ArticleAnnals of surgical oncology2024
ASO Author Reflections: Breast-Conserving Surgery After Neoadjuvant Systemic Therapy for Early-Stage Breast Cancer: Quantitative Biomarkers and Disparities in the Precision-Medicine Era.
Article in Annals of surgical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
In this era of precision medicine, incorporating quantitative measures of estrogen receptor (ER)/progesterone receptor (PR)/Ki-67 expressions and genomic assays could more precisely identify neoadjuvant systemic therapy with the highest likelihood of response and tumor downstaging. In our recent study, we quantified the likelihood of achieving breast-conserving surgery (BCS vs. mastectomy) after neoadjuvant chemotherapy or endocrine therapy as a function of demographics, quantitative ER/PR/Ki-67 expressions, 21-gene recurrence scores, or 70-gene risk scores in early-stage, hormone receptor (HR)-positive/human epidermal growth factor receptor 2 (HER2)-negative breast cancer. Using the 2010-2020 National Cancer Database, we found that the BCS rate after neoadjuvant chemotherapy was higher among patients with high 21-gene recurrence scores, lower ER/PR expression, or higher Ki-67 expression. Most patients who received neoadjuvant endocrine therapy underwent BCS, which was mostly dependent on ER expression. Asian women were less likely than white women to undergo BCS after neoadjuvant treatments. Lack of health insurance was associated with lower odds of BCS in both neoadjuvant settings. Although our study provides insight into the associations of BCS with quantitative biomarkers at a single time point, several questions remain unanswered. With the evolving landscape of neoadjuvant therapies in development for HR-positive/HER2-negative breast cancer, ongoing work using quantitative biomarkers and genomic assay scores is needed to select the right neoadjuvant systemic therapy for the right patient. Given the increasing amount of data available at the time of breast cancer diagnosis, novel computational approaches are needed to integrate patient demographic and tumor-specific factors to predict the optimal treatment strategy and likelihood of BCS.
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