Correlation between gene mutation status and molecular subtypes
Rectum Adenocarcinoma (Primary solid tumor)
22 February 2013 | analyses__2013_02_22
Maintainer Information
Citation Information
doi:10.7908/C19P2ZV9
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/C19P2ZV9
Overview
Introduction

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

Summary

Testing the association between mutation status of 16 genes and 9 molecular subtypes across 69 patients, 3 significant findings detected with P value < 0.05 and Q value < 0.25.

- KRAS mutation correlated to 'MRNASEQ_CHIERARCHICAL'.

- 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 16 genes and 9 molecular subtypes. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 3 significant findings detected.

|  | Clinical Features | MRNA CNMF | MRNA CHIERARCHICAL | CN CNMF | RPPA CNMF | RPPA CHIERARCHICAL | MRNASEQ CNMF | MRNASEQ 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 | Fisher's exact test | Fisher's exact test
TP53 | 45 (65%) | 24 | 0.000229 (0.0311) | 0.00135 (0.181) | 0.21 (1.00) | 0.144 (1.00) | 0.294 (1.00) | 0.223 (1.00) | 0.461 (1.00) | 0.812 (1.00) | 0.288 (1.00)
KRAS | 38 (55%) | 31 | 0.342 (1.00) | 0.18 (1.00) | 0.00233 (0.31) | 0.737 (1.00) | 0.764 (1.00) | 0.00844 (1.00) | 0.000835 (0.113) | 0.292 (1.00) | 0.0465 (1.00)
APC | 57 (83%) | 12 | 0.85 (1.00) | 0.78 (1.00) | 0.0561 (1.00) | 0.456 (1.00) | 0.647 (1.00) | 0.344 (1.00) | 1 (1.00) | 0.337 (1.00) | 1 (1.00)
SMAD4 | 8 (12%) | 61 | 0.105 (1.00) | 0.361 (1.00) | 0.914 (1.00) | 0.597 (1.00) | 0.17 (1.00) | 0.636 (1.00) | 0.126 (1.00) | 0.309 (1.00) | 1 (1.00)
KIAA1804 | 9 (13%) | 60 | 0.322 (1.00) | 0.578 (1.00) | 0.0997 (1.00) | 0.795 (1.00) | 0.471 (1.00) | 0.712 (1.00) | 0.469 (1.00) | 0.805 (1.00) | 1 (1.00)
FBXW7 | 9 (13%) | 60 | 0.148 (1.00) | 0.0962 (1.00) | 0.409 (1.00) | 0.148 (1.00) | 0.855 (1.00) | 0.185 (1.00) | 0.0626 (1.00) | 0.805 (1.00) | 1 (1.00)
NRAS | 5 (7%) | 64 | 0.856 (1.00) | 1 (1.00) | 0.675 (1.00) |  |  | 0.532 (1.00) | 0.716 (1.00) | 0.0969 (1.00) | 1 (1.00)
TCF7L2 | 7 (10%) | 62 | 0.883 (1.00) | 0.683 (1.00) | 0.709 (1.00) | 0.169 (1.00) | 0.461 (1.00) | 0.224 (1.00) | 0.315 (1.00) | 1 (1.00) | 1 (1.00)
PIK3CA | 7 (10%) | 62 | 1 (1.00) | 0.731 (1.00) | 0.814 (1.00) | 0.356 (1.00) | 0.556 (1.00) | 0.866 (1.00) | 0.587 (1.00) | 0.343 (1.00) | 0.328 (1.00)
OPCML | 6 (9%) | 63 | 0.228 (1.00) | 0.0972 (1.00) | 0.508 (1.00) | 0.169 (1.00) | 0.628 (1.00) | 0.942 (1.00) | 0.749 (1.00) | 1 (1.00) | 1 (1.00)
SMAD2 | 5 (7%) | 64 | 1 (1.00) | 1 (1.00) | 0.29 (1.00) | 0.0155 (1.00) | 0.291 (1.00) | 0.369 (1.00) | 0.154 (1.00) | 1 (1.00) | 1 (1.00)
SPATA8 | 3 (4%) | 66 |  |  | 0.388 (1.00) |  |  | 0.694 (1.00) | 0.439 (1.00) | 0.432 (1.00) | 0.176 (1.00)
ERBB2 | 4 (6%) | 65 | 0.54 (1.00) | 1 (1.00) | 0.576 (1.00) | 0.0567 (1.00) | 0.628 (1.00) | 0.614 (1.00) | 0.6 (1.00) | 1 (1.00) | 1 (1.00)
IL1RAPL2 | 5 (7%) | 64 | 0.613 (1.00) | 0.519 (1.00) | 0.785 (1.00) |  |  | 0.473 (1.00) | 0.524 (1.00) | 0.273 (1.00) | 1 (1.00)
FAM123B | 6 (9%) | 63 | 1 (1.00) | 0.731 (1.00) | 0.463 (1.00) | 0.243 (1.00) | 0.375 (1.00) | 0.358 (1.00) | 0.3 (1.00) | 1 (1.00) | 1 (1.00)
ZIM3 | 5 (7%) | 64 | 0.827 (1.00) | 0.668 (1.00) | 0.433 (1.00) | 0.00342 (0.451) | 0.185 (1.00) | 0.445 (1.00) | 0.524 (1.00) | 0.387 (1.00) | 0.28 (1.00)

'KRAS MUTATION STATUS' versus 'MRNASEQ_CHIERARCHICAL'

P value = 0.000835 (Fisher's exact test), Q value = 0.11

Table S1. Gene #2: 'KRAS MUTATION STATUS' versus Clinical Feature #7: 'MRNASEQ_CHIERARCHICAL'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 15 | 24 | 28
KRAS MUTATED | 9 | 19 | 8
KRAS WILD-TYPE | 6 | 5 | 20

Figure S1. Get High-res Image Gene #2: 'KRAS MUTATION STATUS' versus Clinical Feature #7: 'MRNASEQ_CHIERARCHICAL'

'TP53 MUTATION STATUS' versus 'MRNA_CNMF'

P value = 0.000229 (Fisher's exact test), Q value = 0.031

Table S2. Gene #3: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'MRNA_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 24 | 19 | 21
TP53 MUTATED | 8 | 13 | 19
TP53 WILD-TYPE | 16 | 6 | 2

Figure S2. Get High-res Image Gene #3: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'MRNA_CNMF'

'TP53 MUTATION STATUS' versus 'MRNA_CHIERARCHICAL'

P value = 0.00135 (Fisher's exact test), Q value = 0.18

Table S3. Gene #3: 'TP53 MUTATION STATUS' versus Clinical Feature #2: 'MRNA_CHIERARCHICAL'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 17 | 23 | 24
TP53 MUTATED | 15 | 8 | 17
TP53 WILD-TYPE | 2 | 15 | 7

Figure S3. 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 = 16

- Number of Molecular subtypes = 9

- 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.

- 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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