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

Prostate Adenocarcinoma (Primary solid tumor)

21 April 2013 | analyses__2013_04_21

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): Prostate Adenocarcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between copy number variations of arm-level result and molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C1CN71WR

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 29 arm-level results and 6 molecular subtypes across 187 patients, 8 significant findings detected with Q value < 0.25.

7p gain cnv correlated to 'CN_CNMF'.
8q gain cnv correlated to 'CN_CNMF'.
8p loss cnv correlated to 'METHLYATION_CNMF'.
13q loss cnv correlated to 'CN_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 29 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
7p gain	0 (0%)	171	0.000281 (0.0461)	0.0775 (1.00)	0.109 (1.00)	0.139 (1.00)	0.17 (1.00)	0.592 (1.00)
8q gain	0 (0%)	168	5.57e-05 (0.00924)	0.462 (1.00)	0.0856 (1.00)	0.243 (1.00)	0.12 (1.00)	0.00324 (0.518)
8p loss	0 (0%)	140	0.00278 (0.448)	0.000129 (0.0214)	0.00326 (0.518)	0.0331 (1.00)	0.0193 (1.00)	1 (1.00)
13q loss	0 (0%)	175	0.00145 (0.236)	0.214 (1.00)	0.0284 (1.00)	0.0349 (1.00)	0.449 (1.00)	0.648 (1.00)
16q loss	0 (0%)	163	3.66e-05 (0.00611)	0.0391 (1.00)	0.18 (1.00)	0.199 (1.00)	0.0754 (1.00)	0.103 (1.00)
17p loss	0 (0%)	166	6.43e-06 (0.00109)	0.0967 (1.00)	0.0996 (1.00)	0.0171 (1.00)	0.705 (1.00)	0.907 (1.00)
18p loss	0 (0%)	169	2.52e-05 (0.00423)	0.213 (1.00)	0.363 (1.00)	0.257 (1.00)	0.0156 (1.00)	0.122 (1.00)
18q loss	0 (0%)	163	1.1e-08 (1.88e-06)	0.165 (1.00)	0.0886 (1.00)	0.219 (1.00)	0.0398 (1.00)	0.182 (1.00)
1p gain	0 (0%)	184	0.0678 (1.00)	0.0489 (1.00)			0.496 (1.00)	0.41 (1.00)
1q gain	0 (0%)	182	0.139 (1.00)	0.275 (1.00)	0.551 (1.00)	0.3 (1.00)	0.324 (1.00)	0.201 (1.00)
3p gain	0 (0%)	182	0.254 (1.00)	0.519 (1.00)	0.0637 (1.00)	0.362 (1.00)	0.17 (1.00)	0.155 (1.00)
3q gain	0 (0%)	181	0.138 (1.00)	0.664 (1.00)	0.0198 (1.00)	0.00561 (0.887)	0.055 (1.00)	0.0354 (1.00)
7q gain	0 (0%)	173	0.00226 (0.367)	0.101 (1.00)	0.182 (1.00)	0.227 (1.00)	0.477 (1.00)	0.598 (1.00)
8p gain	0 (0%)	179	0.0103 (1.00)	0.347 (1.00)	0.396 (1.00)	0.0935 (1.00)	0.852 (1.00)	0.395 (1.00)
9p gain	0 (0%)	184	0.0678 (1.00)	0.483 (1.00)	0.644 (1.00)	0.625 (1.00)	0.862 (1.00)	0.41 (1.00)
9q gain	0 (0%)	181	0.0762 (1.00)	0.326 (1.00)	0.132 (1.00)	0.229 (1.00)	0.569 (1.00)	0.56 (1.00)
10q gain	0 (0%)	183	0.278 (1.00)	0.566 (1.00)	0.211 (1.00)	0.3 (1.00)	0.55 (1.00)	0.172 (1.00)
12q gain	0 (0%)	184	0.201 (1.00)	0.269 (1.00)			0.567 (1.00)	1 (1.00)
16p gain	0 (0%)	184	0.598 (1.00)	0.783 (1.00)	0.776 (1.00)	0.261 (1.00)	0.496 (1.00)	1 (1.00)
16q gain	0 (0%)	184	0.598 (1.00)	0.783 (1.00)	0.776 (1.00)	0.261 (1.00)	0.496 (1.00)	1 (1.00)
5q loss	0 (0%)	182	0.0138 (1.00)	0.0647 (1.00)	0.0637 (1.00)	0.362 (1.00)	0.09 (1.00)	0.0892 (1.00)
6q loss	0 (0%)	180	0.0157 (1.00)	0.116 (1.00)	0.0714 (1.00)	0.0935 (1.00)	0.319 (1.00)	0.176 (1.00)
8q loss	0 (0%)	183	0.278 (1.00)	0.389 (1.00)	0.329 (1.00)	0.388 (1.00)	0.834 (1.00)	0.309 (1.00)
10p loss	0 (0%)	182	0.439 (1.00)	0.396 (1.00)	0.551 (1.00)	0.3 (1.00)	0.234 (1.00)	0.0246 (1.00)
10q loss	0 (0%)	182	0.439 (1.00)	0.735 (1.00)	0.625 (1.00)	0.866 (1.00)	0.545 (1.00)	1 (1.00)
12p loss	0 (0%)	177	0.198 (1.00)	0.0197 (1.00)	0.139 (1.00)	0.0123 (1.00)	0.0682 (1.00)	0.252 (1.00)
20p loss	0 (0%)	182	0.363 (1.00)	0.0647 (1.00)	0.211 (1.00)	0.133 (1.00)	0.0473 (1.00)	0.0246 (1.00)
21q loss	0 (0%)	183	0.278 (1.00)	0.566 (1.00)	0.329 (1.00)	0.0963 (1.00)	0.352 (1.00)	0.83 (1.00)
22q loss	0 (0%)	182	0.0646 (1.00)	0.396 (1.00)	0.377 (1.00)	0.452 (1.00)	1 (1.00)	1 (1.00)

'7p gain' versus 'CN_CNMF'

P value = 0.000281 (Fisher's exact test), Q value = 0.046

Table S1. Gene #5: '7p gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	34	92	59	2
7P GAIN CNV	1	2	13	0
7P GAIN WILD-TYPE	33	90	46	2

Figure S1. Get High-res Image Gene #5: '7p gain' versus Molecular Subtype #1: 'CN_CNMF'

'8q gain' versus 'CN_CNMF'

P value = 5.57e-05 (Fisher's exact test), Q value = 0.0092

Table S2. Gene #8: '8q gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	34	92	59	2
8Q GAIN CNV	2	2	15	0
8Q GAIN WILD-TYPE	32	90	44	2

Figure S2. Get High-res Image Gene #8: '8q gain' versus Molecular Subtype #1: 'CN_CNMF'

'8p loss' versus 'METHLYATION_CNMF'

P value = 0.000129 (Fisher's exact test), Q value = 0.021

Table S3. Gene #17: '8p loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	55	77	55
8P LOSS CNV	11	31	5
8P LOSS WILD-TYPE	44	46	50

Figure S3. Get High-res Image Gene #17: '8p loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

'13q loss' versus 'CN_CNMF'

P value = 0.00145 (Fisher's exact test), Q value = 0.24

Table S4. Gene #22: '13q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	34	92	59	2
13Q LOSS CNV	1	1	10	0
13Q LOSS WILD-TYPE	33	91	49	2

Figure S4. Get High-res Image Gene #22: '13q loss' versus Molecular Subtype #1: 'CN_CNMF'

'16q loss' versus 'CN_CNMF'

P value = 3.66e-05 (Fisher's exact test), Q value = 0.0061

Table S5. Gene #23: '16q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	34	92	59	2
16Q LOSS CNV	2	4	18	0
16Q LOSS WILD-TYPE	32	88	41	2

Figure S5. Get High-res Image Gene #23: '16q loss' versus Molecular Subtype #1: 'CN_CNMF'

'17p loss' versus 'CN_CNMF'

P value = 6.43e-06 (Fisher's exact test), Q value = 0.0011

Table S6. Gene #24: '17p loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	34	92	59	2
17P LOSS CNV	0	4	17	0
17P LOSS WILD-TYPE	34	88	42	2

Figure S6. Get High-res Image Gene #24: '17p loss' versus Molecular Subtype #1: 'CN_CNMF'

'18p loss' versus 'CN_CNMF'

P value = 2.52e-05 (Fisher's exact test), Q value = 0.0042

Table S7. Gene #25: '18p loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	34	92	59	2
18P LOSS CNV	1	2	15	0
18P LOSS WILD-TYPE	33	90	44	2

Figure S7. Get High-res Image Gene #25: '18p loss' versus Molecular Subtype #1: 'CN_CNMF'

'18q loss' versus 'CN_CNMF'

P value = 1.1e-08 (Fisher's exact test), Q value = 1.9e-06

Table S8. Gene #26: '18q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	34	92	59	2
18Q LOSS CNV	0	3	21	0
18Q LOSS WILD-TYPE	34	89	38	2

Figure S8. Get High-res Image Gene #26: '18q loss' versus Molecular Subtype #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 = 187
Number of significantly arm-level cnvs = 29
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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