Personalized Oncology Suite: integrating next-generation sequencing data and whole-slide bioimages

BMC Bioinformatics

Zlatko Trajanoski¹  Benjamin Hiltpolt³  Stephan Pabinger²  Michael Sperk³  Matthias Baldauf¹  Andreas Dander¹ 

[1]Oncotyrol GmbH, Center for Personalized Cancer Medicine, Karl-Kapferer-Strasse 5, 6020 Innsbruck, Austria

[2]AIT-Austrian Institute of Technology, Health & Environment Department, Molecular Diagnostics, Muthgasse 11, 1190 Vienna, Austria

[3]Division for Bioinformatics, Biocenter, Innsbruck Medical University, Innrain 80-82, 6020 Innsbruck, Austria

关键词: Open-source;    Application;    Whole-slide bioimaging;    Next-generation sequencing;    Data integration;    Personalized oncology;   

Others  :  1085985
DOI  :  10.1186/1471-2105-15-306

 received in 2014-05-26, accepted in 2014-09-15,  发布年份 2014

【 摘 要 】

Background

Cancer immunotherapy has recently entered a remarkable renaissance phase with the approval of several agents for treatment. Cancer treatment platforms have demonstrated profound tumor regressions including complete cure in patients with metastatic cancer. Moreover, technological advances in next-generation sequencing (NGS) as well as the development of devices for scanning whole-slide bioimages from tissue sections and image analysis software for quantitation of tumor-infiltrating lymphocytes (TILs) allow, for the first time, the development of personalized cancer immunotherapies that target patient specific mutations. However, there is currently no bioinformatics solution that supports the integration of these heterogeneous datasets.

Results

We have developed a bioinformatics platform – Personalized Oncology Suite (POS) – that integrates clinical data, NGS data and whole-slide bioimages from tissue sections. POS is a web-based platform that is scalable, flexible and expandable. The underlying database is based on a data warehouse schema, which is used to integrate information from different sources. POS stores clinical data, genomic data (SNPs and INDELs identified from NGS analysis), and scanned whole-slide images. It features a genome browser as well as access to several instances of the bioimage management application Bisque. POS provides different visualization techniques and offers sophisticated upload and download possibilities. The modular architecture of POS allows the community to easily modify and extend the application.

Conclusions

The web-based integration of clinical, NGS, and imaging data represents a valuable resource for clinical researchers and future application in medical oncology. POS can be used not only in the context of cancer immunology but also in other studies in which NGS data and images of tissue sections are generated. The application is open-source and can be downloaded at http://www.icbi.at/POS webcite.

【 授权许可】

   
2014 Dander et al.; licensee BioMed Central Ltd.

【 图 表 】

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