Neoadjuvant therapy in hormone Receptor-Positive/HER2-Negative breast cancer

医学 新辅助治疗 乳腺癌 肿瘤科 生物标志物 内科学 化疗 临床试验 保乳手术 激素疗法 病态的 癌症 乳房切除术 生物化学 化学
作者
Luca Cantini,Dario Trapani,Lorenzo Guidi,Luca Boscolo Bielo,Roberta Scafetta,Marcin Koziej,Laura Vidal,Kamal S. Saini,Giuseppe Curigliano
出处
期刊:Cancer Treatment Reviews [Elsevier BV]
卷期号:123: 102669-102669 被引量:8
标识
DOI:10.1016/j.ctrv.2023.102669
摘要

Neoadjuvant therapy is commonly used in patients with locally advanced or inoperable breast cancer (BC). Neoadjuvant chemotherapy (NACT) represents an established treatment modality able to downstage tumours, facilitate breast-conserving surgery, yet also achieve considerable pathologic complete response (pCR) rates in HER2-positive and triple-negative BC. For patients with HR+/HER2- BC, the choice between NACT and neoadjuvant endocrine therapy (NET) is still based on clinical and pathological features and not guided by biomarkers of defined clinical utility, differently from the adjuvant setting where gene-expression signatures have been widely adopted to drive decision-making. In this review, we summarize the evidence supporting the choice of NACT vs NET in HR+/HER2- BC, discussing the issues surrounding clinical trial design and proper selection of patients for every treatment. It is time to question the binary paradigm of responder vs non-responders as well as the “one size fits all” approach in luminal BC, supporting the utilization of continuous endpoints and the adoption of tissue and plasma-based biomarkers at multiple timepoints. This will eventually unleash the full potential of neoadjuvant therapy which is to modulate patient treatment based on treatment sensitivity and surgical outcomes. We also reviewed the current landscape of neoadjuvant studies for HR+/HER2- BC, focusing on antibody-drug conjugates (ADCs) and immunotherapy combinations. Finally, we proposed a roadmap for future neoadjuvant approaches in HR+/HER2- BC, which should be based on a staggered biomarker-driven treatment selection aiming at impacting long-term relevant endpoints.
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