According to real-world data, EGFR C797X mutation is deemed the leading resistance mechanism to treatment with Tagrisso in patients with non-small cell lung cancer.
Certain features may make patients resistant to lung cancer treatments, meaning that the drugs may stop working in these patient populations.
Such is the case for those with EGFR C797X mutations, which was found to be the leading cause for treatment resistance to Tagrisso (osimertinib) in both the frontline and second-line setting, according to recent research that was presented at the 2022 World Conference on Lung Cancer.
EGFR C797X surpassed MET amplifications, which was previously known as one of the top lung cancer characteristics that predicted possible Tagrisso resistance.
“This real-world study improves our understanding of how resistance mutations to (Tagrisso) emerge over time, with new insights on when EGFR C797X mutations overtake MET amplifications as the most commonly acquired resistance mechanism,” Dr. Suresh S. Ramalingam said in a press release.
“The analysis characterizes the increased frequency of EGFR C797X mutations as patients are treated with first-line (Tagrisso) for longer durations, reinforcing the need for next-generation EGFR inhibitors to address C797X-driven resistance,” said Ramalingam, who is executive director of Winship Cancer Institute of Emory University in Atlanta, a professor in the Department of Hematology and Medical Oncology and the Roberto C. Goizueta Distinguished Chair for Cancer Research at Emory University School of Medicine and associate vice president for cancer at Woodruff Health Sciences Center.
Less common EGFR resistance mechanisms include EGFR L718Q, EGFR S768I, EGFR L792X and EGFR exon 20 insertions. HER2 amplification, BRAF and KRAS mutations and CCNE1 amplification are also known mechanisms of resistance for Tagrisso.
Investigators leveraged data from the GuardantINFORM clinical genomic database to conduct an analysis of real-world detection rate and cooccurrence of EGFR C797X mutations.
The data included 65,273 patients with non-small cell lung cancer who had received DNA testing.
Patients with activating EGFR mutations were reported as follows: EGFR L858R (3,817 patients), EGFR exon 19 deletions (5,489 patients), other EGFR driver detected (1,777 patients) and no EGFR driver detected (60,349 patients).
Once treatment with Tagrisso was applied to the results, 4,288 patients were evaluable, and 2,050 had DNA analysis after treatment: 65.2% received Tagrisso in the first-line setting (1,337 patients), compared to 34.8% in the second line (713 patients).
With a median follow-up of 21 months, EGFR C797X mutations were detected among 87 patients (6.5%) after first-line Tagrisso. After a median follow-up of 26 months in the second-line setting, 99 patients (13.9%) had the mutation.
Further, the results showed that following second-line Tagrisso, the incidence of EGFR C797X mutations exceeded CCNE1 amplification, MET amplification, PIK3CA E545 and E542 mutations, as well as EGFR L718 and BRAF V600E mutations from 12 months.
Further, the median time to C797X detection was 16.8 months compared with 10.5 and 9.1 months for MET and CCNE1 amplification, respectively. Among those who received Tagrisso in the first-line setting, the cumulative incidence of EGFR C797X was 8% after five years. MET and CCNE1 amplification were detected at rates of 6.4% and 7.9%, respectively. In the second-line setting, after five years the initiation the cumulative incidence of CCNE1 amplification and EGFR C797X mutations was 10.3% and 17.5%, respectively. MET amplification was detected at 7.2%.
In an analysis of patients who discontinued Tagrisso with 60 days of cell-free DNA analysis (600 patients) the cumulative incidence of EGFR C797 mutations was 12.5%. This marker was used a proxy for disease progression in the analysis.
The study authors noted that the dynamics of the appearance of resistance mechanisms to Tagrisso remain poorly studied. They also noted that limitations of the study include common health care claims data discrepancies including inherent to healthcare claims data missing clinical information not routinely reported, the potential for misclassification, and representativeness of the study cohort.
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