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 149 genes and 3 clinical features across 126 patients, 2 significant findings detected with Q value < 0.25.

  • OR52J3 mutation correlated to 'Time to Death'.

  • TRAT1 mutation correlated to 'Time to Death'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between mutation status of 149 genes and 3 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
nMutated (%) nWild-Type logrank test t-test Fisher's exact test
OR52J3 8 (6%) 118 0.00028
(0.0521)
0.4
(1.00)
1
(1.00)
TRAT1 9 (7%) 117 0.000994
(0.184)
0.534
(1.00)
0.723
(1.00)
BRAF 65 (52%) 61 0.512
(1.00)
0.693
(1.00)
1
(1.00)
NRAS 37 (29%) 89 0.158
(1.00)
0.706
(1.00)
1
(1.00)
CDKN2A 20 (16%) 106 0.562
(1.00)
0.318
(1.00)
0.802
(1.00)
TP53 22 (17%) 104 0.635
(1.00)
PTEN 8 (6%) 118 0.462
(1.00)
ACSM2B 26 (21%) 100 0.00274
(0.504)
LCE1B 8 (6%) 118 0.462
(1.00)
CADM2 13 (10%) 113 0.767
(1.00)
NAP1L2 12 (10%) 114 0.757
(1.00)
RAC1 9 (7%) 117 0.485
(1.00)
MUC7 9 (7%) 117 1
(1.00)
OR51S1 15 (12%) 111 0.781
(1.00)
FUT9 13 (10%) 113 1
(1.00)
PPP6C 12 (10%) 114 1
(1.00)
USP29 23 (18%) 103 0.339
(1.00)
RPTN 21 (17%) 105 0.808
(1.00)
TAF1A 7 (6%) 119 0.705
(1.00)
CDH9 22 (17%) 104 1
(1.00)
GRXCR1 12 (10%) 114 1
(1.00)
HIST1H2AA 7 (6%) 119 0.705
(1.00)
ZNF679 13 (10%) 113 0.767
(1.00)
C8A 17 (13%) 109 0.0289
(1.00)
RBM11 9 (7%) 117 1
(1.00)
ZNF479 11 (9%) 115 0.159
(1.00)
0.988
(1.00)
0.528
(1.00)
GFRAL 18 (14%) 108 1
(1.00)
DDX3X 15 (12%) 111 0.253
(1.00)
OR4M2 13 (10%) 113 1
(1.00)
FRG2B 10 (8%) 116 0.495
(1.00)
PDE1A 23 (18%) 103 1
(1.00)
PRB2 18 (14%) 108 0.0681
(1.00)
LUZP2 9 (7%) 117 1
(1.00)
NRK 18 (14%) 108 1
(1.00)
PARM1 10 (8%) 116 0.745
(1.00)
SLC38A4 14 (11%) 112 0.254
(1.00)
PRAMEF11 11 (9%) 115 1
(1.00)
USP17L2 13 (10%) 113 0.371
(1.00)
PRB1 10 (8%) 116 0.324
(1.00)
CYLC2 15 (12%) 111 1
(1.00)
DSG3 31 (25%) 95 0.755
(1.00)
0.00824
(1.00)
0.67
(1.00)
VEGFC 9 (7%) 117 1
(1.00)
IL32 3 (2%) 123 0.553
(1.00)
LILRB4 18 (14%) 108 0.598
(1.00)
GLRB 13 (10%) 113 0.767
(1.00)
STXBP5L 27 (21%) 99 0.0321
(1.00)
0.894
(1.00)
0.823
(1.00)
GML 6 (5%) 120 0.414
(1.00)
TLL1 27 (21%) 99 0.00458
(0.838)
0.27
(1.00)
0.261
(1.00)
DEFB118 5 (4%) 121 1
(1.00)
GK2 15 (12%) 111 0.00965
(1.00)
0.525
(1.00)
0.781
(1.00)
OR5J2 10 (8%) 116 0.168
(1.00)
LIN7A 7 (6%) 119 1
(1.00)
MUM1L1 10 (8%) 116 1
(1.00)
MARCH11 6 (5%) 120 1
(1.00)
PSG4 12 (10%) 114 0.757
(1.00)
ZIM3 10 (8%) 116 0.745
(1.00)
CLEC14A 11 (9%) 115 0.326
(1.00)
OR5H2 10 (8%) 116 0.495
(1.00)
TCEB3C 19 (15%) 107 0.0679
(1.00)
PRB4 13 (10%) 113 0.88
(1.00)
0.707
(1.00)
0.767
(1.00)
KLHL4 14 (11%) 112 0.37
(1.00)
0.00656
(1.00)
0.377
(1.00)
HBD 8 (6%) 118 0.462
(1.00)
FAM19A1 6 (5%) 120 0.668
(1.00)
LRRIQ4 11 (9%) 115 0.528
(1.00)
SPINK13 4 (3%) 122 1
(1.00)
SNAP91 13 (10%) 113 0.767
(1.00)
LONRF2 10 (8%) 116 0.745
(1.00)
CLCC1 7 (6%) 119 1
(1.00)
KIAA1257 7 (6%) 119 0.421
(1.00)
SIGLEC14 5 (4%) 121 0.158
(1.00)
SPANXN2 12 (10%) 114 0.112
(1.00)
0.533
(1.00)
DEFB112 5 (4%) 121 1
(1.00)
CD2 12 (10%) 114 0.353
(1.00)
HTR3B 9 (7%) 117 0.153
(1.00)
KIR2DL1 8 (6%) 118 1
(1.00)
OR4N2 16 (13%) 110 0.433
(1.00)
0.912
(1.00)
0.0492
(1.00)
ST18 26 (21%) 100 1
(1.00)
TUBB8 7 (6%) 119 1
(1.00)
C2ORF40 3 (2%) 123 1
(1.00)
PRR23B 8 (6%) 118 1
(1.00)
TFEC 12 (10%) 114 0.757
(1.00)
SGCZ 14 (11%) 112 1
(1.00)
TRIM58 9 (7%) 117 1
(1.00)
ANXA10 8 (6%) 118 0.986
(1.00)
0.229
(1.00)
0.709
(1.00)
ZNF844 3 (2%) 123 0.553
(1.00)
SLC14A1 10 (8%) 116 0.0133
(1.00)
C9 11 (9%) 115 0.528
(1.00)
DSG1 18 (14%) 108 0.798
(1.00)
CCDC11 13 (10%) 113 1
(1.00)
MKX 10 (8%) 116 0.463
(1.00)
0.176
(1.00)
0.745
(1.00)
OR7D2 12 (10%) 114 0.533
(1.00)
STARD6 5 (4%) 121 1
(1.00)
SPATA8 3 (2%) 123 0.553
(1.00)
GRXCR2 11 (9%) 115 1
(1.00)
OR4A15 14 (11%) 112 0.0816
(1.00)
C4ORF22 8 (6%) 118 0.709
(1.00)
CCDC54 10 (8%) 116 0.324
(1.00)
CRISP2 7 (6%) 119 0.705
(1.00)
MOG 5 (4%) 121 0.652
(1.00)
NMS 7 (6%) 119 0.705
(1.00)
DEFB115 5 (4%) 121 0.158
(1.00)
UGT2A3 12 (10%) 114 0.533
(1.00)
ZNF98 12 (10%) 114 0.757
(1.00)
ADH1C 21 (17%) 105 0.466
(1.00)
HBG2 6 (5%) 120 1
(1.00)
HHLA2 9 (7%) 117 1
(1.00)
IDH1 7 (6%) 119 0.257
(1.00)
OR2L3 8 (6%) 118 0.256
(1.00)
OR4F6 9 (7%) 117 0.485
(1.00)
B2M 4 (3%) 122 0.296
(1.00)
ARID2 19 (15%) 107 0.44
(1.00)
LOC649330 15 (12%) 111 0.00458
(0.838)
0.321
(1.00)
1
(1.00)
OR5AC2 13 (10%) 113 0.767
(1.00)
OR5H14 6 (5%) 120 0.0852
(1.00)
SPAG16 9 (7%) 117 0.485
(1.00)
SPRY3 7 (6%) 119 1
(1.00)
STK31 22 (17%) 104 1
(1.00)
TACR3 13 (10%) 113 0.767
(1.00)
SERPINB4 16 (13%) 110 0.165
(1.00)
TSGA13 5 (4%) 121 0.158
(1.00)
GIMAP7 10 (8%) 116 0.324
(1.00)
SDR16C5 17 (13%) 109 0.597
(1.00)
SPOCK3 16 (13%) 110 0.0492
(1.00)
TRHR 14 (11%) 112 1
(1.00)
CD96 8 (6%) 118 1
(1.00)
CLEC4E 9 (7%) 117 0.285
(1.00)
TPTE 32 (25%) 94 0.834
(1.00)
MCART6 7 (6%) 119 1
(1.00)
OR2W1 11 (9%) 115 0.207
(1.00)
MORF4 10 (8%) 116 0.745
(1.00)
RGS18 5 (4%) 121 0.158
(1.00)
BAGE2 9 (7%) 117 0.723
(1.00)
KIAA1644 4 (3%) 122 1
(1.00)
AGXT2 13 (10%) 113 0.767
(1.00)
CLDN4 5 (4%) 121 1
(1.00)
CDH10 20 (16%) 106 0.617
(1.00)
VWC2L 11 (9%) 115 1
(1.00)
ABRA 10 (8%) 116 0.324
(1.00)
ARPP21 22 (17%) 104 0.0151
(1.00)
0.891
(1.00)
0.0548
(1.00)
NR1H4 7 (6%) 119 0.421
(1.00)
GZMA 8 (6%) 118 0.709
(1.00)
DGAT2L6 8 (6%) 118 1
(1.00)
TMCO5A 6 (5%) 120 0.414
(1.00)
KRT26 7 (6%) 119 1
(1.00)
CCNE2 7 (6%) 119 0.705
(1.00)
CCK 3 (2%) 123 1
(1.00)
ADAMTS20 29 (23%) 97 0.281
(1.00)
MPP7 15 (12%) 111 0.262
(1.00)
0.282
(1.00)
1
(1.00)
FAM155A 8 (6%) 118 0.14
(1.00)
'OR52J3 MUTATION STATUS' versus 'Time to Death'

P value = 0.00028 (logrank test), Q value = 0.052

Table S1.  Gene #60: 'OR52J3 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 15 8 0.2 - 131.1 (62.8)
OR52J3 MUTATED 3 3 10.1 - 32.5 (12.6)
OR52J3 WILD-TYPE 12 5 0.2 - 131.1 (72.2)

Figure S1.  Get High-res Image Gene #60: 'OR52J3 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'TRAT1 MUTATION STATUS' versus 'Time to Death'

P value = 0.000994 (logrank test), Q value = 0.18

Table S2.  Gene #82: 'TRAT1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 15 8 0.2 - 131.1 (62.8)
TRAT1 MUTATED 3 3 10.1 - 32.5 (26.4)
TRAT1 WILD-TYPE 12 5 0.2 - 131.1 (72.2)

Figure S2.  Get High-res Image Gene #82: 'TRAT1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

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

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

  • Number of patients = 126

  • Number of significantly mutated genes = 149

  • Number of selected clinical features = 3

  • 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)