Correlation between gene mutation status and selected clinical features
Lung Adenocarcinoma (MOLECULAR_NONSMOKER)
07 February 2013  |  awg_luad__2013_02_07
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 (2013): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C12V2D75
Overview
Introduction

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

Summary

Testing the association between mutation status of 15 genes and 14 clinical features across 50 patients, no significant finding detected with Q value < 0.25.

  • No gene mutations related to clinical features.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between mutation status of 15 genes and 14 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.

Clinical
Features
Time
to
Death
AGE GENDER KARNOFSKY
PERFORMANCE
SCORE
HISTOLOGICAL
TYPE
PATHOLOGY
T
PATHOLOGY
N
PATHOLOGICSPREAD(M) TUMOR
STAGE
RADIATIONS
RADIATION
REGIMENINDICATION
NUMBERPACKYEARSSMOKED STOPPEDSMOKINGYEAR TOBACCOSMOKINGHISTORYINDICATOR YEAROFTOBACCOSMOKINGONSET
nMutated (%) nWild-Type logrank test t-test Fisher's exact test t-test Chi-square test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test t-test t-test Fisher's exact test t-test
TP53 17 (34%) 33 0.166
(1.00)
0.626
(1.00)
0.229
(1.00)
0.554
(1.00)
0.0371
(1.00)
0.318
(1.00)
1
(1.00)
0.354
(1.00)
1
(1.00)
0.282
(1.00)
0.305
(1.00)
0.0101
(1.00)
0.516
(1.00)
EGFR 10 (20%) 40 0.143
(1.00)
0.895
(1.00)
0.0366
(1.00)
0.178
(1.00)
1
(1.00)
0.793
(1.00)
0.495
(1.00)
0.716
(1.00)
1
(1.00)
0.0121
(1.00)
0.109
(1.00)
0.787
(1.00)
CDKN2A 5 (10%) 45 0.384
(1.00)
0.24
(1.00)
0.377
(1.00)
0.959
(1.00)
0.553
(1.00)
0.506
(1.00)
1
(1.00)
0.694
(1.00)
1
(1.00)
0.0176
(1.00)
0.127
(1.00)
0.0511
(1.00)
0.616
(1.00)
SMAD4 5 (10%) 45 0.456
(1.00)
0.109
(1.00)
1
(1.00)
0.993
(1.00)
0.112
(1.00)
0.485
(1.00)
1
(1.00)
0.694
(1.00)
0.423
(1.00)
0.773
(1.00)
KRAS 4 (8%) 46 0.501
(1.00)
0.546
(1.00)
0.289
(1.00)
0.0128
(1.00)
0.262
(1.00)
0.506
(1.00)
1
(1.00)
0.489
(1.00)
1
(1.00)
0.568
(1.00)
0.175
(1.00)
KEAP1 6 (12%) 44 0.928
(1.00)
0.992
(1.00)
0.00244
(0.395)
0.0168
(1.00)
1
(1.00)
0.714
(1.00)
1
(1.00)
0.36
(1.00)
0.487
(1.00)
0.623
(1.00)
0.823
(1.00)
0.082
(1.00)
CSMD3 10 (20%) 40 0.448
(1.00)
0.468
(1.00)
0.494
(1.00)
0.493
(1.00)
0.747
(1.00)
0.474
(1.00)
0.106
(1.00)
0.405
(1.00)
1
(1.00)
0.937
(1.00)
0.591
(1.00)
0.902
(1.00)
0.434
(1.00)
BRAF 5 (10%) 45 0.103
(1.00)
0.528
(1.00)
0.377
(1.00)
0.993
(1.00)
0.448
(1.00)
0.385
(1.00)
1
(1.00)
0.648
(1.00)
0.423
(1.00)
0.297
(1.00)
0.261
(1.00)
SPTA1 7 (14%) 43 0.637
(1.00)
0.511
(1.00)
0.416
(1.00)
0.0126
(1.00)
0.574
(1.00)
0.318
(1.00)
0.525
(1.00)
0.433
(1.00)
1
(1.00)
0.581
(1.00)
0.937
(1.00)
STK11 3 (6%) 47 0.247
(1.00)
0.0582
(1.00)
0.726
(1.00)
1
(1.00)
0.406
(1.00)
1
(1.00)
0.274
(1.00)
1
(1.00)
0.532
(1.00)
0.42
(1.00)
OR4A5 3 (6%) 47 0.593
(1.00)
0.91
(1.00)
0.265
(1.00)
0.97
(1.00)
0.672
(1.00)
0.256
(1.00)
1
(1.00)
0.569
(1.00)
1
(1.00)
0.0427
(1.00)
SETD2 5 (10%) 45 0.00225
(0.367)
0.101
(1.00)
1
(1.00)
0.993
(1.00)
0.785
(1.00)
0.385
(1.00)
0.376
(1.00)
1
(1.00)
0.0718
(1.00)
0.691
(1.00)
0.335
(1.00)
CDH10 4 (8%) 46 0.717
(1.00)
0.184
(1.00)
0.641
(1.00)
0.997
(1.00)
0.0987
(1.00)
0.8
(1.00)
0.086
(1.00)
1
(1.00)
0.353
(1.00)
0.839
(1.00)
0.89
(1.00)
MGAT4C 3 (6%) 47 1
(1.00)
0.97
(1.00)
1
(1.00)
0.256
(1.00)
1
(1.00)
0.276
(1.00)
0.933
(1.00)
0.217
(1.00)
U2AF2 3 (6%) 47 0.934
(1.00)
0.265
(1.00)
0.019
(1.00)
0.428
(1.00)
0.775
(1.00)
1
(1.00)
0.138
(1.00)
Methods & Data
Input
  • Mutation data file = MOLECULAR_NONSMOKER.mutsig.cluster.txt

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

  • Number of patients = 50

  • Number of significantly mutated genes = 15

  • Number of selected clinical features = 14

  • 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.

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] 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)