Oncotarget

Clinical Research Papers:

Improving needle biopsy accuracy in small renal mass using tumor-specific DNA methylation markers

Sameer Chopra, Jie Liu, Mehrdad Alemozaffar, Peter W. Nichols, Manju Aron, Daniel J. Weisenberger, Clayton K. Collings, Sumeet Syan, Brian Hu, Mihir Desai, Monish Aron, Vinay Duddalwar, Inderbir Gill, Gangning Liang and Kimberly D. Siegmund _

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Oncotarget. 2017; 8:5439-5448. https://doi.org/10.18632/oncotarget.12276

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Abstract

Sameer Chopra1,*, Jie Liu2,*, Mehrdad Alemozaffar1, Peter W. Nichols3, Manju Aron3, Daniel J. Weisenberger4, Clayton K. Collings1, Sumeet Syan1, Brian Hu5, Mihir Desai1, Monish Aron1, Vinay Duddalwar6, Inderbir Gill1, Gangning Liang1 and Kimberly D. Siegmund2

1 Department of Urology, Norris Comprehensive Cancer Center, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

2 Department of Preventive Medicine, Norris Comprehensive Cancer Center, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

3 Department of Pathology, Norris Comprehensive Cancer Center, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

4 Department of Biochemistry and Molecular Biology, Norris Comprehensive Cancer Center, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

5 Department of Urology, Loma Linda University, Loma Linda, CA, USA

6 Department of Radiology, Norris Comprehensive Cancer Center, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

* These authors have contributed equally to this work

Correspondence to:

Gangning Liang, email:

Kimberly D. Siegmund , email:

Keywords: small renal mass, tumor classification, DNA methylation, kidney cancer

Received: July 29, 2016 Accepted: September 20, 2016 Published: September 27, 2016

Abstract

Purpose: The clinical management of small renal masses (SRMs) is challenging since the current methods for distinguishing between benign masses and malignant renal cell carcinomas (RCCs) are frequently inaccurate or inconclusive. In addition, renal cancer subtypes also have different treatments and outcomes. High false negative rates increase the risk of cancer progression and indeterminate diagnoses result in unnecessary and potentially morbid surgical procedures.

Experimental Design: We built a predictive classification model for kidney tumors using 697 DNA methylation profiles from six different subgroups: clear cell, papillary and chromophobe RCC, benign angiomylolipomas, oncocytomas, and normal kidney tissues. Furthermore, the DNA methylation-dependent classifier has been validated in 272 ex vivo needle biopsy samples from 100 renal masses (71% SRMs).

Results: In general, the results were highly reproducible (89%, n=70) in predicting identical malignant subtypes from biopsies. Overall, 98% of adjacent-normals (n=102) were correctly classified as normal, while 92% of tumors (n=71) were correctly classified malignant and 86% of benign (n=29) were correctly classified benign by this classification model.

Conclusions: Overall, this study provides molecular-based support for using routine needle biopsies to determine tumor classification of SRMs and support the clinical decision-making.


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