Phylomics aspires to connect asymptomatic patients with a non-invasive blood-based cancer screening. By combining high-sensitivity technologies and an unprecedented application of modern data science, Phylomics will develop products that provide meaningfully and actionable recommendations for patients.
Look at the shadow,
not the sun
Cells produce byproducts that appear in the circulation. These byproducts or metabolites directly correlate with a normal or pathological cell. Cancer cells have a unique set of mutations and byproducts. Attempting the cumbersome task of deep-sequencing the genome is like staring into the sun. But by looking at the shadows, or downstream metabolites we can identify the mutations within the genome without the hassle.
The non-binary problem
The complexity of tumors require techniques that recognize the heterogeneity within cancers of a common origin and across patients. Cancer is a cell subject to random mutations. Such mutations are driven by the principles of evolution, such as natural selection.
Phylomics unique ability to classify tumor specimens based upon the relatedness among clinical specimens allows us to uncover biologically meaningful comparisons that permit clinically relevant interpretations, such as cancer diagnosis and identifying of proteins (i.e., biomarkers) that uniquely characterize specimens or a group of specimens.
Permits comparison among data sets obtained from different machines or by different investigators, which cannot be conducted reliably with other methods.
Our method has been adapted to analyze metabolomics and gene arrays with similar advantages over existing analytical methods.
The output of our computational model produces a visual output that shows the spectrum of the disease from early to advanced stages.
Identifies proteins that uniquely characterize clinical specimens (i.e., potential biomarkers and therapeutic targets).