VUGENE, in collaboration with Vilnius University and Vilnius University Hospital Santaros Klinikos, has announced the results of its research and experimental development (R&D) project, “Development of a Clinical Multi-Omics Platform for the Diagnostics and Advanced Therapy Solutions of Oncological Diseases” (EU grant Implementation of mission-driven science and innovation programmes, NR. 02-002-P-0001).
Over the course of the project, three technological prototypes were created, two of which were developed into full products. To commercialize the project’s results, the spin-off company UAB VUGENE Precision Medicine was established. The company will provide integrated analysis of data from all three omics types to support personalized diagnosis and treatment of kidney cancer.
The project’s goal was to create a clinical multi-omics platform that consolidates diagnostic and advanced therapy solutions for the treatment of oncological diseases. The primary focus was on the molecular biology research of renal cell carcinoma (RCC). A multi-omics data analysis ecosystem was developed, encompassing a clinical methodological framework and an automated bioinformatics infrastructure designed to analyze and interpret whole-genome, epigenome, and single-cell transcriptome data.
One of the project’s most significant achievements is a unique database of 70 kidney cancer patients. This database integrates clinical, pathological, and molecular information, with each sample characterized by over 160 variables. This level of detail significantly surpasses publicly available datasets, which are typically limited to basic characteristics. This dataset is further enhanced by the integration of publicly available data: 262 single-cell RNA sequencing, 548 EPIC methylation, and 402 whole-exome sequencing datasets have been consolidated into the platform.
The bioinformatics infrastructure ensures code versioning and complete traceability of results. The system’s performance was validated by analyzing large volumes of public and project-collected data, including cases where all three omics were analyzed simultaneously on 15 patients.
A significant component of the project is the integration of artificial intelligence into analytical processes. The foundational model “scGPT” was adapted and refined for single-cell annotation, supplemented by the use of machine learning algorithms (GLM, SVM, Random Forest). Additionally, an AI-enabled feature was developed at the prototype level, allowing researchers to adapt scientific visualizations in real time.
All research results have been consolidated into an interactive platform, app.vugene.com, which serves as a multidimensional database and resource for understanding kidney cancer biology, complete with data export and visualization capabilities.
Access to the platform is available for consortium members upon request at email.
Cover image credits: Sebastian Kaulitzki / Adobe Stock
