AUTHOR=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. 

TITLE=Digital quantitative tissue image analysis of hypoxia in resected pancreatic ductal adenocarcinomas

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.926497

DOI=10.3389/fonc.2022.926497

ISSN=2234-943X

ABSTRACT=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.