Shinde V, Burke K E, Chakravarty A, Fleming M, McDonald A A, Berger A, Ecsedy J, Blakemore S J, Tirrell S M, Bowman D
Pharmaceuticals International Co., Cambridge, MA 02139, USA. Email:
Vet Pathol. 2014 Jan;51(1):292-303. doi: 10.1177/0300985813511124. Epub 2013 Nov 14.
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.
基于免疫组织化学的生物标志物常用于了解临床前模型和临床研究中关键癌症通路的靶点抑制情况。自动玻片扫描和先进的高通量图像分析软件技术已发展成为基于免疫组织化学的生物标志物定量分析的常规方法。除了基于实体玻片的传统病理学H评分外,病理学领域也在接纳数字病理学和先进的定量图像分析,这些技术实现了组织和细胞水平的分析。实施了一个自动化工作流程,包括自动染色、玻片扫描和图像分析方法,以探索涉及两个癌症靶点的生物标志物:极光激酶A(Aurora A)和NEDD8激活酶(NAE)。这两个工作流程突出了我们免疫组织化学实验室的发展以及每种生物学检测的不同需求。对从MLN8237(Aurora A抑制剂)1期临床试验获得的皮肤活检样本进行有丝分裂和凋亡指数评估,同时对肿瘤活检样本评估有丝分裂指数以及染色体排列和纺锤体缺陷,以证明Aurora A受到抑制。此外,在临床前异种移植模型和NAE抑制剂MLN4924的急性髓系白血病1期试验中,一种新型图像算法的开发能够测量NAE抑制后下游通路的调节情况。在重点研究中,基于自动图像分析解决方案制定生物标志物策略,使项目团队能够确认靶点和通路抑制,并以更高的通量和定量准确性了解靶点抑制的下游结果。这些案例研究展示了一种将病理学家的专业知识与自动图像分析相结合的策略,以支持肿瘤学药物研发项目。