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Targeting the Top 20 Cancers

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With so much talk about methylation and biomarkers, the two still have a way to go before they’re paired in a commercially successful test to predict or prevent cancer. However, six years into a project that aims to have an early detection biomarker test for the top 20 most deadly cancers in the US, Northwestern University’s Victor Levenson is getting there step by step.

Levenson, a research associate professor at Northwestern’s Robert H. Lurie Comprehensive Cancer Center, recently received a $65,000 Penny Severns grant to continue research into developing a biomarker test that uses methylation profiles within breast and ovarian cancers as a molecular signature for early detection of these two diseases.

The test will profile cell-free plasma DNA derived from heterogeneous tissue samples to detect methylation in 56 different gene promoters. This “composite biomarker,” as Levenson refers to it, can then be used as a comparative base for other samples. In the first incarnation, he says, the biomarker test will be for early detection and diagnosis of breast and ovarian cancers. Later on, he hopes to develop it into a predictive test for drug response and resistance as well.

“The power of this approach is the detection of multiple elements within the same sample,” Levenson says, “and the problem with this approach is that it does not provide any mechanistic insights into the process, but rather provides a lot of qualitative data.”

Typically, it’s difficult to diagnose clinical samples with objectivity, according to Levenson: The tissue being examined is usually heterogeneous and the diagnosis is based on subjective analysis. Error rates are high when pathologists have to eyeball the specimen for signs of cancerous lesions.

“We have to get away from subjective evaluation of data because subjective evaluation of data will always lead to discrepancies,” Levenson says. In his test, instead of employing typical microdissection methods, he will use the entire sample to find the analyte — cell-free plasma DNA — and then select specific genes that are most differentially methylated.

Aiming for the clinic

“From the very beginning we wanted to build a clinically applicable test,” Levenson says. “We have not achieved that goal yet, but we’re trying to get away from anything that is too complicated or requires too complex equipment, or in the end, requires subjective evaluation.” The assay that his team has developed uses methylation-specific restriction enzymes that cut up DNA outside methylated sites, leaving these fragments intact. These genes are then amplified using pre-selected, gene-specific primers that allow them to compare the methylation patterns of diseased and normal tissues. “We’re analyzing 56 fragments in each sample, so this constitutes the profile,” Levenson says. “Then out of this profile, we’re selecting those fragments that are differentially methylated, and then we’re using the whole set of differentially methylated fragments in order to draw a conclusion for the samples that are unknown.”

Epigenetic change is only one measurable cellular response for determining a predictive or diagnostic biomarker, but Levenson chose to focus his assay on DNA methylation because not only is DNA is sturdy and amplifiable, but also methylation patterns within a gene are “multiple, easily detectable, and in a precise position.”

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