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OncoRisk: a state-of-the-art web server for bridging the oncogenic databases and pan-cancer cohorts to the translational oncology
KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.ORCID iD: 0009-0001-3893-682X
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science. KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0009-0002-7604-6139
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology. KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0009-0003-1654-5216
Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum, Turkey.
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2026 (English)In: Communications Biology, E-ISSN 2399-3642, Vol. 9, no 1, article id 519Article in journal (Refereed) Published
Abstract [en]

Accurate interpretation of genomic variants remains a major bottleneck in precision oncology, due in part to fragmented knowledge across databases and limited integration between clinical evidence and population-scale genomic datasets. Here we present OncoRisk, a stand-alone, user-friendly web server that unifies data from over ten oncogenic databases and seven large-scale pan-cancer cohorts, enabling rapid multi-database queries and network-based exploration of genomic variants, gene-gene interactions, and therapy associations. The platform features a semi-automated reporting workflow that generates comprehensive, patient-specific clinical reports from raw tissue sequencing data and categorizes variants into actionable tiers. For translational research, OncoRisk provides modules for data-driven exploration, allowing users to validate findings by interrogating mutation frequencies and clinical associations across real-world patient data. Furthermore, an integrated suite of analytical tools enables comprehensive, cohort-level investigations of mutational landscapes, prognostic biomarkers, and oncogenic signaling pathways. By providing a unified ecosystem that bridges curated knowledge with large-scale cohort data, OncoRisk serves as an effective catalyst for both discovery research and clinical application in oncology. OncoRisk is publicly available at https://www.phenomeportal.org/oncorisk.

Place, publisher, year, edition, pages
Springer Nature , 2026. Vol. 9, no 1, article id 519
National Category
Cancer and Oncology Medical Genetics and Genomics Bioinformatics and Computational Biology
Identifiers
URN: urn:nbn:se:kth:diva-379261DOI: 10.1038/s42003-026-10005-5ISI: 001737028300001PubMedID: 41951849Scopus ID: 2-s2.0-105035510249OAI: oai:DiVA.org:kth-379261DiVA, id: diva2:2053222
Funder
Knut and Alice Wallenberg Foundation, 72110
Note

QC 20260423

Available from: 2026-04-15 Created: 2026-04-15 Last updated: 2026-05-29Bibliographically approved

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Song, XiyaGreen, EmreLiao, XinmengUhlén, MathiasZhang, ChengMardinoglu, Adil

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