Abstract
SummaryMulti-targeted kinase inhibitors, such as sorafenib, have been used in various malignancies, but their efficacy in clinical applications varies among individuals and lacks pretherapeutic prediction measures. We applied the concept of “click chemistry” to pathological staining and established a drug-loaded probe staining assay. We stained the cells and different types of pathological sections and demonstrated that the assay was reliable. We further verified in cells, cell-derived xenograft model, and clinical level that the staining intensity of the probe could reflect drug sensitivity. The stained samples from 300 patients who suffered from hepatocellular carcinoma and used the sorafenib probe also indicated that staining intensity was closely related to clinical information and could be used as an independent marker without undergoing sorafenib therapy for prognosis. This assay provided new ideas for multi-target drug clinical trials, pre-medication prediction, and pathological research.
Highlights
Targeted drug therapy is regarded as the main treatment for malignant tumors
Imatinib Probe Could Bind to CD117 On the basis of the structure–activity relationship of imatinib, we determined that the probe-modified position was a nonpocket-binding functional base (Manley et al, 2010)
Conducting the CCK-8 assay, we tested the effect of imatinib and its probe on the proliferation of the imatinib-sensitive gastrointestinal stromal tumor cell line GIST882
Summary
Targeted drug therapy is regarded as the main treatment for malignant tumors. Next-generation sequencing (NGS) has been widely used as a clinical diagnostic method for targeted drugs (Khotskaya et al, 2017; Prasad et al, 2016). A class of targeted drugs exists, and it includes multi-target kinase inhibitors, such as sorafenib (Wilhelm et al, 2006), which is the first drug approved for the treatment of advanced hepatocellular carcinoma (HCC) (Raoul et al, 2018). This class of drugs cannot rely on NGS for premedication diagnosis. The therapeutic effects of multi-target drugs often vary among individuals, and their therapeutic effects are unstable and random, which are similar to traditional chemotherapy (Garraway and Hahn, 2010). Efficient methods should be developed to predict whether a patient can respond to multi-target drugs and guide clinical use (Llovet et al, 2018).
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