Applications of Pathology-Assisted Image Analysis of Immunohistochemistry-Based Biomarkers in Oncology

伴生诊断 免疫组织化学 数字化病理学 工作流程 生物标志物 生物标志物发现 病理 医学 癌症 生物信息学 计算生物学 计算机科学 生物 内科学 蛋白质组学 生物化学 数据库 基因
作者
Vaishali Shinde,Kristine Burke,Arijit Chakravarty,Mark D. Fleming,Alice McDonald,Allison Berger,J. Ecsedy,Stephen J. Blakemore,Stephen Tirrell,Doug Bowman
出处
期刊:Veterinary Pathology [SAGE]
卷期号:51 (1): 292-303 被引量:23
标识
DOI:10.1177/0300985813511124
摘要

Immunohistochemistry-based biomarkers are commonly used to understand target inhibition in key cancer pathways in preclinical models and clinical studies. Automated slide-scanning and advanced high-throughput image analysis software technologies have evolved into a routine methodology for quantitative analysis of immunohistochemistry-based biomarkers. Alongside the traditional pathology H-score based on physical slides, the pathology world is welcoming digital pathology and advanced quantitative image analysis, which have enabled tissue- and cellular-level analysis. An automated workflow was implemented that includes automated staining, slide-scanning, and image analysis methodologies to explore biomarkers involved in 2 cancer targets: Aurora A and NEDD8-activating enzyme (NAE). The 2 workflows highlight the evolution of our immunohistochemistry laboratory and the different needs and requirements of each biological assay. Skin biopsies obtained from MLN8237 (Aurora A inhibitor) phase 1 clinical trials were evaluated for mitotic and apoptotic index, while mitotic index and defects in chromosome alignment and spindles were assessed in tumor biopsies to demonstrate Aurora A inhibition. Additionally, in both preclinical xenograft models and an acute myeloid leukemia phase 1 trial of the NAE inhibitor MLN4924, development of a novel image algorithm enabled measurement of downstream pathway modulation upon NAE inhibition. In the highlighted studies, developing a biomarker strategy based on automated image analysis solutions enabled project teams to confirm target and pathway inhibition and understand downstream outcomes of target inhibition with increased throughput and quantitative accuracy. These case studies demonstrate a strategy that combines a pathologist's expertise with automated image analysis to support oncology drug discovery and development programs.

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