Reproducible Biological Networks for Personalized Oncology

Die ständig wachsende Menge an Daten in den Biowissenschaften hat die Ära der wirklich personalisierten Medizin eingeleitet. Eine Medizin, in der Behandlungen auf den einzelnen Patienten zugeschnitten sind und therapeutische Entscheidungen auf datengestützten Krankheitsprofilen beruhen. Biologische Netzwerke ermöglichen die computergestützte Verarbeitung der Beziehungen und Eigenschaften zellulärer Prozesse. Sie helfen dabei, neue Eigenschaften und nachgelagerte Wirkungen von Mutationen und pharmazeutischen Eingriffen gleichermaßen aufzuklären. Netzwerkdarstellungen, die therapeutische Entscheidungen unterstützen, müssen in arbeitsintensiven Vorbereitungsphasen manuell zusammengestellt werden, bevor sie von Experten auf dem Gebiet diskutiert werden können. Biologische Netzwerke, die in wissenschaftlichen Veröffentlichungen genutzt oder präsentiert werden, sind häufig weder maschinenlesbar noch ist ihre Herkunft eindeutig ersichtlich. Dies erschwert die Reproduzierbarkeit der Ergebnisse und verhindert ihre effektive Weiterverwendung. Hier stellen wir SBML4j vor, eine serviceorientierte Anwendung, die anpassbare biologische Netzwerke bereitstellt. Sie lässt sich durch die vorhandene REST-basierte Schnittstelle und die intuitive Python-Bibliothek leicht in bestehende klinische Softwareprozesse integrieren. Wir demonstrieren dies anhand der Integration von SBML4j mit einer Annotationspipeline für klinische Varianten in ein webbasiertes Frontend. Dieses ermöglicht die visuelle Untersuchung von Varianten in ihrer genetischen Nachbarschaft. SBML4j erstellt Netzwerk-Mappings aus standardisierten und kuratierten systembiologischen Modellen und Pathways. Ein umfassender Provenance-Report für jedes von SBML4j bereitgestellte Netzwerk gewährleistet die Reproduzierbarkeit. Diese Provenance kann sogar nachverfolgt werden, wenn externe Anwendungen die von SBML4j bereitgestellten Netzwerke nutzen oder wenn extern erstellte Netzwerke als Derivate bestehender Netzwerke in SBML4j hochgeladen werden. Damit liefert SBML4j reproduzierbares biologisches Netzwerkwissen für Ansätze der personalisierten Medizin. Gleichzeitig ermöglicht es Forschungsgruppen, detaillierte Herkunftsinformationen und maschinenlesbare Darstellungen biologischer Netzwerke bereitzustellen. Als Open-Source-Projekt mit einer nicht restriktiven Lizenz wird SBML4j von einer aktiven Forschungsgemeinschaft weiterentwickelt und kann die Zukunft der personalisierten Medizin mitgestalten.An ever increasing amount of data in the life sciences sparked the era of truly personalized medicine. A medicine, where treatments are tailored to individual patients and therapeutic decisions are based on data driven disease profiles. Biological networks enable the computational processing of the relationships and properties of the cellular processes. They help to elucidate emerging properties and downstream effects of mutations and pharmaceutical interventions alike. Network representations of the cellular processes that aid therapeutic decisions have to be manually assembled in labor-intensive preparation phases, before they can be discussed by the experts in the field. In research, most publications that do report biological networks as part of their methodology or results, fail to adequately disclose the provenance of their networks. By not providing machine-readable representations of the processes they discuss, reproducibility of the findings is hampered. Here, we present SBML4j, a service-oriented application that provides customizable biological networks through annotation, filtering and various graph-algorithmic computations. It is easily integrated into existing clinical tool chains through the provided RESTful interface and the intuitive Python library. We demonstrate this with the integration of SBML4j with a clinical variant annotation pipeline and a web-based frontend for visual exploration of variants in their genetic neighborhood. SBML4j creates network mappings from standardized and curated systems biology models and pathways. By using well-defined biological qualifiers and ontologies, the provenance of the networks and their biological entities is clearly defined. A comprehensive provenance report for any network provided by SBML4j ensures reproducibility. This provenance can even be tracked when external applications consume networks that are provided by SBML4j or when externally created networks are uploaded to SBML4j as derivatives of existing networks. With this, SBML4j delivers reproducible biological-network knowledge to personalized medicine approaches. At the same time it enables research groups to provide detailed provenance information and machine-readable representations of biological networks. As an open-source project with a non-restrictive license, SBML4j will be further developed by an active research community and can help to shape the future of personalized medicine

Management of Cerebral Venous Thrombosis Due to Adenoviral COVID-19 Vaccination

Objective Cerebral venous thrombosis (CVT) caused by vaccine-induced immune thrombotic thrombocytopenia (VITT) is a rare adverse effect of adenovirus-based severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) vaccines. In March 2021, after autoimmune pathogenesis of VITT was discovered, treatment recommendations were developed. These comprised immunomodulation, non-heparin anticoagulants, and avoidance of platelet transfusion. The aim of this study was to evaluate adherence to these recommendations and its association with mortality. Methods We used data from an international prospective registry of patients with CVT after the adenovirus-based SARS-CoV-2 vaccination. We analyzed possible, probable, or definite VITT-CVT cases included until January 18, 2022. Immunomodulation entailed administration of intravenous immunoglobulins and/or plasmapheresis. Results Ninety-nine patients with VITT-CVT from 71 hospitals in 17 countries were analyzed. Five of 38 (13%), 11 of 24 (46%), and 28 of 37 (76%) of the patients diagnosed in March, April, and from May onward, respectively, were treated in-line with VITT recommendations (p < 0.001). Overall, treatment according to recommendations had no statistically significant influence on mortality (14/44 [32%] vs 29/55 [52%], adjusted odds ratio [OR] = 0.43, 95% confidence interval [CI] = 0.16-1.19). However, patients who received immunomodulation had lower mortality (19/65 [29%] vs 24/34 [70%], adjusted OR = 0.19, 95% CI = 0.06-0.58). Treatment with non-heparin anticoagulants instead of heparins was not associated with lower mortality (17/51 [33%] vs 13/35 [37%], adjusted OR = 0.70, 95% CI = 0.24-2.04). Mortality was also not significantly influenced by platelet transfusion (17/27 [63%] vs 26/72 [36%], adjusted OR = 2.19, 95% CI = 0.74-6.54). Conclusions In patients with VITT-CVT, adherence to VITT treatment recommendations improved over time. Immunomodulation seems crucial for reducing mortality of VITT-CVT. ANN NEUROL 2022Peer reviewe

Cerebral venous sinus thrombosis due to vaccine-induced immune thrombotic thrombocytopenia in middle-income countries

Background: Adenovirus-based COVID-19 vaccines are extensively used in low- and middle-income countries (LMICs). Remarkably, cases of cerebral venous sinus thrombosis due to vaccine-induced immune thrombotic thrombocytopenia (CVST-VITT) have rarely been reported from LMICs. Aims: We studied the frequency, manifestations, treatment, and outcomes of CVST-VITT in LMICs. Methods: We report data from an international registry on CVST after COVID-19 vaccination. VITT was classified according to the Pavord criteria. We compared CVST-VITT cases from LMICs to cases from high-income countries (HICs). Results: Until August 2022, 228 CVST cases were reported, of which 63 were from LMICs (all middle-income countries [MICs]: Brazil, China, India, Iran, Mexico, Pakistan, Turkey). Of these 63, 32 (51%) met the VITT criteria, compared to 103 of 165 (62%) from HICs. Only 5 of the 32 (16%) CVST-VITT cases from MICs had definite VITT, mostly because anti-platelet factor 4 antibodies were often not tested. The median age was 26 (interquartile range [IQR] 20–37) versus 47 (IQR 32–58) years, and the proportion of women was 25 of 32 (78%) versus 77 of 103 (75%) in MICs versus HICs, respectively. Patients from MICs were diagnosed later than patients from HICs (1/32 [3%] vs. 65/103 [63%] diagnosed before May 2021). Clinical manifestations, including intracranial hemorrhage, were largely similar as was intravenous immunoglobulin use. In-hospital mortality was lower in MICs (7/31 [23%, 95% confidence interval (CI) 11–40]) than in HICs (44/102 [43%, 95% CI 34–53], p = 0.039). Conclusions: The number of CVST-VITT cases reported from LMICs was small despite the widespread use of adenoviral vaccines. Clinical manifestations and treatment of CVST-VITT cases were largely similar in MICs and HICs, while mortality was lower in patients from MICs.</p

Sex differences in cerebral venous sinus thrombosis after adenoviral vaccination against COVID-19

Introduction: Cerebral venous sinus thrombosis associated with vaccine-induced immune thrombotic thrombocytopenia (CVST-VITT) is a severe disease with high mortality. There are few data on sex differences in CVST-VITT. The aim of our study was to investigate the differences in presentation, treatment, clinical course, complications, and outcome of CVST-VITT between women and men. Patients and methods: We used data from an ongoing international registry on CVST-VITT. VITT was diagnosed according to the Pavord criteria. We compared the characteristics of CVST-VITT in women and men. Results: Of 133 patients with possible, probable, or definite CVST-VITT, 102 (77%) were women. Women were slightly younger [median age 42 (IQR 28–54) vs 45 (28–56)], presented more often with coma (26% vs 10%) and had a lower platelet count at presentation [median (IQR) 50x109/L (28–79) vs 68 (30–125)] than men. The nadir platelet count was lower in women [median (IQR) 34 (19–62) vs 53 (20–92)]. More women received endovascular treatment than men (15% vs 6%). Rates of treatment with intravenous immunoglobulins were similar (63% vs 66%), as were new venous thromboembolic events (14% vs 14%) and major bleeding complications (30% vs 20%). Rates of good functional outcome (modified Rankin Scale 0-2, 42% vs 45%) and in-hospital death (39% vs 41%) did not differ. Discussion and conclusions: Three quarters of CVST-VITT patients in this study were women. Women were more severely affected at presentation, but clinical course and outcome did not differ between women and men. VITT-specific treatments were overall similar, but more women received endovascular treatment.</p

Sex differences in cerebral venous sinus thrombosis after adenoviral vaccination against COVID-19

Introduction: Cerebral venous sinus thrombosis associated with vaccine-induced immune thrombotic thrombocytopenia (CVST-VITT) is a severe disease with high mortality. There are few data on sex differences in CVST-VITT. The aim of our study was to investigate the differences in presentation, treatment, clinical course, complications, and outcome of CVST-VITT between women and men. Patients and methods: We used data from an ongoing international registry on CVST-VITT. VITT was diagnosed according to the Pavord criteria. We compared the characteristics of CVST-VITT in women and men. Results: Of 133 patients with possible, probable, or definite CVST-VITT, 102 (77%) were women. Women were slightly younger [median age 42 (IQR 28–54) vs 45 (28–56)], presented more often with coma (26% vs 10%) and had a lower platelet count at presentation [median (IQR) 50x109/L (28–79) vs 68 (30–125)] than men. The nadir platelet count was lower in women [median (IQR) 34 (19–62) vs 53 (20–92)]. More women received endovascular treatment than men (15% vs 6%). Rates of treatment with intravenous immunoglobulins were similar (63% vs 66%), as were new venous thromboembolic events (14% vs 14%) and major bleeding complications (30% vs 20%). Rates of good functional outcome (modified Rankin Scale 0-2, 42% vs 45%) and in-hospital death (39% vs 41%) did not differ. Discussion and conclusions: Three quarters of CVST-VITT patients in this study were women. Women were more severely affected at presentation, but clinical course and outcome did not differ between women and men. VITT-specific treatments were overall similar, but more women received endovascular treatment.</p

Evolving driving controllers using genetic programming

Abstract-Computational gaming requires the automatic generation of virtual opponents for different game levels. We have turned to artificial evolution to automatically generate such game players. In particular, we have used Genetic Programming to automatically evolve computer programs for computer gaming. With Genetic Programming, in theory, it is possible to generate any kind of program. The programs are not constrained as much as they are in other computational learning approaches, e.g. neural networks. We show how Genetic Programming improved upon a manually crafted race car driver (proportional controller). The open race car simulator TORCS was used to evaluate the virtual drivers

De novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet

BACKGROUND: With a growing amount of (multi-)omics data being available, the extraction of knowledge from these datasets is still a difficult problem. Classical enrichment-style analyses require predefined pathways or gene sets that are tested for significant deregulation to assess whether the pathway is functionally involved in the biological process under study. De novo identification of these pathways can reduce the bias inherent in predefined pathways or gene sets. At the same time, the definition and efficient identification of these pathways de novo from large biological networks is a challenging problem. RESULTS: We present a novel algorithm, DeRegNet, for the identification of maximally deregulated subnetworks on directed graphs based on deregulation scores derived from (multi-)omics data. DeRegNet can be interpreted as maximum likelihood estimation given a certain probabilistic model for de-novo subgraph identification. We use fractional integer programming to solve the resulting combinatorial optimization problem. We can show that the approach outperforms related algorithms on simulated data with known ground truths. On a publicly available liver cancer dataset we can show that DeRegNet can identify biologically meaningful subgraphs suitable for patient stratification. DeRegNet can also be used to find explicitly multi-omics subgraphs which we demonstrate by presenting subgraphs with consistent methylation-transcription patterns. DeRegNet is freely available as open-source software. CONCLUSION: The proposed algorithmic framework and its available implementation can serve as a valuable heuristic hypothesis generation tool contextualizing omics data within biomolecular networks