Palliative care is offered along with primary treatment to improve the quality of life of the patient by relieving the symptoms and stress of a serious illness such as cancer. As per modern definitions, palliative care is appropriate at any age and at any stage of the illness, regardless of the eventual outcome. Patient-reported outcomes (PRO), i.e., health status measurements reported directly by the patients or their proxies, and especially their availability in electronic form (ePROs), are gradually gaining popularity as building blocks of innovative palliative care interventions. This paper presents MyPal, an EC-funded collaborative research project that aims to exploit advanced eHealth technologies to develop and evaluate two novel ePRO-based general palliative care interventions for cancer patients. In particular, the paper presents: (1) a short overview of MyPal; (2) the target populations, i.e., adults suffering from chronic lymphocytic leukemia (CLL) or myelodysplastic syndromes (MDS), and children with solid or hematologic malignancies; (3) the ePRO-based interventions being designed for the target populations, (4) the eHealth platform for delivering the interventions under development, and (5) the international, multi-center clinical studies to be conducted for assessing these interventions, i.e., a randomized controlled trial (RCT) and an observational study for adults and children, respectively.

MOTIVATION: Breast cancer is the second leading cause of cancer death among women. Tumors, even of the same histopathological subtype, exhibit a high genotypic diversity that impedes therapy stratification and that hence must be accounted for in the treatment decision-making process. RESULTS: Here, we present ClinOmicsTrailbc, a comprehensive visual analytics tool for breast cancer decision support that provides a holistic assessment of standard-of-care targeted drugs, candidates for drug repositioning and immunotherapeutic approaches. To this end, our tool analyzes and visualizes clinical markers and (epi-)genomics and transcriptomics datasets to identify and evaluate the tumor's main driver mutations, the tumor mutational burden, activity patterns of core cancer-relevant pathways, drug-specific biomarkers, the status of molecular drug targets and pharmacogenomic influences. In order to demonstrate ClinOmicsTrailbc's rich functionality, we present three case studies highlighting various ways in which ClinOmicsTrailbc can support breast cancer precision medicine. ClinOmicsTrailbc is a powerful integrated visual analytics tool for breast cancer research in general and for therapy stratification in particular, assisting oncologists to find the best possible treatment options for their breast cancer patients based on actionable, evidence-based results. AVAILABILITY AND IMPLEMENTATION: ClinOmicsTrailbc can be freely accessed at https://clinomicstrail.bioinf.uni-sb.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The papers in this special section examine current advances on various fronts, focusing on reporting bioinformatics, analysis of molecular, genetic and/or clinical data pertaining to human cancer risk, prevention, outcomes or treatment response.

Motivation: Transcriptional regulators play a major role in most biological processes. Alterations in their activities are associated with a variety of diseases and in particular with tumor development and progression. Hence, it is important to assess the effects of deregulated regulators on pathological processes. Results: Here, we present REGulator-Gene Association Enrichment (REGGAE), a novel method for the identification of key transcriptional regulators that have a significant effect on the expression of a given set of genes, e.g. genes that are differentially expressed between two sample groups. REGGAE uses a Kolmogorov-Smirnov-like test statistic that implicitly combines associations between regulators and their target genes with an enrichment approach to prioritize the influence of transcriptional regulators. We evaluated our method in two different application scenarios, which demonstrate that REGGAE is well suited for uncovering the influence of transcriptional regulators and is a valuable tool for the elucidation of complex regulatory mechanisms. Availability and implementation: REGGAE is freely available at https://regulatortrail.bioinf.uni-sb.de. Supplementary information: Supplementary data are available at Bioinformatics online.

Cancer research has led to more cancer patients being cured, and many more enabled to live with their cancer. As such, some cancers are now considered a chronic disease, where patients and their families face the challenge to take an active role in their own care and in some cases in their treatment. To this direction the iManageCancer project aims to provide a cancer specific self-management platform designed according to the needs of patient groups while focusing, in parallel, on the wellbeing of the cancer patient. In this paper, we present the use-case requirements collected using a survey, a workshop and the analysis of three white papers and then we explain the corresponding system architecture. We describe in detail the main technological components of the designed platform, show the current status of development and we discuss further directions of research.

MOTIVATION: Gene set analysis has revolutionized the interpretation of high-throughput transcriptomic data. Nowadays, with comprehensive studies that measure multiple -omics from the same sample, powerful tools for the integrative analysis of multi-omics datasets are required. RESULTS: Here, we present GeneTrail2, a web service allowing the integrated analysis of transcriptomic, miRNomic, genomic and proteomic datasets. It offers multiple statistical tests, a large number of predefined reference sets, as well as a comprehensive collection of biological categories and enables direct comparisons between the computed results. We used GeneTrail2 to explore pathogenic mechanisms of Wilms tumors. We not only succeeded in revealing signaling cascades that may contribute to the malignancy of blastemal subtype tumors but also identified potential biomarkers for nephroblastoma with adverse prognosis. The presented use-case demonstrates that GeneTrail2 is well equipped for the integrative analysis of comprehensive -omics data and may help to shed light on complex pathogenic mechanisms in cancer and other diseases. AVAILABILITY AND IMPLEMENTATION: GeneTrail2 can be freely accessed under https://genetrail2.bioinf.uni-sb.de CONTACT: : [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Successful implementation of meaningful Clinical Decision Support (CDS) solutions in healthcare has the potential to reduce the knowledge gap between clinical research and practice, especially in a complex genetic disease such as cancer. While significant effort has been invested in the implementation of tools for CDS in the last few decades, their uptake in the clinic has been limited. The barriers to adoption have been extensively discussed in the literature. In oncology, current CDS solutions are not able to support the complex decisions required for stratification and personalized treatment of patients and to keep up with the high rate of change in therapeutic options and knowledge. We propose a CDS framework that facilitates the implementation of decision support that flexibly integrates a large variety of clinical models and can bring to the clinic comprehensive solutions leveraging the latest available knowledge. We include both literature-based models and models built within the p-medicine research project using the available comprehensive datasets from clinical trials and care. The solution is open to the biomedical community, enabling the reuse of existing models for third-party CDS implementations and for the development of new models, and supporting collaboration among modelers, CDS implementers, biomedical researchers and clinicians. To increase adoption and support the complexity of patient management along the care continuum, we also propose to support and leverage the clinical processes defined and adhered to by healthcare organizations. We design an architecture that extends the CDS framework with workflow modeling and execution functionality to leverage the existing clinical processes. The knowledge models are embedded in the workflow models and executed at the right time, when and where the recommendation is needed in the clinical process. Next to supporting the decisions, this solution supports by default the decision processes as well and exploits the knowledge embedded in those processes.

Wilms tumor, or nephroblastoma, is a cancer of the kidneys that typically occurs in children and rarely in adults. Around 10% of Wilms tumor patients are diagnosed having a concurrent syndrome that enhances the risk of Wilms tumor. A screening method for early detection of Wilms tumor in these patients would be beneficial, since the size or stage of a tumor is related to outcome. We introduce a miRNA pathway analysis methodology that takes into account the topology and regulation mechanisms of the gene regulatory networks and identify disrupted sub-paths in known pathways, using miRNA- expressions. The methodology was applied on a miRNA-expression study and a predictive model was developed, using machine-learning (decision-tree induction) approaches. The model is able to identify putative mechanisms that underlie and govern the Wilms tumor phenotype, and discriminate between diseased and healthy subjects. Initial experimental results are promising and in line with the relevant biomedical literature.

In oncology, the risk of Serious Adverse Events (SAEs) is an important factor in the decision for a certain treatment, because SAEs can be life threatening and/or they can reduce the quality of life to a large extent. Prediction models can be made, based on data available from clinical trials. Building such prediction models requires great effort and expertise. On the one hand, data mining expertise is required to mold the data, build and validate the models. On the other hand, clinical insight is required in order to select interesting features and judge the applicability of the model. The greater the involvement of the clinical expert in the generation of a model, the larger the chance that the model will be adopted for further investigation or use in clinical practice. This paper presents a framework which provides a graphical interface which helps a clinical expert to be largely involved in the generation of a (crude) first set of prediction models without great effort or data mining expertise. This allows unpromising paths of research to be easily discarded while promising models can be further teased out by a data mining expert.

Significant Virtual Physiological Human efforts and projects have been concerned with cancer modeling, especially in the European Commission Seventh Framework research program, with the ambitious goal to approach personalized cancer simulation based on patient-specific data and thereby optimize therapy decisions in the clinical setting. However, building realistic in silico predictive models targeting the clinical practice requires interactive, synergetic approaches to integrate the currently fragmented efforts emanating from the systems biology and computational oncology communities all around the globe. To further this goal, we propose an intelligent graphical workflow planning system that exploits the multiscale and modular nature of cancer and allows building complex cancer models by intuitively linking/interchanging highly specialized models. The system adopts and extends current standardization efforts, key tools, and infrastructure in view of building a pool of reliable and reproducible models capable of improving current therapies and demonstrating the potential for clinical translation of these technologies.

This paper outlines the major components and function of the technologically integrated oncosimulator developed primarily within the Advancing Clinico Genomic Trials on Cancer (ACGT) project. The Oncosimulator is defined as an information technology system simulating in vivo tumor response to therapeutic modalities within the clinical trial context. Chemotherapy in the neoadjuvant setting, according to two real clinical trials concerning nephroblastoma and breast cancer, has been considered. The spatiotemporal simulation module embedded in the Oncosimulator is based on the multiscale, predominantly top-down, discrete entity-discrete event cancer simulation technique developed by the In Silico Oncology Group, National Technical University of Athens. The technology modules include multiscale data handling, image processing, invocation of code execution via a spreadsheet-inspired environment portal, execution of the code on the grid, and the visualization of the predictions. A refining scenario for the eventual coupling of the oncosimulator with immunological models is also presented. Parameter values have been adapted to multiscale clinical trial data in a consistent way, thus supporting the predictive potential of the oncosimulator. Indicative results demonstrating various aspects of the clinical adaptation and validation process are presented. Completion of these processes is expected to pave the way for the clinical translation of the system.

Applying engineering precepts to biological systems has spawn the field of systems biology to investigate a network of interacting components, including the coordination of internal systems of living organisms such as endocrine, nervous, and respiratory with gene and gene product expression, and behavior and environmental factors, and understand how these components together contribute to the disease initiation and progression, biological development, and health. Proceeding from systems biology, systems medicine incorporates complex and dynamic biochemical, physiological, and environmental interactions between all components of disease and health that sustain living organisms. The current special issue includes a selected number of papers presented at the 12th IEEE International Conference on BioInformatics and BioEngineering (BIBE 2012), Nov. 11-13, 2012 under a special session with the same theme, in addition to papers submitted following an open call for papers. The Special Issue presents experiences as well as technological and scientific developments stemming from some flagship projects funded by the EU under the FP7 framework programme aiming to bring together researchers working in the fields of infrastructures and technologies for integrative biomedical research, ICT for predictive and translational medicine and the VPH community at large. A total of 15 papers are included under the following scientific subdomains: 1) mHealth,Wearable Systems and Telemonitoring Services (five papers), 2) Medical Imaging (four papers), and 3) Computational Biology (six papers).

The implementation of Clinical Decision Support (CDS) solutions is an important prerequisite for reducing the knowledge gap between clinical research and practice, especially in a complex genetic disease such as cancer. However, current CDS solutions are unable to support all the complex decisions required for personalized treatment of cancer patients and become quickly obsolete due to the high rate of change in therapeutic options and knowledge. Our CDS framework enables the development of decision support tools that flexibly integrate a large variety of multiscale models and can leverage the efforts of a large community of modellers. In our implementation, we combine community-developed models described in the literature (e.g. the St. Gallen stratification for early breast cancer) and models derived by mining the comprehensive datasets from clinical trials and care brought together in the p_Medicine collaborative research project. This framework and its underlying solution for models storage, management and execution will also constitute a platform for continuous validation of existing models on new data. Our goal is to enable the reuse of existing models for CDS and for the development of new models, and to support collaboration among modellers, CDS implementers, biomedical researchers and clinicians. We initially develop and deploy our solution in the context of the p-Medicine project in the oncology domain, but we aim to expand our scope and to reach out to a wide community of users in the biomedical area.

The ongoing need of IT support for advancing personalized medicine has led to a plethora of needs for developing new computational algorithms, informatics resource management infrastructures and tools for extracting patient specific clinico-genomic information, and more recently, predicting and optimizing the therapeutic outcome for the individual patient within the EC VPH initiative. This has led to an unprecedented explosion in proposed tools and models for personalized medicine which in turn need specific frameworks for categorizing, querying and accessing such resources in an interoperable and standardized fashion. The proposed personalized medicine workbench is part of the EC funded p-medicine project and aims to create a semantically annotated repository of tools specific to the advancement of personalized medicine by addressing the project's clinical scenarios. Central to this development is the inclusion of a wide range of tools for personalized medicine encompassing biostatistics, bioinformatics, multi-scale predictive modeling and image analysis clinical applications.

Improving the initial diagnosis and the assessment of response to treatment in malignant gliomas, while avoiding invasive methods as much as justifiable, is one major aspect actual research is focusing on. Imaging studies are used to calculate tumor volume and define vital, necrotic and cystic areas within a tumor. Though the visual interpretation of magnetic resonance (MR) images is based on qualitative observation of variation in signal intensity, a correlation of signal intensities with histological features of a tumor is not possible. Better methods are needed for a reliable interpretation of follow-up studies in single patients. Histograms of signal intensities might serve as a method adding quantitative data to the description of a tumor. Using DoctorEye software, tumors can be easily rendered and histograms of the signal intensities within a tumor as well as mean and median signal intensities are possible to calculate. Our results in glioblastoma suggest that these histograms are an innovative method of gaining new tumor-specific information without performing additional investigations in a patient. It can be an additional diagnostic tool in differentiating various intracranial lesions from each other, as well as in assessing response to treatment or progression of malignant glioma.

Mathematical and computational tumor dynamics models can provide considerable insight into the relative importance and interdependence of related biological mechanisms. They may also suggest the existence of optimal treatment windows in the generic setting. Nevertheless, they cannot be translated into clinical practice unless they undergo a strict and thorough clinical validation and adaptation. In this context one of the major actions of the EC funded project ldquoAdvancing Clinico-Genomic Trials on Cancerrdquo (ACGT) is dedicated to the development of a patient specific four dimensional multiscale tumor model mimicking the nephroblastoma tumor response to chemotherapeutic agents according to the SIOP 2001/GPOH clinical trial. Combined administration of vincristine and dactinomycin is considered. The patient#x2019;s pseudoanonymized imaging, histopathological, molecular and clinical data are carefully exploited. The paper briefly outlines the basics of the model developed by the In Silico Oncology Group and particularly stresses the effect of stem/clonogenic, progenitor and differentiated tumor cells on the overall tumor dynamics. The need for matching the cell category transition rates to the cell category relative populations of free tumor growth for an already large solid tumor at the start of simulation has been clarified. A technique has been suggested and succesfully applied in order to ensure satisfaction of this condition. The concept of a nomogram matching the cell category transition rates to the cell category relative populations at the treatment baseline is introduced. Convergence issues are addressed and indicative numerical results are presented. Qualitative agreement of the modelpsilas behavior with the corresponding clinical trial experience supports its potential to constitute the basis for an optimization system within the clinical environment following completion of its clinical validation and optimization. In silico treatment experimentation in the patient individualized context is expected to constitute the primary application of the model.

The potential of cancer multilevel modeling has been particularly emphasized over the past years. Integration of multiscale experimental and clinical information pertaining to cancer via advanced computer models seems to considerably accelerate optimization of cancer treatment in the patient individualized context. However, a sine qua non prerequisite for such models to reach clinical practice is to be thoroughly tested through clinical trials for validation and optimization purposes. This is one of the major goals of the European Commission funded ldquoadvancing clinico-genomic trials on cancerrdquo (ACGT) project. This paper presents a discrete state based, four dimensional, multiscale tumor dynamics model that has been specially developed by the in silico oncology group in order to mimick the trial of principle (TOP) clinical trial concerning breast cancer treated with epirubicin. The TOP trial constitutes one of the ACGT clinical trials. A substantial part of the model can address other tumor types as well. The actual pseudoanonymized imaging, histopathological, molecular and clinical data of the patient are exploited. Special emphasis is put on the effect of cancer stem/clonogenic, progenitor, differentiated and dead cells, the cell category transition rates and the cell category relative populations within the tumor from the treatment baseline onwards. The importance of adaptation of the cell category relative populations to the cell category transition rates for free tumor growth is revealed and the concept of a pertinent nomogram is introduced. A method which ensures adaptation of these two sets of entities at the beginning of the simulation execution is proposed and subsequently successfully applied. Convergence and code checking issues are addressed. Indicative parametric/sensitivity studies are presented along with specific numerical findings. The modelpsilas behavior substantiates its potential to serve as the basis of a treatment optimization system following an eventually succesful completion of the clinical validation and optimization process.

We present a new source of terminology for transnational data exchange in oncology, emphasizing the integration of both clinical and molecular data. In order to achieve best results in semantic interoperability, the ACGT project provides an ontology on cancer research and management. Besides examining pre-existing sources of terminology, were view methods of ontology development, and present best practices to be employed in the development of the ACGT Master Ontology. The clinical trial management system that is currently developed within ACGTconstitutes a central use of the ontology at this point.