Informace o publikaci

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

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POPOVICI Vlad BUDINSKÁ Eva ČÁPKOVÁ Lenka SCHWARZ Daniel DUŠEK Ladislav FEIT Josef JAGGI Rolf

Druh Článek v odborném periodiku
Časopis / Zdroj BMC Bioinformatics
Fakulta / Pracoviště MU

Lékařská fakulta

Citace
Doi http://dx.doi.org/10.1186/s12859-016-1072-z
Obor Onkologie a hematologie
Klíčová slova Histopathology images; Image analysis; Biomarker discovery; Gene expression; Multimodal data mining
Popis Background: Genomics 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. Results: We 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. Conclusions: The 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.
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