Correlation between gene mutation status and selected clinical features
Uveal Melanoma (Primary solid tumor)
28 January 2016 | analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1668CQ0
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1668CQ0
Overview
Introduction

This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.

Summary

Testing the association between mutation status of 7 genes and 7 clinical features across 80 patients, 3 significant findings detected with Q value < 0.25.

- GNAQ mutation correlated to 'PATHOLOGIC_STAGE'.

- SF3B1 mutation correlated to 'Time to Death'.

- BAP1 mutation correlated to 'Time to Death'.

Results
Overview of the results

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

|  | Clinical Features | Time to Death | YEARS TO BIRTH | PATHOLOGIC STAGE | PATHOLOGY T STAGE | PATHOLOGY M STAGE | GENDER | RADIATION THERAPY
| nMutated (%) | nWild-Type | logrank test | Wilcoxon-test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test
GNAQ | 40 (50%) | 40 | 0.0429 (0.487) | 0.467 (1.00) | 0.00158 (0.0774) | 0.801 (1.00) | 0.0515 (0.487) | 1 (1.00) | 0.615 (1.00)
SF3B1 | 18 (22%) | 62 | 0.00713 (0.147) | 0.231 (1.00) | 0.879 (1.00) | 0.881 (1.00) | 0.573 (1.00) | 0.596 (1.00) | 0.545 (1.00)
BAP1 | 22 (28%) | 58 | 0.00901 (0.147) | 0.119 (0.73) | 0.434 (1.00) | 0.43 (1.00) | 0.265 (1.00) | 0.458 (1.00) | 1 (1.00)
GNA11 | 36 (45%) | 44 | 0.203 (1.00) | 0.749 (1.00) | 0.0597 (0.487) | 0.837 (1.00) | 0.307 (1.00) | 0.653 (1.00) | 0.0828 (0.579)
EIF1AX | 10 (12%) | 70 | 0.255 (1.00) | 0.419 (1.00) | 1 (1.00) | 0.666 (1.00) | 1 (1.00) | 0.32 (1.00) | 1 (1.00)
CYSLTR2 | 3 (4%) | 77 | 0.676 (1.00) | 0.425 (1.00) | 0.684 (1.00) | 0.78 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
SFRS2 | 3 (4%) | 77 | 0.832 (1.00) | 0.704 (1.00) | 1 (1.00) | 0.578 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)

'GNAQ MUTATION STATUS' versus 'PATHOLOGIC_STAGE'

P value = 0.00158 (Fisher's exact test), Q value = 0.077

Table S1. Gene #1: 'GNAQ MUTATION STATUS' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV
--- | --- | --- | --- | --- | --- | ---
ALL | 12 | 27 | 25 | 10 | 1 | 4
GNAQ MUTATED | 8 | 11 | 19 | 2 | 0 | 0
GNAQ WILD-TYPE | 4 | 16 | 6 | 8 | 1 | 4

Figure S1. Get High-res Image Gene #1: 'GNAQ MUTATION STATUS' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'SF3B1 MUTATION STATUS' versus 'Time to Death'

P value = 0.00713 (logrank test), Q value = 0.15

Table S2. Gene #4: 'SF3B1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 80 | 23 | 0.1 - 85.5 (25.8)
SF3B1 MUTATED | 18 | 1 | 14.9 - 82.2 (38.4)
SF3B1 WILD-TYPE | 62 | 22 | 0.1 - 85.5 (23.7)

Figure S2. Get High-res Image Gene #4: 'SF3B1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'BAP1 MUTATION STATUS' versus 'Time to Death'

P value = 0.00901 (logrank test), Q value = 0.15

Table S3. Gene #5: 'BAP1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 80 | 23 | 0.1 - 85.5 (25.8)
BAP1 MUTATED | 22 | 11 | 1.6 - 61.2 (22.1)
BAP1 WILD-TYPE | 58 | 12 | 0.1 - 85.5 (26.4)

Figure S3. Get High-res Image Gene #5: 'BAP1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

Methods & Data
Input

- Mutation data file = sample_sig_gene_table.txt from Mutsig_2CV pipeline

- Processed Mutation data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_Correlate_Genomic_Events_Preprocess/UVM-TP/22572045/transformed.cor.cli.txt

- Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/UVM-TP/22507229/UVM-TP.merged_data.txt

- Number of patients = 80

- Number of significantly mutated genes = 7

- Number of selected clinical features = 7

- Exclude genes that fewer than K tumors have mutations, K = 3

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

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

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] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[2] 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)
[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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