Construction and analysis of tissue microarrays in the era of digital pathology: a pilot study targeting CDX1 and CDX2 in a colon cancer cohort of 612 patients. (13th January 2017) - Nolte, Sarah; Zlobec, Inti; Lugli, Alessandro; Hohenberger, Werner; Croner, Roland; Merkel, Susanne; Hartmann, Arndt; Geppert, Carol I; Rau, Tilman T
Abstract: CDX1 and CDX2 are possibly predictive biomarkers in colorectal cancer. We combined digitally‐guided (next generation) TMA construction (ngTMA) and the utility of digital image analysis (DIA) to assess accuracy, tumour heterogeneity and the selective impact of different combined intensity‐percentage levels on prognosis.CDX1 and CDX2 immunohistochemistry was performed on ngTMAs covering normal tissue, tumour centre and invasive front. The percentages of all epithelial cells per staining intensity per core were analysed digitally. Beyond classical prognosis analysis following REMARK guidelines, we investigated pre‐analytical conditions, three different types of heterogeneity (mosaic‐like, targeted and haphazard) and influences on cohort segregation and patient selection. The ngTMA‐DIA approach produced robust biomarker data with infrequent core loss and excellent on‐target punching. The detailed assessment of tumour heterogeneity could – except for a certain diffuse mosaic‐like heterogeneity – exclude differences between the invasive front and tumour centre, as well as detect haphazard clonal heterogeneous elements. Moreover, lower CDX1 and CDX2 counts correlated with mucinous histology, higher TNM stage, higher tumour grade and worse survival ( p  < 0.01, all). Different protein expression intensity levels shared comparable prognostic power and a great overlap in patient selection. The combination of ngTMA with DIA enhances accuracy and controls for biomarker analysis. Beyond the confirmation of CDX1 and CDX2 as prognostically relevant markers in CRC, this study highlights the greater robustness of CDX2 in comparison to CDX1. For the assessment of CDX2 protein loss, cut‐points as percentage data of complete protein loss can be deduced as a recommendation.