Journal:Personalized Oncology Suite: Integrating next-generation sequencing data and whole-slide bioimages
| Full article title | Personalized Oncology Suite: Integrating next-generation sequencing data and whole-slide bioimages |
|---|---|
| Journal | BMC Bioinformatics |
| Author(s) | Dander, Andreas; Baldauf, Matthias; Sperk, Michael; Pabinger, Stephan; Hiltpolt, Benjamin; Trajanoski, Zlatko |
| Author affiliation(s) | Innsbruck Medical University, AIT-Austrian Institute of Technology, Oncotyrol GmbH |
| Primary contact | Email: zlatko.trajanoski@i-med.ac.at |
| Year published | 2014 |
| Volume and issue | 15 |
| Page(s) | 306 |
| DOI | 10.1186/1471-2105-15-306 |
| ISSN | 1471-2105 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | http://www.biomedcentral.com/1471-2105/15/306 |
| Download | http://www.biomedcentral.com/content/pdf/1471-2105-15-306.pdf (PDF) |
 |
This article should not be considered complete until this message box has been removed. This is a work in progress. |
Abstract
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.
Keywords: Personalized oncology; Data integration; Next-generation sequencing; Whole-slide bioimaging; Application; Open-source
Background
Cancer immunotherapy has recently entered a remarkable renaissance phase with the approval of several agents for treatment.[1] Cancer immunotherapies that involve the use of the adaptive immune system, such as anti-checkpoint antibodies and adoptive T-cell therapies, have demonstrated profound tumor regressions including complete cure in patients with metastatic cancer. Technological advances in next-generation sequencing (NGS) as well as the development of devices for scanning whole-slide images 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. The use of NGS technologies to characterize tumor samples enables one not only to comprehensively study the interactions between human cancers and the immune system, but also to identify targets for patient stratification. Moreover, the quantitation of TILs will improve therapeutic efficacy, even in the absence of immunotherapy. It will enable a precise characterization of the immune infiltrates in the tumor and will help to identify mechanisms of tumor regression and disentangle the complex tumor-immune cell interactions. For example, understanding the molecular basis of the interactions between cytotoxic chemotherapeutics or targeted anti-cancer agents and the immune system is essential for the development of optimal therapeutic schemes and in the long run will result in clinical benefit for the patients.
However, the real value of the disparate datasets can be truly exploited only when the data are integrated. In our experience it is of utmost importance to establish a local database hosting only the necessary data. Only pre-processed and normalized data will be stored in a dedicated relational database whereas primary data are archived at separate locations including public repositories.[2] To this end, a database that integrates clinical, NGS, and bioimaging data would be extremely helpful for clinical cancer research and in near future also for routine applications in medical oncology. However, to the best of our knowledge there is currently no application that supports this integration. As of today there are different applications integrating either clinical data and NGS data or bioimages (Table 1) but no integrated solution has been created. We therefore developed the bioinformatics platform Personalized Oncology Suite (POS) to overcome this bottleneck and support the researchers working in this exciting field.
References
- ↑ Couzin-Frankel, J. (2013). "Cancer Immunotherapy". Science 342 (6165): 1432-1433. doi:10.1126/science.342.6165.1432. PMID 24357284.
- ↑ Hackl, H.; Stocker, G.; Charoentong, P.; Mlecnik, B.; Bindea, G.; Galon, J.; Trajanoski, Z. (2010). "Information technology solutions for integration of biomolecular and clinical data in the identification of new cancer biomarkers and targets for therapy". Pharmacology & Therapeutics 128 (3): 488–498. doi:10.1016/j.pharmthera.2010.08.012. PMID 20832425.
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.
