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Pathology & Oncology Research

, Volume 25, Issue 1, pp 51–58 | Cite as

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

  • Heounjeong Go
  • Mun Jung Kang
  • Pil-Jong Kim
  • Jae-Lyun Lee
  • Ji Y. Park
  • Ja-Min Park
  • Jae Y. Ro
  • Yong Mee Cho
Original Article

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

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

Notes

Acknowledgements

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

Ethical Approval

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.

Supplementary material

12253_2017_323_MOESM1_ESM.docx (25 kb)
ESM 1 (DOCX 24 kb)
12253_2017_323_MOESM2_ESM.docx (31 kb)
ESM 2 (DOCX 31 kb)

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Copyright information

© Arányi Lajos Foundation 2017

Authors and Affiliations

  • Heounjeong Go
    • 1
  • Mun Jung Kang
    • 1
  • Pil-Jong Kim
    • 2
  • Jae-Lyun Lee
    • 3
  • Ji Y. Park
    • 4
  • Ja-Min Park
    • 1
  • Jae Y. Ro
    • 5
  • Yong Mee Cho
    • 1
  1. 1.Department of Pathology, Asan Medical CenterUniversity of Ulsan College of MedicineSeoulRepublic of Korea
  2. 2.Biomedical Knowledge Engineering LaboratorySeoul National University College of Dental MedicineSeoulRepublic of Korea
  3. 3.Department of Oncology, Asan Medical CenterUniversity of Ulsan College of MedicineSeoulRepublic of Korea
  4. 4.Department of PathologyDaegu Catholic University Medical CenterDaeguRepublic of Korea
  5. 5.Department of Pathology and Genomic MedicineThe Methodist Hospital and Weill Medical College of Cornell UniversityHoustonUSA

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