Correlation between copy number variations of arm-level result and molecular subtypes

Prostate Adenocarcinoma (Primary solid tumor)

22 February 2013 | analyses__2013_02_22

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 molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C19W0CQS

Overview

Introduction

This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and subtypes.

Summary

Testing the association between copy number variation 26 arm-level results and 6 molecular subtypes across 177 patients, 8 significant findings detected with Q value < 0.25.

8q gain cnv correlated to 'CN_CNMF'.
8p loss cnv correlated to 'CN_CNMF', 'METHLYATION_CNMF', and 'MIRSEQ_CNMF'.
16q loss cnv correlated to 'CN_CNMF'.
17p loss cnv correlated to 'CN_CNMF'.
18p loss cnv correlated to 'CN_CNMF'.
18q loss cnv correlated to 'CN_CNMF'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 26 arm-level results and 6 molecular subtypes. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 8 significant findings detected.


		Molecular subtypes	CN CNMF	METHLYATION CNMF	MRNASEQ CNMF	MRNASEQ CHIERARCHICAL	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL
	nCNV (%)	nWild-Type	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test
8p loss	46 (26%)	131	0.000144 (0.0196)	0.000228 (0.0305)	0.0164 (1.00)	0.00406 (0.535)	0.000165 (0.0222)	0.681 (1.00)
8q gain	17 (10%)	160	1.19e-05 (0.00163)	0.57 (1.00)	0.438 (1.00)	0.284 (1.00)	0.262 (1.00)	0.112 (1.00)
16q loss	22 (12%)	155	8.59e-06 (0.00119)	0.112 (1.00)	0.189 (1.00)	0.078 (1.00)	0.173 (1.00)	0.615 (1.00)
17p loss	18 (10%)	159	6.04e-06 (0.00084)	0.156 (1.00)	0.299 (1.00)	0.0355 (1.00)	0.499 (1.00)	0.604 (1.00)
18p loss	19 (11%)	158	2.66e-06 (0.000373)	0.201 (1.00)	0.661 (1.00)	0.186 (1.00)	0.0486 (1.00)	0.0116 (1.00)
18q loss	24 (14%)	153	1.56e-09 (2.2e-07)	0.419 (1.00)	0.32 (1.00)	0.0681 (1.00)	0.087 (1.00)	0.0668 (1.00)
1q gain	4 (2%)	173	0.214 (1.00)	0.56 (1.00)			0.53 (1.00)	0.822 (1.00)
3p gain	4 (2%)	173	0.362 (1.00)	0.824 (1.00)			0.15 (1.00)	0.29 (1.00)
3q gain	6 (3%)	171	0.19 (1.00)	0.668 (1.00)	0.109 (1.00)	0.0238 (1.00)	0.288 (1.00)	0.1 (1.00)
7p gain	15 (8%)	162	0.0195 (1.00)	0.09 (1.00)	0.138 (1.00)	0.637 (1.00)	0.787 (1.00)	0.573 (1.00)
7q gain	12 (7%)	165	0.0989 (1.00)	0.171 (1.00)	0.275 (1.00)	0.926 (1.00)	0.663 (1.00)	0.376 (1.00)
8p gain	8 (5%)	169	0.0501 (1.00)	1 (1.00)	0.738 (1.00)	0.44 (1.00)	1 (1.00)	0.707 (1.00)
9q gain	5 (3%)	172	0.0904 (1.00)	0.387 (1.00)	0.266 (1.00)	0.823 (1.00)	0.651 (1.00)	0.845 (1.00)
12q gain	3 (2%)	174	0.16 (1.00)				0.0331 (1.00)	0.0756 (1.00)
16p gain	3 (2%)	174	0.459 (1.00)	0.78 (1.00)			0.458 (1.00)	0.774 (1.00)
16q gain	3 (2%)	174	0.459 (1.00)	0.78 (1.00)			0.458 (1.00)	0.774 (1.00)
5q loss	3 (2%)	174	0.0543 (1.00)	0.0525 (1.00)			0.114 (1.00)	1 (1.00)
6q loss	7 (4%)	170	0.0278 (1.00)	0.156 (1.00)	0.132 (1.00)	0.0111 (1.00)	0.365 (1.00)	0.125 (1.00)
8q loss	4 (2%)	173	0.0327 (1.00)	0.387 (1.00)	0.266 (1.00)	0.102 (1.00)	0.0423 (1.00)	0.395 (1.00)
10p loss	6 (3%)	171	0.162 (1.00)	0.285 (1.00)	0.377 (1.00)	0.448 (1.00)	0.288 (1.00)	0.122 (1.00)
10q loss	5 (3%)	172	0.387 (1.00)	0.735 (1.00)	0.53 (1.00)	1 (1.00)	0.57 (1.00)	1 (1.00)
12p loss	9 (5%)	168	0.161 (1.00)	0.073 (1.00)	0.664 (1.00)	1 (1.00)	0.0539 (1.00)	0.246 (1.00)
13q loss	12 (7%)	165	0.00359 (0.478)	0.204 (1.00)	0.0256 (1.00)	0.302 (1.00)	0.167 (1.00)	0.375 (1.00)
20p loss	4 (2%)	173	0.57 (1.00)	0.188 (1.00)	0.329 (1.00)	0.14 (1.00)	0.00575 (0.753)	0.0161 (1.00)
21q loss	4 (2%)	173	0.214 (1.00)	0.493 (1.00)			0.266 (1.00)	1 (1.00)
22q loss	6 (3%)	171	0.0262 (1.00)	0.388 (1.00)	0.322 (1.00)	0.113 (1.00)	0.388 (1.00)	1 (1.00)

'8q gain mutation analysis' versus 'CN_CNMF'

P value = 1.19e-05 (Fisher's exact test), Q value = 0.0016

Table S1. Gene #7: '8q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	39	83	54	1
8Q GAIN MUTATED	2	1	14	0
8Q GAIN WILD-TYPE	37	82	40	1

Figure S1. Get High-res Image Gene #7: '8q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'8p loss mutation analysis' versus 'CN_CNMF'

P value = 0.000144 (Fisher's exact test), Q value = 0.02

Table S2. Gene #14: '8p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	39	83	54	1
8P LOSS MUTATED	7	13	26	0
8P LOSS WILD-TYPE	32	70	28	1

Figure S2. Get High-res Image Gene #14: '8p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'8p loss mutation analysis' versus 'METHLYATION_CNMF'

P value = 0.000228 (Fisher's exact test), Q value = 0.03

Table S3. Gene #14: '8p loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	53	50	68
8P LOSS MUTATED	11	4	27
8P LOSS WILD-TYPE	42	46	41

Figure S3. Get High-res Image Gene #14: '8p loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

'8p loss mutation analysis' versus 'MIRSEQ_CNMF'

P value = 0.000165 (Fisher's exact test), Q value = 0.022

Table S4. Gene #14: '8p loss mutation analysis' versus Clinical Feature #5: 'MIRSEQ_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	45	46	25	60
8P LOSS MUTATED	22	4	6	13
8P LOSS WILD-TYPE	23	42	19	47

Figure S4. Get High-res Image Gene #14: '8p loss mutation analysis' versus Clinical Feature #5: 'MIRSEQ_CNMF'

'16q loss mutation analysis' versus 'CN_CNMF'

P value = 8.59e-06 (Fisher's exact test), Q value = 0.0012

Table S5. Gene #20: '16q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	39	83	54	1
16Q LOSS MUTATED	2	3	17	0
16Q LOSS WILD-TYPE	37	80	37	1

Figure S5. Get High-res Image Gene #20: '16q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'17p loss mutation analysis' versus 'CN_CNMF'

P value = 6.04e-06 (Fisher's exact test), Q value = 0.00084

Table S6. Gene #21: '17p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	39	83	54	1
17P LOSS MUTATED	0	3	15	0
17P LOSS WILD-TYPE	39	80	39	1

Figure S6. Get High-res Image Gene #21: '17p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'18p loss mutation analysis' versus 'CN_CNMF'

P value = 2.66e-06 (Fisher's exact test), Q value = 0.00037

Table S7. Gene #22: '18p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	39	83	54	1
18P LOSS MUTATED	1	2	16	0
18P LOSS WILD-TYPE	38	81	38	1

Figure S7. Get High-res Image Gene #22: '18p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'18q loss mutation analysis' versus 'CN_CNMF'

P value = 1.56e-09 (Fisher's exact test), Q value = 2.2e-07

Table S8. Gene #23: '18q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	39	83	54	1
18Q LOSS MUTATED	0	3	21	0
18Q LOSS WILD-TYPE	39	80	33	1

Figure S8. Get High-res Image Gene #23: '18q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

Methods & Data

Input

Mutation data file = broad_values_by_arm.mutsig.cluster.txt
Molecular subtypes file = PRAD-TP.transferedmergedcluster.txt
Number of patients = 177
Number of significantly arm-level cnvs = 26
Number of molecular subtypes = 6
Exclude genes that fewer than K tumors have mutations, K = 3

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] 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)

[2] 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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