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R & D

The Sanguine project develops a platform technology that can be adapted to different types of cancer and many different types of tests, including screening and early disease detection, disease classification, minimal residual disease detection, and disease management, depending solely on the samples used in the development process.

The Technology

Cell-free DNA is constantly released into the bloodstream by apoptotic and necrotic cells and provides a systemic snapshot of cell death. The JaxBio team has developed an ultra-sensitive method to detect epigenetic profiles - methylation and hydroxymethylation changes in cfDNA, that occur during cancer initiation and progression.  The proprietary process involves extracting cell-free DNA from the blood, fluorescently labeling epigenetic marks, and hybridizing them onto a custom high-content microarray (discovery array). Pixels on the array light up according to the epigenetic content of the specific DNA sequence captured. Each cancer type presents a unique pixel pattern signature that may be used for classifying the type and stage of the disease. A machine learning algorithm defines a disease-specific panel that presents the strongest biomarkers that differentiate healthy and cancer states, as well as cancer type and stage. The panel is then printed on a targeted array, the HemaChip, which enables performing a fast, simple, and low-cost test for early cancer diagnosis. 

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My Approach
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The technology significantly reduces the cost of testing by eliminating the need for sequencing

Since unlabeled DNA fragments considered as “background DNA” are undetectable on the microarray, the signal to noise is significantly higher than other methods aiming to detect disease-specific methylation profiles, such as sequencing or qPCR.  Thus, this method can identify rare epigenetically-modified populations with increased sensitivity.

Our results show hundreds of distinct genomic loci that can differentiate between lymphoma, acute myeloid leukemia (AML), and healthy samples. Visualized through heat maps, each column represents an individual, categorized as either healthy or diagnosed with cancer. The rows in the heat maps represent specific genomic loci, showing distinct methylation levels between healthy individuals and patients with lymphoma or AML. Importantly, we are expecting these results to improve as more data will accumulate and the AI algorithm is further optimized.

My Approach

Empowering Early Detection, Transforming Lives

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