Digital quantitative tissue image analysis of hypoxia in resected pancreatic ductal adenocarcinomas - Siddiqui, Iram; Bilkey, Jade; McKee, Trevor D.; Serra, Stefano; Pintilie, Melania; Do, Trevor; Xu, Jing; Tsao, Ming-Sound; Gallinger, Steve; Hill, Richard P.; Hedley, David W.; Dhani, Neesha C. (Frontiers in Oncology, 2022)
Background: Tumor hypoxia is attributed to aggressive biology of pancreatic ductal adenocarcinoma (PDAC). We previously reported that hypoxia correlated with rapid tumor growth and metastasis in patient derived xenografts. Anticipating a prognostic relevance of hypoxia in patient tumors, we developed protocols for semi-quantitative image analysis to provide objective, observer-independent measure of hypoxia. We further validated a method which is most efficient and accurate to assess tumor hypoxia, and can be applied broadly. Methods: We studied the performance of three automated image analysis platforms in scoring pimonidazole-detectable hypoxia in resected PDAC (n=10). Multiple stained tumor sections were analyzed on 3 independent image-analysis platforms, Aperio Genie (AG), Definiens Tissue Studio (TS) and Definiens Developer (DD), which comprised of a customized rule-set. Results: The output from, AG had good concordance with manual scoring, but the work-flow was resource-intensive and not suited for high throughput analysis (1). TS analysis had high levels of variability related to misclassification of cells class, while the customized rule-set of DD had a high level of reliability with an intra-class co-efficient of more than 85%. Discussion: This work demonstrates the feasibility of developing a robust, high performance, pipeline for automated, quantitative scoring of pimonidazole-detectable hypoxia in patient tumors.