Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association.

Tissue biomarkers have been of increasing utility for scientific research, diagnosing disease, and treatment response prediction. There has been a steady shift away from qualitative assessment toward providing more quantitative scores for these biomarkers. The application of quantitative image analysis has thus become an indispensable tool for in-depth tissue biomarker interrogation in these contexts. This white paper reviews current technologies being employed for quantitative image analysis, their application and pitfalls, regulatory framework demands, and guidelines established for promoting their safe adoption in clinical practice.

Integrated digital pathology and transcriptome analysis identifies molecular mediators of T-cell exclusion in ovarian cancer.

Close proximity between cytotoxic T lymphocytes and tumour cells is required for effective immunotherapy. However, what controls the spatial distribution of T cells in the tumour microenvironment is not well understood. Here we couple digital pathology and transcriptome analysis on a large ovarian tumour cohort and develop a machine learning approach to molecularly classify and characterize tumour-immune phenotypes. Our study identifies two important hallmarks characterizing T cell excluded tumours: 1) loss of antigen presentation on tumour cells and 2) upregulation of TGFß and activated stroma. Furthermore, we identify TGFß as an important mediator of T cell exclusion. TGFß reduces MHC-I expression in ovarian cancer cells in vitro. TGFß also activates fibroblasts and induces extracellular matrix production as a potential physical barrier to hinder T cell infiltration. Our findings indicate that targeting TGFß might be a promising strategy to overcome T cell exclusion and improve clinical benefits of cancer immunotherapy.

Ovarian Toxicity Assessment in Histopathological Images Using Deep Learning.

As ovarian toxicity is often a safety concern for cancer therapeutics, identification of ovarian pathology is important in early stages of preclinical drug development, particularly when the intended patient population include women of child-bearing potential. Microscopic evaluation by pathologists of hematoxylin and eosin (H&E)-stained tissues is the current gold standard for the assessment of organs in toxicity studies. However, digital pathology and advanced image analysis are being explored with greater frequency and broader applicability to tissue evaluations in toxicologic pathology. Our objective in this work was to develop an automated method that rapidly enumerates rat ovarian corpora lutea on standard H&E-stained slides with comparable accuracy to the gold standard assessment by a pathologist. Herein, we describe an algorithm generated by a deep learning network and tested on 5 rat toxicity studies, which included studies that both had and had not previously been diagnosed with effects on number of ovarian corpora lutea. Our algorithm could not only enumerate corpora lutea accurately in all studies but also revealed distinct trends for studies with and without reproductive toxicity. Our method could be a widely applied tool to aid analysis in general toxicity studies.

Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association.

In this white paper, experts from the Digital Pathology Association (DPA) define terminology and concepts in the emerging field of computational pathology, with a focus on its application to histology images analyzed together with their associated patient data to extract information. This review offers a historical perspective and describes the potential clinical benefits from research and applications in this field, as well as significant obstacles to adoption. Best practices for implementing computational pathology workflows are presented. These include infrastructure considerations, acquisition of training data, quality assessments, as well as regulatory, ethical, and cyber-security concerns. Recommendations are provided for regulators, vendors, and computational pathology practitioners in order to facilitate progress in the field. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Pan-PIM kinase inhibitors enhance Lenalidomide's anti-myeloma activity via cereblon-IKZF1/3 cascade.

Multiple myeloma remains an incurable disease, and continued efforts are required to develop novel agents and novel drug combinations with more effective anti-myeloma activity. Here, we show that the pan-PIM kinase inhibitors SGI1776 and CX6258 exhibit significant anti-myeloma activity and that combining a pan-PIM kinase inhibitor with the immunomodulatory agent lenalidomide in an in vivo myeloma xenograft mouse model resulted in synergistic myeloma cell killing without additional hematologic or hepatic toxicities. Further investigations indicated that treatment with a pan-PIM kinase inhibitor promoted increased ubiquitination and subsequent degradation of IKZF1 and IKZF3, two transcription factors crucial for survival of myeloma cells. Combining a pan-PIM kinase inhibitor with lenalidomide led to more effective degradation of IKZF1 and IKZF3 in multiple myeloma cell lines as well as xenografts of myeloma tumors. We also demonstrated that treatment with a pan-PIM kinase inhibitor resulted in increased expression of cereblon, and that knockdown of cereblon via a shRNA lentivirus abolished the effects of PIM kinase inhibition on the degradation of IKZF1 and IKZF3 and myeloma cell apoptosis, demonstrating a central role of cereblon in pan-PIM kinase inhibitor-mediated down-regulation of IKZF1 and IKZF3 and myeloma cell killing. These data elucidate the mechanism of pan-PIM kinase inhibitor mediated anti-myeloma effect and the rationale for the synergy observed with lenalidomide co-treatment, and provide justification for a clinical trial of the combination of pan-PIM kinase inhibitors and lenalidomide for the treatment of multiple myeloma.

An automated mapping method for Nissl-stained mouse brain histologic sections.

BACKGROUND: Histologic evaluation of the central nervous system is often a critical endpoint in in vivo efficacy studies, and is considered the essential component of neurotoxicity assessment in safety studies. Automated image analysis is a powerful tool that can radically reduce the workload associated with evaluating brain histologic sections. NEW METHOD: We developed an automated brain mapping method that identifies neuroanatomic structures in mouse histologic coronal brain sections. The method utilizes the publicly available Allen Brain Atlas to map brain regions on digitized Nissl-stained sections. RESULTS: The method's accuracy was first assessed by comparing the mapping results to structure delineations from the Franklin and Paxinos (FP) mouse brain atlas. Brain regions mapped from FP Nissl-stained sections and calculated volumes were similar to structure delineations and volumes derived from corresponding FP illustrations. We subsequently applied our method to mouse brain sections from an in vivo study where the hippocampus was the structure of interest. Nissl-stained sections were mapped and hippocampal boundaries transferred to adjacent immunohistochemically stained sections. Optical density quantification results were comparable to those from time-consuming, manually drawn hippocampal delineations on the IHC-stained sections. COMPARISON WITH EXISTING METHODS: Compared to other published methods, our method requires less manual input, and has been validated comprehensively using a secondary atlas, as well as manually annotated brain IHC sections from 68 study mice. CONCLUSIONS: We propose that our automated brain mapping method enables greater efficiency and consistency in mouse neuropathologic assessments.

An Automated Image Analysis Method to Quantify Veterinary Bone Marrow Cellularity on H&E Sections.

Bone marrow toxicity is a common finding when assessing safety of drug candidate molecules. Standard hematoxylin and eosin (H&E) marrow tissue sections are typically manually evaluated to provide a semiquantitative assessment of overall cellularity. Here, we developed an automated image analysis method that allows quantitative assessment of changes in bone marrow cell population in sternal bone. In order to test whether the method was repeatable and sensitive, we compared the automated method with manual subjective histopathology scoring of total cellularity in rat sternal bone marrow samples across 17 independently run studies. The automated method was consistent with manual scoring methodology for detecting altered bone marrow cellularity and, in multiple cases, identified changes at lower doses. The image analysis method allows rapid and more quantitative assessment of bone marrow toxicity compared to manual examination of H&E slides, making it an excellent tool to aid detection of bone marrow cell depletion in preclinical toxicologic studies.

Proof of Concept for an Automated Image Analysis Method to Quantify Rat Bone Marrow Hematopoietic Lineages on H&E Sections.

The bone marrow is an important site for assessment of the hematopoietic toxicity of new drug candidates. Here, we extended our previous work, where we developed a computer algorithm to automatically quantitate overall bone marrow cell density by analyzing digitized images of standard hematoxylin and eosin (H&E) slides of rat bone marrow and further evaluated the capability to quantify myeloid: erythroid + lymphoid (M:EL) ratio and megakaryocyte cell density. We tested the algorithm in a toxicity study, where rats were dosed with two molecules known to affect bone marrow composition, monomethyl auristatin E, and a Bcl-xL inhibitor. The image analysis method detected significant changes in M:EL and megakaryocyte number that were either not found or semiquantitatively described by manual microscopic observation of the same slides. The image analysis results were consistent with other more established but time-consuming methods that measure changes in bone marrow cell composition: smear cytology, flow cytometry, and microscopic assessment. Our work demonstrates the feasibility of a rapid and more quantitative assessment of changes in bone marrow cell lineage composition using a computer algorithm compared to microscopic examination of H&E-stained bone marrow sections.

An entirely automated method to score DSS-induced colitis in mice by digital image analysis of pathology slides.

The DSS (dextran sulfate sodium) model of colitis is a mouse model of inflammatory bowel disease. Microscopic symptoms include loss of crypt cells from the gut lining and infiltration of inflammatory cells into the colon. An experienced pathologist requires several hours per study to score histological changes in selected regions of the mouse gut. In order to increase the efficiency of scoring, Definiens Developer software was used to devise an entirely automated method to quantify histological changes in the whole H&E slide. When the algorithm was applied to slides from historical drug-discovery studies, automated scores classified 88% of drug candidates in the same way as pathologists' scores. In addition, another automated image analysis method was developed to quantify colon-infiltrating macrophages, neutrophils, B cells and T cells in immunohistochemical stains of serial sections of the H&E slides. The timing of neutrophil and macrophage infiltration had the highest correlation to pathological changes, whereas T and B cell infiltration occurred later. Thus, automated image analysis enables quantitative comparisons between tissue morphology changes and cell-infiltration dynamics.

High heregulin expression is associated with activated HER3 and may define an actionable biomarker in patients with squamous cell carcinomas of the head and neck.

PURPOSE: Tumors with oncogenic dependencies on the HER family of receptor tyrosine kinases (RTKs) often respond well to targeted inhibition. Our previous work suggested that many cell lines derived from squamous cell carcinomas of the head and neck (SCCHNs) depend on autocrine signaling driven by HER2/3 dimerization and high-level co-expression of HRG. Additionally, results from a Phase I trial of MEHD7495A, a dual-action antibody that blocks ligand binding to EGFR and HER3, suggest that high-level HRG expression was associated with clinical response in SCCHN patients. Here we explore the hypothesis that high-level HRG expression defines a subpopulation of SCCHNs with activated HER3. EXPERIMENTAL DESIGN: qRT-PCR expression profiling was performed on >750 tumors of diverse origin, including >150 therapy-naïve, primary, and recurrent SCCHNs. Activated HER3, defined by immunoprecipitation of phospho-HER3, was compared to HRG expression in SCCHN samples. Paracrine versus autocrine expression was evaluated using RNA-in situ hybridization. RESULTS: SCCHN tumors express the highest levels of HRG compared to a diverse collection of other tumor types. We show that high HRG expression is associated with activated HER3, whereas low HRG expression is associated with low HER3 activation in SCCHN tumors. Furthermore, HRG expression is higher in recurrent SCCHN compared to patient-matched therapy naïve specimens. CONCLUSIONS: HRG expression levels define a biologically distinct subset of SCCHN patients. We propose that high-level expression of HRG is associated with constitutive activation of HER3 in SCCHN and thus defines an actionable biomarker for interventions targeting HER3.

An automated method to quantify microglia morphology and application to monitor activation state longitudinally in vivo.

Microglia are specialized immune cells of the brain. Upon insult, microglia initiate a cascade of cellular responses including a characteristic change in cell morphology. To study the dynamics of microglia immune response in situ, we developed an automated image analysis method that enables the quantitative assessment of microglia activation state within tissue based solely on cell morphology. Per cell morphometric analysis of fluorescently labeled microglia is achieved through local iterative threshold segmentation, which reduces errors caused by signal-to-noise variation across large volumes. We demonstrate, utilizing systemic application of lipopolysaccharide as a model of immune challenge, that several morphological parameters, including cell perimeter length, cell roundness and soma size, quantitatively distinguish resting versus activated populations of microglia within tissue comparable to traditional immunohistochemistry methods. Furthermore, we provide proof-of-concept data that monitoring soma size enables the longitudinal assessment of microglia activation in the mouse neocortex imaged via 2-photon in vivo microscopy. The ability to quantify microglia activation automatically by shape alone allows unbiased and rapid analysis of both fixed and in vivo central nervous system tissue.

Phosphorous dysregulation induced by MEK small molecule inhibitors in the rat involves blockade of FGF-23 signaling in the kidney.

MEK, a kinase downstream of Ras and Raf oncogenes, constitutes a high priority target in oncology research. MEK small molecule inhibitors cause soft tissue mineralization in rats secondary to serum inorganic phosphorus (iP) elevation, but the molecular mechanism for this toxicity remains undetermined. We performed investigative studies with structurally distinct MEK inhibitors GEN-A and PD325901 (PD-901) in Sprague-Dawley rats. Our data support a mechanism that involves FGF-23 signal blockade in the rat kidney, causing transcriptional upregulation of 25-hydroxyvitamin D(3) 1-alpha-hydroxylase (Cyp27b1), the rate-limiting enzyme in vitamin D activation, and downregulation of 1,25-dihydroxyvitamin D(3) 24-hydroxylase (Cyp24a1), the enzyme that initiates the degradation of the active form of vitamin D. These transcriptional changes increase serum vitamin D levels, which in turn drive the increase in serum iP, leading to soft tissue mineralization in the rat.

Cortical microtubule contacts position the spindle in C. elegans embryos.

Interactions between microtubules and the cell cortex play a critical role in positioning organelles in a variety of biological contexts. Here we used Caenorhabditis elegans as a model system to study how cortex-microtubule interactions position the mitotic spindle in response to polarity cues. Imaging EBP-2::GFP and YFP::alpha-tubulin revealed that microtubules shrink soon after cortical contact, from which we propose that cortical adaptors mediate microtubule depolymerization energy into pulling forces. We also observe association of dynamic microtubules to form astral fibers that persist, despite the catastrophe events of individual microtubules. Computer simulations show that these effects, which are crucially determined by microtubule dynamics, can explain anaphase spindle oscillations and posterior displacement in 3D.