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乳腺癌个体化医学的预测生物标志物。

Predictive biomarkers for personalized medicine in breast cancer.

机构信息

Gustave Roussy Institute, INSERM U981, Prédicteurs moléculaires et nouvelles cibles en oncologie, Villejuif, France; LabEx LERMIT, Université Paris-Saclay, 92296 Châtenay-Malabry, France; Inovarion, 75005, Paris, France.

Gustave Roussy Institute, INSERM U981, Prédicteurs moléculaires et nouvelles cibles en oncologie, Villejuif, France; LabEx LERMIT, Université Paris-Saclay, 92296 Châtenay-Malabry, France.

出版信息

Cancer Lett. 2022 Oct 1;545:215828. doi: 10.1016/j.canlet.2022.215828. Epub 2022 Jul 16.

Abstract

Breast cancer is one of the most frequent malignancies among women worldwide. Based on clinical and molecular features of breast tumors, patients are treated with chemotherapy, hormonal therapy and/or radiotherapy and more recently with immunotherapy or targeted therapy. These different therapeutic options have markedly improved patient outcomes. However, further improvement is needed to fight against resistance to treatment. In the rapidly growing area of research for personalized medicine, predictive biomarkers - which predict patient response to therapy - are essential tools to select the patients who are most likely to benefit from the treatment, with the aim to give the right therapy to the right patient and avoid unnecessary overtreatment. The search for predictive biomarkers is an active field of research that includes genomic, proteomic and/or machine learning approaches. In this review, we describe current strategies and innovative tools to identify, evaluate and validate new biomarkers. We also summarize current predictive biomarkers in breast cancer and discuss companion biomarkers of targeted therapy in the context of precision medicine.

摘要

乳腺癌是全球女性中最常见的恶性肿瘤之一。根据乳腺癌肿瘤的临床和分子特征,患者接受化疗、激素治疗和/或放疗,最近还接受免疫治疗或靶向治疗。这些不同的治疗选择显著改善了患者的预后。然而,仍需要进一步改进以对抗治疗耐药性。在个性化医学的快速发展的研究领域中,预测生物标志物——预测患者对治疗的反应——是选择最有可能受益于治疗的患者的重要工具,目的是为合适的患者提供合适的治疗,并避免不必要的过度治疗。寻找预测生物标志物是一个活跃的研究领域,包括基因组、蛋白质组和/或机器学习方法。在这篇综述中,我们描述了当前用于识别、评估和验证新生物标志物的策略和创新工具。我们还总结了乳腺癌的当前预测生物标志物,并讨论了精准医学背景下靶向治疗的伴随生物标志物。

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