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 48 arm-level results and 7 clinical features across 75 patients, 3 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'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 48 arm-level results and 7 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)
	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
1q gain	6 (8%)	69	0.000488 (0.139)	0.276 (1.00)	1 (1.00)		0.00462 (1.00)	1 (1.00)	0.0952 (1.00)
6q gain	3 (4%)	72	4.21e-05 (0.0121)	0.23 (1.00)	0.551 (1.00)		0.708 (1.00)		0.0302 (1.00)
17p gain	34 (45%)	41	0.517 (1.00)	0.374 (1.00)	0.0207 (1.00)	0.423 (1.00)	0.00037 (0.106)	0.396 (1.00)	0.493 (1.00)
2p gain	10 (13%)	65	0.25 (1.00)	0.249 (1.00)	1 (1.00)	0.495 (1.00)	0.888 (1.00)	0.311 (1.00)	0.0158 (1.00)
2q gain	11 (15%)	64	0.339 (1.00)	0.294 (1.00)	1 (1.00)	0.495 (1.00)	1 (1.00)	0.639 (1.00)	0.0421 (1.00)
3p gain	20 (27%)	55	0.47 (1.00)	0.856 (1.00)	0.152 (1.00)		0.0315 (1.00)		0.553 (1.00)
3q gain	22 (29%)	53	0.323 (1.00)	0.898 (1.00)	0.0928 (1.00)		0.0656 (1.00)	1 (1.00)	0.356 (1.00)
4p gain	4 (5%)	71	0.114 (1.00)	0.0312 (1.00)	1 (1.00)		0.441 (1.00)	0.583 (1.00)	0.0202 (1.00)
4q gain	3 (4%)	72	0.667 (1.00)	0.027 (1.00)	1 (1.00)		0.496 (1.00)
5p gain	8 (11%)	67	0.634 (1.00)	0.11 (1.00)	0.223 (1.00)		0.04 (1.00)	1 (1.00)	0.485 (1.00)
5q gain	9 (12%)	66	0.165 (1.00)	0.29 (1.00)	0.435 (1.00)		0.124 (1.00)	0.655 (1.00)	0.671 (1.00)
6p gain	4 (5%)	71	0.0959 (1.00)	0.312 (1.00)	0.314 (1.00)		0.335 (1.00)	0.583 (1.00)	0.0292 (1.00)
7p gain	38 (51%)	37	0.482 (1.00)	0.579 (1.00)	0.133 (1.00)	0.399 (1.00)	0.051 (1.00)	1 (1.00)	0.71 (1.00)
7q gain	38 (51%)	37	0.219 (1.00)	0.847 (1.00)	0.318 (1.00)	0.357 (1.00)	0.051 (1.00)	0.686 (1.00)	0.42 (1.00)
8p gain	4 (5%)	71	0.508 (1.00)	0.517 (1.00)	1 (1.00)		0.335 (1.00)	0.583 (1.00)	1 (1.00)
8q gain	7 (9%)	68	0.0107 (1.00)	0.702 (1.00)	0.412 (1.00)		0.03 (1.00)	0.372 (1.00)	0.485 (1.00)
10p gain	3 (4%)	72			1 (1.00)		0.496 (1.00)
12p gain	22 (29%)	53	0.82 (1.00)	0.615 (1.00)	0.265 (1.00)		0.717 (1.00)	0.67 (1.00)	0.077 (1.00)
12q gain	22 (29%)	53	0.82 (1.00)	0.615 (1.00)	0.265 (1.00)		0.717 (1.00)	0.67 (1.00)	0.077 (1.00)
13q gain	10 (13%)	65	0.649 (1.00)	0.295 (1.00)	1 (1.00)	0.399 (1.00)	0.447 (1.00)		0.42 (1.00)
16p gain	34 (45%)	41	0.847 (1.00)	0.419 (1.00)	0.0207 (1.00)	0.357 (1.00)	0.764 (1.00)	1 (1.00)	0.915 (1.00)
16q gain	30 (40%)	45	0.0714 (1.00)	0.528 (1.00)	0.07 (1.00)	0.399 (1.00)	0.402 (1.00)	0.396 (1.00)	0.169 (1.00)
17q gain	45 (60%)	30	0.668 (1.00)	0.673 (1.00)	0.123 (1.00)		0.48 (1.00)	1 (1.00)	0.379 (1.00)
18p gain	6 (8%)	69	0.277 (1.00)	0.522 (1.00)	0.664 (1.00)	0.495 (1.00)	1 (1.00)		0.378 (1.00)
18q gain	4 (5%)	71	0.574 (1.00)	0.513 (1.00)	1 (1.00)		0.781 (1.00)		0.639 (1.00)
20p gain	20 (27%)	55	0.462 (1.00)	0.104 (1.00)	0.777 (1.00)	0.462 (1.00)	0.378 (1.00)	0.655 (1.00)	0.728 (1.00)
20q gain	22 (29%)	53	0.462 (1.00)	0.211 (1.00)	1 (1.00)	0.462 (1.00)	0.348 (1.00)	0.655 (1.00)	0.906 (1.00)
1p loss	7 (9%)	68	0.619 (1.00)	0.736 (1.00)	1 (1.00)		0.531 (1.00)		0.623 (1.00)
3p loss	5 (7%)	70	0.273 (1.00)	0.096 (1.00)	0.313 (1.00)		0.3 (1.00)	1 (1.00)	0.378 (1.00)
4p loss	5 (7%)	70	0.168 (1.00)	0.0966 (1.00)	0.147 (1.00)		0.235 (1.00)	1 (1.00)	1 (1.00)
4q loss	5 (7%)	70	0.762 (1.00)	0.567 (1.00)	0.627 (1.00)		0.677 (1.00)		1 (1.00)
5p loss	4 (5%)	71	0.000961 (0.273)	0.181 (1.00)	0.576 (1.00)		0.0778 (1.00)	0.0873 (1.00)	0.183 (1.00)
5q loss	3 (4%)	72	0.659 (1.00)	0.00881 (1.00)	1 (1.00)		0.0849 (1.00)	0.583 (1.00)	0.1 (1.00)
6p loss	7 (9%)	68	0.0267 (1.00)	0.685 (1.00)	0.184 (1.00)	0.317 (1.00)	0.531 (1.00)	1 (1.00)	0.802 (1.00)
6q loss	9 (12%)	66	0.36 (1.00)	0.729 (1.00)	0.0159 (1.00)	0.317 (1.00)	0.417 (1.00)	0.6 (1.00)	1 (1.00)
9p loss	10 (13%)	65	0.0938 (1.00)	0.451 (1.00)	0.00538 (1.00)	0.416 (1.00)	0.264 (1.00)	1 (1.00)	0.364 (1.00)
9q loss	11 (15%)	64	0.156 (1.00)	0.624 (1.00)	0.0711 (1.00)	0.416 (1.00)	0.426 (1.00)	1 (1.00)	0.641 (1.00)
10q loss	4 (5%)	71	0.00303 (0.858)	0.741 (1.00)	1 (1.00)		0.335 (1.00)		0.296 (1.00)
11p loss	6 (8%)	69	0.157 (1.00)	0.159 (1.00)	0.664 (1.00)		0.155 (1.00)		0.323 (1.00)
11q loss	7 (9%)	68	0.0196 (1.00)	0.198 (1.00)	1 (1.00)		0.03 (1.00)	1 (1.00)	0.623 (1.00)
13q loss	5 (7%)	70	0.0222 (1.00)	0.334 (1.00)	0.024 (1.00)		0.235 (1.00)	0.583 (1.00)	1 (1.00)
14q loss	13 (17%)	62	0.507 (1.00)	0.733 (1.00)	0.507 (1.00)	0.416 (1.00)	0.616 (1.00)	0.67 (1.00)	0.418 (1.00)
15q loss	6 (8%)	69	0.0652 (1.00)	0.462 (1.00)	0.351 (1.00)		0.386 (1.00)	1 (1.00)	1 (1.00)
17p loss	3 (4%)	72			0.204 (1.00)		0.0849 (1.00)		0.1 (1.00)
18p loss	9 (12%)	66	0.00507 (1.00)	0.734 (1.00)	1 (1.00)		0.124 (1.00)	0.639 (1.00)	0.266 (1.00)
18q loss	10 (13%)	65	0.00507 (1.00)	0.734 (1.00)	1 (1.00)		0.0584 (1.00)	0.639 (1.00)	0.291 (1.00)
21q loss	10 (13%)	65	0.149 (1.00)	0.368 (1.00)	1 (1.00)		0.79 (1.00)	1 (1.00)	0.862 (1.00)
22q loss	15 (20%)	60	0.731 (1.00)	0.54 (1.00)	0.12 (1.00)	0.423 (1.00)	0.0791 (1.00)	1 (1.00)	0.133 (1.00)

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

P value = 0.000488 (logrank test), Q value = 0.14

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	72	13	0.5 - 182.7 (19.7)
1Q GAIN MUTATED	6	2	0.9 - 25.4 (7.8)
1Q GAIN WILD-TYPE	66	11	0.5 - 182.7 (22.3)

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 = 4.21e-05 (logrank test), Q value = 0.012

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	72	13	0.5 - 182.7 (19.7)
6Q GAIN MUTATED	3	2	7.9 - 13.6 (9.6)
6Q GAIN WILD-TYPE	69	11	0.5 - 182.7 (21.6)

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 = 0.00037 (Fisher's exact test), Q value = 0.11

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

nPatients	T1	T2	T3+T4
ALL	38	9	28
17P GAIN MUTATED	22	7	5
17P GAIN WILD-TYPE	16	2	23

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

Methods & Data

Input

Mutation data file = broad_values_by_arm.mutsig.cluster.txt
Clinical data file = KIRP.clin.merged.picked.txt
Number of patients = 75
Number of significantly arm-level cnvs = 48
Number of selected clinical features = 7
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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