NCT01565200.

Precision medicine aims to use patient genomic, epigenomic, specific drug dose, and other data to define disease patterns that may potentially lead to an improved treatment outcome. Personalized dosing regimens based on tumor drug penetration can play a critical role in this approach. State-of-the-art techniques to measure tumor drug penetration focus on systemic exposure, tissue penetration, cellular or molecular engagement, and expression of pharmacological activity. Using in silico methods, this information can be integrated to bridge the gap between the therapeutic regimen and the pharmacological link with clinical outcome. These methodologies are described, and challenges ahead are discussed. Supported by many examples, this review shows how the combination of these techniques provides enhanced patient-specific information on drug accessibility at the tumor tissue level, target binding, and downstream pharmacology. Our vision of how to apply tumor drug penetration measurements offers a roadmap for the clinical implementation of precision dosing.

Since the approval of trastuzumab, a humanized monoclonal antibody against the extracellular domain of human epidermal growth factor receptor 2 (HER2), 3 other HER2-targeting agents have gained regulatory approval: lapatinib, pertuzumab, and trastuzumab-emtansine. These agents have revolutionized the management of HER2-positive breast cancer, highlighting the concept that targeted therapies are successful when patients exhibit tumor-selective expression of a molecular target-in this case, HER2. However, response prediction and innate or acquired resistance remain serious concerns. Predictive biomarkers of a response-which could help in the selection of patients who might benefit from a selected targeted therapy-are currently lacking. Molecular imaging with anti-HER2 probes allows the noninvasive, whole-body assessment of HER2 tumor burden and has the potential to improve patient selection, optimize the dose and schedule, and rationalize assessment of the response to anti-HER2 therapies. Furthermore, unlike biopsy-based HER2 assessment, this approach can reveal inter-or intratumoral heterogeneity as well as variations in HER2 expression over time. This review summarizes the available literature and the current status of molecular imaging as a tool for the assessment of HER2 (target) expression or the prediction of an early treatment response in early and advanced HER2-positive breast cancer. Human epidermal growth factor receptor 2 (HER2) is overexpressed in several cancers, including breast, gastric, ovary, prostate, bladder, and lung cancers, and is a proven therapeutic target in the first 2 tumor types (1,2). In breast cancer (BC), in particular, HER2 overexpression is found in 15%-25% of patients and is associated with a clinically aggressive course. Successful targeting of HER2 with a range of anti-HER2 drugs has resulted in markedly improved patient outcomes in both advanced and early disease settings (3).However, the presence of a target (in this case, HER2) does not always guarantee benefit from matched targeted therapies because downstream signaling resistance or escape can occur. In HER2-overexpressing BC, it has been estimated that about 50% of patients with metastases do not benefit from anti-HER2 therapies (4), and not a single biomarker for identifying nonresponding patients has yet been validated (5).Furthermore, there is increasing evidence of temporal and spatial heterogeneity in BC HER2 overexpression. Patients with negative test results at diagnosis can have positive test results later in the disease course and vice versa, a fact that explains why biopsy of metastatic disease is a strong recommendation of many clinical treatment guidelines (6). Heterogeneity in biomarker expression at metastatic sites is only beginning to be recognized, with growing appreciation for molecular imaging.This article focuses on HER2-positive BC and provides a comprehensive overview of the present and future roles of molecular imaging in improving target mapping, predicting benefit from chemotherapy with or without ...