Corti Chiara, Binboğa Kurt Busem, Koca Beyza, Rahman Tasnim, Conforti Fabio, Pala Laura, Bianchini Giampaolo, Criscitiello Carmen, Curigliano Giuseppe, Garrido-Castro Ana C, Kabraji Sheheryar K, Waks Adrienne G, Mittendorf Elizabeth A, Tolaney Sara M
Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy.
Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Division of Pathology, Brigham and Women's Hospital, Boston, MA, USA.
Cancer Treat Rev. 2025 Jan;132:102852. doi: 10.1016/j.ctrv.2024.102852. Epub 2024 Nov 13.
Neoadjuvant chemoimmunotherapy (NACIT) has been shown to improve pathologic complete response (pCR) rates and survival outcomes in stage II-III triple-negative breast cancer (TNBC). Promising pCR rate improvements have also been documented for selected patients with estrogen receptor-positive (ER+) human epidermal growth factor receptor 2-negative (HER2-) breast cancer (BC). However, one size does not fit all and predicting which patients will benefit from NACIT remains challenging. Accurate predictions would be useful to minimize immune-related toxicity, which can be severe, irreversible, and potentially impact fertility and quality of life, and to identify patients in need of alternative treatments. This review aims to capitalize on the existing translational and clinical evidence on predictors of treatment response in patients with early-stage BC treated with neoadjuvant chemotherapy (NACT) and NACIT. It summarizes evidence suggesting that NACT/NACIT effectiveness may correlate with pre-treatment tumor characteristics, including mutational profiles, ER expression and signaling, immune cell presence and spatial organization, specific gene signatures, and the levels of proliferating versus quiescent cancer cells. However, the predominantly qualitative and descriptive nature of many studies highlights the challenges in integrating various potential response determinants into a validated, comprehensive, and multimodal predictive model. The potential of novel multi-modal approaches, such as those based on artificial intelligence, to overcome current challenges remains unclear, as these tools are not free from bias and shortcut learning. Despite these limitations, the rapid evolution of these technologies, coupled with further efforts in basic and translational research, holds promise for improving treatment outcome predictions in early HER2- BC.
新辅助化疗免疫疗法(NACIT)已被证明可提高II-III期三阴性乳腺癌(TNBC)的病理完全缓解(pCR)率和生存结果。对于部分雌激素受体阳性(ER+)、人表皮生长因子受体2阴性(HER2-)乳腺癌(BC)患者,也有文献记载pCR率有显著提高。然而,一刀切并不适用于所有情况,预测哪些患者将从NACIT中获益仍然具有挑战性。准确的预测有助于将可能严重、不可逆且可能影响生育能力和生活质量的免疫相关毒性降至最低,并识别需要替代治疗的患者。本综述旨在利用关于接受新辅助化疗(NACT)和NACIT治疗的早期BC患者治疗反应预测指标的现有转化和临床证据。它总结了证据表明,NACT/NACIT的有效性可能与治疗前的肿瘤特征相关,包括突变谱、ER表达和信号传导、免疫细胞的存在和空间组织、特定基因特征以及增殖与静止癌细胞的水平。然而,许多研究主要是定性和描述性的,这凸显了将各种潜在反应决定因素整合到一个经过验证的、全面的多模式预测模型中的挑战。基于人工智能等新型多模式方法克服当前挑战的潜力仍不明确,因为这些工具并非没有偏差和捷径学习。尽管存在这些局限性,但这些技术的快速发展,再加上基础和转化研究的进一步努力,有望改善早期HER2- BC的治疗结果预测。