Automated tumor analysis for molecular profiling in lung cancer

Peter W. Hamilton, Yinhai Wang, Clinton Boyd, Jacqueline A. James, Maurice B. Loughrey, Joseph P. Houghton, David P. Boyle, Paul Kelly, Perry Maxwell, David McCleary, James Diamond, Darragh G. McArt, Jonathon Tunstall, Peter Bankhead, Manuel Salto-Tellez

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29 Citations (Scopus)
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Abstract

The discovery and clinical application of molecular biomarkers in solid tumors, increasingly relies on nucleic acid extraction from FFPE tissue sections and subsequent molecular profiling. This in turn requires the pathological review of haematoxylin & eosin (H&E) stained slides, to ensure sample quality, tumor DNA sufficiency by visually estimating the percentage tumor nuclei and tumor annotation for manual macrodissection. In this study on NSCLC, we demonstrate considerable variation in tumor nuclei percentage between pathologists, potentially undermining the precision of NSCLC molecular evaluation and emphasising the need for quantitative tumor evaluation. We subsequently describe the development and validation of a system called TissueMark for automated tumor annotation and percentage tumor nuclei measurement in NSCLC using computerized image analysis. Evaluation of 245 NSCLC slides showed precise automated tumor annotation of cases using Tissuemark, strong concordance with manually drawn boundaries and identical EGFR mutational status, following manual macrodissection from the image analysis generated tumor boundaries. Automated analysis of cell counts for % tumor measurements by Tissuemark showed reduced variability and significant correlation (p < 0.001) with benchmark tumor cell counts. This study demonstrates a robust image analysis technology that can facilitate the automated quantitative analysis of tissue samples for molecular profiling in discovery and diagnostics.
Original languageEnglish
Number of pages9
JournalOncotarget
DOIs
Publication statusPublished - 03 Aug 2015

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