Couldn't find what you looking for?

TRY OUR SEARCH!

Making Gleevec More Effective Against Leukemia

by — Last updated: 2015-09-18 in Cancer

Table of Contents

Thirty-two year-old Austrian-born biomathematician Franziska Michor believes that cancer cells may behave in predictable ways that can be understood with advances in mathematics.
Dr Michor's prize-winning work focused on dosing and timing Gleevec, the main treatment for chronic myeloid leukemia. As chemotherapies go, Gleevec is probably the best that modern medicine has. Because it is keyed to compensating for a single defect in a single gene, rather than a complex series of mutations, it brings most cases of chronic myeloid leukemia into remission. The problem is, as soon as the treatment is stopped, the cancer returns, sometimes even more severely than before treatment.
 
Michor realized that the problem probably was that the right dose of Gleevec was not being delivered at the right time. To finesse dosing regimens, she took a look at a massive amount of bloodwork data provided by a colleague in Australia. 
 
 
What the Harvard-trained scientist discovered was that:
  • Gleevec kills leukemia cells, but has no effect on the stem cells from which they originate.
  • Gleevec is superbly suited for treating this form of leukemia, but it can never bring about a cure.
  • Even though Gleevec is a "smart" drug for this form of cancer, the cancer is smarter.
This led Michor to a question that just about every cancer specialist considers for every patient, which is better, continuous low doses of chemotherapy spread over time, or high doses of chemotherapy interspersed with "chemotherapy vacations" to minimize toxic side effects. The answer, Michor has computed, is that high-dose Gleevec interrupted by breaks to give the body a chance to recover from side effects actually works better at keeping the cancer at bey. 
 
Michor, however, is the first person to have reached this conclusion from cold, hard, factual data, rather than clinical experience.
 
It turns out that many doctors don't like the idea of an algorithm's doing their work. Rather than being hailed for her innovative work in the mathematics of cancer, Michor was attacked. Most investigators refused to share data with her for analysis. However, Michor was offered a position at New York City's Sloan-Kettering cancer center, where she now has access to massive amounts of data about multiple cancers and how they respond to conventional treatments.
 
Since Michor left Harvard (and later returned to Harvard), her research has focused on some very basic, patient-centered questions:
  • When treating patients for glioblastoma, a particularly aggressive form of brain cancer, it is necessary to wheel them into radiation in the middle of the night, so as not to waste even part of a single day before initiating treatment? Michor and her colleagues found that sticking to an 8 a.m. to 5 p.m. schedule worked just as well (and certainly gave patients and their families more opportunities to rest).
  • Is one kind of chemotherapy enough, or should cancers be attacked with multiple chemotherapy drugs? Michor and her colleagues have developed a concept of "stochastic tunneling," in which cancers caused by one mutation may develop a second mutation before the first mutation can be addressed by treatment. The implication is that one kind of chemo usually will not bring a cancer into remission.
  • Does the five-year survival rate tell us anything about how well patients feel in the years after they are diagnosed with cancer? Michor and her colleagues determined that the data indicate that survival is an indicator of how well patients feel, not just how late they die.
Michor has collaborated on over 100 studies of the mathematics of cancer. Her non-invasive conceptual tools may lead to better treatments with fewer side effects for millions of people who have cancer.

Your thoughts on this

User avatar Guest
Captcha