Measuring cell growth after cancer treatment can help determine the best treatment approach.
Choosing the best treatment for a cancer patient is often an inexact science. Drugs that work well for some patients may not help others, and tumors that are initially susceptible to a drug can later become resistant.
In a new approach to devising more personalized treatments, researchers at MIT and Dana-Farber Cancer Institute have developed a novel way to test tumors for drug susceptibility. Using a device that measures the masses of single cells, they can predict whether a particular drug will kill tumor cells, based on how it affects their growth rates.
The researchers successfully tested this approach with a very aggressive type of brain cancer called glioblastoma and a type of blood cancer known as acute lymphoblastic leukemia. They reported their results in the Oct. 10 issue of Nature Biotechnology.
“We’ve developed a functional assay that can measure drug response of individual cells while maintaining viability for downstream analysis such as sequencing,” says Scott Manalis, the Andrew (1956) and Erna Viterbi Professor in the MIT departments of Biological Engineering and Mechanical Engineering and a member of MIT’s Koch Institute for Integrative Cancer Research, who is one of the paper’s senior authors.
David Weinstock and Keith Ligon of Dana-Farber Cancer Institute are also senior authors of the paper. The lead authors are Mark Stevens, a former MIT graduate student who is now a research scientist at Dana-Farber; MIT graduate student Nigel Chou; and Dana-Farber postdocs Cecile Maire and Mark Murakami.
Measuring cell growth
In recent years, scientists have been trying to identify genetic markers in tumors that suggest susceptibility to targeted cancer drugs. However, useful markers have been found for only a small percentage of cancers so far, and even when there is a predictive test, it is not accurate for all patients with that type of cancer.
Read Full Article: A new strategy for choosing cancer drugs | MIT News
|Read Full Article: A new strategy for choosing cancer drugs | MIT News|