miRoncol Health offers a blood-based, real-time multi-cancer early risk assessment utilizing microRNA biomarkers and AI pattern recognition. This molecular pre-screen provides insights across more than a dozen common solid tumor types to inform physician-guided follow-up decisions. The service complements established screening programs by offering proactive, molecular-level risk evaluation for individuals and clinical partners.
Funding
Funding not disclosed
Founders
Product
Problem
Current cancer screening methods often fail to detect solid tumor cancers in their early stages, leading to lower survival rates. Existing screening options are also limited, with less than 20% of cancers being found through routine procedures, and many early-stage tumors are discovered incidentally rather than through proactive screening programs.
Solution
miRoncol is developing a blood-based test for the early detection of multiple solid tumor cancers, utilizing microRNA (miRNA) biomarkers and PCR technology. The test aims to screen for over 12 types of solid tumor cancers, which account for approximately 60% of total cancer deaths. By leveraging the properties of miRNA, which are stable and detectable in blood and exhibit different expression profiles in cancer patients, miRoncol's test offers a non-invasive and convenient method for early cancer detection during routine check-ups. The company's approach involves training machine learning models on blood samples to identify shared patterns among various solid tumors, with the goal of providing a highly accurate and affordable solution for proactive cancer risk assessment.
Target Audience
The primary target audience includes individuals seeking proactive cancer risk assessment and healthcare providers looking for accessible and accurate early cancer detection tools.
Features
- Blood-based multi-cancer early detection test
- Detects 12+ types of solid tumor cancers
- Utilizes microRNA (miRNA) biomarkers
- Employs PCR technology for analysis
- High sensitivity for early-stage cancers
- High specificity to minimize false positives
- Non-invasive and convenient blood draw procedure
- Machine learning models trained on 11,000+ blood samples