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

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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:
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
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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

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


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.

Table 1. This table compares POS with other applications in the context of data integration
Application Edit Clinical TNM Sequencing Vis. Bioimages Public Ref.
BioIMAX Y N N N N Whole-slide N [3]
Bisque Y N N N N Whole-slide N [4]
Galaxy LIMS Y N N Y N N N [5]
NG6 N N N Raw 454 and HiSeq N N N [6]
NGS tools Y N N Raw Illumina N N DAS[7] [8]
ONCO-i2b2 N Y Y N N N SNOMED [10]
openBIS Y N N Raw Illumina N N N [11]
Taverna N N N Y N N Y [12]
POS Y Y Y vcf Files Y Whole-slide COSMIC[13] -
The columns Clinical and TNM show if these data types are available. Sequencing depicts which type of next-generation sequencing data can be uploaded, and the column Vis. shows if mutations can be visualized. The column Bioimages shows which type of images can be used and the final column Public states available public annotations.


In order to be scalable, flexible, and expandable, POS makes use of state of the art software engineering techniques and architectures like the Java Enterprise Edition 6 (J2EE 6) technology stack. It is a web-based platform relying on the JBoss Application Server in version 7.1.1. The modular three-tier architecture (web frontend, application core, database backend) and the release under the open-source license GNU AGPL enables the community to easily modify and extend the application with further functionalities. Figure 1 outlines the software architecture of POS and depicts the main used libraries. PrimeFaces and PrimeFaces Extensions are used for the creation of JSF components, whereas Hibernate Validator provides input validation of user entries. As access scopes of Java Beans are crucial within JavaEE applications, Apache CODI is used to include additional scopes. Due to the fact that POS deals with different types of collections, the Guava libraries were chosen to support POS with a set of helpful functionalities regarding collections. Used Bisque instances are shown at the top of Figure 1. Furthermore, the standalone application POS Image Uploader allows batch uploading of numerous images at once.

Fig1 Dander BMCBioinformatics2014 15.jpg

Figure 1: Software architecture of POS. The JBoss Application Server of POS is shown as the central rectangle containing different JSF libraries.
On the right hand side the attached Authorization and Authentication System (AAS) is depicted. POS uses PostgreSQL as database management
system and applies EclipseLink for the object-relational mapping. The Bioconductor package Gviz handles rendering of the genome browser
tracks, and the R package Rserver provides an R server available through a network connection. On top, distributed Bisque instances are shown.
The POS Image Uploader, a JavaFX based standalone application, is outlined on the top left in the figure. This application enables users
to upload several images at once.

The underlying database is based on a data warehouse schema. This schema was chosen as it is widely used for integrating information from different sources. All defined entities are outlined in Additional file 1. POS uses the Java library EclipseLink for object-relational mapping. In the default configuration POS runs with PostgreSQL, but can be easily exchanged with another relational database.

Additional file 1. Database schema of the Personalized Oncology Suite.
The database schema of POS is based on a data warehouse schema. Therefore, the central entity represents patients. Each patient belongs to an institute, which stores the externalid referencing an institute within the attached Authorization and Authentication System. It can be seen that each institute can be connected to an imagerepository containing information about the connection to a Bisque instance. The entity institutesharing manages information about shared data. Clinicaldata as well as tnm and immunoscore for staging of cancer are related to the entity patient. For the integration of somatic mutations within POS the entities snp and indel are used. The entity analysis contains metadata about the next-generation sequencing itself. Image manages the attributes externalimageid and externalresourceid which are IDs used for accessing the image within Bisque. The attached imagetype contains information about the staining of the image. All entities with a name like <name>_audittrail hold information about documented changes made to the attached entity <name>. It is shown that the timestamp, the name of the author and the performed changes are recorded within these entities. Several entities comprise a deleted flag. If such an entity gets deleted it will not be removed within the database, but will not be shown in the frontend. This has the advantage that deleted entities can be restored by a database administrator.

Format: PNG; Size: 1.5MB Download file

Results and discussion

The Personalized Oncology Suite is an application combining biological and clinical data into one integrated solution. In this context, clinical data comprises information about cancer patients, TNM staging, and density values of TILs used for immune score estimation. Biological data describes mutations found via next-generation sequencing and whole-slide bioimages, which can be uploaded to the application. POS features different types of visualization techniques for all integrated data types. Furthermore, publicly available data from the COSMIC database[13] is integrated into POS. As data types are stored in different file formats, POS includes several data import and export possibilities for the most important formats. In addition, different filters on the data can be applied either individually or in combination (e.g. age at diagnosis and TNM stage). In the current implementation queries can be done only for single modalities (e.g. genetic features, images, or clinical parameters). Furthermore, patients can be selected based on their UICC stage or Immunoscore by using a range slider. POS features different user interface languages using internationalization and is fault tolerant as all inputs are validated. Furthermore, POS implements exception handling as well as an intelligent logging functionality.

POS has been released under the open-source license GNU AGPL to allow integration of new components from the scientific community. The source code can be accessed via the project website Figure 2 outlines the different layers between the JSF based frontend and the relational database.

Fig2 Dander BMCBioinformatics2014 15.jpg

Figure 2: Software layers of POS. a) The JSF based presentation layer on top (blue) access the relational database (green) via the Java backend. This backend
can be accessed via the classes Controller or LazyDataModel. These classes make use of the DataAccessObject which provides a single access point to the
relational database by using object-relational mapping. b) The Controller is responsible for secure CRUD (create, read, update and delete) operations.
c) The LazyDataModel is used for providing immutable, filtered, sorted, and paginated collections to the presentation layer.

Clinical data and tumor staging

POS integrates clinical data of cancer patients including various attributes such as gender, date of diagnosis, disease duration, adjuvant therapy, and relapse. We have included compliant measures in the design of the software ensuring that patient-identifying data such as name, academic title, address, telephone number, e-mail address, date and place of birth, as well as date and place of death[14] are not collected. Patients are uniquely defined by alphanumerical identifiers. Thus, data from multiple clinical visits or any other information can be unambiguously assigned to an individual patient.

Tumor staging

In addition to clinical data, POS integrates the TNM cancer staging system. The T, N, and M categories are stored separately within the database and the resulting AJCC/UICC stage (0-IV) is determined from this information. POS facilitates the creation of descriptive plots for comparing patients in different TNM stages. These plots can also be used for comparison of data among different participating institutes, provided the user has permission to access the information. In addition to input data manually, clinical and staging data can be uploaded to POS in CSV format. Furthermore, these data types can be exported as XLS, CSV and PDF files.

Next-generation sequencing data

With the use of next-generation sequencing, biologists are able to determine the order of nucleotide bases composing the DNA. The identification of somatic mutations can be performed by sequencing tumor/normal pairs and subsequently comparing cancerous to healthy tissue. Several different applications exist which are able to analyze tumor/normal pairs regarding their somatic SNPs and INDELs.[15] Since there are a plethora of methods for analyses of NGS data, the design of the software focused on the integration of disparate data types rather than the analyses of raw data. Furthermore, as the methods and available tools improve at fast pace, integration of processed data makes the system more flexible and versatile. POS is able to integrate somatic SNPs and INDELs identified by such experiments. For uploading this type of data, users first need to define an analysis, which can be used to attach called mutations. POS supports data upload via VCF (Variant Call Format) files or by manual input of mutations.

Genome browser

As visualization of identified mutations tremendously supports the interpretation of these results, genome browsers have been developed to display mutations in the context of a reference genome.[15] POS includes a genome browser that features a combined view of different tracks, each containing a dedicated plot. Figure 3 shows a screenshot of the genome browser view within POS. On top of the genome browser panel, the region of interest can be specified by defining chromosome number, start and end position or by choosing a gene of interest. The user can select several patients at once and mutations for each patient will be displayed in separate tracks.

Fig3 Dander BMCBioinformatics2014 15.jpg

Figure 3: View of the genome browser. A genome browser visualizing mutations in the context of the reference genome including publicly available annotations is shown. The first track depicts the ideogram of the chosen chromosome, followed by an axis showing its genomic coordinates. Next, publicly available annotations derived from BioMart[16] are depicted. The bottom track holds information about uploaded mutations. The patient name and the shown mutations were randomly generated.


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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. Table 1 has also been modified to a Y/N format from its original check mark/X format.