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 74 genes and 7 clinical features across 105 patients, 7 significant findings detected with Q value < 0.25.

  • CCNE2 mutation correlated to 'Time to Death'.

  • BAGE2 mutation correlated to 'LYMPH.NODE.METASTASIS'.

  • PPP6C mutation correlated to 'LYMPH.NODE.METASTASIS'.

  • OXA1L mutation correlated to 'LYMPH.NODE.METASTASIS'.

  • LIN7A mutation correlated to 'Time to Death'.

  • VGLL1 mutation correlated to 'LYMPH.NODE.METASTASIS'.

  • ACTL7B mutation correlated to 'LYMPH.NODE.METASTASIS'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between mutation status of 74 genes and 7 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 7 significant findings detected.

Clinical
Features
Time
to
Death
AGE GENDER DISTANT
METASTASIS
LYMPH
NODE
METASTASIS
TUMOR
STAGECODE
NEOPLASM
DISEASESTAGE
nMutated (%) nWild-Type logrank test t-test Fisher's exact test Fisher's exact test Chi-square test t-test Chi-square test
CCNE2 10 (10%) 95 1.6e-05
(0.00697)
0.804
(1.00)
0.477
(1.00)
1
(1.00)
0.939
(1.00)
0.344
(1.00)
BAGE2 7 (7%) 98 0.478
(1.00)
0.203
(1.00)
0.419
(1.00)
0.126
(1.00)
0.000138
(0.0598)
0.0694
(1.00)
PPP6C 10 (10%) 95 0.934
(1.00)
0.843
(1.00)
0.72
(1.00)
0.209
(1.00)
0.000234
(0.101)
0.58
(1.00)
OXA1L 4 (4%) 101 0.136
(1.00)
1
(1.00)
1
(1.00)
0.000303
(0.13)
0.0748
(1.00)
LIN7A 8 (8%) 97 0.000224
(0.0967)
0.662
(1.00)
1
(1.00)
1
(1.00)
0.052
(1.00)
0.712
(1.00)
VGLL1 3 (3%) 102 0.111
(1.00)
0.553
(1.00)
1
(1.00)
0.00012
(0.0523)
0.451
(1.00)
ACTL7B 7 (7%) 98 0.848
(1.00)
0.419
(1.00)
1
(1.00)
1.49e-05
(0.0065)
0.0874
(1.00)
BRAF 59 (56%) 46 0.435
(1.00)
0.298
(1.00)
1
(1.00)
1
(1.00)
0.564
(1.00)
0.817
(1.00)
CDKN2A 16 (15%) 89 0.871
(1.00)
0.919
(1.00)
0.773
(1.00)
1
(1.00)
0.871
(1.00)
0.686
(1.00)
NRAS 29 (28%) 76 0.635
(1.00)
0.266
(1.00)
0.816
(1.00)
0.744
(1.00)
0.72
(1.00)
0.949
(1.00)
STK19 7 (7%) 98 0.424
(1.00)
0.598
(1.00)
0.419
(1.00)
1
(1.00)
0.997
(1.00)
0.909
(1.00)
TTN 83 (79%) 22 0.208
(1.00)
0.888
(1.00)
0.44
(1.00)
0.177
(1.00)
0.0997
(1.00)
0.301
(1.00)
TP53 18 (17%) 87 0.59
(1.00)
0.00159
(0.682)
0.397
(1.00)
1
(1.00)
0.386
(1.00)
0.383
(1.00)
PTEN 11 (10%) 94 0.163
(1.00)
0.163
(1.00)
0.295
(1.00)
0.229
(1.00)
0.937
(1.00)
0.155
(1.00)
TPTE 29 (28%) 76 0.612
(1.00)
0.223
(1.00)
0.816
(1.00)
1
(1.00)
0.856
(1.00)
0.487
(1.00)
PRB4 15 (14%) 90 0.305
(1.00)
0.0512
(1.00)
0.221
(1.00)
1
(1.00)
0.319
(1.00)
0.839
(1.00)
CDH9 22 (21%) 83 0.738
(1.00)
0.102
(1.00)
1
(1.00)
1
(1.00)
0.517
(1.00)
0.309
(1.00)
SERPINB4 17 (16%) 88 0.412
(1.00)
0.417
(1.00)
0.0904
(1.00)
1
(1.00)
0.226
(1.00)
0.768
(1.00)
PRB2 19 (18%) 86 0.124
(1.00)
0.00736
(1.00)
0.175
(1.00)
1
(1.00)
0.595
(1.00)
0.466
(1.00)
TCEB3C 14 (13%) 91 0.47
(1.00)
0.481
(1.00)
0.223
(1.00)
1
(1.00)
0.956
(1.00)
0.845
(1.00)
ADH1C 17 (16%) 88 0.438
(1.00)
0.0388
(1.00)
0.773
(1.00)
1
(1.00)
0.0633
(1.00)
0.231
(1.00)
C15ORF23 7 (7%) 98 0.469
(1.00)
0.78
(1.00)
1
(1.00)
1
(1.00)
0.798
(1.00)
0.903
(1.00)
SDR16C5 16 (15%) 89 0.221
(1.00)
0.114
(1.00)
0.773
(1.00)
0.503
(1.00)
0.597
(1.00)
0.342
(1.00)
PDE1A 19 (18%) 86 0.714
(1.00)
0.141
(1.00)
1
(1.00)
1
(1.00)
0.109
(1.00)
0.522
(1.00)
TLL1 24 (23%) 81 0.747
(1.00)
0.144
(1.00)
0.799
(1.00)
1
(1.00)
0.224
(1.00)
0.373
(1.00)
RAC1 7 (7%) 98 0.83
(1.00)
0.522
(1.00)
0.671
(1.00)
1
(1.00)
0.922
(1.00)
0.679
(1.00)
RERG 8 (8%) 97 0.734
(1.00)
0.0304
(1.00)
1
(1.00)
1
(1.00)
0.0311
(1.00)
0.304
(1.00)
OR52L1 12 (11%) 93 0.704
(1.00)
0.845
(1.00)
1
(1.00)
1
(1.00)
0.0469
(1.00)
0.799
(1.00)
HBD 8 (8%) 97 0.546
(1.00)
0.858
(1.00)
0.691
(1.00)
1
(1.00)
0.992
(1.00)
0.562
(1.00)
VEGFC 12 (11%) 93 0.509
(1.00)
0.823
(1.00)
1
(1.00)
1
(1.00)
0.24
(1.00)
0.337
(1.00)
OR1N2 13 (12%) 92 0.897
(1.00)
0.726
(1.00)
0.336
(1.00)
1
(1.00)
0.223
(1.00)
0.685
(1.00)
HNF4G 12 (11%) 93 0.259
(1.00)
0.105
(1.00)
1
(1.00)
0.0231
(1.00)
0.559
(1.00)
TRAT1 6 (6%) 99 0.00681
(1.00)
0.331
(1.00)
1
(1.00)
1
(1.00)
0.0349
(1.00)
0.721
(1.00)
AREG 5 (5%) 100 0.445
(1.00)
0.631
(1.00)
1
(1.00)
0.00334
(1.00)
0.528
(1.00)
ST18 24 (23%) 81 0.662
(1.00)
0.0401
(1.00)
0.621
(1.00)
1
(1.00)
0.585
(1.00)
0.545
(1.00)
TAF1A 8 (8%) 97 0.135
(1.00)
0.843
(1.00)
0.691
(1.00)
0.301
(1.00)
0.678
(1.00)
0.735
(1.00)
GRXCR2 9 (9%) 96 0.568
(1.00)
0.455
(1.00)
0.722
(1.00)
1
(1.00)
0.0863
(1.00)
0.474
(1.00)
OR9K2 11 (10%) 94 0.687
(1.00)
0.058
(1.00)
0.501
(1.00)
1
(1.00)
0.186
(1.00)
0.457
(1.00)
A2BP1 14 (13%) 91 0.66
(1.00)
0.297
(1.00)
0.754
(1.00)
1
(1.00)
0.315
(1.00)
0.215
(1.00)
HIST1H2AA 5 (5%) 100 0.787
(1.00)
0.631
(1.00)
1
(1.00)
0.979
(1.00)
0.99
(1.00)
LILRA1 21 (20%) 84 0.407
(1.00)
0.618
(1.00)
0.294
(1.00)
0.597
(1.00)
0.313
(1.00)
0.974
(1.00)
GALNTL5 10 (10%) 95 0.384
(1.00)
0.428
(1.00)
0.275
(1.00)
1
(1.00)
0.028
(1.00)
0.503
(1.00)
HSD11B1 6 (6%) 99 0.499
(1.00)
0.177
(1.00)
0.357
(1.00)
1
(1.00)
0.0131
(1.00)
0.514
(1.00)
ANKRD20A4 4 (4%) 101 0.218
(1.00)
0.455
(1.00)
0.317
(1.00)
1
(1.00)
0.00258
(1.00)
0.297
(1.00)
GPR141 8 (8%) 97 0.0436
(1.00)
0.375
(1.00)
1
(1.00)
1
(1.00)
0.0799
(1.00)
0.452
(1.00)
NAP1L2 10 (10%) 95 0.829
(1.00)
0.0919
(1.00)
0.72
(1.00)
1
(1.00)
0.0586
(1.00)
0.855
(1.00)
C18ORF26 13 (12%) 92 0.06
(1.00)
0.704
(1.00)
0.336
(1.00)
1
(1.00)
0.18
(1.00)
0.312
(1.00)
DEFB110 5 (5%) 100 0.28
(1.00)
0.963
(1.00)
0.318
(1.00)
1
(1.00)
0.00258
(1.00)
0.378
(1.00)
IDH1 6 (6%) 99 0.963
(1.00)
0.0206
(1.00)
0.357
(1.00)
1
(1.00)
0.984
(1.00)
0.378
(1.00)
SNAP91 15 (14%) 90 0.453
(1.00)
0.238
(1.00)
0.221
(1.00)
0.503
(1.00)
0.515
(1.00)
0.202
(1.00)
CA1 6 (6%) 99 0.152
(1.00)
0.668
(1.00)
1
(1.00)
0.712
(1.00)
0.891
(1.00)
SERPINB11 12 (11%) 93 0.874
(1.00)
0.675
(1.00)
0.0166
(1.00)
1
(1.00)
0.177
(1.00)
0.488
(1.00)
UGT2B15 13 (12%) 92 0.887
(1.00)
0.359
(1.00)
0.102
(1.00)
0.394
(1.00)
0.211
(1.00)
0.484
(1.00)
ACSM2B 21 (20%) 84 0.504
(1.00)
0.95
(1.00)
0.00611
(1.00)
1
(1.00)
0.614
(1.00)
0.946
(1.00)
C16ORF78 8 (8%) 97 0.671
(1.00)
0.383
(1.00)
1
(1.00)
1
(1.00)
0.0585
(1.00)
0.507
(1.00)
TFEC 10 (10%) 95 0.128
(1.00)
0.158
(1.00)
0.72
(1.00)
1
(1.00)
0.00184
(0.789)
0.533
(1.00)
TRHDE 24 (23%) 81 0.276
(1.00)
0.0878
(1.00)
0.134
(1.00)
1
(1.00)
0.442
(1.00)
0.48
(1.00)
OR5AC2 13 (12%) 92 0.322
(1.00)
0.783
(1.00)
0.751
(1.00)
1
(1.00)
0.838
(1.00)
0.394
(1.00)
CCDC102B 11 (10%) 94 0.465
(1.00)
0.928
(1.00)
1
(1.00)
1
(1.00)
0.187
(1.00)
0.6
(1.00)
GRXCR1 12 (11%) 93 0.501
(1.00)
0.019
(1.00)
1
(1.00)
1
(1.00)
0.937
(1.00)
0.42
(1.00)
OR4M2 13 (12%) 92 0.858
(1.00)
0.00192
(0.82)
1
(1.00)
1
(1.00)
0.475
(1.00)
0.537
(1.00)
PLAC8L1 3 (3%) 102 0.226
(1.00)
0.553
(1.00)
1
(1.00)
0.991
(1.00)
0.783
(1.00)
POM121 10 (10%) 95 0.0543
(1.00)
0.921
(1.00)
0.72
(1.00)
1
(1.00)
0.0981
(1.00)
0.0476
(1.00)
RBM11 9 (9%) 96 0.238
(1.00)
0.0272
(1.00)
1
(1.00)
1
(1.00)
0.0733
(1.00)
0.644
(1.00)
RUNX1T1 16 (15%) 89 0.814
(1.00)
0.127
(1.00)
0.141
(1.00)
1
(1.00)
0.4
(1.00)
0.984
(1.00)
RGS7 17 (16%) 88 0.339
(1.00)
0.0121
(1.00)
1
(1.00)
0.27
(1.00)
0.0912
(1.00)
0.845
(1.00)
CLVS2 11 (10%) 94 0.496
(1.00)
0.442
(1.00)
0.501
(1.00)
1
(1.00)
0.147
(1.00)
0.845
(1.00)
DEFB118 6 (6%) 99 0.66
(1.00)
0.715
(1.00)
1
(1.00)
1
(1.00)
0.0446
(1.00)
0.589
(1.00)
OR52A5 12 (11%) 93 0.221
(1.00)
0.272
(1.00)
1
(1.00)
1
(1.00)
0.18
(1.00)
0.623
(1.00)
SPTLC3 13 (12%) 92 0.689
(1.00)
0.875
(1.00)
1
(1.00)
1
(1.00)
0.319
(1.00)
0.281
(1.00)
PHGDH 7 (7%) 98 0.59
(1.00)
0.156
(1.00)
0.102
(1.00)
1
(1.00)
0.0446
(1.00)
0.0424
(1.00)
RGPD3 18 (17%) 87 0.494
(1.00)
0.00612
(1.00)
0.0204
(1.00)
0.575
(1.00)
0.152
(1.00)
0.774
(1.00)
WDR12 7 (7%) 98 0.0135
(1.00)
0.552
(1.00)
0.102
(1.00)
1
(1.00)
0.97
(1.00)
0.968
(1.00)
MPP7 13 (12%) 92 0.816
(1.00)
0.138
(1.00)
0.336
(1.00)
0.25
(1.00)
0.323
(1.00)
0.67
(1.00)
'CCNE2 MUTATION STATUS' versus 'Time to Death'

P value = 1.6e-05 (logrank test), Q value = 0.007

Table S1.  Gene #16: 'CCNE2 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 55 32 1.0 - 98.8 (13.3)
CCNE2 MUTATED 7 7 1.1 - 13.3 (10.3)
CCNE2 WILD-TYPE 48 25 1.0 - 98.8 (15.0)

Figure S1.  Get High-res Image Gene #16: 'CCNE2 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'BAGE2 MUTATION STATUS' versus 'LYMPH.NODE.METASTASIS'

P value = 0.000138 (Chi-square test), Q value = 0.06

Table S2.  Gene #25: 'BAGE2 MUTATION STATUS' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 N1A N1B N2 N2A N2B N2C N3 NX
ALL 58 1 3 9 1 3 7 1 12 1
BAGE2 MUTATED 2 0 1 0 1 0 0 1 2 0
BAGE2 WILD-TYPE 56 1 2 9 0 3 7 0 10 1

Figure S2.  Get High-res Image Gene #25: 'BAGE2 MUTATION STATUS' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'PPP6C MUTATION STATUS' versus 'LYMPH.NODE.METASTASIS'

P value = 0.000234 (Chi-square test), Q value = 0.1

Table S3.  Gene #27: 'PPP6C MUTATION STATUS' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 N1A N1B N2 N2A N2B N2C N3 NX
ALL 58 1 3 9 1 3 7 1 12 1
PPP6C MUTATED 2 0 2 1 1 0 1 1 2 0
PPP6C WILD-TYPE 56 1 1 8 0 3 6 0 10 1

Figure S3.  Get High-res Image Gene #27: 'PPP6C MUTATION STATUS' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'OXA1L MUTATION STATUS' versus 'LYMPH.NODE.METASTASIS'

P value = 0.000303 (Chi-square test), Q value = 0.13

Table S4.  Gene #35: 'OXA1L MUTATION STATUS' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 N1A N1B N2 N2A N2B N2C N3 NX
ALL 58 1 3 9 1 3 7 1 12 1
OXA1L MUTATED 2 0 1 0 1 0 0 0 0 0
OXA1L WILD-TYPE 56 1 2 9 0 3 7 1 12 1

Figure S4.  Get High-res Image Gene #35: 'OXA1L MUTATION STATUS' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'LIN7A MUTATION STATUS' versus 'Time to Death'

P value = 0.000224 (logrank test), Q value = 0.097

Table S5.  Gene #39: 'LIN7A MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 55 32 1.0 - 98.8 (13.3)
LIN7A MUTATED 4 3 1.0 - 12.2 (1.1)
LIN7A WILD-TYPE 51 29 2.9 - 98.8 (14.7)

Figure S5.  Get High-res Image Gene #39: 'LIN7A MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'VGLL1 MUTATION STATUS' versus 'LYMPH.NODE.METASTASIS'

P value = 0.00012 (Chi-square test), Q value = 0.052

Table S6.  Gene #40: 'VGLL1 MUTATION STATUS' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 N1A N1B N2 N2A N2B N2C N3 NX
ALL 58 1 3 9 1 3 7 1 12 1
VGLL1 MUTATED 1 0 0 0 1 0 0 0 1 0
VGLL1 WILD-TYPE 57 1 3 9 0 3 7 1 11 1

Figure S6.  Get High-res Image Gene #40: 'VGLL1 MUTATION STATUS' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'ACTL7B MUTATION STATUS' versus 'LYMPH.NODE.METASTASIS'

P value = 1.49e-05 (Chi-square test), Q value = 0.0065

Table S7.  Gene #70: 'ACTL7B MUTATION STATUS' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 N1A N1B N2 N2A N2B N2C N3 NX
ALL 58 1 3 9 1 3 7 1 12 1
ACTL7B MUTATED 3 0 0 0 1 0 0 0 0 1
ACTL7B WILD-TYPE 55 1 3 9 0 3 7 1 12 0

Figure S7.  Get High-res Image Gene #70: 'ACTL7B MUTATION STATUS' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

Methods & Data
Input
  • Mutation data file = SKCM-Regional_LN.mutsig.cluster.txt

  • Clinical data file = SKCM-Regional_LN.clin.merged.picked.txt

  • Number of patients = 105

  • Number of significantly mutated genes = 74

  • 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

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

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.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] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[5] 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)