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 64 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%)	58	0.328 (1.00)	0.000258 (0.0602)	0.406 (1.00)	0.028 (1.00)
1p gain	7 (11%)	57	0.217 (1.00)	0.607 (1.00)	0.22 (1.00)
1q gain	14 (22%)	50	0.107 (1.00)	0.039 (1.00)	0.53 (1.00)
2p gain	13 (20%)	51	0.354 (1.00)	0.699 (1.00)	0.00679 (1.00)
3p gain	13 (20%)	51	0.492 (1.00)	0.741 (1.00)	1 (1.00)	0.51 (1.00)
3q gain	16 (25%)	48	0.198 (1.00)	0.0812 (1.00)	0.143 (1.00)	0.67 (1.00)
4p gain	6 (9%)	58	0.134 (1.00)	0.76 (1.00)	0.655 (1.00)
5p gain	17 (27%)	47	0.991 (1.00)	0.628 (1.00)	1 (1.00)	0.233 (1.00)
5q gain	9 (14%)	55	0.531 (1.00)	0.328 (1.00)	0.707 (1.00)	0.233 (1.00)
6p gain	4 (6%)	60	0.222 (1.00)	0.55 (1.00)	0.603 (1.00)
7p gain	19 (30%)	45	0.285 (1.00)	0.573 (1.00)	1 (1.00)	0.278 (1.00)
7q gain	19 (30%)	45	0.511 (1.00)	0.829 (1.00)	0.406 (1.00)	0.16 (1.00)
8p gain	10 (16%)	54	0.725 (1.00)	0.968 (1.00)	1 (1.00)
8q gain	21 (33%)	43	0.695 (1.00)	0.85 (1.00)	0.269 (1.00)	0.861 (1.00)
9p gain	8 (12%)	56	0.0299 (1.00)	0.0599 (1.00)	0.245 (1.00)	0.861 (1.00)
9q gain	9 (14%)	55	0.0555 (1.00)	0.0507 (1.00)	1 (1.00)	0.703 (1.00)
10p gain	15 (23%)	49	0.592 (1.00)	0.213 (1.00)	0.549 (1.00)	0.861 (1.00)
10q gain	4 (6%)	60	0.601 (1.00)	0.426 (1.00)	0.603 (1.00)
11q gain	3 (5%)	61	0.65 (1.00)	0.452 (1.00)	0.545 (1.00)
12p gain	11 (17%)	53	0.919 (1.00)	0.764 (1.00)	0.304 (1.00)
12q gain	11 (17%)	53	0.364 (1.00)	0.924 (1.00)	0.0805 (1.00)
13q gain	11 (17%)	53	0.352 (1.00)	0.33 (1.00)	0.304 (1.00)	0.206 (1.00)
14q gain	5 (8%)	59	0.744 (1.00)	0.932 (1.00)	1 (1.00)	0.533 (1.00)
15q gain	4 (6%)	60	0.198 (1.00)	0.851 (1.00)	1 (1.00)
16p gain	5 (8%)	59	0.0014 (0.324)	0.473 (1.00)	0.329 (1.00)
16q gain	6 (9%)	58	0.0101 (1.00)	0.25 (1.00)	0.17 (1.00)
17p gain	5 (8%)	59	0.678 (1.00)	0.289 (1.00)	0.329 (1.00)
17q gain	12 (19%)	52	0.511 (1.00)	0.713 (1.00)	0.737 (1.00)
18p gain	11 (17%)	53	0.635 (1.00)	0.882 (1.00)	1 (1.00)	0.703 (1.00)
18q gain	6 (9%)	58	0.0848 (1.00)	0.857 (1.00)	0.406 (1.00)
19p gain	5 (8%)	59	0.718 (1.00)	0.218 (1.00)	0.329 (1.00)
19q gain	14 (22%)	50	0.249 (1.00)	0.928 (1.00)	0.208 (1.00)	0.206 (1.00)
20p gain	26 (41%)	38	0.235 (1.00)	0.793 (1.00)	0.116 (1.00)	0.26 (1.00)
20q gain	27 (42%)	37	0.894 (1.00)	0.967 (1.00)	0.792 (1.00)	0.87 (1.00)
21q gain	12 (19%)	52	0.198 (1.00)	0.384 (1.00)	0.737 (1.00)	0.703 (1.00)
22q gain	5 (8%)	59	0.0593 (1.00)	0.633 (1.00)	0.155 (1.00)	0.703 (1.00)
Xq gain	4 (6%)	60	0.741 (1.00)	0.167 (1.00)	1 (1.00)
2p loss	3 (5%)	61	0.803 (1.00)	0.00627 (1.00)	1 (1.00)	0.925 (1.00)
2q loss	5 (8%)	59	0.326 (1.00)	0.146 (1.00)	1 (1.00)	0.925 (1.00)
3p loss	5 (8%)	59	0.991 (1.00)	0.235 (1.00)	0.329 (1.00)
4p loss	11 (17%)	53	0.52 (1.00)	0.834 (1.00)	0.49 (1.00)	0.028 (1.00)
4q loss	12 (19%)	52	0.633 (1.00)	0.902 (1.00)	1 (1.00)	0.103 (1.00)
5p loss	7 (11%)	57	0.1 (1.00)	0.76 (1.00)	0.22 (1.00)	0.028 (1.00)
5q loss	14 (22%)	50	0.269 (1.00)	0.361 (1.00)	0.53 (1.00)	0.059 (1.00)
6p loss	6 (9%)	58	0.257 (1.00)	0.223 (1.00)	1 (1.00)
6q loss	12 (19%)	52	0.175 (1.00)	0.801 (1.00)	0.737 (1.00)	0.0263 (1.00)
8p loss	18 (28%)	46	0.118 (1.00)	0.0798 (1.00)	0.568 (1.00)	0.699 (1.00)
9p loss	20 (31%)	44	0.937 (1.00)	0.77 (1.00)	0.156 (1.00)	0.285 (1.00)
9q loss	17 (27%)	47	0.867 (1.00)	0.735 (1.00)	0.375 (1.00)	0.451 (1.00)
10p loss	7 (11%)	57	0.456 (1.00)	0.694 (1.00)	0.684 (1.00)
10q loss	7 (11%)	57	0.626 (1.00)	0.615 (1.00)	0.684 (1.00)
11p loss	22 (34%)	42	0.608 (1.00)	0.349 (1.00)	0.58 (1.00)	0.919 (1.00)
11q loss	17 (27%)	47	0.482 (1.00)	0.519 (1.00)	1 (1.00)	0.516 (1.00)
12q loss	3 (5%)	61	0.636 (1.00)	0.576 (1.00)	0.545 (1.00)
13q loss	10 (16%)	54	0.821 (1.00)	0.491 (1.00)	0.144 (1.00)
14q loss	12 (19%)	52	0.431 (1.00)	0.768 (1.00)	0.521 (1.00)	0.703 (1.00)
15q loss	8 (12%)	56	0.456 (1.00)	0.24 (1.00)	0.43 (1.00)	0.861 (1.00)
16p loss	7 (11%)	57	0.2 (1.00)	0.184 (1.00)	0.406 (1.00)	0.342 (1.00)
16q loss	6 (9%)	58	0.487 (1.00)	0.409 (1.00)	0.655 (1.00)	0.592 (1.00)
17p loss	18 (28%)	46	0.12 (1.00)	0.109 (1.00)	0.771 (1.00)	0.53 (1.00)
17q loss	3 (5%)	61	0.759 (1.00)	0.874 (1.00)	1 (1.00)
18p loss	8 (12%)	56	0.24 (1.00)	0.431 (1.00)	0.43 (1.00)
18q loss	16 (25%)	48	0.0489 (1.00)	0.364 (1.00)	0.143 (1.00)	0.87 (1.00)
19p loss	3 (5%)	61	0.529 (1.00)	0.00624 (1.00)	0.27 (1.00)
22q loss	14 (22%)	50	0.51 (1.00)	0.655 (1.00)	0.0584 (1.00)	0.588 (1.00)

'21q loss mutation analysis' versus 'AGE'

P value = 0.000258 (t-test), Q value = 0.06

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

	nPatients	Mean (Std.Dev)
ALL	64	67.7 (10.5)
21Q LOSS MUTATED	6	57.3 (4.4)
21Q LOSS WILD-TYPE	58	68.7 (10.4)

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 = 64
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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