Intratumoral heterogeneity analysis reveals hidden associations between protein expression losses and patient survival in clear cell renal cell carcinoma
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Wei Jiang1,*, Essel Dulaimi2,*, Karthik Devarajan3, Theodore Parsons1, Qiong Wang1, Raymond O'Neill1, Charalambos Solomides1, Stephen C. Peiper1, Joseph R. Testa4, Robert Uzzo5, Haifeng Yang1
1Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Philadelphia, PA, United States
2Department of Pathology, Fox Chase Cancer Center, Philadelphia, PA, United States
3Biostatistics, Fox Chase Cancer Center, Philadelphia, PA, United States
4Cancer Biology, Fox Chase Cancer Center, Philadelphia, PA, United States
5Department of Surgical Oncology, Fox Chase Cancer Center, Philadelphia, PA, United States
*These authors contributed equally to this work
Haifeng Yang, email: [email protected]
Karthik Devarajan, email: [email protected]
Keywords: clear cell renal cell carcinoma, immunohistochemistry, intratumoral heterogeneity, overall survival, SWI/SNF
Received: February 14, 2017 Accepted: March 27, 2017 Published: April 08, 2017
Intratumoral heterogeneity (ITH) is a prominent feature of kidney cancer. It is not known whether it has utility in finding associations between protein expression and clinical parameters. We used ITH that is detected by immunohistochemistry (IHC) to aid the association analysis between the loss of SWI/SNF components and clinical parameters.160 ccRCC tumors (40 per tumor stage) were used to generate tissue microarray (TMA). Four foci from different regions of each tumor were selected. IHC was performed against PBRM1, ARID1A, SETD2, SMARCA4, and SMARCA2. Statistical analyses were performed to correlate biomarker losses with patho-clinical parameters. Categorical variables were compared between groups using Fisher’s exact tests. Univariate and multivariable analyses were used to correlate biomarker changes and patient survivals. Multivariable analyses were performed by constructing decision trees using the classification and regression trees (CART) methodology. IHC detected widespread ITH in ccRCC tumors. The statistical analysis of the “Truncal loss” (root loss) found additional correlations between biomarker losses and tumor stages than the traditional “Loss in tumor (total)”. Losses of SMARCA4 or SMARCA2 significantly improved prognosis for overall survival (OS). Losses of PBRM1, ARID1A or SETD2 had the opposite effect. Thus “Truncal Loss” analysis revealed hidden links between protein losses and patient survival in ccRCC.
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