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.
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
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
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.