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Pathol Oncol Res. 2019 Jan;25(1):51-58. doi: 10.1007/s12253-017-0323-2. Epub 2017 Sep 29.

Development of Response Classifier for Vascular Endothelial Growth Factor Receptor (VEGFR)-Tyrosine Kinase Inhibitor (TKI) in Metastatic Renal Cell Carcinoma.

Author information

1
Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea.
2
Biomedical Knowledge Engineering Laboratory, Seoul National University College of Dental Medicine, 101 Daehakno, Jongni-gu, Seoul, 03080, Republic of Korea.
3
Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea.
4
Department of Pathology, Daegu Catholic University Medical Center, 33, Duryugongwon-ro 17-gil, Nam-gu, Daegu, 42472, Republic of Korea.
5
Department of Pathology and Genomic Medicine, The Methodist Hospital and Weill Medical College of Cornell University, 6565 Fannin St, Houston, TX, 77030, USA.
6
Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea. yongcho@amc.seoul.kr.

Abstract

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.

KEYWORDS:

Machine learning; Metastatic renal cell carcinoma; Response classifier; Tyrosine kinase inhibitors; Vascular endothelial growth factor signaling

PMID:
28963640
DOI:
10.1007/s12253-017-0323-2
[Indexed for MEDLINE]

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