Summary: Qlucore has released a new version of Qlucore Insights tailored for lung cancer, capable of classifying both primary and metastatic lesions, developed in collaboration with Heidelberg University Hospital and supported by a Eurostars Joint European Programme grant.


  1. The new Qlucore Insights model can classify samples into 18 subgroups, including primary lung cancer types, metastatic cancers, and certain infections/inflammatory conditions.
  2. Developed using RNA data from over 300 samples and advanced AI-based machine learning, the model also detects and reports gene fusions.
  3. The model offers easy and fast data processing and results using standard RNA-seq kits, workflows, and NGS instruments.

Qlucore has released a new version of Qlucore Insights specifically designed for the lung cancer field, with the capability to classify primary lung samples as well as metastatic lesions. The lung cancer model is being developed with Heidelberg University Hospital, supported by a Eurostars Joint European Programme grant.

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, e.g. Sarcoidosis and TBC. 

Qlucore Insights Model Based on RNA Data

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.