Correlation between gene mutation status and selected clinical features
Overview
Introduction

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

Summary

Testing the association between mutation status of 32 genes and 8 clinical features across 278 patients, 3 significant findings detected with Q value < 0.25.

  • IDH1 mutation correlated to 'Time to Death' and 'AGE'.

  • ATRX mutation correlated to 'AGE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between mutation status of 32 genes and 8 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
AGE GENDER KARNOFSKY
PERFORMANCE
SCORE
HISTOLOGICAL
TYPE
RADIATIONS
RADIATION
REGIMENINDICATION
RACE ETHNICITY
nMutated (%) nWild-Type logrank test Wilcoxon-test Fisher's exact test Wilcoxon-test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test
IDH1 14 (5%) 264 0.000145
(0.0366)
3.92e-06
(0.000996)
0.392
(1.00)
0.123
(1.00)
0.494
(1.00)
0.04
(1.00)
0.0273
(1.00)
1
(1.00)
ATRX 16 (6%) 262 0.00321
(0.796)
1.49e-05
(0.00376)
0.593
(1.00)
0.057
(1.00)
0.545
(1.00)
0.279
(1.00)
0.0249
(1.00)
1
(1.00)
TP53 78 (28%) 200 0.0013
(0.324)
0.32
(1.00)
0.677
(1.00)
0.102
(1.00)
0.35
(1.00)
0.202
(1.00)
0.593
(1.00)
1
(1.00)
PIK3R1 32 (12%) 246 0.625
(1.00)
0.495
(1.00)
0.434
(1.00)
0.76
(1.00)
1
(1.00)
0.69
(1.00)
0.676
(1.00)
1
(1.00)
RB1 23 (8%) 255 0.214
(1.00)
0.949
(1.00)
0.654
(1.00)
0.0253
(1.00)
0.683
(1.00)
1
(1.00)
0.601
(1.00)
1
(1.00)
NF1 29 (10%) 249 0.165
(1.00)
0.134
(1.00)
0.84
(1.00)
0.241
(1.00)
1
(1.00)
0.54
(1.00)
1
(1.00)
1
(1.00)
PTEN 85 (31%) 193 0.393
(1.00)
0.324
(1.00)
0.588
(1.00)
0.829
(1.00)
0.0608
(1.00)
0.678
(1.00)
1
(1.00)
0.227
(1.00)
PIK3CA 28 (10%) 250 0.403
(1.00)
0.997
(1.00)
0.684
(1.00)
0.751
(1.00)
0.497
(1.00)
0.676
(1.00)
0.423
(1.00)
1
(1.00)
STAG2 12 (4%) 266 0.00285
(0.71)
0.849
(1.00)
0.761
(1.00)
0.0422
(1.00)
1
(1.00)
0.54
(1.00)
0.613
(1.00)
1
(1.00)
SEMG1 8 (3%) 270 0.252
(1.00)
0.176
(1.00)
0.715
(1.00)
0.284
(1.00)
1
(1.00)
0.722
(1.00)
0.493
(1.00)
1
(1.00)
RPL5 7 (3%) 271 0.838
(1.00)
0.803
(1.00)
0.256
(1.00)
0.423
(1.00)
1
(1.00)
0.431
(1.00)
0.449
(1.00)
1
(1.00)
SLC26A3 6 (2%) 272 0.686
(1.00)
0.279
(1.00)
0.424
(1.00)
0.678
(1.00)
1
(1.00)
0.183
(1.00)
1
(1.00)
1
(1.00)
MAP3K1 6 (2%) 272 0.909
(1.00)
0.295
(1.00)
0.424
(1.00)
0.652
(1.00)
1
(1.00)
0.401
(1.00)
1
(1.00)
1
(1.00)
BRAF 6 (2%) 272 0.142
(1.00)
0.918
(1.00)
1
(1.00)
0.279
(1.00)
0.027
(1.00)
0.401
(1.00)
0.399
(1.00)
1
(1.00)
EGFR 73 (26%) 205 0.705
(1.00)
0.695
(1.00)
0.257
(1.00)
0.382
(1.00)
0.451
(1.00)
0.565
(1.00)
0.413
(1.00)
1
(1.00)
PDGFRA 11 (4%) 267 0.494
(1.00)
0.0539
(1.00)
1
(1.00)
0.411
(1.00)
1
(1.00)
0.187
(1.00)
1
(1.00)
1
(1.00)
KDR 8 (3%) 270 0.621
(1.00)
0.499
(1.00)
0.266
(1.00)
0.754
(1.00)
0.323
(1.00)
0.722
(1.00)
1
(1.00)
1
(1.00)
TMPRSS6 6 (2%) 272 0.965
(1.00)
0.274
(1.00)
0.67
(1.00)
0.284
(1.00)
1
(1.00)
0.667
(1.00)
1
(1.00)
1
(1.00)
CHD8 8 (3%) 270 0.951
(1.00)
0.0224
(1.00)
0.141
(1.00)
0.238
(1.00)
1
(1.00)
0.446
(1.00)
1
(1.00)
1
(1.00)
SEMA3C 11 (4%) 267 0.21
(1.00)
0.995
(1.00)
0.751
(1.00)
0.739
(1.00)
1
(1.00)
0.514
(1.00)
0.0563
(1.00)
0.129
(1.00)
MMP13 5 (2%) 273 0.461
(1.00)
0.12
(1.00)
0.657
(1.00)
0.0658
(1.00)
1
(1.00)
0.174
(1.00)
1
(1.00)
1
(1.00)
PRKCD 3 (1%) 275 0.778
(1.00)
0.123
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
0.224
(1.00)
1
(1.00)
REN 5 (2%) 273 0.685
(1.00)
0.617
(1.00)
0.657
(1.00)
0.333
(1.00)
0.08
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
LRRC55 6 (2%) 272 0.00121
(0.304)
0.719
(1.00)
0.67
(1.00)
0.0955
(1.00)
1
(1.00)
0.667
(1.00)
0.4
(1.00)
1
(1.00)
MUC17 21 (8%) 257 0.325
(1.00)
0.5
(1.00)
0.817
(1.00)
0.782
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
CD209 5 (2%) 273 0.996
(1.00)
0.717
(1.00)
0.0579
(1.00)
0.605
(1.00)
1
(1.00)
0.667
(1.00)
1
(1.00)
1
(1.00)
DDX5 3 (1%) 275 0.117
(1.00)
0.0388
(1.00)
0.555
(1.00)
0.135
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
TP63 6 (2%) 272 0.659
(1.00)
0.994
(1.00)
0.67
(1.00)
0.479
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
0.0531
(1.00)
CD1D 4 (1%) 274 0.543
(1.00)
0.253
(1.00)
0.621
(1.00)
0.706
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
LZTR1 10 (4%) 268 0.668
(1.00)
0.234
(1.00)
0.101
(1.00)
0.739
(1.00)
0.17
(1.00)
0.735
(1.00)
1
(1.00)
0.00427
(1.00)
TEX15 8 (3%) 270 0.0768
(1.00)
0.94
(1.00)
0.715
(1.00)
0.748
(1.00)
1
(1.00)
0.277
(1.00)
0.494
(1.00)
1
(1.00)
FBN3 11 (4%) 267 0.245
(1.00)
0.94
(1.00)
0.532
(1.00)
0.943
(1.00)
0.189
(1.00)
0.348
(1.00)
0.612
(1.00)
1
(1.00)
'IDH1 MUTATION STATUS' versus 'Time to Death'

P value = 0.000145 (logrank test), Q value = 0.037

Table S1.  Gene #6: 'IDH1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 278 210 0.1 - 73.8 (8.9)
IDH1 MUTATED 14 4 3.4 - 50.5 (18.8)
IDH1 WILD-TYPE 264 206 0.1 - 73.8 (8.6)

Figure S1.  Get High-res Image Gene #6: 'IDH1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'IDH1 MUTATION STATUS' versus 'AGE'

P value = 3.92e-06 (Wilcoxon-test), Q value = 0.001

Table S2.  Gene #6: 'IDH1 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 278 61.0 (13.0)
IDH1 MUTATED 14 40.0 (15.1)
IDH1 WILD-TYPE 264 62.2 (11.9)

Figure S2.  Get High-res Image Gene #6: 'IDH1 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

'ATRX MUTATION STATUS' versus 'AGE'

P value = 1.49e-05 (Wilcoxon-test), Q value = 0.0038

Table S3.  Gene #13: 'ATRX MUTATION STATUS' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 278 61.0 (13.0)
ATRX MUTATED 16 42.7 (16.4)
ATRX WILD-TYPE 262 62.2 (11.9)

Figure S3.  Get High-res Image Gene #13: 'ATRX MUTATION STATUS' versus Clinical Feature #2: 'AGE'

Methods & Data
Input
  • Mutation data file = transformed.cor.cli.txt

  • Clinical data file = GBM-TP.merged_data.txt

  • Number of patients = 278

  • Number of significantly mutated genes = 32

  • Number of selected clinical features = 8

  • 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)