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

Adrenocortical Carcinoma (Primary solid tumor)

16 April 2014 | analyses__2014_04_16

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 (2014): Correlation between copy number variations of arm-level result and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1X63KHD

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 59 arm-level events and 6 clinical features across 33 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 59 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	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	GENDER
	nCNV (%)	nWild-Type	logrank test	t-test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test
3p gain	5 (15%)	28	0.131 (1.00)	0.634 (1.00)	0.916 (1.00)	0.292 (1.00)	0.538 (1.00)	0.656 (1.00)
3q gain	5 (15%)	28	0.131 (1.00)	0.634 (1.00)	0.916 (1.00)	0.292 (1.00)	0.538 (1.00)	0.656 (1.00)
4p gain	15 (45%)	18	0.096 (1.00)	0.77 (1.00)	0.617 (1.00)	0.679 (1.00)	1 (1.00)	0.732 (1.00)
4q gain	14 (42%)	19	0.196 (1.00)	0.736 (1.00)	0.525 (1.00)	0.507 (1.00)	1 (1.00)	1 (1.00)
5p gain	20 (61%)	13	0.426 (1.00)	0.515 (1.00)	0.846 (1.00)	1 (1.00)	0.268 (1.00)	0.481 (1.00)
5q gain	18 (55%)	15	0.592 (1.00)	0.584 (1.00)	1 (1.00)	1 (1.00)	0.113 (1.00)	1 (1.00)
6p gain	5 (15%)	28	0.252 (1.00)	0.214 (1.00)	0.531 (1.00)	0.424 (1.00)	1 (1.00)	1 (1.00)
6q gain	5 (15%)	28	0.786 (1.00)	0.877 (1.00)	0.829 (1.00)	0.704 (1.00)	1 (1.00)	0.656 (1.00)
7p gain	17 (52%)	16	0.297 (1.00)	0.257 (1.00)	0.706 (1.00)	1 (1.00)	0.113 (1.00)	0.732 (1.00)
7q gain	16 (48%)	17	0.297 (1.00)	0.332 (1.00)	0.425 (1.00)	1 (1.00)	0.103 (1.00)	0.494 (1.00)
8p gain	12 (36%)	21	0.986 (1.00)	0.48 (1.00)	0.665 (1.00)	0.767 (1.00)	0.268 (1.00)	0.481 (1.00)
8q gain	15 (45%)	18	0.82 (1.00)	0.781 (1.00)	0.523 (1.00)	0.679 (1.00)	0.602 (1.00)	0.732 (1.00)
9p gain	6 (18%)	27	0.341 (1.00)	0.562 (1.00)	0.157 (1.00)	0.554 (1.00)	0.0181 (1.00)	0.656 (1.00)
9q gain	9 (27%)	24	0.079 (1.00)	0.379 (1.00)	0.144 (1.00)	0.117 (1.00)	0.0046 (1.00)	0.118 (1.00)
10p gain	6 (18%)	27	0.997 (1.00)	0.592 (1.00)	0.172 (1.00)	1 (1.00)	0.538 (1.00)	1 (1.00)
10q gain	7 (21%)	26	0.524 (1.00)	0.247 (1.00)	0.124 (1.00)	0.554 (1.00)	1 (1.00)	1 (1.00)
12p gain	21 (64%)	12	0.569 (1.00)	0.295 (1.00)	0.936 (1.00)	0.853 (1.00)	1 (1.00)	0.282 (1.00)
12q gain	21 (64%)	12	0.241 (1.00)	0.839 (1.00)	0.674 (1.00)	1 (1.00)	1 (1.00)	0.282 (1.00)
14q gain	5 (15%)	28	0.122 (1.00)	0.622 (1.00)	0.0136 (1.00)	0.176 (1.00)	0.0093 (1.00)	1 (1.00)
15q gain	4 (12%)	29	0.515 (1.00)	0.152 (1.00)	0.161 (1.00)	0.105 (1.00)	1 (1.00)	1 (1.00)
16p gain	18 (55%)	15	0.293 (1.00)	0.74 (1.00)	0.95 (1.00)	1 (1.00)	0.613 (1.00)	0.166 (1.00)
16q gain	16 (48%)	17	0.421 (1.00)	0.786 (1.00)	0.944 (1.00)	1 (1.00)	0.598 (1.00)	0.0381 (1.00)
19p gain	19 (58%)	14	0.607 (1.00)	0.553 (1.00)	0.665 (1.00)	1 (1.00)	0.632 (1.00)	0.728 (1.00)
19q gain	17 (52%)	16	0.869 (1.00)	0.698 (1.00)	0.816 (1.00)	1 (1.00)	0.602 (1.00)	0.303 (1.00)
20p gain	15 (45%)	18	0.066 (1.00)	0.993 (1.00)	0.445 (1.00)	0.679 (1.00)	0.602 (1.00)	0.0149 (1.00)
20q gain	17 (52%)	16	0.0467 (1.00)	0.717 (1.00)	0.949 (1.00)	0.681 (1.00)	0.602 (1.00)	0.0149 (1.00)
21q gain	10 (30%)	23	0.588 (1.00)	0.842 (1.00)	0.31 (1.00)	0.644 (1.00)	0.069 (1.00)	0.465 (1.00)
xq gain	9 (27%)	24	0.0815 (1.00)	0.172 (1.00)	0.587 (1.00)	0.738 (1.00)	1 (1.00)	0.708 (1.00)
1p loss	16 (48%)	17	0.814 (1.00)	0.329 (1.00)	0.617 (1.00)	0.679 (1.00)	1 (1.00)	0.732 (1.00)
1q loss	11 (33%)	22	0.672 (1.00)	0.0191 (1.00)	0.617 (1.00)	0.866 (1.00)	0.584 (1.00)	0.721 (1.00)
2p loss	11 (33%)	22	0.43 (1.00)	0.44 (1.00)	1 (1.00)	0.853 (1.00)	1 (1.00)	0.721 (1.00)
2q loss	11 (33%)	22	0.43 (1.00)	0.44 (1.00)	1 (1.00)	0.853 (1.00)	1 (1.00)	0.721 (1.00)
3p loss	10 (30%)	23	0.00865 (1.00)	0.633 (1.00)	0.0759 (1.00)	0.0641 (1.00)	0.284 (1.00)	0.465 (1.00)
3q loss	10 (30%)	23	0.0972 (1.00)	0.349 (1.00)	0.343 (1.00)	0.4 (1.00)	0.284 (1.00)	1 (1.00)
4p loss	6 (18%)	27	0.00167 (0.592)	0.97 (1.00)	0.312 (1.00)	0.0368 (1.00)	1 (1.00)	0.656 (1.00)
4q loss	6 (18%)	27	0.00167 (0.592)	0.97 (1.00)	0.312 (1.00)	0.0368 (1.00)	1 (1.00)	0.656 (1.00)
5q loss	3 (9%)	30	0.824 (1.00)	0.318 (1.00)	0.161 (1.00)	0.704 (1.00)	1 (1.00)	0.601 (1.00)
6p loss	8 (24%)	25	0.249 (1.00)	0.994 (1.00)	0.343 (1.00)	0.213 (1.00)	0.0475 (1.00)	0.688 (1.00)
6q loss	9 (27%)	24	0.0575 (1.00)	0.975 (1.00)	0.144 (1.00)	0.117 (1.00)	0.069 (1.00)	1 (1.00)
8p loss	8 (24%)	25	0.44 (1.00)	0.648 (1.00)	0.237 (1.00)	0.832 (1.00)	0.225 (1.00)	1 (1.00)
8q loss	7 (21%)	26	0.958 (1.00)	0.661 (1.00)	0.576 (1.00)	1 (1.00)	0.169 (1.00)	1 (1.00)
9p loss	10 (30%)	23	0.285 (1.00)	0.92 (1.00)	0.538 (1.00)	0.394 (1.00)	1 (1.00)	0.259 (1.00)
9q loss	6 (18%)	27	0.839 (1.00)	0.562 (1.00)	0.615 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
10p loss	6 (18%)	27	0.043 (1.00)	0.54 (1.00)	0.502 (1.00)	0.554 (1.00)	0.169 (1.00)	0.656 (1.00)
10q loss	6 (18%)	27	0.043 (1.00)	0.54 (1.00)	0.502 (1.00)	0.554 (1.00)	0.169 (1.00)	0.656 (1.00)
11p loss	15 (45%)	18	0.503 (1.00)	0.697 (1.00)	0.245 (1.00)	1 (1.00)	0.29 (1.00)	0.732 (1.00)
11q loss	15 (45%)	18	0.965 (1.00)	0.643 (1.00)	0.364 (1.00)	0.883 (1.00)	1 (1.00)	0.732 (1.00)
13q loss	17 (52%)	16	0.242 (1.00)	0.842 (1.00)	0.792 (1.00)	0.763 (1.00)	1 (1.00)	0.303 (1.00)
14q loss	6 (18%)	27	0.607 (1.00)	0.617 (1.00)	0.411 (1.00)	0.377 (1.00)	1 (1.00)	1 (1.00)
15q loss	8 (24%)	25	0.333 (1.00)	0.108 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.688 (1.00)
16p loss	3 (9%)	30	0.0631 (1.00)	0.121 (1.00)	0.602 (1.00)	0.704 (1.00)	0.36 (1.00)	1 (1.00)
17p loss	15 (45%)	18	0.0125 (1.00)	0.834 (1.00)	0.0664 (1.00)	0.0839 (1.00)	1 (1.00)	0.732 (1.00)
17q loss	12 (36%)	21	0.19 (1.00)	0.476 (1.00)	0.404 (1.00)	0.394 (1.00)	0.584 (1.00)	0.481 (1.00)
18p loss	18 (55%)	15	0.216 (1.00)	0.993 (1.00)	0.259 (1.00)	0.456 (1.00)	0.602 (1.00)	0.491 (1.00)
18q loss	16 (48%)	17	0.479 (1.00)	0.824 (1.00)	0.72 (1.00)	0.592 (1.00)	0.315 (1.00)	0.169 (1.00)
20p loss	6 (18%)	27	0.563 (1.00)	0.556 (1.00)	0.411 (1.00)	1 (1.00)	0.538 (1.00)	0.175 (1.00)
21q loss	6 (18%)	27	0.119 (1.00)	0.688 (1.00)	0.411 (1.00)	0.377 (1.00)	1 (1.00)	0.656 (1.00)
22q loss	18 (55%)	15	0.0648 (1.00)	0.156 (1.00)	0.215 (1.00)	0.679 (1.00)	0.602 (1.00)	0.491 (1.00)
xq loss	9 (27%)	24	0.452 (1.00)	0.0028 (0.984)	0.352 (1.00)	0.712 (1.00)	1 (1.00)	1 (1.00)

Methods & Data

Input

Copy number data file = transformed.cor.cli.txt
Clinical data file = ACC-TP.merged_data.txt
Number of patients = 33
Number of significantly arm-level cnvs = 59
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)

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