by a rigorously qualified fluorescent scanning process. After scanning qualification, the MuscleMap (Flagship Biosciences, Westminster, Colorado) algorithm was validated by comparing high-power microscopic field total and dystrophin-positive fiber counts obtained by trained pathologists to data derived by MuscleMap. Next, the algorithm was tested on whole-slide images of immunofluorescent-labeled muscle sections from Duchenne muscular dystrophy, Becker muscular dystrophy, and control patients. RESULTS.­: When used under the guidance of a trained pathologist, the digital image analysis tool met predefined validation criteria and demonstrated functional and statistical equivalence with manual assessment. This work is the first, to our knowledge, to qualify and validate immunofluorescent scanning and digital tissue image-analysis workflow, respectively, with the rigor required to support the clinical trial environments. CONCLUSIONS.­: MuscleMap enables analysis of all fibers within an entire muscle biopsy section and provides data on a fiber-by-fiber basis. This will allow future clinical trials to objectively investigate myofibers' dystrophin expression at a greater level of consistency and detail.

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.

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 patient's 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 (HER2(cont)). 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.

The evaluation of E-Cadherin and vimentin as biomarkers of clinical outcomes among patients with non-small cell lung cancer treated with erlotinib as second- or third-line therapy.

E-Cadherin and vimentin protein expression was assessed in late stage non-small cell lung cancer tumors from the placebo controlled clinical trial, NCIC-CTG BR.21, to determine if these markers had the potential to predict outcome of erlotinib therapy. E-Cadherin and vimentin protein expression levels were assessed in tumors from 95 patients, who were representative of the overall population, using semi-quantitative immunohistochemistry. The percentage of tumor cells with grades 0, 1, 2, or 3 membrane staining of E-cadherin and cytoplasmic staining of vimentin was measured. Three scoring methods and multiple cut-offs were explored to determine if these markers were able to divide patients into groups with different overall survival (OS). A cut-off point for E-cadherin of ≥40% tumor cells with staining of +2 and +3 and a cut-off for vimentin of ≥10% of tumors cell with any staining provided the optimal stratification. The OS hazard ratio (HR) for E-cadherin(+) versus E-cadherin(-) in the erlotinib-treated patients was 0.68 (0.35-1.33) compared with 1.48 (0.69-3.15) in the placebo patients and the OS (HR) for erlotinib versus placebo was 0.47 (0.26-0.88) in E-cadherin(+) patients compared with 1.12 (0.52-2.44) in the E-cadherin(-) patients. The OS (HR) for vimentin(+) versus vimentin(-) in the erlotinib-treated patients was 0.65 (0.31-1.38) compared to 2.32 (1.09-4.94) in the placebo-treated patients and the OS (HR) for erlotinib versus placebo was 0.26 (0.11-0.63) in vimentin(+) compared to 0.99 (0.55-1.76) in the vimentin(-) patients. Similar trends were observed for progression-free survival and response rate. E-Cadherin and vimentin are biomarkers worthy of additional study as predictive markers of outcome of erlotinib therapy.

Inducible expression of TGFß, 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 ß (aTGFß), 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 aTGFß 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 aTGFß induced a more complex EMT, in which changes in extracellular matrix remodeling components were pronounced.