No biomarker beyond HER2 itself, which suffers from a low positive predictive value, has demonstrated clinical utility in breast cancer, despite numerous attempts to improve treatment tailoring for the growing number of anti-HER2 targeted therapies. This prompted us to examine the body of evidence, using a systematic approach, to identify putative predictive biomarkers in HER2-positive breast cancer, and discuss the hitherto failure to address the needs of patients. In the future, it is hoped immune-based biomarkers will predict benefit from anti-HER2 treatments in the neoadjuvant and adjuvant settings. In advanced-stage disease, the quantification of tumour heterogeneity using molecular-imaging technology has generated informative data on the success or failure of the antibody-drug conjugate T-DM1. Treatment tailoring remains a high priority, in cost-constrained health-care systems, but such tailoring will require a dramatic shift in the way translational research is being conducted, with the establishment of large, easily accessible, and well-annotated databases of candidate predictive biomarkers. Single-centre biomarker research should become a thing of the past.

尽管进行了许多尝试以针对越来越多的抗HER2靶向疗法进行适应性治疗，但HER2本身以外的生物标志物均未在乳腺癌中显示出临床实用性，而HER2本身的生物标志物具有较低的阳性预测值。这促使我们使用系统的方法来检查证据，以鉴定HER2阳性乳腺癌的假定预测性生物标志物，并讨论迄今为止未能解决患者需求的问题。希望将来基于免疫的生物标记物能够预测新辅助和辅助环境中抗HER2治疗的益处。在晚期疾病中，使用分子成像技术对肿瘤异质性进行量化已生成有关抗体-药物偶联物T-DM1成功或失败的信息性数据。量身定制治疗仍然是重中之重，在成本受限的医疗保健系统中，但这种剪裁将要求进行转化研究的方式发生重大变化，并建立大型的，易于访问的且注释正确的候选预测性生物标志物数据库。单中心生物标志物研究应该成为过去。