Genetic variability may help predict tumor aggressiveness and treatment success

17 Apr

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.

cancer definition
The researchers say if their findings are confirmed in further studies, their new tool – which measures genetic variability in tumors –

could help doctors identify the best treatment for patients and predict their prognosis.

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.

High genetic variability in tumors correlated with lower patient survival

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


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.