Kidney Renal Papillary Cell Carcinoma: Correlation between copy number variations of arm-level result 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 arm-level copy number variations (cnvs) and selected clinical features.

Summary

Testing the association between copy number variation 53 arm-level results and 8 clinical features across 95 patients, 7 significant findings detected with Q value < 0.25.

1q gain cnv correlated to 'Time to Death'.
6q gain cnv correlated to 'Time to Death'.
17p gain cnv correlated to 'PATHOLOGY.T' and 'TUMOR.STAGE'.
5p loss cnv correlated to 'Time to Death'.
11p loss cnv correlated to 'Time to Death'.
17p 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 53 arm-level results and 8 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 7 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
17p gain	48 (51%)	47	0.213 (1.00)	0.0264 (1.00)	0.00707 (1.00)	0.223 (1.00)	0.000566 (0.203)	0.264 (1.00)	0.0287 (1.00)	0.000422 (0.151)
1q gain	8 (8%)	87	0.000112 (0.0406)	0.725 (1.00)	0.229 (1.00)		0.00427 (1.00)	1 (1.00)	0.124 (1.00)	0.00784 (1.00)
6q gain	3 (3%)	92	6.38e-06 (0.00231)	0.243 (1.00)	0.553 (1.00)		0.68 (1.00)		0.00801 (1.00)	0.0741 (1.00)
5p loss	3 (3%)	92	0.000245 (0.0881)	0.189 (1.00)	0.207 (1.00)		0.061 (1.00)
11p loss	6 (6%)	89	0.000578 (0.206)	0.146 (1.00)	1 (1.00)		0.344 (1.00)		0.335 (1.00)	0.258 (1.00)
17p loss	4 (4%)	91	2.62e-07 (9.5e-05)	0.897 (1.00)	0.0754 (1.00)		0.154 (1.00)		0.0754 (1.00)	0.0682 (1.00)
2p gain	10 (11%)	85	0.196 (1.00)	0.281 (1.00)	1 (1.00)	0.539 (1.00)	1 (1.00)	0.219 (1.00)	0.0317 (1.00)	0.0413 (1.00)
2q gain	11 (12%)	84	0.305 (1.00)	0.528 (1.00)	0.726 (1.00)	0.539 (1.00)	1 (1.00)	0.053 (1.00)	0.0832 (1.00)	0.00966 (1.00)
3p gain	23 (24%)	72	0.408 (1.00)	0.926 (1.00)	0.0653 (1.00)	0.0981 (1.00)	0.0187 (1.00)	1 (1.00)	0.747 (1.00)	0.285 (1.00)
3q gain	25 (26%)	70	0.283 (1.00)	0.974 (1.00)	0.0392 (1.00)	0.219 (1.00)	0.0458 (1.00)	1 (1.00)	0.642 (1.00)	0.38 (1.00)
4p gain	4 (4%)	91	0.0729 (1.00)	0.0368 (1.00)	1 (1.00)		0.311 (1.00)	0.352 (1.00)	0.0252 (1.00)	0.0741 (1.00)
4q gain	3 (3%)	92	0.693 (1.00)	0.0347 (1.00)	1 (1.00)		0.319 (1.00)
5p gain	9 (9%)	86	0.766 (1.00)	0.174 (1.00)	0.442 (1.00)	0.394 (1.00)	0.0502 (1.00)	0.816 (1.00)	0.355 (1.00)	0.0735 (1.00)
5q gain	9 (9%)	86	0.318 (1.00)	0.259 (1.00)	0.442 (1.00)	0.394 (1.00)	0.235 (1.00)	0.816 (1.00)	0.655 (1.00)	0.283 (1.00)
6p gain	4 (4%)	91	0.0775 (1.00)	0.332 (1.00)	0.316 (1.00)		0.154 (1.00)	0.509 (1.00)	0.00878 (1.00)	0.0682 (1.00)
7p gain	51 (54%)	44	0.0752 (1.00)	0.569 (1.00)	0.0764 (1.00)	0.192 (1.00)	0.00337 (1.00)	0.581 (1.00)	0.101 (1.00)	0.00319 (1.00)
7q gain	52 (55%)	43	0.0752 (1.00)	0.569 (1.00)	0.176 (1.00)	0.192 (1.00)	0.0107 (1.00)	0.581 (1.00)	0.116 (1.00)	0.0112 (1.00)
8p gain	5 (5%)	90	0.585 (1.00)	0.263 (1.00)	0.63 (1.00)		0.0877 (1.00)	0.575 (1.00)	0.75 (1.00)	0.146 (1.00)
8q gain	7 (7%)	88	0.00545 (1.00)	0.675 (1.00)	0.417 (1.00)		0.0136 (1.00)	0.166 (1.00)	0.491 (1.00)	0.0116 (1.00)
10p gain	3 (3%)	92			1 (1.00)		0.319 (1.00)
12p gain	29 (31%)	66	0.997 (1.00)	0.95 (1.00)	0.0937 (1.00)	0.272 (1.00)	1 (1.00)	0.478 (1.00)	0.181 (1.00)	0.461 (1.00)
12q gain	29 (31%)	66	0.997 (1.00)	0.95 (1.00)	0.0937 (1.00)	0.272 (1.00)	1 (1.00)	0.478 (1.00)	0.181 (1.00)	0.461 (1.00)
13q gain	10 (11%)	85	0.711 (1.00)	0.329 (1.00)	1 (1.00)	0.219 (1.00)	0.787 (1.00)		1 (1.00)	0.918 (1.00)
16p gain	42 (44%)	53	0.524 (1.00)	0.478 (1.00)	0.00601 (1.00)	0.533 (1.00)	0.543 (1.00)	0.446 (1.00)	0.719 (1.00)	0.804 (1.00)
16q gain	39 (41%)	56	0.0993 (1.00)	0.592 (1.00)	0.013 (1.00)	0.279 (1.00)	0.403 (1.00)	0.228 (1.00)	0.185 (1.00)	0.819 (1.00)
17q gain	57 (60%)	38	0.744 (1.00)	0.127 (1.00)	0.108 (1.00)	0.569 (1.00)	0.254 (1.00)	0.32 (1.00)	0.0735 (1.00)	0.164 (1.00)
18p gain	6 (6%)	89	0.201 (1.00)	0.551 (1.00)	0.667 (1.00)	0.539 (1.00)	0.831 (1.00)		0.385 (1.00)	0.442 (1.00)
18q gain	5 (5%)	90	0.606 (1.00)	0.921 (1.00)	1 (1.00)		0.632 (1.00)		1 (1.00)	0.542 (1.00)
20p gain	27 (28%)	68	0.431 (1.00)	0.0169 (1.00)	0.455 (1.00)	0.642 (1.00)	0.564 (1.00)	1 (1.00)	0.559 (1.00)	0.629 (1.00)
20q gain	29 (31%)	66	0.431 (1.00)	0.0435 (1.00)	0.626 (1.00)	0.642 (1.00)	0.372 (1.00)	1 (1.00)	0.677 (1.00)	0.417 (1.00)
Xq gain	4 (4%)	91	0.418 (1.00)	0.329 (1.00)	0.0754 (1.00)		0.758 (1.00)		0.38 (1.00)	0.33 (1.00)
1p loss	8 (8%)	87	0.777 (1.00)	0.815 (1.00)	1 (1.00)		0.546 (1.00)		0.621 (1.00)	0.298 (1.00)
1q loss	4 (4%)	91	0.745 (1.00)	0.15 (1.00)	1 (1.00)		1 (1.00)		1 (1.00)	1 (1.00)
3p loss	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)
4p loss	6 (6%)	89	0.133 (1.00)	0.283 (1.00)	0.0601 (1.00)		0.27 (1.00)	1 (1.00)	1 (1.00)	0.144 (1.00)
4q loss	6 (6%)	89	0.705 (1.00)	0.878 (1.00)	0.355 (1.00)		0.698 (1.00)		1 (1.00)	0.609 (1.00)
5q loss	3 (3%)	92	0.0288 (1.00)	0.495 (1.00)	0.207 (1.00)		0.68 (1.00)
6p loss	8 (8%)	87	0.0118 (1.00)	0.367 (1.00)	0.229 (1.00)	0.219 (1.00)	0.157 (1.00)	0.213 (1.00)	1 (1.00)	0.194 (1.00)
6q loss	9 (9%)	86	0.217 (1.00)	0.805 (1.00)	0.0178 (1.00)	0.356 (1.00)	0.111 (1.00)	0.408 (1.00)	0.446 (1.00)	0.308 (1.00)
9p loss	12 (13%)	83	0.0601 (1.00)	0.475 (1.00)	0.00502 (1.00)	0.422 (1.00)	0.129 (1.00)	1 (1.00)	0.428 (1.00)	0.341 (1.00)
9q loss	12 (13%)	83	0.102 (1.00)	0.448 (1.00)	0.0369 (1.00)	0.422 (1.00)	0.129 (1.00)	1 (1.00)	0.643 (1.00)	0.477 (1.00)
10p loss	3 (3%)	92	0.0285 (1.00)	0.774 (1.00)	1 (1.00)		0.319 (1.00)
10q loss	3 (3%)	92	0.000937 (0.334)	0.758 (1.00)	0.553 (1.00)		0.68 (1.00)		0.124 (1.00)	0.33 (1.00)
11q loss	7 (7%)	88	0.0121 (1.00)	0.182 (1.00)	1 (1.00)		0.0136 (1.00)	0.575 (1.00)	0.307 (1.00)	0.0263 (1.00)
13q loss	6 (6%)	89	0.0137 (1.00)	0.688 (1.00)	0.00801 (1.00)		0.27 (1.00)	0.352 (1.00)	1 (1.00)	0.361 (1.00)
14q loss	15 (16%)	80	0.973 (1.00)	0.868 (1.00)	0.363 (1.00)	0.422 (1.00)	0.64 (1.00)	0.449 (1.00)	0.602 (1.00)	0.308 (1.00)
15q loss	8 (8%)	87	0.0335 (1.00)	0.248 (1.00)	0.69 (1.00)		0.157 (1.00)	1 (1.00)	0.621 (1.00)	0.298 (1.00)
16q loss	3 (3%)	92			0.0237 (1.00)		0.68 (1.00)		0.38 (1.00)	0.33 (1.00)
18p loss	13 (14%)	82	0.00233 (0.826)	0.729 (1.00)	0.749 (1.00)	0.356 (1.00)	0.192 (1.00)	0.49 (1.00)	0.749 (1.00)	0.431 (1.00)
18q loss	14 (15%)	81	0.00233 (0.826)	0.729 (1.00)	1 (1.00)	0.356 (1.00)	0.108 (1.00)	0.49 (1.00)	0.677 (1.00)	0.245 (1.00)
21q loss	10 (11%)	85	0.15 (1.00)	0.0392 (1.00)	0.474 (1.00)		0.885 (1.00)		1 (1.00)	0.43 (1.00)
22q loss	15 (16%)	80	0.654 (1.00)	0.537 (1.00)	0.13 (1.00)	0.473 (1.00)	0.13 (1.00)	1 (1.00)	0.353 (1.00)	0.152 (1.00)
Xq loss	3 (3%)	92	0.745 (1.00)	0.967 (1.00)	1 (1.00)		1 (1.00)			1 (1.00)

'1q gain mutation analysis' versus 'Time to Death'

P value = 0.000112 (logrank test), Q value = 0.041

Table S1. Gene #1: '1q gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	88	14	0.0 - 182.7 (15.7)
1Q GAIN MUTATED	7	2	0.7 - 25.4 (7.6)
1Q GAIN WILD-TYPE	81	12	0.0 - 182.7 (20.1)

Figure S1. Get High-res Image Gene #1: '1q gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

'6q gain mutation analysis' versus 'Time to Death'

P value = 6.38e-06 (logrank test), Q value = 0.0023

Table S2. Gene #11: '6q gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	88	14	0.0 - 182.7 (15.7)
6Q GAIN MUTATED	3	2	7.9 - 13.6 (9.6)
6Q GAIN WILD-TYPE	85	12	0.0 - 182.7 (19.2)

Figure S2. Get High-res Image Gene #11: '6q gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

'17p gain mutation analysis' versus 'PATHOLOGY.T'

P value = 0.000566 (Fisher's exact test), Q value = 0.2

Table S3. Gene #22: '17p gain mutation analysis' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3+T4
ALL	54	10	31
17P GAIN MUTATED	34	7	7
17P GAIN WILD-TYPE	20	3	24

Figure S3. Get High-res Image Gene #22: '17p gain mutation analysis' versus Clinical Feature #5: 'PATHOLOGY.T'

'17p gain mutation analysis' versus 'TUMOR.STAGE'

P value = 0.000422 (Fisher's exact test), Q value = 0.15

Table S4. Gene #22: '17p gain mutation analysis' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	49	5	22	9
17P GAIN MUTATED	32	2	4	2
17P GAIN WILD-TYPE	17	3	18	7

Figure S4. Get High-res Image Gene #22: '17p gain mutation analysis' versus Clinical Feature #8: 'TUMOR.STAGE'

'5p loss mutation analysis' versus 'Time to Death'

P value = 0.000245 (logrank test), Q value = 0.088

Table S5. Gene #34: '5p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	88	14	0.0 - 182.7 (15.7)
5P LOSS MUTATED	3	2	0.0 - 22.9 (11.1)
5P LOSS WILD-TYPE	85	12	0.1 - 182.7 (15.9)

Figure S5. Get High-res Image Gene #34: '5p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

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

P value = 0.000578 (logrank test), Q value = 0.21

Table S6. Gene #42: '11p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	88	14	0.0 - 182.7 (15.7)
11P LOSS MUTATED	5	2	0.7 - 21.6 (8.8)
11P LOSS WILD-TYPE	83	12	0.0 - 182.7 (15.9)

Figure S6. Get High-res Image Gene #42: '11p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

'17p loss mutation analysis' versus 'Time to Death'

P value = 2.62e-07 (logrank test), Q value = 9.5e-05

Table S7. Gene #48: '17p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	88	14	0.0 - 182.7 (15.7)
17P LOSS MUTATED	3	2	0.7 - 11.1 (9.6)
17P LOSS WILD-TYPE	85	12	0.0 - 182.7 (19.2)

Figure S7. Get High-res Image Gene #48: '17p 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 = KIRP.clin.merged.picked.txt
Number of patients = 95
Number of significantly arm-level cnvs = 53
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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