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 30 genes and 10 clinical features across 441 patients, 4 significant findings detected with Q value < 0.25.

  • BAP1 mutation correlated to 'Time to Death',  'NEOPLASM_DISEASESTAGE', and 'PATHOLOGY_T_STAGE'.

  • TRIM6 mutation correlated to 'Time to Death'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between mutation status of 30 genes and 10 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 4 significant findings detected.

Clinical
Features 		Time
to
Death 		YEARS
TO
BIRTH 		NEOPLASM
DISEASESTAGE 		PATHOLOGY
T
STAGE 		PATHOLOGY
N
STAGE 		PATHOLOGY
M
STAGE 		GENDER KARNOFSKY
PERFORMANCE
SCORE 		RACE ETHNICITY
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 Wilcoxon-test Fisher's exact test Fisher's exact test
BAP1 44 (10%) 397 0.00182
(0.182) 		0.916
(1.00) 		7e-05
(0.0105) 		6e-05
(0.0105) 		0.245
(1.00) 		0.0152
(0.364) 		0.00713
(0.267) 		1
(1.00) 		0.724
(1.00)
TRIM6 5 (1%) 436 0.00322
(0.242) 		0.196
(0.944) 		0.634
(1.00) 		0.599
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00)
SETD2 46 (10%) 395 0.1
(0.916) 		0.241
(1.00) 		0.299
(1.00) 		0.191
(0.944) 		0.379
(1.00) 		0.285
(1.00) 		0.332
(1.00) 		0.732
(1.00) 		0.71
(1.00)
PBRM1 139 (32%) 302 0.505
(1.00) 		0.185
(0.944) 		0.986
(1.00) 		0.186
(0.944) 		0.163
(0.944) 		0.781
(1.00) 		0.592
(1.00) 		0.547
(1.00) 		0.255
(1.00) 		0.234
(1.00)
KDM5C 27 (6%) 414 0.0591
(0.771) 		0.0331
(0.584) 		0.98
(1.00) 		0.975
(1.00) 		1
(1.00) 		1
(1.00) 		0.00615
(0.267) 		1
(1.00) 		0.379
(1.00)
VHL 171 (39%) 270 0.898
(1.00) 		0.726
(1.00) 		0.137
(0.916) 		0.111
(0.916) 		0.791
(1.00) 		0.287
(1.00) 		0.474
(1.00) 		0.278
(1.00) 		0.0156
(0.364) 		0.821
(1.00)
PTEN 17 (4%) 424 0.855
(1.00) 		0.575
(1.00) 		0.0871
(0.916) 		0.328
(1.00) 		0.158
(0.944) 		0.494
(1.00) 		0.0405
(0.675) 		0.427
(1.00) 		0.658
(1.00) 		0.61
(1.00)
MTOR 28 (6%) 413 0.138
(0.916) 		0.12
(0.916) 		0.276
(1.00) 		0.378
(1.00) 		0.356
(1.00) 		0.182
(0.944) 		0.103
(0.916) 		0.0256
(0.511) 		0.179
(0.944) 		1
(1.00)
TP53 12 (3%) 429 0.00989
(0.33) 		0.875
(1.00) 		0.507
(1.00) 		0.262
(1.00) 		1
(1.00) 		0.416
(1.00) 		0.76
(1.00) 		0.529
(1.00) 		1
(1.00)
FAM200A 5 (1%) 436 0.495
(1.00) 		0.685
(1.00) 		0.759
(1.00) 		0.486
(1.00) 		1
(1.00) 		1
(1.00) 		0.0538
(0.74) 		1
(1.00) 		1
(1.00)
NEFH 5 (1%) 436 0.669
(1.00) 		0.543
(1.00) 		0.128
(0.916) 		0.197
(0.944) 		1
(1.00) 		0.58
(1.00) 		0.661
(1.00) 		1
(1.00) 		1
(1.00)
PTCH1 7 (2%) 434 0.0543
(0.74) 		0.245
(1.00) 		0.366
(1.00) 		0.0758
(0.843) 		1
(1.00) 		1
(1.00) 		0.701
(1.00) 		1
(1.00) 		1
(1.00)
NF2 6 (1%) 435 0.139
(0.916) 		0.322
(1.00) 		0.457
(1.00) 		0.471
(1.00) 		0.0143
(0.364) 		0.244
(1.00) 		1
(1.00) 		0.31
(1.00) 		0.358
(1.00)
CCDC120 4 (1%) 437 0.134
(0.916) 		0.856
(1.00) 		0.105
(0.916) 		0.122
(0.916) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		0.198
(0.944)
PIK3CA 12 (3%) 429 0.618
(1.00) 		0.83
(1.00) 		0.57
(1.00) 		0.593
(1.00) 		1
(1.00) 		1
(1.00) 		0.0296
(0.556) 		1
(1.00) 		0.56
(1.00)
ATM 12 (3%) 429 0.96
(1.00) 		0.332
(1.00) 		0.136
(0.916) 		0.00711
(0.267) 		0.132
(0.916) 		0.416
(1.00) 		0.358
(1.00) 		1
(1.00) 		0.405
(1.00)
KIAA1751 6 (1%) 435 0.9
(1.00) 		0.186
(0.944) 		0.497
(1.00) 		0.878
(1.00) 		0.317
(1.00) 		0.596
(1.00) 		0.67
(1.00) 		1
(1.00) 		1
(1.00)
GUSB 4 (1%) 437 0.826
(1.00) 		0.0707
(0.839) 		0.168
(0.944) 		0.396
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00)
ARID1A 11 (2%) 430 0.866
(1.00) 		0.735
(1.00) 		0.0518
(0.74) 		0.112
(0.916) 		1
(1.00) 		1
(1.00) 		0.528
(1.00) 		1
(1.00) 		0.127
(0.916)
GPR50 3 (1%) 438 0.272
(1.00) 		0.9
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		0.283
(1.00) 		0.168
(0.944) 		1
(1.00)
PCK1 5 (1%) 436 0.187
(0.944) 		0.345
(1.00) 		0.18
(0.944) 		0.278
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00)
DPCR1 6 (1%) 435 0.384
(1.00) 		0.517
(1.00) 		0.103
(0.916) 		0.877
(1.00) 		1
(1.00) 		0.0536
(0.74) 		1
(1.00) 		0.31
(1.00) 		1
(1.00)
NFAT5 6 (1%) 435 0.88
(1.00) 		0.628
(1.00) 		0.708
(1.00) 		1
(1.00) 		1
(1.00) 		0.596
(1.00) 		0.67
(1.00) 		1
(1.00) 		1
(1.00)
EGFR 7 (2%) 434 0.017
(0.364) 		0.595
(1.00) 		0.0161
(0.364) 		0.00624
(0.267) 		0.262
(1.00) 		0.308
(1.00) 		0.43
(1.00) 		1
(1.00) 		1
(1.00)
RBMX 4 (1%) 437 0.701
(1.00) 		0.217
(1.00) 		1
(1.00) 		1
(1.00) 		0.14
(0.916) 		0.5
(1.00) 		0.302
(1.00) 		1
(1.00) 		1
(1.00)
GPR172B 4 (1%) 437 0.576
(1.00) 		0.798
(1.00) 		0.885
(1.00) 		0.805
(1.00) 		0.204
(0.954) 		1
(1.00) 		0.127
(0.916) 		1
(1.00) 		0.0707
(0.839)
FGFR3 4 (1%) 437 0.973
(1.00) 		0.666
(1.00) 		0.283
(1.00) 		0.496
(1.00) 		0.0727
(0.839) 		1
(1.00) 		0.302
(1.00) 		1
(1.00) 		1
(1.00)
ARAP3 3 (1%) 438 0.733
(1.00) 		0.544
(1.00) 		0.819
(1.00) 		0.735
(1.00) 		0.405
(1.00) 		0.283
(1.00) 		0.169
(0.944) 		1
(1.00)
OPTC 4 (1%) 437 0.946
(1.00) 		0.636
(1.00) 		0.884
(1.00) 		0.808
(1.00) 		1
(1.00) 		1
(1.00) 		0.615
(1.00) 		1
(1.00) 		1
(1.00)
GOLGA5 5 (1%) 436 0.44
(1.00) 		0.725
(1.00) 		0.181
(0.944) 		0.276
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00)
'BAP1 MUTATION STATUS' versus 'Time to Death'

P value = 0.00182 (logrank test), Q value = 0.18

Table S1.  Gene #2: 'BAP1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 441 146 0.1 - 120.6 (37.6)
BAP1 MUTATED 44 24 0.1 - 93.3 (28.3)
BAP1 WILD-TYPE 397 122 0.2 - 120.6 (39.2)

Figure S1.  Get High-res Image Gene #2: 'BAP1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'BAP1 MUTATION STATUS' versus 'NEOPLASM_DISEASESTAGE'

P value = 7e-05 (Fisher's exact test), Q value = 0.01

Table S2.  Gene #2: 'BAP1 MUTATION STATUS' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 211 45 115 70
BAP1 MUTATED 7 7 18 12
BAP1 WILD-TYPE 204 38 97 58

Figure S2.  Get High-res Image Gene #2: 'BAP1 MUTATION STATUS' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'BAP1 MUTATION STATUS' versus 'PATHOLOGY_T_STAGE'

P value = 6e-05 (Fisher's exact test), Q value = 0.01

Table S3.  Gene #2: 'BAP1 MUTATION STATUS' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 216 56 162 7
BAP1 MUTATED 8 11 25 0
BAP1 WILD-TYPE 208 45 137 7

Figure S3.  Get High-res Image Gene #2: 'BAP1 MUTATION STATUS' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'TRIM6 MUTATION STATUS' versus 'Time to Death'

P value = 0.00322 (logrank test), Q value = 0.24

Table S4.  Gene #24: 'TRIM6 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 441 146 0.1 - 120.6 (37.6)
TRIM6 MUTATED 5 3 0.2 - 34.4 (12.3)
TRIM6 WILD-TYPE 436 143 0.1 - 120.6 (38.5)

Figure S4.  Get High-res Image Gene #24: 'TRIM6 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/KIRC-TP/15182434/transformed.cor.cli.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/KIRC-TP/15081616/KIRC-TP.merged_data.txt

  • Number of patients = 441

  • Number of significantly mutated genes = 30

  • Number of selected clinical features = 10

  • 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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