Correlation between gene mutation status and selected clinical features
Uveal Melanoma (Primary solid tumor)
13 July 2018 | None
Maintainer Information
Maintained by Broad Institute GDAC (Broad Institute of MIT & Harvard)
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, 2 significant findings detected with Q value < 0.25.

- GNAQ mutation correlated to 'PATHOLOGIC_STAGE'.

- SF3B1 mutation correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

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, 2 significant findings detected.

|  | Clinical Features | DAYS TO DEATH OR LAST FUP | 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 | 0.0614 (0.501) | 0.514 (1.00) | 0.00121 (0.0558) | 0.692 (1.00) | 0.0515 (0.501) | 1 (1.00) | 0.61 (1.00)
SF3B1 | 18 (22%) | 62 | 0.00228 (0.0558) | 0.177 (1.00) | 0.964 (1.00) | 1 (1.00) | 0.573 (1.00) | 0.785 (1.00) | 0.527 (1.00)
EIF1AX | 10 (12%) | 70 | 0.278 (1.00) | 0.413 (1.00) | 0.98 (1.00) | 0.661 (1.00) | 1 (1.00) | 0.313 (1.00) | 1 (1.00)
BAP1 | 20 (25%) | 60 | 0.227 (1.00) | 0.0428 (0.501) | 0.412 (1.00) | 0.199 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
GNA11 | 35 (44%) | 45 | 0.233 (1.00) | 0.793 (1.00) | 0.0363 (0.501) | 0.824 (1.00) | 0.307 (1.00) | 0.647 (1.00) | 0.571 (1.00)
SRSF2 | 3 (4%) | 77 | 0.787 (1.00) | 0.71 (1.00) | 1 (1.00) | 0.558 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
CYSLTR2 | 3 (4%) | 77 | 0.624 (1.00) | 0.426 (1.00) | 0.679 (1.00) | 0.77 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)

'GNAQ MUTATION STATUS' versus 'PATHOLOGIC_STAGE'

P value = 0.00121 (Fisher's exact test), Q value = 0.056

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

nPatients | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV
--- | --- | --- | --- | --- | --- | ---
ALL | 11 | 27 | 25 | 10 | 1 | 4
GNAQ MUTATED | 8 | 11 | 19 | 2 | 0 | 0
GNAQ WILD-TYPE | 3 | 16 | 6 | 8 | 1 | 4

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

'SF3B1 MUTATION STATUS' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.00228 (logrank test), Q value = 0.056

Table S2. Gene #5: 'SF3B1 MUTATION STATUS' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 79 | 22 | 0.1 - 85.5 (26.1)
SF3B1 MUTATED | 17 | 0 | 19.7 - 82.2 (38.7)
SF3B1 WILD-TYPE | 62 | 22 | 0.1 - 85.5 (23.7)

Figure S2. Get High-res Image Gene #5: 'SF3B1 MUTATION STATUS' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

Methods & Data
Input

- Mutation data file = sample_sig_gene_table.txt from Mutsig_2CV pipeline

- Processed Mutation data file = /cromwell_root/fc-f5144117-2d5a-42c2-8998-5b38e52db5d9/72c5c88f-c1dc-4684-91a3-27070eb9950e/correlate_genomic_events_all/27256aee-9d75-4241-8356-5fe45cf78cd4/call-preprocess_genomic_event/transformed.cor.cli.txt

- Clinical data file = /cromwell_root/fc-2289d790-de74-4808-9b0a-cefafc34d859/0d7c7dcf-18e0-4b2d-afc0-a0b2ee1e45ff/preprocess_clinical_workflow/70152ac6-f707-4277-8d60-8770b1b366c6/call-preprocess_clinical/TCGA-UVM-TP.clin.merged.picked.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.

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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