Correlation between gene mutation status and molecular subtypes
Rectum Adenocarcinoma (Primary solid tumor)
23 September 2013 | analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C1WS8RKT
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 gene mutation status and molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C1WS8RKT
Overview
Introduction

This pipeline computes the correlation between significantly recurrent gene mutations and molecular subtypes.

Summary

Testing the association between mutation status of 3 genes and 7 molecular subtypes across 69 patients, 4 significant findings detected with P value < 0.05 and Q value < 0.25.

- KRAS mutation correlated to 'MRNA_CHIERARCHICAL' and 'CN_CNMF'.

- TP53 mutation correlated to 'MRNA_CNMF' and 'MRNA_CHIERARCHICAL'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 3 genes and 7 molecular subtypes. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 4 significant findings detected.

|  | Clinical Features | MRNA CNMF | MRNA CHIERARCHICAL | CN CNMF | RPPA CNMF | RPPA CHIERARCHICAL | MIRSEQ CNMF | MIRSEQ CHIERARCHICAL
| nMutated (%) | nWild-Type | Fisher's exact test | Fisher's exact test | Chi-square test | Fisher's exact test | Chi-square test | Fisher's exact test | Fisher's exact test
KRAS | 38 (55%) | 31 | 0.249 (1.00) | 0.00641 (0.115) | 0.00233 (0.0467) | 0.83 (1.00) | 0.817 (1.00) | 0.292 (1.00) | 0.0465 (0.79)
TP53 | 45 (65%) | 24 | 0.000428 (0.00899) | 0.00307 (0.0584) | 0.21 (1.00) | 0.16 (1.00) | 0.603 (1.00) | 0.812 (1.00) | 0.288 (1.00)
APC | 57 (83%) | 12 | 0.568 (1.00) | 0.849 (1.00) | 0.0561 (0.897) | 0.362 (1.00) | 0.598 (1.00) | 0.337 (1.00) | 1 (1.00)

'KRAS MUTATION STATUS' versus 'MRNA_CHIERARCHICAL'

P value = 0.00641 (Fisher's exact test), Q value = 0.12

Table S1. Gene #2: 'KRAS MUTATION STATUS' versus Clinical Feature #2: 'MRNA_CHIERARCHICAL'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 23 | 26 | 15
KRAS MUTATED | 16 | 8 | 11
KRAS WILD-TYPE | 7 | 18 | 4

Figure S1. Get High-res Image Gene #2: 'KRAS MUTATION STATUS' versus Clinical Feature #2: 'MRNA_CHIERARCHICAL'

'KRAS MUTATION STATUS' versus 'CN_CNMF'

P value = 0.00233 (Chi-square test), Q value = 0.047

Table S2. Gene #2: 'KRAS MUTATION STATUS' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 1 | 15 | 17 | 25 | 10
KRAS MUTATED | 1 | 3 | 7 | 19 | 8
KRAS WILD-TYPE | 0 | 12 | 10 | 6 | 2

Figure S2. Get High-res Image Gene #2: 'KRAS MUTATION STATUS' versus Clinical Feature #3: 'CN_CNMF'

'TP53 MUTATION STATUS' versus 'MRNA_CNMF'

P value = 0.000428 (Fisher's exact test), Q value = 0.009

Table S3. Gene #3: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'MRNA_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 24 | 21 | 19
TP53 MUTATED | 8 | 15 | 17
TP53 WILD-TYPE | 16 | 6 | 2

Figure S3. Get High-res Image Gene #3: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'MRNA_CNMF'

'TP53 MUTATION STATUS' versus 'MRNA_CHIERARCHICAL'

P value = 0.00307 (Fisher's exact test), Q value = 0.058

Table S4. Gene #3: 'TP53 MUTATION STATUS' versus Clinical Feature #2: 'MRNA_CHIERARCHICAL'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 23 | 26 | 15
TP53 MUTATED | 8 | 20 | 12
TP53 WILD-TYPE | 15 | 6 | 3

Figure S4. Get High-res Image Gene #3: 'TP53 MUTATION STATUS' versus Clinical Feature #2: 'MRNA_CHIERARCHICAL'

Methods & Data
Input

- Mutation data file = READ-TP.mutsig.cluster.txt

- Molecular subtypes file = READ-TP.transferedmergedcluster.txt

- Number of patients = 69

- Number of significantly mutated genes = 3

- Number of Molecular subtypes = 7

- 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

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.

- 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)
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