Kidney Renal Clear Cell Carcinoma: Correlation between copy number variations of arm-level result and selected clinical features<BR>(primary solid tumor cohort)

Kidney Renal Clear Cell Carcinoma: Correlation between copy number variations of arm-level result and selected clinical features
(primary solid tumor cohort)

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Overview

Introduction

This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.

Summary

Testing the association between copy number variation 71 arm-level results and 8 clinical features across 493 patients, 3 significant findings detected with Q value < 0.25.

5p gain cnv correlated to 'AGE'.
5q gain cnv correlated to 'AGE'.
11p loss cnv 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 71 arm-level results and 8 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 3 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
5p gain	141 (29%)	352	0.124 (1.00)	0.000246 (0.132)	0.116 (1.00)	0.563 (1.00)	0.211 (1.00)	1 (1.00)	0.783 (1.00)	0.288 (1.00)
5q gain	156 (32%)	337	0.297 (1.00)	0.00037 (0.198)	0.156 (1.00)	0.482 (1.00)	0.21 (1.00)	0.615 (1.00)	0.594 (1.00)	0.4 (1.00)
11p loss	5 (1%)	488	3.9e-05 (0.0209)	0.943 (1.00)	0.663 (1.00)		0.618 (1.00)	0.141 (1.00)	0.569 (1.00)	0.908 (1.00)
1p gain	5 (1%)	488	0.498 (1.00)	0.942 (1.00)	1 (1.00)		0.405 (1.00)	0.141 (1.00)	1 (1.00)	0.368 (1.00)
1q gain	32 (6%)	461	0.96 (1.00)	0.185 (1.00)	0.565 (1.00)	0.115 (1.00)	0.066 (1.00)	0.33 (1.00)	0.803 (1.00)	0.0528 (1.00)
2p gain	48 (10%)	445	0.58 (1.00)	0.14 (1.00)	0.201 (1.00)	0.563 (1.00)	0.758 (1.00)	1 (1.00)	0.206 (1.00)	0.302 (1.00)
2q gain	49 (10%)	444	0.411 (1.00)	0.205 (1.00)	0.0387 (1.00)	0.159 (1.00)	0.871 (1.00)	0.702 (1.00)	0.208 (1.00)	0.383 (1.00)
3p gain	7 (1%)	486	0.95 (1.00)	0.429 (1.00)	0.243 (1.00)		0.508 (1.00)	0.264 (1.00)	0.295 (1.00)	0.12 (1.00)
3q gain	27 (5%)	466	0.575 (1.00)	0.948 (1.00)	1 (1.00)	0.0261 (1.00)	0.249 (1.00)	0.359 (1.00)	0.164 (1.00)	0.312 (1.00)
4p gain	8 (2%)	485	0.235 (1.00)	0.875 (1.00)	0.72 (1.00)		0.327 (1.00)	0.369 (1.00)	0.616 (1.00)	0.0885 (1.00)
4q gain	8 (2%)	485	0.653 (1.00)	0.389 (1.00)	1 (1.00)		0.561 (1.00)	0.369 (1.00)	0.616 (1.00)	0.223 (1.00)
6p gain	6 (1%)	487	0.224 (1.00)	0.236 (1.00)	1 (1.00)		0.662 (1.00)	1 (1.00)	1 (1.00)	0.756 (1.00)
6q gain	5 (1%)	488	0.183 (1.00)	0.442 (1.00)	0.663 (1.00)		0.405 (1.00)	1 (1.00)	0.569 (1.00)	0.368 (1.00)
7p gain	127 (26%)	366	0.862 (1.00)	0.314 (1.00)	0.0846 (1.00)	0.893 (1.00)	0.261 (1.00)	0.599 (1.00)	0.0451 (1.00)	0.179 (1.00)
7q gain	129 (26%)	364	0.517 (1.00)	0.38 (1.00)	0.0673 (1.00)	0.699 (1.00)	0.113 (1.00)	1 (1.00)	0.00661 (1.00)	0.0455 (1.00)
8p gain	14 (3%)	479	0.574 (1.00)	0.761 (1.00)	0.0417 (1.00)	0.757 (1.00)	0.00637 (1.00)	1 (1.00)	0.462 (1.00)	0.00287 (1.00)
8q gain	34 (7%)	459	0.856 (1.00)	0.427 (1.00)	0.192 (1.00)	0.471 (1.00)	0.00565 (1.00)	0.657 (1.00)	1 (1.00)	0.00264 (1.00)
9p gain	8 (2%)	485	0.123 (1.00)	0.0377 (1.00)	0.0232 (1.00)		0.614 (1.00)	1 (1.00)	0.616 (1.00)	0.378 (1.00)
9q gain	8 (2%)	485	0.769 (1.00)	0.97 (1.00)	0.457 (1.00)		0.00758 (1.00)	1 (1.00)	0.356 (1.00)	0.111 (1.00)
10p gain	6 (1%)	487	0.72 (1.00)	0.511 (1.00)	0.422 (1.00)		0.33 (1.00)	1 (1.00)	0.233 (1.00)	0.288 (1.00)
10q gain	4 (1%)	489	0.511 (1.00)	0.169 (1.00)	0.123 (1.00)		0.646 (1.00)	1 (1.00)	0.489 (1.00)	0.377 (1.00)
11p gain	17 (3%)	476	0.974 (1.00)	0.8 (1.00)	0.608 (1.00)	0.115 (1.00)	0.021 (1.00)	1 (1.00)	0.16 (1.00)	0.0198 (1.00)
11q gain	15 (3%)	478	0.317 (1.00)	0.811 (1.00)	0.409 (1.00)		0.0709 (1.00)	1 (1.00)	0.713 (1.00)	0.0435 (1.00)
12p gain	78 (16%)	415	0.111 (1.00)	0.115 (1.00)	0.364 (1.00)	0.404 (1.00)	0.0448 (1.00)	1 (1.00)	0.122 (1.00)	0.0325 (1.00)
12q gain	78 (16%)	415	0.142 (1.00)	0.167 (1.00)	0.364 (1.00)	0.404 (1.00)	0.0448 (1.00)	0.748 (1.00)	0.122 (1.00)	0.0325 (1.00)
13q gain	14 (3%)	479	0.291 (1.00)	0.851 (1.00)	0.779 (1.00)		0.321 (1.00)	1 (1.00)	1 (1.00)	0.24 (1.00)
14q gain	5 (1%)	488	0.438 (1.00)	0.524 (1.00)	1 (1.00)		0.0216 (1.00)	1 (1.00)	0.569 (1.00)	0.131 (1.00)
15q gain	14 (3%)	479	0.868 (1.00)	0.939 (1.00)	0.779 (1.00)		0.231 (1.00)	0.46 (1.00)	0.462 (1.00)	0.415 (1.00)
16p gain	65 (13%)	428	0.611 (1.00)	0.0654 (1.00)	0.212 (1.00)	0.4 (1.00)	0.804 (1.00)	0.0837 (1.00)	0.713 (1.00)	0.719 (1.00)
16q gain	58 (12%)	435	0.487 (1.00)	0.0864 (1.00)	0.106 (1.00)	0.191 (1.00)	0.649 (1.00)	0.143 (1.00)	1 (1.00)	0.715 (1.00)
17p gain	16 (3%)	477	0.456 (1.00)	0.533 (1.00)	0.795 (1.00)		0.279 (1.00)	0.501 (1.00)	0.149 (1.00)	0.149 (1.00)
17q gain	22 (4%)	471	0.143 (1.00)	0.539 (1.00)	0.647 (1.00)	0.161 (1.00)	0.327 (1.00)	0.607 (1.00)	0.553 (1.00)	0.439 (1.00)
18p gain	18 (4%)	475	0.0884 (1.00)	0.782 (1.00)	0.0223 (1.00)		0.963 (1.00)	1 (1.00)	0.333 (1.00)	0.526 (1.00)
18q gain	18 (4%)	475	0.0884 (1.00)	0.782 (1.00)	0.0223 (1.00)		0.963 (1.00)	1 (1.00)	0.333 (1.00)	0.526 (1.00)
19p gain	25 (5%)	468	0.935 (1.00)	0.703 (1.00)	1 (1.00)	0.161 (1.00)	0.52 (1.00)	0.301 (1.00)	0.251 (1.00)	0.223 (1.00)
19q gain	28 (6%)	465	0.884 (1.00)	0.709 (1.00)	0.683 (1.00)	0.161 (1.00)	0.469 (1.00)	0.359 (1.00)	0.174 (1.00)	0.23 (1.00)
20p gain	66 (13%)	427	0.274 (1.00)	0.268 (1.00)	0.0127 (1.00)	0.315 (1.00)	0.00519 (1.00)	0.485 (1.00)	0.0429 (1.00)	0.0202 (1.00)
20q gain	68 (14%)	425	0.428 (1.00)	0.22 (1.00)	0.0195 (1.00)	0.315 (1.00)	0.0086 (1.00)	0.485 (1.00)	0.106 (1.00)	0.0317 (1.00)
21q gain	33 (7%)	460	0.339 (1.00)	0.324 (1.00)	0.851 (1.00)	0.161 (1.00)	0.508 (1.00)	1 (1.00)	1 (1.00)	0.894 (1.00)
22q gain	25 (5%)	468	0.964 (1.00)	0.83 (1.00)	0.526 (1.00)		0.196 (1.00)	1 (1.00)	0.251 (1.00)	0.439 (1.00)
Xq gain	11 (2%)	482	0.44 (1.00)	0.723 (1.00)	0.0546 (1.00)		0.871 (1.00)	1 (1.00)	0.0733 (1.00)	0.0503 (1.00)
1p loss	32 (6%)	461	0.877 (1.00)	0.0984 (1.00)	0.565 (1.00)	0.115 (1.00)	0.0145 (1.00)	0.607 (1.00)	0.45 (1.00)	0.0696 (1.00)
1q loss	21 (4%)	472	0.779 (1.00)	0.599 (1.00)	0.644 (1.00)		0.0938 (1.00)	1 (1.00)	0.756 (1.00)	0.263 (1.00)
2p loss	10 (2%)	483	0.471 (1.00)	0.932 (1.00)	0.506 (1.00)		0.0915 (1.00)	1 (1.00)	1 (1.00)	0.481 (1.00)
2q loss	11 (2%)	482	0.856 (1.00)	0.983 (1.00)	0.344 (1.00)		0.193 (1.00)	1 (1.00)	1 (1.00)	0.726 (1.00)
3p loss	304 (62%)	189	0.556 (1.00)	0.355 (1.00)	0.0797 (1.00)	0.906 (1.00)	0.0168 (1.00)	0.462 (1.00)	0.0145 (1.00)	0.018 (1.00)
3q loss	73 (15%)	420	0.948 (1.00)	0.0175 (1.00)	0.000815 (0.434)	0.573 (1.00)	0.319 (1.00)	0.702 (1.00)	0.729 (1.00)	0.61 (1.00)
4p loss	35 (7%)	458	0.0602 (1.00)	0.558 (1.00)	0.854 (1.00)	0.0176 (1.00)	0.0236 (1.00)	1 (1.00)	0.808 (1.00)	0.118 (1.00)
4q loss	28 (6%)	465	0.0183 (1.00)	0.992 (1.00)	0.841 (1.00)	0.0261 (1.00)	0.00329 (1.00)	0.607 (1.00)	0.174 (1.00)	0.025 (1.00)
6p loss	58 (12%)	435	0.235 (1.00)	0.187 (1.00)	0.00303 (1.00)	0.499 (1.00)	0.757 (1.00)	0.189 (1.00)	0.439 (1.00)	0.797 (1.00)
6q loss	82 (17%)	411	0.548 (1.00)	0.719 (1.00)	0.127 (1.00)	0.851 (1.00)	0.969 (1.00)	0.0592 (1.00)	0.868 (1.00)	0.767 (1.00)
8p loss	94 (19%)	399	0.512 (1.00)	0.0949 (1.00)	0.281 (1.00)	0.0817 (1.00)	0.603 (1.00)	0.322 (1.00)	0.429 (1.00)	0.514 (1.00)
8q loss	37 (8%)	456	0.279 (1.00)	0.915 (1.00)	0.0469 (1.00)	0.00419 (1.00)	1 (1.00)	0.608 (1.00)	0.244 (1.00)	0.858 (1.00)
9p loss	87 (18%)	406	0.0111 (1.00)	0.0624 (1.00)	0.0128 (1.00)	0.0261 (1.00)	0.185 (1.00)	0.745 (1.00)	0.0209 (1.00)	0.0902 (1.00)
9q loss	92 (19%)	401	0.00542 (1.00)	0.034 (1.00)	0.0155 (1.00)	0.0473 (1.00)	0.0165 (1.00)	1 (1.00)	0.0035 (1.00)	0.00434 (1.00)
10p loss	32 (6%)	461	0.507 (1.00)	0.49 (1.00)	0.565 (1.00)	0.0261 (1.00)	0.0141 (1.00)	1 (1.00)	0.803 (1.00)	0.866 (1.00)
10q loss	48 (10%)	445	0.742 (1.00)	0.258 (1.00)	0.268 (1.00)	0.0535 (1.00)	0.00839 (1.00)	0.621 (1.00)	0.677 (1.00)	0.618 (1.00)
11q loss	7 (1%)	486	0.148 (1.00)	0.638 (1.00)	0.43 (1.00)		0.799 (1.00)	0.264 (1.00)	1 (1.00)	1 (1.00)
13q loss	31 (6%)	462	0.0012 (0.639)	0.0513 (1.00)	0.334 (1.00)	0.00419 (1.00)	0.00133 (0.707)	1 (1.00)	0.0394 (1.00)	0.0118 (1.00)
14q loss	158 (32%)	335	0.251 (1.00)	0.203 (1.00)	0.265 (1.00)	0.508 (1.00)	0.0194 (1.00)	0.0682 (1.00)	0.142 (1.00)	0.0235 (1.00)
15q loss	12 (2%)	481	0.257 (1.00)	0.13 (1.00)	1 (1.00)		0.282 (1.00)	0.417 (1.00)	0.408 (1.00)	0.266 (1.00)
16q loss	5 (1%)	488	0.0864 (1.00)	0.0696 (1.00)	0.663 (1.00)		0.00635 (1.00)	1 (1.00)	0.569 (1.00)	0.0726 (1.00)
17p loss	25 (5%)	468	0.0387 (1.00)	0.658 (1.00)	0.288 (1.00)	0.0473 (1.00)	0.131 (1.00)	1 (1.00)	0.0396 (1.00)	0.223 (1.00)
17q loss	12 (2%)	481	0.697 (1.00)	0.831 (1.00)	0.232 (1.00)		0.0534 (1.00)	1 (1.00)	1 (1.00)	0.47 (1.00)
18p loss	46 (9%)	447	0.317 (1.00)	0.106 (1.00)	0.626 (1.00)	0.518 (1.00)	0.102 (1.00)	0.691 (1.00)	0.204 (1.00)	0.36 (1.00)
18q loss	48 (10%)	445	0.699 (1.00)	0.22 (1.00)	0.268 (1.00)	0.518 (1.00)	0.432 (1.00)	0.702 (1.00)	0.292 (1.00)	0.692 (1.00)
19p loss	4 (1%)	489	0.000514 (0.274)	0.89 (1.00)	0.612 (1.00)		0.014 (1.00)	1 (1.00)	1 (1.00)	0.0681 (1.00)
20p loss	6 (1%)	487	0.751 (1.00)	0.967 (1.00)	1 (1.00)		0.881 (1.00)	1 (1.00)	1 (1.00)	0.931 (1.00)
21q loss	32 (6%)	461	0.779 (1.00)	0.944 (1.00)	1 (1.00)		0.00608 (1.00)	0.359 (1.00)	0.45 (1.00)	0.181 (1.00)
22q loss	11 (2%)	482	0.228 (1.00)	0.0446 (1.00)	0.344 (1.00)		0.272 (1.00)	1 (1.00)	1 (1.00)	0.191 (1.00)
Xq loss	7 (1%)	486	0.507 (1.00)	0.749 (1.00)	0.102 (1.00)		0.63 (1.00)	1 (1.00)	0.602 (1.00)	0.574 (1.00)

'5p gain mutation analysis' versus 'AGE'

P value = 0.000246 (t-test), Q value = 0.13

Table S1. Gene #9: '5p gain mutation analysis' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	492	60.6 (12.2)
5P GAIN MUTATED	141	63.7 (12.0)
5P GAIN WILD-TYPE	351	59.3 (12.1)

Figure S1. Get High-res Image Gene #9: '5p gain mutation analysis' versus Clinical Feature #2: 'AGE'

'5q gain mutation analysis' versus 'AGE'

P value = 0.00037 (t-test), Q value = 0.2

Table S2. Gene #10: '5q gain mutation analysis' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	492	60.6 (12.2)
5Q GAIN MUTATED	155	63.5 (12.3)
5Q GAIN WILD-TYPE	337	59.2 (11.9)

Figure S2. Get High-res Image Gene #10: '5q gain mutation analysis' versus Clinical Feature #2: 'AGE'

'11p loss mutation analysis' versus 'Time to Death'

P value = 3.9e-05 (logrank test), Q value = 0.021

Table S3. Gene #57: '11p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	490	158	0.1 - 111.0 (35.2)
11P LOSS MUTATED	5	4	1.8 - 28.5 (12.1)
11P LOSS WILD-TYPE	485	154	0.1 - 111.0 (35.5)

Figure S3. Get High-res Image Gene #57: '11p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

Methods & Data

Input

Mutation data file = broad_values_by_arm.mutsig.cluster.txt
Clinical data file = KIRC-TP.clin.merged.picked.txt
Number of patients = 493
Number of significantly arm-level cnvs = 71
Number of selected clinical features = 8
Exclude genes that fewer than K tumors have mutations, K = 3

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations 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 mutations 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)

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