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

Pancreatic Adenocarcinoma (Primary solid tumor)

23 May 2013 | analyses__2013_05_23

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/C1833Q3R

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 19 arm-level results and 8 molecular subtypes across 50 patients, 5 significant findings detected with Q value < 0.25.

6q loss cnv correlated to 'METHLYATION_CNMF'.
9p loss cnv correlated to 'CN_CNMF'.
17p loss cnv correlated to 'CN_CNMF'.
18q loss cnv correlated to 'CN_CNMF'.
21q 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 19 arm-level results and 8 molecular subtypes. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 5 significant findings detected.


		Molecular subtypes	CN CNMF	METHLYATION CNMF	MRNASEQ CNMF	MRNASEQ CHIERARCHICAL	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL	MIRSEQ MATURE CNMF	MIRSEQ MATURE CHIERARCHICAL
	nCNV (%)	nWild-Type	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Chi-square test	Fisher's exact test	Fisher's exact test	Fisher's exact test
6q loss	0 (0%)	42	0.014 (1.00)	0.000163 (0.0228)	0.0212 (1.00)	0.0341 (1.00)	0.00374 (0.49)	0.00756 (0.944)	0.0211 (1.00)	0.00756 (0.944)
9p loss	0 (0%)	42	0.0014 (0.192)	0.00254 (0.343)	0.186 (1.00)	0.0341 (1.00)	0.0649 (1.00)	0.119 (1.00)	0.0211 (1.00)	0.119 (1.00)
17p loss	0 (0%)	40	1.83e-05 (0.00258)	0.00202 (0.275)	0.412 (1.00)	0.0101 (1.00)	0.184 (1.00)	0.0367 (1.00)	0.0212 (1.00)	0.0367 (1.00)
18q loss	0 (0%)	43	0.000968 (0.135)	0.00694 (0.882)	0.664 (1.00)	0.0341 (1.00)	0.0461 (1.00)	0.396 (1.00)	0.185 (1.00)	0.396 (1.00)
21q loss	0 (0%)	43	0.000968 (0.135)	0.00694 (0.882)	0.0212 (1.00)	0.0341 (1.00)	0.0625 (1.00)	0.119 (1.00)	0.0211 (1.00)	0.119 (1.00)
1q gain	0 (0%)	47	0.0449 (1.00)	0.0562 (1.00)	0.488 (1.00)				0.488 (1.00)
8q gain	0 (0%)	43	0.00605 (0.774)	0.0405 (1.00)	0.186 (1.00)	0.258 (1.00)	0.219 (1.00)	0.0305 (1.00)	0.0946 (1.00)	0.0305 (1.00)
18p gain	0 (0%)	45	0.0105 (1.00)	0.12 (1.00)	0.233 (1.00)	0.313 (1.00)	0.404 (1.00)	0.0637 (1.00)	0.108 (1.00)	0.0637 (1.00)
20q gain	0 (0%)	46	0.00306 (0.41)	0.538 (1.00)	1 (1.00)	1 (1.00)	0.353 (1.00)	0.515 (1.00)	0.607 (1.00)	0.515 (1.00)
6p loss	0 (0%)	45	0.0152 (1.00)	0.00491 (0.638)	0.0486 (1.00)	0.0605 (1.00)	0.0184 (1.00)	0.018 (1.00)	0.0485 (1.00)	0.018 (1.00)
9q loss	0 (0%)	47	0.237 (1.00)	0.0562 (1.00)	1 (1.00)				1 (1.00)
10p loss	0 (0%)	47	0.0173 (1.00)	0.0562 (1.00)	0.488 (1.00)		0.0309 (1.00)	0.217 (1.00)	0.233 (1.00)	0.217 (1.00)
10q loss	0 (0%)	47	0.0786 (1.00)	0.0562 (1.00)	0.488 (1.00)		0.198 (1.00)	0.217 (1.00)	0.233 (1.00)	0.217 (1.00)
12p loss	0 (0%)	47	0.0449 (1.00)	0.0562 (1.00)	0.233 (1.00)	0.313 (1.00)	0.198 (1.00)	0.217 (1.00)	0.233 (1.00)	0.217 (1.00)
12q loss	0 (0%)	45	0.0152 (1.00)	0.00491 (0.638)	0.607 (1.00)	0.184 (1.00)	0.422 (1.00)	0.515 (1.00)	0.108 (1.00)	0.515 (1.00)
13q loss	0 (0%)	47	0.0173 (1.00)	0.341 (1.00)	0.488 (1.00)		0.0309 (1.00)	0.217 (1.00)	0.233 (1.00)	0.217 (1.00)
17q loss	0 (0%)	46	0.00306 (0.41)	0.0168 (1.00)	1 (1.00)	0.313 (1.00)	0.114 (1.00)	0.515 (1.00)	0.108 (1.00)	0.515 (1.00)
18p loss	0 (0%)	46	0.0318 (1.00)	0.0168 (1.00)	1 (1.00)	0.313 (1.00)	0.568 (1.00)	0.79 (1.00)	1 (1.00)	0.79 (1.00)
22q loss	0 (0%)	46	0.00306 (0.41)	0.12 (1.00)	0.233 (1.00)	0.313 (1.00)	0.198 (1.00)	0.217 (1.00)	0.233 (1.00)	0.217 (1.00)

'6q loss' versus 'METHLYATION_CNMF'

P value = 0.000163 (Fisher's exact test), Q value = 0.023

Table S1. Gene #6: '6q loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	18	19	12
6Q LOSS CNV	8	0	0
6Q LOSS WILD-TYPE	10	19	12

Figure S1. Get High-res Image Gene #6: '6q loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

'9p loss' versus 'CN_CNMF'

P value = 0.0014 (Fisher's exact test), Q value = 0.19

Table S2. Gene #7: '9p loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	12	28	10
9P LOSS CNV	6	1	1
9P LOSS WILD-TYPE	6	27	9

Figure S2. Get High-res Image Gene #7: '9p loss' versus Molecular Subtype #1: 'CN_CNMF'

'17p loss' versus 'CN_CNMF'

P value = 1.83e-05 (Fisher's exact test), Q value = 0.0026

Table S3. Gene #14: '17p loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	12	28	10
17P LOSS CNV	7	0	3
17P LOSS WILD-TYPE	5	28	7

Figure S3. Get High-res Image Gene #14: '17p loss' versus Molecular Subtype #1: 'CN_CNMF'

'18q loss' versus 'CN_CNMF'

P value = 0.000968 (Fisher's exact test), Q value = 0.13

Table S4. Gene #17: '18q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	12	28	10
18Q LOSS CNV	5	0	2
18Q LOSS WILD-TYPE	7	28	8

Figure S4. Get High-res Image Gene #17: '18q loss' versus Molecular Subtype #1: 'CN_CNMF'

'21q loss' versus 'CN_CNMF'

P value = 0.000968 (Fisher's exact test), Q value = 0.13

Table S5. Gene #18: '21q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	12	28	10
21Q LOSS CNV	5	0	2
21Q LOSS WILD-TYPE	7	28	8

Figure S5. Get High-res Image Gene #18: '21q loss' versus Molecular Subtype #1: 'CN_CNMF'

Methods & Data

Input

Mutation data file = broad_values_by_arm.mutsig.cluster.txt
Molecular subtypes file = PAAD-TP.transferedmergedcluster.txt
Number of patients = 50
Number of significantly arm-level cnvs = 19
Number of molecular subtypes = 8
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

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.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] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)

[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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