Correlation between copy number variations of arm-level result and selected clinical features

Prostate Adenocarcinoma (Primary solid tumor)

23 September 2013 | analyses__2013_09_23

Maintainer Information

Citation Information

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

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between copy number variations of arm-level result and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1V69GX6

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 50 arm-level events and 6 clinical features across 160 patients, no significant finding detected with Q value < 0.25.

No arm-level cnvs related to clinical features.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 50 arm-level events and 6 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.


		Clinical Features	Time to Death	AGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	COMPLETENESS OF RESECTION	NUMBER OF LYMPH NODES
	nCNV (%)	nWild-Type	logrank test	t-test	Fisher's exact test	Fisher's exact test	Fisher's exact test	t-test
1P GAIN MUTATION ANALYSIS	4 (2%)	156	100 (1.00)	0.937 (1.00)	0.0341 (1.00)	0.366 (1.00)	1 (1.00)	0.849 (1.00)
1Q GAIN MUTATION ANALYSIS	7 (4%)	153	100 (1.00)	0.996 (1.00)	0.0944 (1.00)	0.553 (1.00)	0.682 (1.00)	0.717 (1.00)
3P GAIN MUTATION ANALYSIS	12 (8%)	148	100 (1.00)	0.0107 (1.00)	0.00486 (1.00)	1 (1.00)	0.776 (1.00)	0.248 (1.00)
3Q GAIN MUTATION ANALYSIS	16 (10%)	144	100 (1.00)	0.00344 (0.986)	0.0058 (1.00)	0.68 (1.00)	0.517 (1.00)	0.488 (1.00)
4P GAIN MUTATION ANALYSIS	4 (2%)	156	100 (1.00)	0.0834 (1.00)	0.673 (1.00)	1 (1.00)	0.613 (1.00)	0.00152 (0.45)
4Q GAIN MUTATION ANALYSIS	3 (2%)	157	100 (1.00)	0.247 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.00152 (0.45)
5P GAIN MUTATION ANALYSIS	3 (2%)	157	100 (1.00)	0.728 (1.00)	0.322 (1.00)	0.288 (1.00)	0.16 (1.00)	0.722 (1.00)
7P GAIN MUTATION ANALYSIS	30 (19%)	130	100 (1.00)	0.257 (1.00)	0.182 (1.00)	0.487 (1.00)	0.121 (1.00)	0.312 (1.00)
7Q GAIN MUTATION ANALYSIS	27 (17%)	133	100 (1.00)	0.472 (1.00)	0.206 (1.00)	1 (1.00)	0.15 (1.00)	0.138 (1.00)
8P GAIN MUTATION ANALYSIS	19 (12%)	141	100 (1.00)	0.275 (1.00)	0.301 (1.00)	0.121 (1.00)	1 (1.00)	0.908 (1.00)
8Q GAIN MUTATION ANALYSIS	30 (19%)	130	100 (1.00)	0.905 (1.00)	0.439 (1.00)	0.498 (1.00)	0.366 (1.00)	0.491 (1.00)
9P GAIN MUTATION ANALYSIS	7 (4%)	153	100 (1.00)	0.217 (1.00)	0.571 (1.00)	0.0266 (1.00)	0.649 (1.00)	0.24 (1.00)
9Q GAIN MUTATION ANALYSIS	14 (9%)	146	100 (1.00)	0.244 (1.00)	0.137 (1.00)	0.00535 (1.00)	0.283 (1.00)	0.156 (1.00)
10P GAIN MUTATION ANALYSIS	6 (4%)	154	100 (1.00)	0.671 (1.00)	0.746 (1.00)	1 (1.00)	0.083 (1.00)	0.00152 (0.45)
10Q GAIN MUTATION ANALYSIS	7 (4%)	153	100 (1.00)	0.56 (1.00)	0.571 (1.00)	0.497 (1.00)	0.0783 (1.00)	0.441 (1.00)
11P GAIN MUTATION ANALYSIS	5 (3%)	155	100 (1.00)	0.664 (1.00)	0.473 (1.00)	0.435 (1.00)	0.344 (1.00)	0.995 (1.00)
11Q GAIN MUTATION ANALYSIS	7 (4%)	153	100 (1.00)	0.967 (1.00)	0.0944 (1.00)	0.162 (1.00)	0.266 (1.00)	0.652 (1.00)
16P GAIN MUTATION ANALYSIS	8 (5%)	152	100 (1.00)	0.573 (1.00)	0.0247 (1.00)	0.553 (1.00)	0.325 (1.00)	0.717 (1.00)
16Q GAIN MUTATION ANALYSIS	3 (2%)	157	100 (1.00)	0.148 (1.00)	0.322 (1.00)	1 (1.00)	1 (1.00)
18P GAIN MUTATION ANALYSIS	6 (4%)	154	100 (1.00)	0.312 (1.00)	0.362 (1.00)	0.0878 (1.00)	1 (1.00)	0.45 (1.00)
20Q GAIN MUTATION ANALYSIS	5 (3%)	155	100 (1.00)	0.0647 (1.00)	0.0258 (1.00)	1 (1.00)	0.344 (1.00)	0.00152 (0.45)
21Q GAIN MUTATION ANALYSIS	3 (2%)	157	100 (1.00)	0.0824 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.00152 (0.45)
1P LOSS MUTATION ANALYSIS	3 (2%)	157	100 (1.00)	0.621 (1.00)	0.322 (1.00)	1 (1.00)	0.532 (1.00)	0.00152 (0.45)
5P LOSS MUTATION ANALYSIS	4 (2%)	156	100 (1.00)	0.448 (1.00)	0.0341 (1.00)	0.0561 (1.00)	1 (1.00)	0.365 (1.00)
5Q LOSS MUTATION ANALYSIS	5 (3%)	155	100 (1.00)	0.468 (1.00)	0.0258 (1.00)	0.0878 (1.00)	1 (1.00)	0.359 (1.00)
6P LOSS MUTATION ANALYSIS	5 (3%)	155	100 (1.00)	0.0618 (1.00)	0.473 (1.00)	0.0878 (1.00)	0.613 (1.00)	0.273 (1.00)
6Q LOSS MUTATION ANALYSIS	10 (6%)	150	100 (1.00)	0.538 (1.00)	0.348 (1.00)	0.0744 (1.00)	0.739 (1.00)	0.179 (1.00)
8P LOSS MUTATION ANALYSIS	53 (33%)	107	100 (1.00)	0.156 (1.00)	0.04 (1.00)	0.565 (1.00)	0.175 (1.00)	0.143 (1.00)
8Q LOSS MUTATION ANALYSIS	10 (6%)	150	100 (1.00)	0.0526 (1.00)	0.0305 (1.00)	0.0744 (1.00)	0.537 (1.00)	0.157 (1.00)
9P LOSS MUTATION ANALYSIS	6 (4%)	154	100 (1.00)	0.889 (1.00)	0.102 (1.00)	0.0163 (1.00)	1 (1.00)	0.205 (1.00)
10P LOSS MUTATION ANALYSIS	10 (6%)	150	100 (1.00)	0.53 (1.00)	0.00136 (0.406)	0.0744 (1.00)	0.537 (1.00)	0.304 (1.00)
10Q LOSS MUTATION ANALYSIS	12 (8%)	148	100 (1.00)	0.945 (1.00)	0.0611 (1.00)	0.025 (1.00)	0.776 (1.00)	0.262 (1.00)
12P LOSS MUTATION ANALYSIS	14 (9%)	146	100 (1.00)	0.849 (1.00)	0.539 (1.00)	0.00779 (1.00)	0.478 (1.00)	0.178 (1.00)
12Q LOSS MUTATION ANALYSIS	7 (4%)	153	100 (1.00)	0.529 (1.00)	0.24 (1.00)	0.0266 (1.00)	1 (1.00)	0.29 (1.00)
13Q LOSS MUTATION ANALYSIS	25 (16%)	135	100 (1.00)	0.102 (1.00)	0.788 (1.00)	0.0143 (1.00)	1 (1.00)	0.169 (1.00)
14Q LOSS MUTATION ANALYSIS	8 (5%)	152	100 (1.00)	0.282 (1.00)	0.0247 (1.00)	0.203 (1.00)	0.266 (1.00)	0.509 (1.00)
15Q LOSS MUTATION ANALYSIS	10 (6%)	150	100 (1.00)	0.806 (1.00)	0.00366 (1.00)	0.00127 (0.38)	1 (1.00)	0.0595 (1.00)
16P LOSS MUTATION ANALYSIS	13 (8%)	147	100 (1.00)	0.73 (1.00)	0.376 (1.00)	1 (1.00)	0.228 (1.00)	0.248 (1.00)
16Q LOSS MUTATION ANALYSIS	34 (21%)	126	100 (1.00)	0.312 (1.00)	0.28 (1.00)	0.331 (1.00)	0.649 (1.00)	0.368 (1.00)
17P LOSS MUTATION ANALYSIS	25 (16%)	135	100 (1.00)	0.726 (1.00)	0.0741 (1.00)	0.136 (1.00)	1 (1.00)	0.199 (1.00)
17Q LOSS MUTATION ANALYSIS	5 (3%)	155	100 (1.00)	0.765 (1.00)	0.0258 (1.00)	0.435 (1.00)	0.344 (1.00)	0.995 (1.00)
18P LOSS MUTATION ANALYSIS	23 (14%)	137	100 (1.00)	0.262 (1.00)	0.124 (1.00)	0.254 (1.00)	0.606 (1.00)	0.198 (1.00)
18Q LOSS MUTATION ANALYSIS	33 (21%)	127	100 (1.00)	0.324 (1.00)	0.226 (1.00)	0.0834 (1.00)	0.398 (1.00)	0.251 (1.00)
19P LOSS MUTATION ANALYSIS	3 (2%)	157	100 (1.00)	0.0335 (1.00)	0.625 (1.00)	1 (1.00)	1 (1.00)	0.00152 (0.45)
19Q LOSS MUTATION ANALYSIS	4 (2%)	156	100 (1.00)	0.00278 (0.802)	0.107 (1.00)	0.366 (1.00)	1 (1.00)	0.849 (1.00)
20P LOSS MUTATION ANALYSIS	6 (4%)	154	100 (1.00)	0.72 (1.00)	0.028 (1.00)	0.435 (1.00)	0.344 (1.00)	0.632 (1.00)
20Q LOSS MUTATION ANALYSIS	3 (2%)	157	100 (1.00)	0.581 (1.00)	0.0474 (1.00)	1 (1.00)	0.16 (1.00)	0.00152 (0.45)
21Q LOSS MUTATION ANALYSIS	8 (5%)	152	100 (1.00)	0.688 (1.00)	0.175 (1.00)	0.00248 (0.717)	1 (1.00)	0.102 (1.00)
22Q LOSS MUTATION ANALYSIS	14 (9%)	146	100 (1.00)	0.662 (1.00)	0.00747 (1.00)	0.0442 (1.00)	0.618 (1.00)	0.297 (1.00)
XQ LOSS MUTATION ANALYSIS	7 (4%)	153	100 (1.00)	0.913 (1.00)	0.308 (1.00)	0.162 (1.00)	1 (1.00)	0.298 (1.00)

Methods & Data

Input

Copy number data file = transformed.cor.cli.txt
Clinical data file = PRAD-TP.clin.merged.picked.txt
Number of patients = 160
Number of significantly arm-level cnvs = 50
Number of selected clinical features = 6
Exclude regions 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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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)

Made with Nozzle