Correlation between gene mutation status and selected clinical features
Sarcoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1N8798P
Overview
Introduction

This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.

Summary

Testing the association between mutation status of 17 genes and 9 clinical features across 247 patients, one significant finding detected with Q value < 0.25.

  • TP53 mutation correlated to 'HISTOLOGICAL_TYPE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between mutation status of 17 genes and 9 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, one significant finding detected.

Clinical
Features
Time
to
Death
YEARS
TO
BIRTH
TUMOR
TISSUE
SITE
GENDER RADIATION
THERAPY
HISTOLOGICAL
TYPE
RESIDUAL
TUMOR
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 Fisher's exact test Fisher's exact test
TP53 85 (34%) 162 0.748
(1.00)
0.268
(1.00)
0.157
(1.00)
0.592
(1.00)
1
(1.00)
1e-05
(0.00153)
0.739
(1.00)
0.745
(1.00)
0.176
(1.00)
RB1 24 (10%) 223 0.935
(1.00)
0.965
(1.00)
0.914
(1.00)
0.203
(1.00)
0.0959
(1.00)
0.038
(0.831)
0.609
(1.00)
1
(1.00)
1
(1.00)
ATRX 36 (15%) 211 0.226
(1.00)
0.00621
(0.475)
0.874
(1.00)
0.592
(1.00)
0.317
(1.00)
0.226
(1.00)
0.816
(1.00)
0.573
(1.00)
1
(1.00)
NUMBL 8 (3%) 239 0.82
(1.00)
0.0505
(0.966)
0.968
(1.00)
1
(1.00)
0.442
(1.00)
0.468
(1.00)
0.225
(1.00)
1
(1.00)
1
(1.00)
MSH3 7 (3%) 240 0.527
(1.00)
0.637
(1.00)
0.646
(1.00)
0.127
(1.00)
1
(1.00)
0.731
(1.00)
0.88
(1.00)
1
(1.00)
0.131
(1.00)
LTBP3 5 (2%) 242 0.552
(1.00)
0.71
(1.00)
0.37
(1.00)
0.664
(1.00)
0.325
(1.00)
0.676
(1.00)
0.476
(1.00)
0.397
(1.00)
1
(1.00)
EOMES 5 (2%) 242 0.672
(1.00)
0.77
(1.00)
0.114
(1.00)
0.664
(1.00)
0.0274
(0.831)
0.101
(1.00)
1
(1.00)
0.398
(1.00)
1
(1.00)
PTEN 7 (3%) 240 0.673
(1.00)
0.293
(1.00)
0.0201
(0.767)
1
(1.00)
0.364
(1.00)
0.826
(1.00)
0.522
(1.00)
1
(1.00)
1
(1.00)
KRTAP5-5 7 (3%) 240 0.94
(1.00)
0.146
(1.00)
0.703
(1.00)
0.456
(1.00)
0.423
(1.00)
0.385
(1.00)
0.0983
(1.00)
1
(1.00)
0.152
(1.00)
CABLES1 3 (1%) 244 0.536
(1.00)
0.848
(1.00)
0.397
(1.00)
0.597
(1.00)
1
(1.00)
1
(1.00)
0.707
(1.00)
1
(1.00)
1
(1.00)
TRAF7 4 (2%) 243 0.711
(1.00)
0.848
(1.00)
0.416
(1.00)
0.338
(1.00)
1
(1.00)
0.345
(1.00)
0.61
(1.00)
1
(1.00)
1
(1.00)
LOR 6 (2%) 241 0.758
(1.00)
0.848
(1.00)
0.294
(1.00)
0.419
(1.00)
0.673
(1.00)
0.192
(1.00)
0.411
(1.00)
1
(1.00)
1
(1.00)
R3HDM1 4 (2%) 243 0.948
(1.00)
0.207
(1.00)
0.126
(1.00)
1
(1.00)
0.583
(1.00)
0.638
(1.00)
0.478
(1.00)
1
(1.00)
1
(1.00)
NF1 9 (4%) 238 0.517
(1.00)
0.268
(1.00)
0.194
(1.00)
0.736
(1.00)
0.456
(1.00)
0.0372
(0.831)
0.118
(1.00)
1
(1.00)
1
(1.00)
COL4A3 4 (2%) 243 0.14
(1.00)
0.542
(1.00)
0.176
(1.00)
1
(1.00)
0.323
(1.00)
0.17
(1.00)
0.118
(1.00)
1
(1.00)
1
(1.00)
MEGF9 3 (1%) 244 0.225
(1.00)
0.292
(1.00)
0.0143
(0.728)
0.597
(1.00)
1
(1.00)
0.473
(1.00)
0.36
(1.00)
1
(1.00)
1
(1.00)
SCAP 3 (1%) 244 0.505
(1.00)
0.36
(1.00)
0.296
(1.00)
0.0969
(1.00)
0.557
(1.00)
0.894
(1.00)
0.358
(1.00)
1
(1.00)
1
(1.00)
'TP53 MUTATION STATUS' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0015

Table S1.  Gene #1: 'TP53 MUTATION STATUS' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients DEDIFFERENTIATED LIPOSARCOMA DESMOID TUMOR GIANT CELL 'MFH' / UNDIFFERENTIATED PLEOMORPHIC SARCOMA WITH GIANT CELLS LEIOMYOSARCOMA (LMS) MALIGNANT PERIPHERAL NERVE SHEATH TUMORS (MPNST) MYXOFIBROSARCOMA PLEOMORPHIC 'MFH' / UNDIFFERENTIATED PLEOMORPHIC SARCOMA SARCOMA; SYNOVIAL; POORLY DIFFERENTIATED SYNOVIAL SARCOMA - BIPHASIC SYNOVIAL SARCOMA - MONOPHASIC UNDIFFERENTIATED PLEOMORPHIC SARCOMA (UPS)
ALL 56 2 1 98 8 22 29 2 2 6 21
TP53 MUTATED 5 0 0 50 1 8 12 0 0 0 9
TP53 WILD-TYPE 51 2 1 48 7 14 17 2 2 6 12

Figure S1.  Get High-res Image Gene #1: 'TP53 MUTATION STATUS' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

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/SARC-TP/22573934/transformed.cor.cli.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/SARC-TP/22507207/SARC-TP.merged_data.txt

  • Number of patients = 247

  • Number of significantly mutated genes = 17

  • Number of selected clinical features = 9

  • 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.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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)