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 digital approach from scientists at the Johns Hopkins University—a computational strategy transforms highly complex information into a simplified format that emphasizes patient-to-patient variation in the molecular signatures of cancer cells—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 JHU’s Department of Applied Mathematics and Statistics and 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.

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