Bladder Urothelial 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 65 arm-level results and 4 clinical features across 65 patients, one significant finding detected with Q value < 0.25.

21q loss cnv correlated to 'AGE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 65 arm-level results and 4 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, one significant finding detected.


		Clinical Features	Time to Death	AGE	GENDER	KARNOFSKY PERFORMANCE SCORE
	nCNV (%)	nWild-Type	logrank test	t-test	Fisher's exact test	t-test
21q loss	6 (9%)	59	0.383 (1.00)	0.000265 (0.0618)	0.398 (1.00)	0.028 (1.00)
1p gain	7 (11%)	58	0.194 (1.00)	0.606 (1.00)	0.215 (1.00)
1q gain	14 (22%)	51	0.0841 (1.00)	0.0383 (1.00)	0.527 (1.00)
2p gain	13 (20%)	52	0.423 (1.00)	0.701 (1.00)	0.00649 (1.00)
3p gain	13 (20%)	52	0.571 (1.00)	0.743 (1.00)	1 (1.00)	0.51 (1.00)
3q gain	16 (25%)	49	0.239 (1.00)	0.0805 (1.00)	0.137 (1.00)	0.67 (1.00)
4p gain	6 (9%)	59	0.164 (1.00)	0.76 (1.00)	0.655 (1.00)
5p gain	17 (26%)	48	0.894 (1.00)	0.63 (1.00)	1 (1.00)	0.233 (1.00)
5q gain	9 (14%)	56	0.601 (1.00)	0.329 (1.00)	0.706 (1.00)	0.233 (1.00)
6p gain	4 (6%)	61	0.237 (1.00)	0.55 (1.00)	0.599 (1.00)
7p gain	19 (29%)	46	0.227 (1.00)	0.575 (1.00)	1 (1.00)	0.278 (1.00)
7q gain	19 (29%)	46	0.431 (1.00)	0.831 (1.00)	0.399 (1.00)	0.16 (1.00)
8p gain	10 (15%)	55	0.796 (1.00)	0.967 (1.00)	1 (1.00)
8q gain	21 (32%)	44	0.807 (1.00)	0.848 (1.00)	0.275 (1.00)	0.861 (1.00)
9p gain	8 (12%)	57	0.0403 (1.00)	0.0601 (1.00)	0.248 (1.00)	0.861 (1.00)
9q gain	9 (14%)	56	0.0685 (1.00)	0.0508 (1.00)	1 (1.00)	0.703 (1.00)
10p gain	15 (23%)	50	0.7 (1.00)	0.214 (1.00)	0.757 (1.00)	0.861 (1.00)
10q gain	4 (6%)	61	0.544 (1.00)	0.426 (1.00)	0.599 (1.00)
11q gain	3 (5%)	62	0.654 (1.00)	0.452 (1.00)	0.545 (1.00)
12p gain	11 (17%)	54	0.827 (1.00)	0.763 (1.00)	0.308 (1.00)
12q gain	11 (17%)	54	0.44 (1.00)	0.925 (1.00)	0.082 (1.00)
13q gain	11 (17%)	54	0.295 (1.00)	0.329 (1.00)	0.308 (1.00)	0.206 (1.00)
14q gain	5 (8%)	60	0.808 (1.00)	0.932 (1.00)	1 (1.00)	0.533 (1.00)
15q gain	4 (6%)	61	0.174 (1.00)	0.851 (1.00)	1 (1.00)
16p gain	5 (8%)	60	0.00113 (0.263)	0.472 (1.00)	0.326 (1.00)
16q gain	6 (9%)	59	0.00863 (1.00)	0.25 (1.00)	0.168 (1.00)
17p gain	4 (6%)	61	0.355 (1.00)	0.464 (1.00)	0.599 (1.00)
17q gain	11 (17%)	54	0.722 (1.00)	0.558 (1.00)	1 (1.00)
18p gain	11 (17%)	54	0.555 (1.00)	0.88 (1.00)	1 (1.00)	0.703 (1.00)
18q gain	6 (9%)	59	0.0769 (1.00)	0.856 (1.00)	0.398 (1.00)
19p gain	5 (8%)	60	0.698 (1.00)	0.218 (1.00)	0.326 (1.00)
19q gain	14 (22%)	51	0.297 (1.00)	0.927 (1.00)	0.204 (1.00)	0.206 (1.00)
20p gain	26 (40%)	39	0.181 (1.00)	0.796 (1.00)	0.112 (1.00)	0.26 (1.00)
20q gain	28 (43%)	37	0.687 (1.00)	0.971 (1.00)	0.797 (1.00)	0.87 (1.00)
21q gain	12 (18%)	53	0.218 (1.00)	0.382 (1.00)	0.521 (1.00)	0.703 (1.00)
22q gain	5 (8%)	60	0.069 (1.00)	0.633 (1.00)	0.158 (1.00)	0.703 (1.00)
Xq gain	4 (6%)	61	0.789 (1.00)	0.168 (1.00)	1 (1.00)
2p loss	3 (5%)	62	0.854 (1.00)	0.0065 (1.00)	1 (1.00)	0.925 (1.00)
2q loss	5 (8%)	60	0.281 (1.00)	0.146 (1.00)	1 (1.00)	0.925 (1.00)
3p loss	5 (8%)	60	0.934 (1.00)	0.234 (1.00)	0.326 (1.00)
4p loss	11 (17%)	54	0.578 (1.00)	0.833 (1.00)	0.487 (1.00)	0.028 (1.00)
4q loss	12 (18%)	53	0.719 (1.00)	0.9 (1.00)	1 (1.00)	0.103 (1.00)
5p loss	7 (11%)	58	0.118 (1.00)	0.761 (1.00)	0.215 (1.00)	0.028 (1.00)
5q loss	14 (22%)	51	0.334 (1.00)	0.36 (1.00)	0.527 (1.00)	0.059 (1.00)
6p loss	6 (9%)	59	0.243 (1.00)	0.223 (1.00)	1 (1.00)
6q loss	12 (18%)	53	0.204 (1.00)	0.799 (1.00)	0.521 (1.00)	0.0263 (1.00)
8p loss	18 (28%)	47	0.16 (1.00)	0.0792 (1.00)	0.573 (1.00)	0.699 (1.00)
9p loss	21 (32%)	44	0.789 (1.00)	0.766 (1.00)	0.0992 (1.00)	0.285 (1.00)
9q loss	18 (28%)	47	0.631 (1.00)	0.729 (1.00)	0.257 (1.00)	0.451 (1.00)
10p loss	8 (12%)	57	0.24 (1.00)	0.682 (1.00)	1 (1.00)
10q loss	8 (12%)	57	0.29 (1.00)	0.602 (1.00)	1 (1.00)
11p loss	23 (35%)	42	0.44 (1.00)	0.346 (1.00)	0.587 (1.00)	0.919 (1.00)
11q loss	19 (29%)	46	0.456 (1.00)	0.653 (1.00)	1 (1.00)	0.516 (1.00)
12q loss	3 (5%)	62	0.613 (1.00)	0.576 (1.00)	0.545 (1.00)
13q loss	10 (15%)	55	0.919 (1.00)	0.492 (1.00)	0.145 (1.00)
14q loss	13 (20%)	52	0.26 (1.00)	0.761 (1.00)	0.516 (1.00)	0.703 (1.00)
15q loss	8 (12%)	57	0.532 (1.00)	0.239 (1.00)	0.427 (1.00)	0.861 (1.00)
16p loss	7 (11%)	58	0.229 (1.00)	0.184 (1.00)	0.408 (1.00)	0.342 (1.00)
16q loss	6 (9%)	59	0.524 (1.00)	0.408 (1.00)	0.655 (1.00)	0.592 (1.00)
17p loss	19 (29%)	46	0.0725 (1.00)	0.11 (1.00)	0.779 (1.00)	0.53 (1.00)
17q loss	3 (5%)	62	0.794 (1.00)	0.874 (1.00)	1 (1.00)
18p loss	8 (12%)	57	0.276 (1.00)	0.431 (1.00)	0.427 (1.00)
18q loss	16 (25%)	49	0.0675 (1.00)	0.365 (1.00)	0.137 (1.00)	0.87 (1.00)
19p loss	3 (5%)	62	0.569 (1.00)	0.0062 (1.00)	0.263 (1.00)
22q loss	14 (22%)	51	0.439 (1.00)	0.653 (1.00)	0.0558 (1.00)	0.588 (1.00)

'21q loss mutation analysis' versus 'AGE'

P value = 0.000265 (t-test), Q value = 0.062

Table S1. Gene #64: '21q loss mutation analysis' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	65	67.7 (10.5)
21Q LOSS MUTATED	6	57.3 (4.4)
21Q LOSS WILD-TYPE	59	68.7 (10.3)

Figure S1. Get High-res Image Gene #64: '21q loss mutation analysis' versus Clinical Feature #2: 'AGE'

Methods & Data

Input

Mutation data file = broad_values_by_arm.mutsig.cluster.txt
Clinical data file = BLCA.clin.merged.picked.txt
Number of patients = 65
Number of significantly arm-level cnvs = 65
Number of selected clinical features = 4
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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