Development of Response Classifier for Vascular Endothelial Growth Factor Receptor (VEGFR)-Tyrosine Kinase Inhibitor (TKI) in Metastatic Renal Cell Carcinoma
Vascular endothelial growth factor receptor (VEGFR)-targeted therapy improved the outcome of metastatic renal cell carcinoma (mRCC) patients. However, a prediction of the response to VEGFR-tyrosine kinase inhibitor (TKI) remains to be elucidated. We aimed to develop a classifier for VEGFR-TKI responsiveness in mRCC patients. Among 101 mRCC patients, ones with complete response, partial response, or ≥24 weeks stable disease in response to VEGFR-TKI treatment were defined as clinical benefit group, whereas patients with <24 weeks stable disease or progressive disease were classified as clinical non-benefit group. Clinicolaboratory-histopathological data, 41 gene mutations, 20 protein expression levels and 1733 miRNA expression levels were compared between clinical benefit and non-benefit groups. The classifier was built using support vector machine (SVM). Seventy-three patients were clinical benefit group, and 28 patients were clinical non-benefit group. Significantly different features between the groups were as follows: age, time from diagnosis to TKI initiation, thrombocytosis, tumor size, pT stage, ISUP grade, sarcomatoid change, necrosis, lymph node metastasis and expression of pAKT, PD-L1, PD-L2, FGFR2, pS6, PDGFRβ, HIF-1α, IL-8, CA9 and miR-421 (all, P < 0.05). A classifier including necrosis, sarcomatoid component and HIF-1α was built with 0.87 accuracy using SVM. When the classifier was checked against all patients, the apparent accuracy was 0.875 (95% CI, 0.782–0.938). The classifier can be presented as a simple decision tree for clinical use. We developed a VEGFR-TKI response classifier based on comprehensive inclusion of clinicolaboratory-histopathological, immunohistochemical, mutation and miRNA features that may help to guide appropriate treatment in mRCC patients.
KeywordsMetastatic renal cell carcinoma Vascular endothelial growth factor signaling Tyrosine kinase inhibitors Response classifier Machine learning
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and future Planning (2015R1A2A2A01006958).
Compliance with Ethical Standards
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee (2012–0788) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this type of study formal consent is not required.
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