SKCM-TM: Correlation between gene mutation status and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)
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.

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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)
Copyright © 2013 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle