Latest findings from a subgroup analysis of the phase III EMERALD trial are reshaping the approach to treatment decision-making in metastatic breast cancer, particularly regarding the interpretation of ESR1 mutations in liquid biopsies.
Dr. Virginia Kaklamani, co-director of the San Antonio Breast Cancer Symposium (SABCS) and researcher at UT Health Sciences Center in San Antonio, emphasized that clinicians should focus on the presence of ESR1 mutations rather than their variant allele frequencies (VAF) when determining treatment strategies.
Key Clinical Implications
The analysis specifically examined variant allele frequencies of both ESR1 and PIK3CA mutations in estrogen receptor-positive, HER2-negative metastatic breast cancer patients. A notable finding was that elacestrant (Orserdu) maintained its therapeutic efficacy even in cases where PIK3CA mutation VAF exceeded that of ESR1 mutations.
"We still are not understanding how to interpret variant allele frequencies," Dr. Kaklamani explained. "The bottom line is, at least as of now, we should not look at variant allele frequency to make a decision."
Impact on Treatment Approach
This insight carries significant implications for clinical practice, particularly in the interpretation of liquid biopsy results. While liquid biopsies provide detailed information about mutation frequencies, the research suggests that the binary presence or absence of ESR1 mutations should be the primary consideration for treatment decisions.
The findings are especially relevant for healthcare providers utilizing elacestrant, as the drug's efficacy appears to be maintained regardless of the relative VAF levels between PIK3CA and ESR1 mutations. This simplifies the decision-making process and provides clearer guidance for clinicians managing metastatic breast cancer patients.
Clinical Practice Recommendations
Dr. Kaklamani's guidance is clear and actionable: practitioners should base their treatment decisions on the simple presence of ESR1 mutations, rather than getting caught up in the complexities of variant allele frequencies. This straightforward approach could help streamline treatment decisions while ensuring optimal patient care.
