Mathematicians and cancer scientists have found a way to simplify complex biomolecular data about tumors, in principle making it easier to prescribe the appropriate treatment for a specific patient.
The new computational strategy transforms highly complex information into a simplified format that emphasizes patient-to-patient variation in the molecular signatures of cancer cells, the researchers say.
The digital approach from scientists at the Johns Hopkins University was detailed recently in the journal Proceedings of the National Academy of Sciences.
“The main point of this paper was to introduce this methodology,” said Donald Geman, a professor in the Department of Applied Mathematics and Statistics who was senior author of the PNAS article. “And it also reports on some preliminary experiments using the method to distinguish between closely related cancer phenotypes.”
A key challenge for doctors is that each primary form of cancer, such as breast or prostate, may have multiple subtypes, each of which responds differently to a given treatment.
“One of the things that people in this field have noticed over the past 10 years–and, in fact, it has been startling–is how much heterogeneity there is even between two patients with the same subtype of cancer,” Geman said. “By that,
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