Abstract 429: Establishment of the classifier for a response to vascular endothelial growth factor (VEGF)-tyrosine kinase inhibitor (TKI) in metastatic renal cell carcinoma

Abstract

Vascular endothelial growth factor (VEGF)-targeted therapy has been demonstrated to improve the outcome of metastatic renal cell carcinoma (mRCC) patients. However, validated predictors that predict response and prognosis to VEGF-tyrosine kinase inhibitors (VEGF-TKIs) remain to be elucidated. We aimed to define a classifier for VEGF-TKI response in mRCC patients. Among 101 mRCC patients treated with VEGF-TKIs, 73 patients were responder defined as patients showing complete or partial response, or ≥24 weeks stable disease to VEGF-TKI; and 28 patients were nonresponder defined as <24 weeks stable or progressive disease. Clinical and laboratory data were obtained from the medical records. Histologic features of all tumors were reviewed. Twenty-one protein expressions such as VEGF, pAKT, PD-L1, PD-L2, HIF-1α and HIF-2α were evaluated on immunohistochemical staining. Mutation of various cancer-related genes was investigated on OncoMap ver. 4.4 core. Microarray and quantitative real-time reverse transcriptase-polymerase chain reaction (qRT-PCR) were also performed to compare the expression patterns of miRNAs between responders and nonresponders in tissues samples. Patient's age, time from diagnosis to TKI initiation and thrombodytosis were different between responder and nonresponder (all, p <0.05). Histologically, tumor size, T stage, ISUP grade, sarcomatous change, necrosis and lymph node metastasis of responder were significantly lower or smaller than nonresponder (all, p <0.05). pAKT, PD-L1, PD-L2, FGFR2, pS6 and PDGFRβ showed more expression in nonresponder than in responder; on the other hands, HIF-1α, IL-8 and CA9 showed more expression in responder than in nonresponder (all, p <0.05). Expression of miR-421 was higher in responder than nonresponder (p = 0.008). A classifier to VEGF-TKI response was developed with various clinicopathological features, protein expression and miRNA expression. Tumor size, T stage, ISUP grade, necrosis, sarcomatoid change, pAKT, PD-L1, CA9, pS6, HIF-1α and miR-421 were selected by chi-square test with p <0.01. Using 10-fold cross validation (CV) by support vector machine, 3 features, i.e., necrosis, sarcomatoid change and HIF-1α, were finally selected as features for classifier and accuracy of 10-fold CV was 0.87. When the classifier was checked with all patients, apparent accuracy was 0.875 (95% CI, 0.782-0.938). In addition, the classifier could be presented by a simple decision tree for clinical use. In conclusion, we built a VEGF-TKI response classifier by comprehensive inclusion of clinical, laboratorical, histological and immunihistochemical features, mutation of cancer-related genes and miRNA expression using machine learning method and it may be helpful to receive a proper treatment in mRCC patients.

Citation Format: Heounjeong Go, Mun Jung Kang, Pil-Jong Kim, Ja-Min Park, Jae-Lyun Lee, Ji Young Park, Yong Mee Cho. Establishment of the classifier for a response to vascular endothelial growth factor (VEGF)-tyrosine kinase inhibitor (TKI) in metastatic renal cell carcinoma. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 429.