Cancer Treatment Outcomes Influenced by Genetics


Anticancer medication response strongly linked to genetic ancestry.

Anticancer medication response strongly linked to genetic ancestry.

Surviving cancer may depend significantly based on the genetics of the patient, a recent study indicates.

Published in Pharmacogenomics, researchers analyzed lymphoblastoid cell lines from 589 patients to find associations between genetic variants and varying responses to drugs. Additionally, the role of ethnicity in drug potency and efficacy of 28 chemotherapeutic compounds was studied.

The ethnicity of these patients was self-reported as Hispanic or non-Hispanic/Caucasian.

Drug response variability across many of the treatments apparently correlates with genetic ancestry and self-reported race the researchers found. Unique results were found among Hispanic and Caucasian samples, which points to a complex relationship between genome, drug response and treatment outcomes, according to the study.

"This elegant study addresses questions on the role of ethnicity in drug response and the part played by individual genes in drug response," said Commissioning Editor Sarah Jones. "Using state-of-the-art techniques, researchers observed significant differences in drug response by self-reported ethnicity, which led to hypotheses regarding the genetic differences underlying changes in concentration response."

Specifically, a notable association in outcomes and genetics was found in the brain tumor treatment temozolomide. Meanwhile, cancer drugs etoposide and mitomycin also suggest an association, but these findings should be seen as “hypothesis generation,” the study noted.

"Based on the cell lines of hundreds of individuals, our research suggests that the genetic ancestry of a person is strongly related to a person's response to anticancer drug treatment," said lead author John Jack, PhD, a research scientist at North Carolina State University. "The developing field of ‘personalized’ or ‘precision medicine’ will leverage these types of data to help inform a doctor's decision on selecting the optimal drug and dose for each patient."

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