Image-based computational quantification and visualization of genetic alterations and tumour heterogeneity

Qing Zhong, Jan H. Rüschoff, Tiannan Guo, Maria Gabrani, Peter J. Schüffler, Markus Rechsteiner, Yansheng Liu, Thomas J. Fuchs, Niels J. Rupp, Christian Fankhauser, Joachim M. Buhmann, Sven Perner, Cédric Poyet, Miriam Blattner, Davide Soldini, Holger Moch, Mark A. Rubin, Aurelia Noske, Josef Rüschoff, Michael C. HaffnerWolfram Jochum, Peter J. Wild*

*Corresponding author for this work
20 Citations (Scopus)


Recent large-scale genome analyses of human tissue samples have uncovered a high degree of genetic alterations and tumour heterogeneity in most tumour entities, independent of morphological phenotypes and histopathological characteristics. Assessment of genetic copy-number variation (CNV) and tumour heterogeneity by fluorescence in situ hybridization (ISH) provides additional tissue morphology at single-cell resolution, but it is labour intensive with limited throughput and high inter-observer variability. We present an integrative method combining bright-field dual-colour chromogenic and silver ISH assays with an image-based computational workflow (ISHProfiler), for accurate detection of molecular signals, high-throughput evaluation of CNV, expressive visualization of multi-level heterogeneity (cellular, inter- and intra-tumour heterogeneity), and objective quantification of heterogeneous genetic deletions (PTEN) and amplifications (19q12, HER2) in diverse human tumours (prostate, endometrial, ovarian and gastric), using various tissue sizes and different scanners, with unprecedented throughput and reproducibility.

Original languageEnglish
Article number24146
JournalScientific Reports
Publication statusPublished - 07.04.2016


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