HLA-DR15 Molecules Jointly Shape an Autoreactive T Cell Repertoire in Multiple Sclerosis.

The HLA-DR15 haplotype is the strongest genetic risk factor for multiple sclerosis (MS), but our understanding of how it contributes to MS is limited. Because autoreactive CD4+ T cells and B cells as antigen-presenting cells are involved in MS pathogenesis, we characterized the immunopeptidomes of the two HLA-DR15 allomorphs DR2a and DR2b of human primary B cells and monocytes, thymus, and MS brain tissue. Self-peptides from HLA-DR molecules, particularly from DR2a and DR2b themselves, are abundant on B cells and thymic antigen-presenting cells. Furthermore, we identified autoreactive CD4+ T cell clones that can cross-react with HLA-DR-derived self-peptides (HLA-DR-SPs), peptides from MS-associated foreign agents (Epstein-Barr virus and Akkermansia muciniphila), and autoantigens presented by DR2a and DR2b. Thus, both HLA-DR15 allomorphs jointly shape an autoreactive T cell repertoire by serving as antigen-presenting structures and epitope sources and by presenting the same foreign peptides and autoantigens to autoreactive CD4+ T cells in MS.

Microbial peptides activate tumour-infiltrating lymphocytes in glioblastoma.

Microbial organisms have key roles in numerous physiological processes in the human body and have recently been shown to modify the response to immune checkpoint inhibitors1,2. Here we aim to address the role of microbial organisms and their potential role in immune reactivity against glioblastoma. We demonstrate that HLA molecules of both glioblastoma tissues and tumour cell lines present bacteria-specific peptides. This finding prompted us to examine whether tumour-infiltrating lymphocytes (TILs) recognize tumour-derived bacterial peptides. Bacterial peptides eluted from HLA class II molecules are recognized by TILs, albeit very weakly. Using an unbiased antigen discovery approach to probe the specificity of a TIL CD4+ T cell clone, we show that it recognizes a broad spectrum of peptides from pathogenic bacteria, commensal gut microbiota and also glioblastoma-related tumour antigens. These peptides were also strongly stimulatory for bulk TILs and peripheral blood memory cells, which then respond to tumour-derived target peptides. Our data hint at how bacterial pathogens and bacterial gut microbiota can be involved in specific immune recognition of tumour antigens. The unbiased identification of microbial target antigens for TILs holds promise for future personalized tumour vaccination approaches.

Diverse tumorigenic consequences of human papillomavirus integration in primary oropharyngeal cancers.

Human papillomavirus (HPV) causes 5% of all cancers and frequently integrates into host chromosomes. The HPV oncoproteins E6 and E7 are necessary but insufficient for cancer formation, indicating that additional secondary genetic events are required. Here, we investigate potential oncogenic impacts of virus integration. Analysis of 105 HPV-positive oropharyngeal cancers by whole-genome sequencing detects virus integration in 77%, revealing five statistically significant sites of recurrent integration near genes that regulate epithelial stem cell maintenance (i.e., SOX2, TP63, FGFR, MYC) and immune evasion (i.e., CD274). Genomic copy number hyperamplification is enriched 16-fold near HPV integrants, and the extent of focal host genomic instability increases with their local density. The frequency of genes expressed at extreme outlier levels is increased 86-fold within ±150 kb of integrants. Across 95% of tumors with integration, host gene transcription is disrupted via intragenic integrants, chimeric transcription, outlier expression, gene breaking, and/or de novo expression of noncoding or imprinted genes. We conclude that virus integration can contribute to carcinogenesis in a large majority of HPV-positive oropharyngeal cancers by inducing extensive disruption of host genome structure and gene expression.

Bioinformatics for precision oncology.

Molecular profiling of tumor biopsies plays an increasingly important role not only in cancer research, but also in the clinical management of cancer patients. Multi-omics approaches hold the promise of improving diagnostics, prognostics and personalized treatment. To deliver on this promise of precision oncology, appropriate bioinformatics methods for managing, integrating and analyzing large and complex data are necessary. Here, we discuss the specific requirements of bioinformatics methods and software that arise in the setting of clinical oncology, owing to a stricter regulatory environment and the need for rapid, highly reproducible and robust procedures. We describe the workflow of a molecular tumor board and the specific bioinformatics support that it requires, from the primary analysis of raw molecular profiling data to the automatic generation of a clinical report and its delivery to decision-making clinical oncologists. Such workflows have to various degrees been implemented in many clinical trials, as well as in molecular tumor boards at specialized cancer centers and university hospitals worldwide. We review these and more recent efforts to include other high-dimensional multi-omics patient profiles into the tumor board, as well as the state of clinical decision support software to translate molecular findings into treatment recommendations.

ClinVAP: a reporting strategy from variants to therapeutic options.

MOTIVATION: Next-generation sequencing has become routine in oncology and opens up new avenues of therapies, particularly in personalized oncology setting. An increasing number of cases also implies a need for a more robust, automated and reproducible processing of long lists of variants for cancer diagnosis and therapy. While solutions for the large-scale analysis of somatic variants have been implemented, existing solutions often have issues with reproducibility, scalability and interoperability. RESULTS: Clinical Variant Annotation Pipeline (ClinVAP) is an automated pipeline which annotates, filters and prioritizes somatic single nucleotide variants provided in variant call format. It augments the variant information with documented or predicted clinical effect. These annotated variants are prioritized based on driver gene status and druggability. ClinVAP is available as a fully containerized, self-contained pipeline maximizing reproducibility and scalability allowing the analysis of larger scale data. The resulting JSON-based report is suited for automated downstream processing, but ClinVAP can also automatically render the information into a user-defined template to yield a human-readable report. AVAILABILITY AND IMPLEMENTATION: ClinVAP is available at https://github.com/PersonalizedOncology/ClinVAP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile.

The gastrointestinal tracts of mammals are colonized by hundreds of microbial species that contribute to health, including colonization resistance against intestinal pathogens. Many antibiotics destroy intestinal microbial communities and increase susceptibility to intestinal pathogens. Among these, Clostridium difficile, a major cause of antibiotic-induced diarrhoea, greatly increases morbidity and mortality in hospitalized patients. Which intestinal bacteria provide resistance to C. difficile infection and their in vivo inhibitory mechanisms remain unclear. Here we correlate loss of specific bacterial taxa with development of infection, by treating mice with different antibiotics that result in distinct microbiota changes and lead to varied susceptibility to C. difficile. Mathematical modelling augmented by analyses of the microbiota of hospitalized patients identifies resistance-associated bacteria common to mice and humans. Using these platforms, we determine that Clostridium scindens, a bile acid 7α-dehydroxylating intestinal bacterium, is associated with resistance to C. difficile infection and, upon administration, enhances resistance to infection in a secondary bile acid dependent fashion. Using a workflow involving mouse models, clinical studies, metagenomic analyses, and mathematical modelling, we identify a probiotic candidate that corrects a clinically relevant microbiome deficiency. These findings have implications for the rational design of targeted antimicrobials as well as microbiome-based diagnostics and therapeutics for individuals at risk of C. difficile infection.

NGS-pipe: a flexible, easily extendable and highly configurable framework for NGS analysis.

Motivation: Next-generation sequencing is now an established method in genomics, and massive amounts of sequencing data are being generated on a regular basis. Analysis of the sequencing data is typically performed by lab-specific in-house solutions, but the agreement of results from different facilities is often small. General standards for quality control, reproducibility and documentation are missing. Results: We developed NGS-pipe, a flexible, transparent and easy-to-use framework for the design of pipelines to analyze whole-exome, whole-genome and transcriptome sequencing data. NGS-pipe facilitates the harmonization of genomic data analysis by supporting quality control, documentation, reproducibility, parallelization and easy adaptation to other NGS experiments. Availability and implementation: https://github.com/cbg-ethz/NGS-pipe. Contact: niko.beerenwinkel@bsse.ethz.ch.

Genomic Surveillance Reveals Diversity of Multidrug-Resistant Organism Colonization and Infection: A Prospective Cohort Study in Liver Transplant Recipients.

Background: Multidrug-resistant organisms (MDROs) are an important cause of morbidity and mortality after solid organ transplantation. We aimed to characterize MDRO colonization dynamics and infection in liver transplant (LT) recipients through innovative use of active surveillance and whole-genome sequencing (WGS). Methods: We prospectively enrolled consecutive adult patients undergoing LT from March 2014 to March 2016. Fecal samples were collected at multiple timepoints from time of enrollment to 12 months posttransplant. Samples were screened for carbapenem-resistant Enterobacteriaceae (CRE), Enterobacteriaceae resistant to third-generation cephalosporins (Ceph-RE), and vancomycin-resistant enterococci. We performed WGS of CRE and selected Ceph-RE isolates. We also collected clinical data including demographics, transplant characteristics, and infection data. Results: We collected 998 stool samples and 119 rectal swabs from 128 patients. MDRO colonization was detected in 86 (67%) patients at least once and was significantly associated with subsequent MDRO infection (0 vs 19.8%, P = .002). Child-Turcotte-Pugh score at LT and duration of post-LT hospitalization were independent predictors of both MDRO colonization and infection. Temporal dynamics differed between MDROs with respect to onset of colonization, clearance, and infections. We detected an unexpected diversity of CRE colonizing isolates and previously unrecognized transmission that spanned Ceph-RE and CRE phenotypes, as well as a cluster of mcr-1-producing isolates. Conclusions: Active surveillance and WGS showed that MDRO colonization is a highly dynamic and complex process after LT. Understanding that complexity is crucial for informing decisions regarding MDRO infection control, use of therapeutic decolonization, and empiric treatment regimens.

SwissMTB: establishing comprehensive molecular cancer diagnostics in Swiss clinics.

BACKGROUND: Molecular precision oncology is an emerging practice to improve cancer therapy by decreasing the risk of choosing treatments that lack efficacy or cause adverse events. However, the challenges of integrating molecular profiling into routine clinical care are manifold. From a computational perspective these include the importance of a short analysis turnaround time, the interpretation of complex drug-gene and gene-gene interactions, and the necessity of standardized high-quality workflows. In addition, difficulties faced when integrating molecular diagnostics into clinical practice are ethical concerns, legal requirements, and limited availability of treatment options beyond standard of care as well as the overall lack of awareness of their existence. METHODS: To the best of our knowledge, we are the first group in Switzerland that established a workflow for personalized diagnostics based on comprehensive high-throughput sequencing of tumors at the clinic. Our workflow, named SwissMTB (Swiss Molecular Tumor Board), links genetic tumor alterations and gene expression to therapeutic options and clinical trial opportunities. The resulting treatment recommendations are summarized in a clinical report and discussed in a molecular tumor board at the clinic to support therapy decisions. RESULTS: Here we present results from an observational pilot study including 22 late-stage cancer patients. In this study we were able to identify actionable variants and corresponding therapies for 19 patients. Half of the patients were analyzed retrospectively. In two patients we identified resistance-associated variants explaining lack of therapy response. For five out of eleven patients analyzed before treatment the SwissMTB diagnostic influenced treatment decision. CONCLUSIONS: SwissMTB enables the analysis and clinical interpretation of large numbers of potentially actionable molecular targets. Thus, our workflow paves the way towards a more frequent use of comprehensive molecular diagnostics in Swiss hospitals.

Proton Pump Inhibitors Alter Specific Taxa in the Human Gastrointestinal Microbiome: A Crossover Trial.

We conducted an open-label crossover trial to test whether proton pump inhibitors (PPIs) affect the gastrointestinal microbiome to facilitate Clostridium difficile infection (CDI). Twelve healthy volunteers each donated 2 baseline fecal samples, 4 weeks apart (at weeks 0 and 4). They then took PPIs for 4 weeks (40 mg omeprazole, twice daily) and fecal samples were collected at week 8. Six individuals took the PPIs for an additional 4 weeks (from week 8 to 12) and fecal samples were collected from all subjects at week 12. Samples were analyzed by 16S ribosomal RNA gene sequencing. We found no significant within-individual difference in microbiome diversity when we compared changes during baseline vs changes on PPIs. There were, however, significant changes during PPI use in taxa associated with CDI (increased Enterococcaceae and Streptococcaceae, decreased Clostridiales) and taxa associated with gastrointestinal bacterial overgrowth (increased Micrococcaceae and Staphylococcaceae). In a functional analysis, there were no changes in bile acids on PPIs, but there was an increase in genes involved in bacterial invasion. These alterations could provide a mechanism by which PPIs predispose to CDI. ClinicalTrials.gov ID NCT01901276.

Loss of Microbiota-Mediated Colonization Resistance to Clostridium difficile Infection With Oral Vancomycin Compared With Metronidazole.

Antibiotic administration disrupts the intestinal microbiota, increasing susceptibility to pathogens such as Clostridium difficile. Metronidazole or oral vancomycin can cure C. difficile infection, and administration of these agents to prevent C. difficile infection in high-risk patients, although not sanctioned by Infectious Disease Society of America guidelines, has been considered. The relative impacts of metronidazole and vancomycin on the intestinal microbiota and colonization resistance are unknown. We investigated the effect of brief treatment with metronidazole and/or oral vancomycin on susceptibility to C. difficile, vancomycin-resistant Enterococcus, carbapenem-resistant Klebsiella pneumoniae, and Escherichia coli infection in mice. Although metronidazole resulted in transient loss of colonization resistance, oral vancomycin markedly disrupted the microbiota, leading to prolonged loss of colonization resistance to C. difficile infection and dense colonization by vancomycin-resistant Enterococcus, K. pneumoniae, and E. coli. Our results demonstrate that vancomycin, and to a lesser extent metronidazole, are associated with marked intestinal microbiota destruction and greater risk of colonization by nosocomial pathogens.

Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota.

The intestinal microbiota is a microbial ecosystem of crucial importance to human health. Understanding how the microbiota confers resistance against enteric pathogens and how antibiotics disrupt that resistance is key to the prevention and cure of intestinal infections. We present a novel method to infer microbial community ecology directly from time-resolved metagenomics. This method extends generalized Lotka-Volterra dynamics to account for external perturbations. Data from recent experiments on antibiotic-mediated Clostridium difficile infection is analyzed to quantify microbial interactions, commensal-pathogen interactions, and the effect of the antibiotic on the community. Stability analysis reveals that the microbiota is intrinsically stable, explaining how antibiotic perturbations and C. difficile inoculation can produce catastrophic shifts that persist even after removal of the perturbations. Importantly, the analysis suggests a subnetwork of bacterial groups implicated in protection against C. difficile. Due to its generality, our method can be applied to any high-resolution ecological time-series data to infer community structure and response to external stimuli.

Intestinal microbiota containing Barnesiella species cures vancomycin-resistant Enterococcus faecium colonization.

Bacteria causing infections in hospitalized patients are increasingly antibiotic resistant. Classical infection control practices are only partially effective at preventing spread of antibiotic-resistant bacteria within hospitals. Because the density of intestinal colonization by the highly antibiotic-resistant bacterium vancomycin-resistant Enterococcus (VRE) can exceed 10(9) organisms per gram of feces, even optimally implemented hygiene protocols often fail. Decreasing the density of intestinal colonization, therefore, represents an important approach to limit VRE transmission. We demonstrate that reintroduction of a diverse intestinal microbiota to densely VRE-colonized mice eliminates VRE from the intestinal tract. While oxygen-tolerant members of the microbiota are ineffective at eliminating VRE, administration of obligate anaerobic commensal bacteria to mice results in a billionfold reduction in the density of intestinal VRE colonization. 16S rRNA gene sequence analysis of intestinal bacterial populations isolated from mice that cleared VRE following microbiota reconstitution revealed that recolonization with a microbiota that contains Barnesiella correlates with VRE elimination. Characterization of the fecal microbiota of patients undergoing allogeneic hematopoietic stem cell transplantation demonstrated that intestinal colonization with Barnesiella confers resistance to intestinal domination and bloodstream infection with VRE. Our studies indicate that obligate anaerobic bacteria belonging to the Barnesiella genus enable clearance of intestinal VRE colonization and may provide novel approaches to prevent the spread of highly antibiotic-resistant bacteria.

Acquired resistance to anti-PD1 therapy in patients with NSCLC associates with immunosuppressive T cell phenotype.

Immune checkpoint inhibitor treatment has the potential to prolong survival in non-small cell lung cancer (NSCLC), however, some of the patients develop resistance following initial response. Here, we analyze the immune phenotype of matching tumor samples from a cohort of NSCLC patients showing good initial response to immune checkpoint inhibitors, followed by acquired resistance at later time points. By using imaging mass cytometry and whole exome and RNA sequencing, we detect two patterns of resistance¨: One group of patients is characterized by reduced numbers of tumor-infiltrating CD8+ T cells and reduced expression of PD-L1 after development of resistance, whereas the other group shows high CD8+ T cell infiltration and high expression of PD-L1 in addition to markedly elevated expression of other immune-inhibitory molecules. In two cases, we detect downregulation of type I and II IFN pathways following progression to resistance, which could lead to an impaired anti-tumor immune response. This study thus captures the development of immune checkpoint inhibitor resistance as it progresses and deepens our mechanistic understanding of immunotherapy response in NSCLC.

Drug screening and genome editing in human pancreatic cancer organoids identifies drug-gene interactions and candidates for off-label treatment.

Pancreatic cancer (PDAC) is a highly aggressive malignancy for which the identification of novel therapies is urgently needed. Here, we establish a human PDAC organoid biobank from 31 genetically distinct lines, covering a representative range of tumor subtypes, and demonstrate that these reflect the molecular and phenotypic heterogeneity of primary PDAC tissue. We use CRISPR-Cas9 genome editing and drug screening to characterize drug-gene interactions with ARID1A and BRCA2. We find that missense- but not frameshift mutations in the PDAC driver gene ARID1A are associated with increased sensitivity to the kinase inhibitors dasatinib (p < 0.0001) and VE-821 (p < 0.0001). We conduct an automated drug-repurposing screen with 1,172 FDA-approved compounds, identifying 26 compounds that effectively kill PDAC organoids, including 19 chemotherapy drugs currently approved for other cancer types. We validate the activity of these compounds in vitro and in vivo. The in vivo validated hits include emetine and ouabain, compounds which are approved for non-cancer indications and which perturb the ability of PDAC organoids to respond to hypoxia. Our study provides proof-of-concept for advancing precision oncology and identifying candidates for drug repurposing via genome editing and drug screening in tumor organoid biobanks.

Vaccination with Designed Neopeptides Induces Intratumoral, Cross-reactive CD4+ T-cell Responses in Glioblastoma.

PURPOSE: The low mutational load of some cancers is considered one reason for the difficulty to develop effective tumor vaccines. To overcome this problem, we developed a strategy to design neopeptides through single amino acid mutations to enhance their immunogenicity. EXPERIMENTAL DESIGN: Exome and RNA sequencing as well as in silico HLA-binding predictions to autologous HLA molecules were used to identify candidate neopeptides. Subsequently, in silico HLA-anchor placements were used to deduce putative T-cell receptor (TCR) contacts of peptides. Single amino acids of TCR contacting residues were then mutated by amino acid replacements. Overall, 175 peptides were synthesized and sets of 25 each containing both peptides designed to bind to HLA class I and II molecules applied in the vaccination. Upon development of a tumor recurrence, the tumor-infiltrating lymphocytes (TIL) were characterized in detail both at the bulk and clonal level. RESULTS: The immune response of peripheral blood T cells to vaccine peptides, including natural peptides and designed neopeptides, gradually increased with repetitive vaccination, but remained low. In contrast, at the time of tumor recurrence, CD8+ TILs and CD4+ TILs responded to 45% and 100%, respectively, of the vaccine peptides. Furthermore, TIL-derived CD4+ T-cell clones showed strong responses and tumor cell lysis not only against the designed neopeptide but also against the unmutated natural peptides of the tumor. CONCLUSIONS: Turning tumor self-peptides into foreign antigens by introduction of designed mutations is a promising strategy to induce strong intratumoral CD4+ T-cell responses in a cold tumor like glioblastoma.

OptiTope--a web server for the selection of an optimal set of peptides for epitope-based vaccines.

Epitope-based vaccines (EVs) have recently been attracting significant interest. They trigger an immune response by confronting the immune system with immunogenic peptides derived from, e.g. viral- or cancer-related proteins. Binding of these peptides to proteins from the major histocompatibility complex (MHC) is crucial for immune system activation. However, since the MHC is highly polymorphic, different patients typically bind different repertoires of peptides. Furthermore, economical and regulatory issues impose strong limitations on the number of peptides that can be included in an EV. Hence, it is crucial to identify the optimal set of peptides for a vaccine, given constraints such as MHC allele probabilities in the target population, peptide mutation rates and maximum number of selected peptides. OptiTope aims at assisting immunologists in this critical task. With OptiTope, we provide an easy-to-use tool to determine a provably optimal set of epitopes with respect to overall immunogenicity in a specific individual (personalized medicine) or a target population (e.g. a certain ethnic group). OptiTope is available at http://www.epitoolkit.org/optitope.

Tracing Clonal Dynamics Reveals that Two- and Three-dimensional Patient-derived Cell Models Capture Tumor Heterogeneity of Clear Cell Renal Cell Carcinoma.

BACKGROUND: Extensive DNA sequencing has led to an unprecedented view of the diversity of individual genomes and their evolution among patients with clear cell renal cell carcinoma (ccRCC). OBJECTIVE: To understand subclonal architecture and dynamics of patient-derived two-dimensional (2D) and three-dimensional (3D) ccRCC models in vitro, in order to determine whether they mirror ccRCC inter- and intratumor heterogeneity. DESIGN, SETTING, AND PARTICIPANTS: We have established a comprehensive platform of living renal cancer cell models from ccRCC surgical specimens. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We confirmed the concordance of 2D and 3D patient-derived cell (PDC) models with the original tumor tissue in terms of histology, biomarker expression, cancer driver mutations, and copy number alterations. We addressed inter- and intrapatient heterogeneity by analyzing clonal dynamics during serial passaging. RESULTS AND LIMITATIONS: In-depth genetic characterization verified the presence of heterogeneous cell populations, and revealed a high degree of similarity between subclonal compositions of monolayer and organoid cell cultures and the corresponding parental ccRCCs. Clonal dynamics were evident during serial passaging of cells in vitro, suggesting that PDC cultures can offer insights into evolutionary potential and treatment susceptibility of ccRCC subclones in vivo. Proof-of-concept drug profiling using selected ccRCC-targeted therapy agents highlighted patient-specific vulnerabilities in PDC models that could not be anticipated by interrogating commercially available cell lines. CONCLUSIONS: We demonstrate that PDC models mirror inter- and intratumor heterogeneity of ccRCC in vitro. Based on our findings, we envision that the use of these models will advance our understanding of the trajectories that cause genetic diversity and their consequences for treatment on an individual level. PATIENT SUMMARY: In this study, we developed two- and three-dimensional patient-derived models from clear cell renal cell carcinoma (ccRCC) as "mini-tumors in a dish." We show that these cell models retain important features of the human ccRCCs such as the profound tumor heterogeneity, thus highlighting their importance for cancer research and precision medicine.

The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support.

The application and integration of molecular profiling technologies create novel opportunities for personalized medicine. Here, we introduce the Tumor Profiler Study, an observational trial combining a prospective diagnostic approach to assess the relevance of in-depth tumor profiling to support clinical decision-making with an exploratory approach to improve the biological understanding of the disease.

Inferring latent task structure for Multitask Learning by Multiple Kernel Learning.

BACKGROUND: The lack of sufficient training data is the limiting factor for many Machine Learning applications in Computational Biology. If data is available for several different but related problem domains, Multitask Learning algorithms can be used to learn a model based on all available information. In Bioinformatics, many problems can be cast into the Multitask Learning scenario by incorporating data from several organisms. However, combining information from several tasks requires careful consideration of the degree of similarity between tasks. Our proposed method simultaneously learns or refines the similarity between tasks along with the Multitask Learning classifier. This is done by formulating the Multitask Learning problem as Multiple Kernel Learning, using the recently published q-Norm MKL algorithm. RESULTS: We demonstrate the performance of our method on two problems from Computational Biology. First, we show that our method is able to improve performance on a splice site dataset with given hierarchical task structure by refining the task relationships. Second, we consider an MHC-I dataset, for which we assume no knowledge about the degree of task relatedness. Here, we are able to learn the task similarities ab initio along with the Multitask classifiers. In both cases, we outperform baseline methods that we compare against. CONCLUSIONS: We present a novel approach to Multitask Learning that is capable of learning task similarity along with the classifiers. The framework is very general as it allows to incorporate prior knowledge about tasks relationships if available, but is also able to identify task similarities in absence of such prior information. Both variants show promising results in applications from Computational Biology.