Cancer therapy: Investigating how race and ethnicity may affect results
Randomized clinical trials are used in medical research to help determine the effectiveness of new treatments. Although these trials are rigorously designed, they often include an underrepresentation of minority participants, making it difficult to assess whether trial results can be generalized for patients of diverse races and ethnicities.
Now, a Texas A&M Engineering Medicine (EnMed) researcher has found that analyzing real-world data can help identify minority patients who might have better than expected outcomes for cancer therapies. It is a finding the researcher says should prompt clinical trials to include more diverse populations for a more complete evaluation of therapeutic outcomes. The research was recently published in The Oncologist.
“It is well known that African Americans have a higher likelihood of not only developing, but also dying from certain cancers,” said Kamlesh Yadav, instructional associate professor for EnMed at Texas A&M University. “This study is one of the first to use real-world clinical data to identify that African Americans respond favorably to lung cancer immunotherapies.”
Yadav worked with a team of researchers to analyze outcomes of 249 lung cancer patients, from a dataset of more than 11,000 patients, who received immunotherapy treatments and had clinical follow-up data available to review. They found that African Americans had significantly longer time-to-treatment discontinuation, which is the date of starting a medication to the date of treatment discontinuation or death, as well as overall survival when compared to the rest of the group. Yadav says these results have clinical implications for lung cancer patients treated with immunotherapies.
“The clinical trials that led to the U.S. Food and Drug Administration, or FDA, approval of these immunotherapies only had 1–4 percent African Americans,” Yadav said. “In addition, a race-based subgroup analysis was never carried out. Thus, this kind of retrospective analysis of real-world data can help patient stratification for improved therapeutic outcomes.”