Frontiers in Oncology (Aug 2022)

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

  • Iram Siddiqui,
  • Jade Bilkey,
  • Trevor D. McKee,
  • Stefano Serra,
  • Melania Pintilie,
  • Trevor Do,
  • Jing Xu,
  • Ming-Sound Tsao,
  • Steve Gallinger,
  • Steve Gallinger,
  • Richard P. Hill,
  • David W. Hedley,
  • Neesha C. Dhani

DOI
https://doi.org/10.3389/fonc.2022.926497
Journal volume & issue
Vol. 12

Abstract

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BackgroundTumor hypoxia is theorized to contribute to the 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 automated semi-quantitative image analysis to provide an objective, observer-independent measure of hypoxia. We further validated this method which can reproducibly estimate pimonidazole-detectable hypoxia in a high-through put manner.MethodsWe studied the performance of three automated image analysis platforms in scoring pimonidazole-detectable hypoxia in resected PDAC (n = 10) in a cohort of patients enrolled in PIMO-PANC. Multiple stained tumor sections were analyzed on three independent image-analysis platforms, Aperio Genie (AG), Definiens Tissue Studio (TS), and Definiens Developer (DD), which comprised of a customized rule set.ResultsThe output from Aperio Genie (AG) had good concordance with manual scoring, but the workflow was resource-intensive and not suited for high-throughput analysis. 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 intraclass coefficient of more than 85%.DiscussionThis work demonstrates the feasibility of developing a robust, high-performance pipeline for an automated, quantitative scoring of pimonidazole-detectable hypoxia in patient tumors.

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