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Lung Cancer model (RuO)
Qlucore Insights Lung Cancer model uses molecular data to define subclasses as well as to pinpoint the tissue of origin for lung metastases. Built on AI-based machine learning and transcriptomics the model opens new ways for fast, cost efficient and objective sample analysis. Utilizing standard lab workflows, the program supports analysis of lung tumor and lung disease from fixed and embedded tissue specimens.
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Current Routine Diagnostics of Lung cancer
A typical patient presents symptoms like a persistent cough or chest pain, often with a smoking history. Standard workflow begins with a medical history, physical exam, and imaging tests like X-rays and CT scans. Common tests (not extensive list) involve:
Imaging: CT, MRI, PET scans. Biopsies: Needle biopsy, bronchoscopy. Histopathology and molecular tests.
Stages: Ranges from stage 1 (localized) to stage 4 (spread to other parts).
Challenges:
• Histopathology requires highly skilled staff to analyze data
• There is a subjective component in image analysis
• Relying on small and fixed assays is not sustainable
• Identifying the origin of metastases to the lung
• High costs due to specialized personnel/several tests
RNA-seq - the emerging clinical diagnostic solution
RNA sequencing plays a crucial role in overcoming present challenges in lung cancer diagnostics by measuring two key parameters: chimeric gene fusions and gene expression levels. These measurements help in accurately classifying cancer subtypes and determining the cancer’s origin.
RNA sequencing is set to become widely used in clinical settings by:
• The ability to detect key genetic alterations
• By lowering sequencing costs
• By ease of automation
• Enabling same lab workflow across many different diseases
Many leading laboratories are already offering these analyses.
Performance (RoU)
The Qlucore Insights (RoU) classifier model for lung cancer allows gene expression-based subtype classification of the groups listed below. The cover is broad, with 19 subtypes included. In addition to this, its inbuilt gene fusion analysis supports the identification of druggable as well as novel gene fusions.
Performance in terms of accuracy is described in this POSTER.
Example of a visualization of classification in a PCA plot, using Qlucore Insights.
Report - classification of Lung Cancer
View a draft example of how a report might look. Note that the example report is generated with Qlucore Insights (Research Use Only).
Poster - presented at ESMO Congress 2024
This study demonstrates that Qlucore Insights can analyze lung tumors and lung diseases with high accuracy.