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)