Kidney Renal Papillary Cell Carcinoma: Correlation between copy number variation genes and selected clinical features

Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)

Overview

Introduction

This pipeline computes the correlation between significant copy number variation (cnv) genes and selected clinical features.

Summary

Testing the association between copy number variation of 17 peak regions and 8 clinical features across 95 patients, 2 significant findings detected with Q value < 0.25.

Del Peak 16(Xq21.31) cnvs correlated to 'Time to Death'.
Del Peak 17(Xq28) cnvs correlated to 'Time to Death'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 17 regions and 8 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 2 significant findings detected.


		Clinical Features	Time to Death	AGE	GENDER	KARNOFSKY PERFORMANCE SCORE	PATHOLOGY T	PATHOLOGY N	PATHOLOGICSPREAD(M)	TUMOR STAGE
	nCNV (%)	nWild-Type	logrank test	t-test	Fisher's exact test	t-test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test
Del Peak 16(Xq21 31)	12 (13%)	83	3.65e-05 (0.00457)	0.148 (1.00)	0.328 (1.00)	0.466 (1.00)	0.0321 (1.00)	0.714 (1.00)	0.223 (1.00)	0.121 (1.00)
Del Peak 17(Xq28)	11 (12%)	84	7.66e-06 (0.000965)	0.204 (1.00)	0.291 (1.00)	0.498 (1.00)	0.241 (1.00)	0.49 (1.00)	0.726 (1.00)	0.341 (1.00)
Amp Peak 1(19p13 2)	11 (12%)	84	0.99 (1.00)	0.176 (1.00)	1 (1.00)	0.152 (1.00)	0.0558 (1.00)	0.687 (1.00)	0.826 (1.00)	0.579 (1.00)
Del Peak 1(1p36 31)	18 (19%)	77	0.206 (1.00)	0.415 (1.00)	0.776 (1.00)	0.422 (1.00)	0.0802 (1.00)	0.406 (1.00)	0.0078 (0.959)	0.0507 (1.00)
Del Peak 2(2p11 2)	3 (3%)	92	0.355 (1.00)	0.686 (1.00)	1 (1.00)		0.68 (1.00)		1 (1.00)	0.552 (1.00)
Del Peak 3(2q37 3)	5 (5%)	90	0.656 (1.00)	0.357 (1.00)	0.15 (1.00)		0.0141 (1.00)		0.448 (1.00)	0.0175 (1.00)
Del Peak 4(3p22 1)	5 (5%)	90	0.216 (1.00)	0.106 (1.00)	0.317 (1.00)		0.164 (1.00)	1 (1.00)	0.385 (1.00)	0.105 (1.00)
Del Peak 5(4q32 1)	10 (11%)	85	0.0681 (1.00)	0.605 (1.00)	1 (1.00)		0.429 (1.00)	0.763 (1.00)	0.152 (1.00)	0.359 (1.00)
Del Peak 6(5p15 33)	7 (7%)	88	0.111 (1.00)	0.339 (1.00)	0.417 (1.00)		0.036 (1.00)	0.0263 (1.00)	0.428 (1.00)	0.23 (1.00)
Del Peak 7(5q15)	7 (7%)	88	0.615 (1.00)	0.123 (1.00)	0.417 (1.00)		0.0953 (1.00)	0.509 (1.00)	0.136 (1.00)	0.361 (1.00)
Del Peak 8(5q35 2)	7 (7%)	88	0.615 (1.00)	0.233 (1.00)	0.417 (1.00)		0.036 (1.00)	0.509 (1.00)	0.428 (1.00)	0.0867 (1.00)
Del Peak 9(6q22 31)	10 (11%)	85	0.0284 (1.00)	0.73 (1.00)	0.0595 (1.00)	0.356 (1.00)	0.0449 (1.00)	0.816 (1.00)	0.204 (1.00)	0.223 (1.00)
Del Peak 10(9p21 3)	15 (16%)	80	0.0059 (0.732)	0.545 (1.00)	0.0352 (1.00)	0.422 (1.00)	0.0112 (1.00)	0.729 (1.00)	0.0322 (1.00)	0.0271 (1.00)
Del Peak 12(14q11 2)	20 (21%)	75	0.223 (1.00)	0.423 (1.00)	0.586 (1.00)	0.473 (1.00)	0.157 (1.00)	0.656 (1.00)	0.0948 (1.00)	0.0789 (1.00)
Del Peak 13(14q23 3)	19 (20%)	76	0.203 (1.00)	0.821 (1.00)	0.574 (1.00)	0.422 (1.00)	0.0923 (1.00)	0.243 (1.00)	0.107 (1.00)	0.0474 (1.00)
Del Peak 14(14q32 2)	19 (20%)	76	0.203 (1.00)	0.776 (1.00)	0.786 (1.00)	0.422 (1.00)	0.311 (1.00)	0.243 (1.00)	0.0561 (1.00)	0.0551 (1.00)
Del Peak 15(19q13 42)	5 (5%)	90	0.422 (1.00)	0.333 (1.00)	1 (1.00)		0.0877 (1.00)	0.687 (1.00)	0.448 (1.00)	0.204 (1.00)

'Del Peak 16(Xq21.31) mutation analysis' versus 'Time to Death'

P value = 3.65e-05 (logrank test), Q value = 0.0046

Table S1. Gene #16: 'Del Peak 16(Xq21.31) mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	88	14	0.0 - 182.7 (15.7)
DEL PEAK 16(XQ21.31) MUTATED	11	5	0.0 - 79.8 (9.6)
DEL PEAK 16(XQ21.31) WILD-TYPE	77	9	0.5 - 182.7 (20.1)

Figure S1. Get High-res Image Gene #16: 'Del Peak 16(Xq21.31) mutation analysis' versus Clinical Feature #1: 'Time to Death'

'Del Peak 17(Xq28) mutation analysis' versus 'Time to Death'

P value = 7.66e-06 (logrank test), Q value = 0.00097

Table S2. Gene #17: 'Del Peak 17(Xq28) mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	88	14	0.0 - 182.7 (15.7)
DEL PEAK 17(XQ28) MUTATED	10	4	0.0 - 22.9 (10.3)
DEL PEAK 17(XQ28) WILD-TYPE	78	10	0.5 - 182.7 (22.3)

Figure S2. Get High-res Image Gene #17: 'Del Peak 17(Xq28) mutation analysis' versus Clinical Feature #1: 'Time to Death'

Methods & Data

Input

Copy number data file = All Lesions File (all_lesions.conf_##.txt, where ## is the confidence level). The all lesions file is from GISTIC pipeline and summarizes the results from the GISTIC run. It contains data about the significant regions of amplification and deletion as well as which samples are amplified or deleted in each of these regions. The identified regions are listed down the first column, and the samples are listed across the first row, starting in column 10.
Clinical data file = KIRP.clin.merged.picked.txt
Number of patients = 95
Number of copy number variation regions = 17
Number of selected clinical features = 8
Exclude regions that fewer than K tumors have alterations, K = 3

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene cnvs were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between tumors with and without gene cnvs using 't.test' function in R

Fisher's exact test

For binary or multi-class clinical features (nominal or ordinal), two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References

[1] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)

[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[3] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)

[4] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)

[5] GISTIC_version2

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