Joint analysis of histopathology image features and gene expression in breast cancer

BMC Bioinformatics

Research Article

Rolf Jaggi¹  Daniel Schwarz²  Ladislav Dušek²  Lenka Čápková²  Vlad Popovici²  Josef Feit²  Eva Budinská³ 

[1] Department of Clinical Research, Faculty of Medicine, University of Bern, Bern, Switzerland;Institute of Biostatistics and Analyses, Faculty of Medicine, Masarykova Univerzita, Kamenice 5, 62500, Brno, Czech Republic;Institute of Biostatistics and Analyses, Faculty of Medicine, Masarykova Univerzita, Kamenice 5, 62500, Brno, Czech Republic;RECETOX, Masarykova Univerzita, Kamenice 5, 62500, Brno, Czech Republic;

关键词: Histopathology images;    Image analysis;    Biomarker discovery;    Gene expression;    Multimodal data mining;   

DOI  :  10.1186/s12859-016-1072-z

 received in 2015-12-09, accepted in 2016-05-04,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundGenomics and proteomics are nowadays the dominant techniques for novel biomarker discovery. However, histopathology images contain a wealth of information related to the tumor histology, morphology and tumor-host interactions that is not accessible through these techniques. Thus, integrating the histopathology images in the biomarker discovery workflow could potentially lead to the identification of new image-based biomarkers and the refinement or even replacement of the existing genomic and proteomic signatures. However, extracting meaningful and robust image features to be mined jointly with genomic (and clinical, etc.) data represents a real challenge due to the complexity of the images.ResultsWe developed a framework for integrating the histopathology images in the biomarker discovery workflow based on the bag-of-features approach – a method that has the advantage of being assumption-free and data-driven. The images were reduced to a set of salient patterns and additional measurements of their spatial distribution, with the resulting features being directly used in a standard biomarker discovery application. We demonstrated this framework in a search for prognostic biomarkers in breast cancer which resulted in the identification of several prognostic image features and a promising multimodal (imaging and genomic) prognostic signature. The source code for the image analysis procedures is freely available.ConclusionsThe framework proposed allows for a joint analysis of images and gene expression data. Its application to a set of breast cancer cases resulted in image-based and combined (image and genomic) prognostic scores for relapse-free survival.

【 授权许可】

CC BY   
© Popovici et al. 2016

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