Qlucore - listed on Nasdaq First North - is expanding its collaboration in precision diagnostics with Lund University with the aim of developing solutions for improved diagnostics of more forms of blood cancer.

Combining the software expertise of Qlucore with the cancer diagnostics expertise of Lund University, the collaboration aims to develop and evaluate so-called classification models for improved diagnostics of two different forms of blood cancer, acute lymphoblastic and acute myeloid leukemia. The classification models are then used together with the AI-based products Qlucore Diagnostics and Qlucore Insights. The goal is to support more customized cancer treatment and to obtain CE marking for Qlucore Diagnostics.

"The expansion of the collaboration with Lund University will facilitate coverage of both bladder cancer and two forms of acute leukemia. This will strengthen our product plan and speed up the introduction of advanced molecular diagnostics of cancer, benefitting many patients," says Carl-Johan Ivarsson, CEO at Qlucore.

Researchers in clinical genetics at Lund University have many years of experience using high-resolution genetic analysis methods to study why acute leukemias occur and how they can be better diagnosed. Their findings have been described in several well-known scientific journals such as Nature Genetics, Nature Communications, and Blood.

"We are very happy that the successful research that takes place at Lund University can be translated into improved diagnostics of various forms of cancer through collaboration between our researchers and companies,” says Eddie Thordarson, portfolio manager at LU Holding, Lund University's investment and licensing business.

Precision diagnostics for cancer has developed rapidly in recent years, driven by so-called next generation sequencing (NGS). Until now, the focus has been on mutations and variants in the genetic code at the DNA level, which have been used for both patient classification and cancer treatment decisions. For some time now, interest has increased in techniques based on measurements of gene activity levels as this provides improved opportunities to determine which diagnosis a patient receives, and which treatment is most appropriate.

Qlucore Diagnostics fills an important void in the clinical precision diagnostics workflow. With disease-specific models based on machine learning that enable classification, and a feature that supports clinical decisions using, amongst other things, user-friendly 3D visualizations, it becomes possible to predict patients' response to treatment. Qlucore Diagnostics is the future solution for customized cancer treatment and the goal is to obtain a CE mark. Qlucore Insights is intended for research and enables early testing and evaluation.

Qlucore Insights is available to hospitals, clinics and laboratories via the Qlucore team in Europe and the USA.