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 34 genes and 8 clinical features across 293 patients, 2 significant findings detected with Q value < 0.25.

  • BAP1 mutation correlated to 'TUMOR.STAGE'.

  • TP53 mutation correlated to 'Time to Death'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between mutation status of 34 genes and 8 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 2 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE GENDER KARNOFSKY
PERFORMANCE
SCORE 		PATHOLOGY
T 		PATHOLOGY
N 		PATHOLOGICSPREAD(M) TUMOR
STAGE
nMutated (%) nWild-Type logrank test t-test Fisher's exact test t-test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test
BAP1 27 (9%) 266 0.0136
(1.00) 		0.742
(1.00) 		0.00238
(0.561) 		0.00295
(0.692) 		0.627
(1.00) 		0.00411
(0.962) 		0.000388
(0.0927)
TP53 6 (2%) 287 0.000354
(0.0849) 		0.259
(1.00) 		0.188
(1.00) 		0.0158
(1.00) 		0.271
(1.00) 		0.579
(1.00) 		0.0249
(1.00)
PBRM1 106 (36%) 187 0.525
(1.00) 		0.892
(1.00) 		0.16
(1.00) 		0.429
(1.00) 		0.86
(1.00) 		0.746
(1.00) 		0.592
(1.00) 		0.901
(1.00)
SETD2 33 (11%) 260 0.747
(1.00) 		0.136
(1.00) 		0.119
(1.00) 		0.192
(1.00) 		0.605
(1.00) 		0.059
(1.00) 		0.0967
(1.00)
SV2C 3 (1%) 290 0.731
(1.00) 		0.0205
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00)
VHL 138 (47%) 155 0.668
(1.00) 		0.0301
(1.00) 		0.269
(1.00) 		0.359
(1.00) 		0.0815
(1.00) 		0.756
(1.00) 		1
(1.00) 		0.169
(1.00)
KDM5C 18 (6%) 275 0.0803
(1.00) 		0.206
(1.00) 		0.00486
(1.00) 		0.622
(1.00) 		0.516
(1.00) 		1
(1.00) 		0.856
(1.00)
MTOR 24 (8%) 269 0.123
(1.00) 		0.203
(1.00) 		0.119
(1.00) 		0.298
(1.00) 		0.0602
(1.00) 		1
(1.00) 		0.346
(1.00)
EBPL 6 (2%) 287 0.48
(1.00) 		0.527
(1.00) 		0.00161
(0.384) 		0.117
(1.00) 		1
(1.00) 		1
(1.00) 		0.0646
(1.00)
MUC4 41 (14%) 252 0.454
(1.00) 		0.247
(1.00) 		0.597
(1.00) 		0.00706
(1.00) 		0.217
(1.00) 		0.0431
(1.00) 		0.0021
(0.497)
NBPF10 19 (6%) 274 0.173
(1.00) 		0.305
(1.00) 		0.133
(1.00) 		0.147
(1.00) 		0.113
(1.00) 		0.0274
(1.00) 		0.115
(1.00)
PTEN 9 (3%) 284 0.769
(1.00) 		0.219
(1.00) 		0.503
(1.00) 		0.347
(1.00) 		0.0286
(1.00) 		0.613
(1.00) 		0.0971
(1.00)
TOR1A 3 (1%) 290 0.0532
(1.00) 		0.704
(1.00) 		1
(1.00) 		1
(1.00) 		0.0753
(1.00) 		1
(1.00) 		1
(1.00)
UQCRFS1 3 (1%) 290 0.321
(1.00) 		0.518
(1.00) 		0.278
(1.00) 		0.0452
(1.00) 		1
(1.00) 		1
(1.00) 		0.0554
(1.00)
PIK3CA 10 (3%) 283 0.253
(1.00) 		0.808
(1.00) 		0.743
(1.00) 		0.342
(1.00) 		1
(1.00) 		1
(1.00) 		0.336
(1.00)
WDR52 9 (3%) 284 0.967
(1.00) 		0.395
(1.00) 		0.724
(1.00) 		0.647
(1.00) 		1
(1.00) 		0.342
(1.00) 		0.842
(1.00)
TSPAN19 4 (1%) 289 0.319
(1.00) 		0.467
(1.00) 		1
(1.00) 		0.458
(1.00) 		1
(1.00) 		0.437
(1.00) 		0.223
(1.00)
CR1 10 (3%) 283 0.594
(1.00) 		0.711
(1.00) 		0.743
(1.00) 		0.902
(1.00) 		1
(1.00) 		0.628
(1.00) 		0.777
(1.00)
CNTNAP4 9 (3%) 284 0.485
(1.00) 		0.5
(1.00) 		0.724
(1.00) 		0.51
(1.00) 		1
(1.00) 		1
(1.00) 		0.513
(1.00)
ABCB1 8 (3%) 285 0.116
(1.00) 		0.582
(1.00) 		0.718
(1.00) 		0.882
(1.00) 		1
(1.00) 		0.603
(1.00) 		0.784
(1.00)
BAGE2 4 (1%) 289 0.961
(1.00) 		0.667
(1.00) 		0.612
(1.00) 		0.796
(1.00) 		1
(1.00) 		1
(1.00) 		0.589
(1.00)
MSN 4 (1%) 289 0.63
(1.00) 		0.578
(1.00) 		1
(1.00) 		0.19
(1.00) 		1
(1.00) 		1
(1.00) 		0.464
(1.00)
STAG2 9 (3%) 284 0.971
(1.00) 		0.0169
(1.00) 		1
(1.00) 		0.112
(1.00) 		1
(1.00) 		1
(1.00) 		0.161
(1.00)
NPNT 6 (2%) 287 0.0806
(1.00) 		0.222
(1.00) 		0.668
(1.00) 		0.342
(1.00) 		1
(1.00) 		1
(1.00) 		0.219
(1.00)
SPAM1 5 (2%) 288 0.27
(1.00) 		0.192
(1.00) 		0.661
(1.00) 		0.096
(1.00) 		1
(1.00) 		1
(1.00) 		0.288
(1.00)
CLIC6 3 (1%) 290 0.364
(1.00) 		0.379
(1.00) 		0.278
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00)
NFE2L2 5 (2%) 288 0.163
(1.00) 		0.162
(1.00) 		1
(1.00) 		0.827
(1.00) 		1
(1.00) 		0.513
(1.00) 		0.619
(1.00)
SLC2A14 3 (1%) 290 0.713
(1.00) 		0.897
(1.00) 		1
(1.00) 		0.519
(1.00) 		1
(1.00) 		1
(1.00) 		0.38
(1.00)
TPTE2 7 (2%) 286 0.3
(1.00) 		0.309
(1.00) 		1
(1.00) 		0.48
(1.00) 		1
(1.00) 		0.599
(1.00) 		0.596
(1.00)
CCNB2 5 (2%) 288 0.784
(1.00) 		0.459
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		0.911
(1.00)
ZNF800 6 (2%) 287 0.166
(1.00) 		0.599
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		0.579
(1.00) 		0.806
(1.00)
ADCY8 5 (2%) 288 0.706
(1.00) 		0.582
(1.00) 		0.167
(1.00) 		0.262
(1.00) 		0.0753
(1.00) 		0.513
(1.00) 		0.0946
(1.00)
LPAR4 4 (1%) 289 0.534
(1.00) 		0.457
(1.00) 		0.612
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		0.768
(1.00)
C5ORF13 3 (1%) 290 0.32
(1.00) 		0.465
(1.00) 		0.278
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00)
'BAP1 MUTATION STATUS' versus 'TUMOR.STAGE'

P value = 0.000388 (Fisher's exact test), Q value = 0.093

Table S1.  Gene #5: 'BAP1 MUTATION STATUS' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 144 31 76 42
BAP1 MUTATED 5 5 7 10
BAP1 WILD-TYPE 139 26 69 32

Figure S1.  Get High-res Image Gene #5: 'BAP1 MUTATION STATUS' versus Clinical Feature #8: 'TUMOR.STAGE'

'TP53 MUTATION STATUS' versus 'Time to Death'

P value = 0.000354 (logrank test), Q value = 0.085

Table S2.  Gene #10: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 291 77 0.1 - 109.6 (34.3)
TP53 MUTATED 6 3 0.2 - 25.7 (9.8)
TP53 WILD-TYPE 285 74 0.1 - 109.6 (35.2)

Figure S2.  Get High-res Image Gene #10: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

Methods & Data
Input

  • Mutation data file = KIRC-TP.mutsig.cluster.txt

  • Clinical data file = KIRC-TP.clin.merged.picked.txt

  • Number of patients = 293

  • Number of significantly mutated genes = 34

  • 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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between tumors with and without gene mutations using 't.test' 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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[3] 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)
[4] 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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