Correlation between copy number variation genes (focal events) and selected clinical features

Mesothelioma (Primary solid tumor)

02 April 2015 | analyses__2015_04_02

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 (2015): Correlation between copy number variation genes (focal events) and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1ZS2VKD

Overview

Introduction

This pipeline computes the correlation between significant copy number variation (cnv focal) genes and selected clinical features.

Summary

Testing the association between copy number variation 21 focal events and 8 clinical features across 77 patients, 3 significant findings detected with Q value < 0.25.

del_1p36.31 cnv correlated to 'Time to Death'.
del_9p21.3 cnv correlated to 'Time to Death'.
del_10p15.3 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 21 focal events and 8 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 3 significant findings detected.


		Clinical Features	Time to Death	YEARS TO BIRTH	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	PATHOLOGY M STAGE	GENDER	KARNOFSKY PERFORMANCE SCORE
	nCNV (%)	nWild-Type	logrank test	Wilcoxon-test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Wilcoxon-test
del 1p36 31	32 (42%)	45	0.000935 (0.0524)	0.992 (1.00)	0.98 (1.00)	0.578 (1.00)	0.798 (1.00)	0.5 (1.00)	0.772 (1.00)
del 9p21 3	48 (62%)	29	0.000132 (0.0111)	0.32 (0.933)	0.994 (1.00)	0.44 (1.00)	0.75 (1.00)	0.134 (0.681)	1 (1.00)	0.653 (1.00)
del 10p15 3	26 (34%)	51	1.18e-05 (0.00198)	0.918 (1.00)	0.0402 (0.473)	0.0517 (0.491)	0.333 (0.933)	1 (1.00)	1 (1.00)	0.261 (0.877)
del 1p21 3	34 (44%)	43	0.227 (0.877)	0.178 (0.81)	0.635 (1.00)	0.418 (1.00)	0.57 (1.00)	1 (1.00)	0.564 (1.00)
del 2q35	15 (19%)	62	0.0339 (0.473)	0.119 (0.667)	0.737 (1.00)	0.453 (1.00)	0.232 (0.877)	1 (1.00)	0.00716 (0.288)
del 3p21 1	41 (53%)	36	0.416 (1.00)	0.951 (1.00)	0.24 (0.877)	0.947 (1.00)	0.574 (1.00)	1 (1.00)	0.58 (1.00)
del 4q26	35 (45%)	42	0.0154 (0.369)	0.984 (1.00)	0.583 (1.00)	0.0687 (0.563)	0.71 (1.00)	0.495 (1.00)	0.254 (0.877)
del 4q34 3	38 (49%)	39	0.0291 (0.473)	0.33 (0.933)	0.845 (1.00)	0.252 (0.877)	0.801 (1.00)	0.235 (0.877)	0.401 (1.00)
del 5q23 2	15 (19%)	62	0.0327 (0.473)	0.0393 (0.473)	0.968 (1.00)	0.59 (1.00)	0.77 (1.00)	0.325 (0.933)	0.277 (0.91)
del 6q22 1	37 (48%)	40	0.447 (1.00)	0.98 (1.00)	0.111 (0.667)	0.542 (1.00)	0.359 (0.99)	1 (1.00)	0.256 (0.877)
del 6q26	32 (42%)	45	0.136 (0.681)	0.587 (1.00)	0.211 (0.877)	0.851 (1.00)	0.0854 (0.624)	0.523 (1.00)	1 (1.00)
del 8p23 2	16 (21%)	61	0.107 (0.667)	0.209 (0.877)	0.102 (0.656)	0.038 (0.473)	0.785 (1.00)	1 (1.00)	0.723 (1.00)
del 10q25 2	28 (36%)	49	0.045 (0.473)	0.844 (1.00)	0.506 (1.00)	0.221 (0.877)	0.607 (1.00)	1 (1.00)	0.771 (1.00)
del 11q23 2	16 (21%)	61	0.0988 (0.656)	0.606 (1.00)	0.317 (0.933)	0.695 (1.00)	0.914 (1.00)	1 (1.00)	0.723 (1.00)
del 12p13 31	9 (12%)	68	0.0969 (0.656)	0.918 (1.00)	0.311 (0.933)	0.876 (1.00)	0.673 (1.00)	1 (1.00)	0.68 (1.00)
del 13q14 11	41 (53%)	36	0.117 (0.667)	0.759 (1.00)	0.247 (0.877)	0.96 (1.00)	0.0725 (0.563)	0.492 (1.00)	0.148 (0.699)
del 14q32 31	36 (47%)	41	0.043 (0.473)	0.834 (1.00)	0.72 (1.00)	0.138 (0.681)	0.754 (1.00)	0.49 (1.00)	0.0411 (0.473)
del 15q15 1	25 (32%)	52	0.00858 (0.288)	0.87 (1.00)	0.624 (1.00)	0.783 (1.00)	0.875 (1.00)	1 (1.00)	1 (1.00)
del 16p13 3	5 (6%)	72	0.557 (1.00)	0.0555 (0.491)	0.15 (0.699)	0.0737 (0.563)	0.323 (0.933)	1 (1.00)	0.249 (0.877)
del 16q24 1	20 (26%)	57	0.0109 (0.304)	0.406 (1.00)	0.653 (1.00)	0.98 (1.00)	0.331 (0.933)	1 (1.00)	0.054 (0.491)
del 22q12 2	60 (78%)	17	0.136 (0.681)	0.745 (1.00)	0.539 (1.00)	0.282 (0.91)	0.858 (1.00)	0.419 (1.00)	0.73 (1.00)

'del_1p36.31' versus 'Time to Death'

P value = 0.000935 (logrank test), Q value = 0.052

Table S1. Gene #1: 'del_1p36.31' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	76	65	0.7 - 91.7 (15.1)
DEL PEAK 1(1P36.31) MUTATED	31	29	1.3 - 41.5 (11.8)
DEL PEAK 1(1P36.31) WILD-TYPE	45	36	0.7 - 91.7 (19.4)

Figure S1. Get High-res Image Gene #1: 'del_1p36.31' versus Clinical Feature #1: 'Time to Death'

'del_9p21.3' versus 'Time to Death'

P value = 0.000132 (logrank test), Q value = 0.011

Table S2. Gene #11: 'del_9p21.3' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	76	65	0.7 - 91.7 (15.1)
DEL PEAK 11(9P21.3) MUTATED	47	43	0.7 - 41.5 (13.3)
DEL PEAK 11(9P21.3) WILD-TYPE	29	22	1.6 - 91.7 (24.9)

Figure S2. Get High-res Image Gene #11: 'del_9p21.3' versus Clinical Feature #1: 'Time to Death'

'del_10p15.3' versus 'Time to Death'

P value = 1.18e-05 (logrank test), Q value = 0.002

Table S3. Gene #12: 'del_10p15.3' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	76	65	0.7 - 91.7 (15.1)
DEL PEAK 12(10P15.3) MUTATED	25	23	0.7 - 27.7 (10.8)
DEL PEAK 12(10P15.3) WILD-TYPE	51	42	1.3 - 91.7 (22.6)

Figure S3. Get High-res Image Gene #12: 'del_10p15.3' versus Clinical Feature #1: 'Time to Death'

Methods & Data

Input

Copy number data file = all_lesions.txt from GISTIC pipeline
Processed Copy number data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_Correlate_Genomic_Events_Preprocess/MESO-TP/15089884/transformed.cor.cli.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/MESO-TP/15084766/MESO-TP.merged_data.txt
Number of patients = 77
Number of significantly focal cnvs = 21
Number of selected clinical features = 8
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

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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