A new scoring method that measures the genetic variability in a tumor may one day help identify patients with aggressive cancers that are less likely to respond to therapy. Such a tool could improve clinical decisions based on the unique characteristics of a patient’s cancer.
Such is the hope of a research team led by James Rocco, a professor in the Department of Otolaryngology – Head and Neck Surgery, at Ohio State University in Columbus, and colleagues who report a new study in the journal PLOS Medicine.
Cancer occurs when abnormal cells grow and multiply because of changes in the genes that control the way they function. As these mutated cells grow and divide, they accumulate more, different mutations, allowing them to grow faster, invade other parts of the body and resist therapy.
As the mutations accumulate, subpopulations of cells form within a tumor, each characterized by its own cluster of mutations.
Researchers have a theory that this “intra-tumor heterogeneity” leads to worse treatment outcomes because therapies often target specific mutations, not several independent ones.
However, currently there is no easy, single clinical tool to help oncologists measure tumor heterogeneity so they can make clinical decisions and better assess disease prognosis.
As a first step toward meeting this need, Prof. Rocco and colleagues developed a method that scores the genetic variability among cancer cells within tumors of patients with head and neck cancers.
For their study, they collected retrospective data from 305 head and neck squamous cell carcinoma patients from The Cancer Genome Atlas (TCGA) and used the new tool to analyze the genetic variability of their tumors.
The team showed that high scores on the tool – called MATH (mutant-allele tumor heterogeneity) – corresponded to tumors with a high degree of variation among the gene mutations in their cancer cells.
And they found that high genetic variability – higher MATH scores – correlated with lower patient survival.
Each 10% increase in MATH score corresponded to an 8.8% increased likelihood of death.
If their findings are confirmed in further studies and with other cancers, the team suggests MATH scores could help doctors identify the best treatment for cancer patients and predict their prognosis.
Prof. Rocco sums up the findings and their implications:
“Our retrospective analysis showed that patients with high heterogeneity tumors were more than twice as likely to die compared to patients with low heterogeneity tumors. This type of information could refine the dialog about how we tackle cancer by helping us predict a patient’s treatment success and justify clinical decisions based on the unique makeup of a patient’s tumor.”
Meanwhile, Medical News Today recently reported how researchers at the University of Michigan have identified a biomarker for aggressive prostate cancer. The researchers found a protein called Runx2 that produces bone may also control the growth of prostate cells and could be a new target for anticancer drugs.