A new computational strategy paves the way for more personalized cancer treatment
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.
“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, I mean that in two patients who were both diagnosed with melanoma, the skin lesions may look quite similar to the naked eye, but the cancerous cells may be very different at the molecular level. They may have different forms of dysregulation, including different genetic variants and different gene expression profiles.”
Knowing as much as possible about the genetic makeup and impaired biological pathways of a particular patient could help physicians make more informed decisions about the prognosis and treatment, adjusting them to the particular molecular profile.