A cancer patient's chances of survival may be predicted through a new method measuring the variety of genetic mutations within tumors, according to a new study published in the journal Cancer.

Researchers have long hypothesized that tumors whose subgroups of cells undergo different mutations at different DNA sites, a result of genetic heterogeneity, are more difficult to treat because particular subgroups might be more likely to survive radiation, drug treatments or to have spread before diagnosis, according to the researchers.

However, while recent studies have identified specific genes and proteins responsible for conferring treatment resistance in tumors, there has never been an easy way of measuring genetic heterogeneity - until now.

Dr. James Rocco, principal investigator at the Massachusetts General Hospital (MGH) Center for Cancer Research and senior author of the study, and his colleagues have developed a new system of measuring genetic heterogeneity by analyzing advanced gene sequencing data that, in turn, reflects the degree of genetic diversity in a tumor.

Though their system, called MATH, was first revealed in the March 2013 issue of Oral Oncology, the paper was nonetheless limited in proving its efficacy.

In the current study, however, the investigators were able to prove that MATH is capable of predicting patients’ survival rates more accurately than any other previous predictions based on all other risk factors the researchers examined.

“Our results have important implications for the future of oncology care,” Rocco said in a press release about the discovery. “MATH offers a simple, quantitative way to test hypotheses about intratumor genetic heterogeneity, including the likelihood that targeted therapy will succeed.”

This was most true in the cases of patients treated with chemotherapy, perhaps reflecting a greater likelihood that highly heterogeneous tumors contain treatment-resistant cells, according to Edmund Mroz of the MGH Center for Cancer Research and lead author on the study.

Moreover, Mroz noted that what appears to reduce the chance of survival are the subgroups of cells with different mutations within a tumor rather than the process of mutation itself.

“If all the tumor cells have gone through the same series of mutations, a single treatment might be able to kill them all,” he said. “But if there are subgroups with different sets of mutations, one subgroup might be resistant to one type of treatment, while another subgroup might resist a different therapy.”

In fact, one day, Mroz believes their findings could affect the level of treatment a patient receives, including more aggressive therapies for those who receive higher MATH values upon examination versus more standard therapies for those with lower values.