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We work with scientific experts to develop disease specific classifier models for subtype determination. The machine learning (AI) based classifiers are combined with user-friendly visualizations and also supports gene fusion detection and analysis. Currently four models are available and more are being developed.
Read more about Qlucore as part of the EIC Accelerator precision oncology portfolio in Nature Biotechnology.
The Qlucore Insights Models are for research use only (RuO). Development of models for Qlucore Diagnostics is ongoing with a target for apporval according to IVDR.
Gene expression based subtype classification in combination with comprehensive gene fusion analysis. With the inclusion of the DUX4-rearranged and ETV6::RUNX1-like subtypes up to 7% more of the children are covered compared to standard methods. Supported subtypes:
1. High hyperdiploidy
2. ETV6::RUNX1 + ETV6::RUNX1-like
3. KMT2A(MLL)-rearranged (MLL)
4. TCF3::PBX1
5. BCR::ABL1 + BCR::ABL1-like
6. DUX4-rearranged
More information regarding BCP-ALL
The lung cancer 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.
Gene fusion detection and analysis using up to three different gene fusion callers.
Sample: FFPE
More information regarding Lung Cancer.
This model is under develoment and further improvements are expected. The first generation detects the following subtypes:
Sample type: Fresh frozen
Gene expression based subtyping into five subtypes:
Three benefits based on correct molecular subtype classification:
For grade T1 tumours (NMIBC): Identification of Basal/Squamous-like subtype (Ba/Sq-like) and Genomically Unstable subtype (GU). Ba/Sq and GU should be considered for radical surgery due to increased risk of progression. Improves on current risk score algorithm.
For grade T2-T4 tumours (MIBC): De-escalate the use of neoadjuvant chemotherapy (NAC) by refraining from NAC in patients with Basal/Squamous-like subtype (Ba/Sq-like) (Ba/Sq-like tumours respond less frequently).
Metastatic disease: Prediction of response on PD1 & PDL1 inhibitors. More frequently response in the GU subtype.