The new Qlucore Insights model provides a comprehensive overview of the lesion that is being analyzed. The model assigns a sample to one of 18 subgroups, where the subgroups include the major primary lung cancer subtypes, twelve subgroups for metastatic cancer such as breast, colorectal, or kidney cell cancer and two subtypes for infections/inflammatory conditions, i.e. Sarcoidosis and TBC. 

The Qlucore Insights lung cancer model is based on RNA data from more than 300 carefully selected samples (FFPE). The model is developed using modern and tailored AI-based machine learning techniques. Gene fusions are also detected and reported. Data processing and results are easy and fast to generate with the foundation in standard RNA-seq kits, standard workflows and NGS-instruments. 

Although there have been major advancements in the treatment of lung cancer over the past decades, it remains a major cause of death worldwide. To further improve outcomes, the scientific community favours RNA analysis which unlike DNA, fluctuates considerably both in presence and relative concentrations reflecting the cellular characteristics of a tumor. RNA analysis gives a snapshot of the current state of a potential tumor sample. Qlucore uses this snapshot to assign a sample to the correct subgroup. 

"The collaboration with the team from Heidelberg University Hospital is working very well, and we are delighted to see the new Qlucore Insights model being launched", says Carl-Johan Ivarsson, CEO of Qlucore.

Qlucore Insights is intended for research use only and enables early testing and evaluation. Qlucore Insights is provided to hospitals, clinics, and laboratories.