Non-Small Cell Lung Cancer Coverage from Every Angle
Advertisement
Advertisement

Novel Algorithm for Selecting Patients for Lung Cancer Treatment: EGFR Mutations Are Key

By: Melissa Steele-Ogus
Posted: Friday, May 15, 2020

According to a recent study published in Oncotarget, a novel algorithm for plasma monitoring during the first month of treatment with tyrosine kinase inhibitors (TKIs) in patients with non–small cell lung cancer (NSCLC) may aid in selecting patients for appropriate therapy. Antonio Marchetti, MD, PhD, of the University of Chieti, Italy, focused on EGFR mutations as a predictor of resistance to TKI therapy.

A total of 116 patients with EGFR-positive lung adenocarcinomas were treated with first/second-generation TKIs. The patients’ tumors were biopsied for the sensitizing EGFR-mutant allele with 64 testing positive. Patients were then treated with osimertinib, a third-generation EGFR TKI. The EGFR status of these patients was then monitored by plasma tests every 3 to 8 days with reverse transcriptase–polymerase chain reaction (RT-PCR) for 1 month. Patients were screened again at the end of the second month and subsequently at every 2 months.

Of these patients, 57 showed a rapid decrease in levels of the sensitizing EGFR-mutant allele, down to an undetectable level in the first month of treatment; they were classified as good responders. As for the seven patients whose levels did not decrease to an undetectable level; they were classified as poor responders. The seven patients underwent massive parallel sequencing to look for other resistant mutations. In addition, progression-free survival was significantly worse (P < .0001) in poor responders (median time of 4.3 ± 1.1 months) than in good responders (13.3 ± 1.2 months).

“Our data indicate that plasma monitoring by a simple RT-PCR-based EGFR mutation test in the first month of treatment may be useful for rapid identification of patients to be subjected to further characterization by massive parallel sequencing,” the study authors proposed.

Disclosure: The authors reported no conflicts of interest.



By continuing to browse this site you permit us and our partners to place identification cookies on your browser and agree to our use of cookies to identify you for marketing. Read our Privacy Policy to learn more.