Correlation between copy number variation genes and molecular subtypes
Kidney Chromophobe (Primary solid tumor)
22 February 2013 | analyses__2013_02_22
Maintainer Information
Citation Information
doi:10.7908/C19C6VMB
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 variation genes and molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C19C6VMB
Overview
Introduction

This pipeline computes the correlation between significant copy number variation (cnv) genes and molecular subtypes.

Summary

Testing the association between copy number variation of 2 peak regions and 6 molecular subtypes across 66 patients, 4 significant findings detected with Q value < 0.25.

- Amp Peak 1(8q11.23) cnvs correlated to 'MRNASEQ_CNMF' and 'MRNASEQ_CHIERARCHICAL'.

- Amp Peak 2(15q22.31) cnvs correlated to 'MRNASEQ_CNMF' and 'MRNASEQ_CHIERARCHICAL'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 2 regions and 6 molecular subtypes. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 4 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 | Chi-square test | Fisher's exact test | Fisher's exact test
Amp Peak 1(8q11 23) | 18 (27%) | 48 | 0.165 (1.00) | 0.0644 (0.515) | 0.00906 (0.0906) | 0.0143 (0.129) | 0.939 (1.00) | 1 (1.00)
Amp Peak 2(15q22 31) | 22 (33%) | 44 | 0.147 (1.00) | 0.942 (1.00) | 0.00546 (0.0655) | 0.00823 (0.0906) | 0.513 (1.00) | 0.253 (1.00)

'Amp Peak 1(8q11.23) mutation analysis' versus 'MRNASEQ_CNMF'

P value = 0.00906 (Fisher's exact test), Q value = 0.091

Table S1. Gene #1: 'Amp Peak 1(8q11.23) mutation analysis' versus Clinical Feature #3: 'MRNASEQ_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4
--- | --- | --- | --- | ---
ALL | 19 | 22 | 15 | 10
AMP PEAK 1(8Q11.23) MUTATED | 3 | 12 | 2 | 1
AMP PEAK 1(8Q11.23) WILD-TYPE | 16 | 10 | 13 | 9

Figure S1. Get High-res Image Gene #1: 'Amp Peak 1(8q11.23) mutation analysis' versus Clinical Feature #3: 'MRNASEQ_CNMF'

'Amp Peak 1(8q11.23) mutation analysis' versus 'MRNASEQ_CHIERARCHICAL'

P value = 0.0143 (Chi-square test), Q value = 0.13

Table S2. Gene #1: 'Amp Peak 1(8q11.23) mutation analysis' versus Clinical Feature #4: 'MRNASEQ_CHIERARCHICAL'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 10 | 10 | 17 | 6 | 23
AMP PEAK 1(8Q11.23) MUTATED | 2 | 2 | 10 | 0 | 4
AMP PEAK 1(8Q11.23) WILD-TYPE | 8 | 8 | 7 | 6 | 19

Figure S2. Get High-res Image Gene #1: 'Amp Peak 1(8q11.23) mutation analysis' versus Clinical Feature #4: 'MRNASEQ_CHIERARCHICAL'

'Amp Peak 2(15q22.31) mutation analysis' versus 'MRNASEQ_CNMF'

P value = 0.00546 (Fisher's exact test), Q value = 0.066

Table S3. Gene #2: 'Amp Peak 2(15q22.31) mutation analysis' versus Clinical Feature #3: 'MRNASEQ_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4
--- | --- | --- | --- | ---
ALL | 19 | 22 | 15 | 10
AMP PEAK 2(15Q22.31) MUTATED | 5 | 13 | 4 | 0
AMP PEAK 2(15Q22.31) WILD-TYPE | 14 | 9 | 11 | 10

Figure S3. Get High-res Image Gene #2: 'Amp Peak 2(15q22.31) mutation analysis' versus Clinical Feature #3: 'MRNASEQ_CNMF'

'Amp Peak 2(15q22.31) mutation analysis' versus 'MRNASEQ_CHIERARCHICAL'

P value = 0.00823 (Chi-square test), Q value = 0.091

Table S4. Gene #2: 'Amp Peak 2(15q22.31) mutation analysis' versus Clinical Feature #4: 'MRNASEQ_CHIERARCHICAL'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 10 | 10 | 17 | 6 | 23
AMP PEAK 2(15Q22.31) MUTATED | 4 | 1 | 11 | 0 | 6
AMP PEAK 2(15Q22.31) WILD-TYPE | 6 | 9 | 6 | 6 | 17

Figure S4. Get High-res Image Gene #2: 'Amp Peak 2(15q22.31) mutation analysis' versus Clinical Feature #4: 'MRNASEQ_CHIERARCHICAL'

Methods & Data
Input

- Copy number data file = All Lesions File (all_lesions.conf_##.txt, where ## is the confidence level). The all lesions file is from GISTIC pipeline and summarizes the results from the GISTIC run. It contains data about the significant regions of amplification and deletion as well as which samples are amplified or deleted in each of these regions. The identified regions are listed down the first column, and the samples are listed across the first row, starting in column 10.

- Molecular subtype file = KICH-TP.transferedmergedcluster.txt

- Number of patients = 66

- Number of copy number variation regions = 2

- Number of molecular subtypes = 6

- Exclude regions that fewer than K tumors have alterations, 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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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)
[4] GISTIC_version2
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