Qlucore, leading software provider of powerful visualization-based bioinformatics data analysis tools for research and precision diagnostics, is working with leading scientific experts at Lund University (Sweden), Gothenburg University (Sweden) and in China to develop new diagnostics methods and software solutions to significantly improve disease stratification and treatment selection of patients diagnosed with cancer.
A recent grant awarded by VINNOVA, the Swedish Governmental Agency for Innovation Systems, will allow Qlucore to expand Qlucore Diagnostics, the new diagnostic software solution for improved clinical diagnostics of cancer, with a focus on RNA-profiling of glioma and acute leukemia. Glioma and acute leukemia are among the most lethal human malignancies and share common molecular genetic alterations. Cancer is caused by several genetic changes and currently most efforts in a diagnostic setting are focused on molecular profiling at the DNA-level using next-generation sequencing (NGS). International research efforts have demonstrated that transcriptional profiling (RNA seq) of cancer provides critical insights into cancer biology and has a great clinical utility. RNA-seq can reliably detect the expression of gene fusions and measure gene expression levels that can be used for classification, but a major reason for not being utilized in clinical diagnostic settings is the lack of standardized assays and the complexity of data analysis.
To improve cancer precision medicine, there is a need to more quickly move new research findings into clinical use. The VINNOVA grant will enable us to more rapidly develop a clinical grade diagnostics software tool that can be used by the individual clinical lab. This will be an important step for future development of precision cancer medicine.
Carl-Johan Ivarsson, President, Qlucore.
Qlucore is building the software on a flexible and generic platform based on standardized NGS workflows with a focus on superfast visualization and capabilities to handle integrative models with data from different sources. Given that the great majority of all cancer subtypes in the future are anticipated to be transcriptionally profiled, the software solution will also be applicable to all major cancer forms in the future.