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

Pheochromocytoma and Paraganglioma (Primary solid tumor)

17 October 2014 | analyses__2014_10_17

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/C1416W0P

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 63 arm-level events and 4 clinical features across 129 patients, one significant finding detected with Q value < 0.25.

16p loss cnv correlated to 'Time to Death'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 63 arm-level events 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	RACE
	nCNV (%)	nWild-Type	logrank test	Wilcoxon-test	Fisher's exact test	Fisher's exact test
16p loss	6 (5%)	123	7.64e-06 (0.00193)	0.524 (1.00)	1 (1.00)	1 (1.00)
1p gain	6 (5%)	123	0.606 (1.00)	0.395 (1.00)	0.0847 (1.00)	1 (1.00)
1q gain	15 (12%)	114	0.616 (1.00)	0.056 (1.00)	0.0937 (1.00)	0.777 (1.00)
2p gain	3 (2%)	126	0.779 (1.00)	0.667 (1.00)	0.257 (1.00)	0.126 (1.00)
4p gain	3 (2%)	126	0.667 (1.00)	0.537 (1.00)	0.579 (1.00)	0.128 (1.00)
4q gain	3 (2%)	126	0.728 (1.00)	0.399 (1.00)	0.579 (1.00)	1 (1.00)
5p gain	9 (7%)	120	0.577 (1.00)	0.227 (1.00)	0.731 (1.00)	1 (1.00)
5q gain	6 (5%)	123	0.635 (1.00)	0.893 (1.00)	0.233 (1.00)	1 (1.00)
6p gain	10 (8%)	119	0.637 (1.00)	0.0649 (1.00)	0.512 (1.00)	1 (1.00)
6q gain	4 (3%)	125	0.782 (1.00)	0.159 (1.00)	0.632 (1.00)	1 (1.00)
7p gain	22 (17%)	107	0.432 (1.00)	0.491 (1.00)	0.637 (1.00)	0.548 (1.00)
7q gain	18 (14%)	111	0.517 (1.00)	0.744 (1.00)	0.127 (1.00)	0.581 (1.00)
8p gain	7 (5%)	122	0.576 (1.00)	0.4 (1.00)	0.698 (1.00)	0.571 (1.00)
8q gain	10 (8%)	119	0.285 (1.00)	0.795 (1.00)	0.746 (1.00)	0.336 (1.00)
9p gain	3 (2%)	126	0.732 (1.00)	0.944 (1.00)	1 (1.00)	1 (1.00)
10p gain	9 (7%)	120	0.623 (1.00)	0.654 (1.00)	0.731 (1.00)	1 (1.00)
10q gain	8 (6%)	121	0.621 (1.00)	0.671 (1.00)	0.727 (1.00)	1 (1.00)
11p gain	5 (4%)	124	0.778 (1.00)	0.705 (1.00)	0.387 (1.00)	1 (1.00)
12p gain	7 (5%)	122	0.637 (1.00)	0.483 (1.00)	0.698 (1.00)	1 (1.00)
12q gain	10 (8%)	119	0.564 (1.00)	0.982 (1.00)	0.512 (1.00)	1 (1.00)
13q gain	7 (5%)	122	0.688 (1.00)	0.716 (1.00)	1 (1.00)	1 (1.00)
15q gain	10 (8%)	119	0.579 (1.00)	0.317 (1.00)	0.746 (1.00)	0.449 (1.00)
16p gain	5 (4%)	124	0.711 (1.00)	0.534 (1.00)	1 (1.00)	1 (1.00)
16q gain	4 (3%)	125	0.732 (1.00)	0.19 (1.00)	1 (1.00)	1 (1.00)
17q gain	4 (3%)	125	0.732 (1.00)	0.876 (1.00)	0.316 (1.00)	0.38 (1.00)
18p gain	4 (3%)	125	0.655 (1.00)	0.514 (1.00)	0.632 (1.00)	1 (1.00)
18q gain	7 (5%)	122	0.563 (1.00)	0.592 (1.00)	0.698 (1.00)	1 (1.00)
19p gain	16 (12%)	113	0.618 (1.00)	0.098 (1.00)	0.42 (1.00)	0.801 (1.00)
19q gain	11 (9%)	118	0.492 (1.00)	0.0822 (1.00)	0.756 (1.00)	1 (1.00)
20p gain	10 (8%)	119	0.593 (1.00)	0.459 (1.00)	0.512 (1.00)	1 (1.00)
20q gain	7 (5%)	122	0.623 (1.00)	0.181 (1.00)	0.698 (1.00)	1 (1.00)
xq gain	4 (3%)	125	0.692 (1.00)	0.582 (1.00)	0.632 (1.00)	1 (1.00)
1p loss	78 (60%)	51	0.169 (1.00)	0.609 (1.00)	1 (1.00)	0.743 (1.00)
1q loss	17 (13%)	112	0.526 (1.00)	0.953 (1.00)	0.439 (1.00)	0.372 (1.00)
2p loss	7 (5%)	122	0.782 (1.00)	0.218 (1.00)	1 (1.00)	0.569 (1.00)
2q loss	8 (6%)	121	0.0582 (1.00)	0.718 (1.00)	0.727 (1.00)	0.622 (1.00)
3p loss	47 (36%)	82	0.11 (1.00)	0.334 (1.00)	0.268 (1.00)	0.0474 (1.00)
3q loss	70 (54%)	59	0.259 (1.00)	0.0683 (1.00)	0.722 (1.00)	0.809 (1.00)
4p loss	8 (6%)	121	0.697 (1.00)	0.83 (1.00)	0.727 (1.00)	1 (1.00)
4q loss	8 (6%)	121	0.662 (1.00)	0.718 (1.00)	0.727 (1.00)	1 (1.00)
5p loss	4 (3%)	125	0.808 (1.00)	0.649 (1.00)	0.316 (1.00)	1 (1.00)
5q loss	6 (5%)	123	0.669 (1.00)	0.389 (1.00)	0.402 (1.00)	0.51 (1.00)
6q loss	16 (12%)	113	0.45 (1.00)	0.825 (1.00)	0.177 (1.00)	1 (1.00)
7q loss	4 (3%)	125	0.651 (1.00)	0.957 (1.00)	0.632 (1.00)	0.00181 (0.454)
8p loss	15 (12%)	114	0.553 (1.00)	0.532 (1.00)	0.267 (1.00)	0.398 (1.00)
8q loss	9 (7%)	120	0.222 (1.00)	0.93 (1.00)	0.731 (1.00)	1 (1.00)
9p loss	7 (5%)	122	0.101 (1.00)	0.783 (1.00)	0.138 (1.00)	0.572 (1.00)
9q loss	8 (6%)	121	0.249 (1.00)	0.488 (1.00)	0.465 (1.00)	0.247 (1.00)
11p loss	41 (32%)	88	0.882 (1.00)	0.573 (1.00)	0.448 (1.00)	0.25 (1.00)
11q loss	34 (26%)	95	0.807 (1.00)	0.386 (1.00)	1 (1.00)	0.826 (1.00)
13q loss	6 (5%)	123	0.688 (1.00)	0.599 (1.00)	0.697 (1.00)	0.161 (1.00)
14q loss	16 (12%)	113	0.473 (1.00)	0.453 (1.00)	0.789 (1.00)	1 (1.00)
15q loss	3 (2%)	126	0.707 (1.00)	0.399 (1.00)	0.257 (1.00)	0.0327 (1.00)
16q loss	4 (3%)	125	0.753 (1.00)	0.447 (1.00)	0.632 (1.00)	1 (1.00)
17p loss	50 (39%)	79	0.536 (1.00)	0.672 (1.00)	0.275 (1.00)	0.138 (1.00)
17q loss	16 (12%)	113	0.414 (1.00)	0.319 (1.00)	0.293 (1.00)	1 (1.00)
18p loss	10 (8%)	119	0.644 (1.00)	0.481 (1.00)	0.329 (1.00)	0.449 (1.00)
18q loss	3 (2%)	126	0.89 (1.00)	0.365 (1.00)	1 (1.00)	0.298 (1.00)
19q loss	4 (3%)	125	0.651 (1.00)	0.467 (1.00)	0.632 (1.00)	0.173 (1.00)
20q loss	3 (2%)	126	0.844 (1.00)	0.913 (1.00)	1 (1.00)	1 (1.00)
21q loss	30 (23%)	99	0.315 (1.00)	0.286 (1.00)	0.0982 (1.00)	0.395 (1.00)
22q loss	47 (36%)	82	0.0767 (1.00)	0.512 (1.00)	0.713 (1.00)	0.548 (1.00)
xq loss	38 (29%)	91	0.932 (1.00)	0.723 (1.00)	0.697 (1.00)	0.0911 (1.00)

'16p loss' versus 'Time to Death'

P value = 7.64e-06 (logrank test), Q value = 0.0019

Table S1. Gene #53: '16p loss' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	129	4	0.1 - 303.1 (15.0)
16P LOSS MUTATED	6	2	0.8 - 106.8 (9.8)
16P LOSS WILD-TYPE	123	2	0.1 - 303.1 (16.6)

Figure S1. Get High-res Image Gene #53: '16p loss' versus Clinical Feature #1: 'Time to Death'

Methods & Data

Input

Copy number data file = transformed.cor.cli.txt
Clinical data file = PCPG-TP.merged_data.txt
Number of patients = 129
Number of significantly arm-level cnvs = 63
Number of selected clinical features = 4
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

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] 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)

[3] 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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