Development of a Consensus-Based Risk Stratification Algorithm for HR+/HER2- Early Breast Cancer to Guide Adjuvant CDK4/6i Therapy: Outcomes from a Multidisciplinary Workshop in India.
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Abstract
Purpose: Hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR+/HER2-) early breast cancer (EBC) is treated with curative intent, but risk of recurrence (RoR) remains a challenge. The identification of high RoR EBC patients is inconsistent, highlighting the need for a simplified approach. This study aimed to develop an evidence-informed, consensus-based tool to support recurrence risk stratification and treatment decision-making in patients with HR+/HER2- EBC, incorporating multidisciplinary perspectives and evolving evidence from guidelines and trials.
Methods: Five structured, multidisciplinary workshops were conducted across India, involving 135 experts, to integrate evidence and expert consensus into a practical algorithm for RoR assessment. The workshops included initial polling on clinical practices, followed by case-based discussions and literature review, with post-discussion polling used to capture shifts in expert perspectives.
Results: Polling responses demonstrated consensus (defined as ≥70% agreement by experts) that all N1 patients are at a high RoR (94.3%) and that tumors ≥5 cm are associated with high risk (90.0%). Given that low-grade (G1) tumors are associated with a favorable prognosis (low risk) and high-grade (G3) tumors indicate a poorer prognosis (high risk), Ki-67-based treatment decision-making was considered most relevant for the moderate-grade (G2) subgroup. Accordingly, experts agreed that combining tumor grade G2 with Ki-67 assessment improves risk stratification in N0 patients with tumors <5 cm (71.6%). A Ki-67 cut-off of ≥20% was considered critical (96.3%). The experts agreed that adjuvant cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) have an invasive disease-free survival (iDFS) advantage in all N1 (93.8%) and high risk N0 (91.1%) patients with HR+/HER2- EBC.
Conclusion: The developed algorithm provides a user-friendly framework to facilitate uniform risk stratification and optimize HR+/HER2- EBC treatment in India. Further prospective and real-world validation is required to establish its clinical utility and evaluate its impact on treatment decisions.
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