A team of researchers from Dartmouth College, Brown University, Oregon State University, the University of Minnesota, and the University of California have been using “Big Data” analytical techniques to discover differences in the biochemical patterns of white blood cells. Dr. Devin Koestler (pictured), a bio-statistician and lead researcher on the project, says that the approach has been yielding some promising results and could lead to new early diagnostic techniques for non-blood cancers.
Methylation is a common biochemical process in which a molecule known as a methyl group attaches itself to DNA. Different types of white blood cells, known as leukocyte subsets, occur in different proportions in a given blood sample, depending on the disease that is present. Each leukocyte subset also displays different signature methylation patterns which can be used to characterize the proportion of distinct leukocyte subsets in a blood sample and potentially identify the presence of a specific disease.
According to Dartmouth’s press release, previous research had identified distinct differences between methylation patterns in biopsied tissues. The present study has characterized the methylation patterns in blood samples derived from non-blood cancers. The researchers analyzed large volumes of data from studies of ovarian, bladder, and head and neck cancers and demonstrated significant correlations between the specific cancers and the methylation patterns of leukocyte subsets.
A major advantages of this approach is that a simple blood test, instead of an invasive biopsy, is all that is potentially required to screen for the presence of non-blood cancers. Furthermore, archived blood samples frozen from previous studies can also be used to add to the test volume data for further validation of the technique.
The researcher findings have been published in the journal Cancer Epidemiology, Biomarkers & Prevention…
Press release: Blood Cells May Offer Telltale Clues in Cancer Diagnosis