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.
Cancer is a cell subject to random mutations. Such mutations are driven by the principles of evolution, such as natural selection. The complexity of tumors require techniques that recognize the heterogeneity within cancers of a common origin and across patients.
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.
A longer look
Cells produce byproducts that appear in the systemic circulation. These byproducts or metabolites directly correlate with the genetic code of the cancer cells from which they are derived.
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).