Correlation between gene mutation status and selected clinical features

Liver Hepatocellular Carcinoma (Primary solid tumor)

17 October 2014 | analyses__2014_10_17

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 (2014): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1FJ2FPQ

Overview

Introduction

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

Summary

Testing the association between mutation status of 18 genes and 11 clinical features across 188 patients, 4 significant findings detected with Q value < 0.25.

TP53 mutation correlated to 'GENDER' and 'RACE'.
CTNNB1 mutation correlated to 'GENDER'.
BAP1 mutation correlated to 'GENDER'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 18 genes and 11 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 4 significant findings detected.


		Clinical Features	Time to Death	AGE	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	PATHOLOGY M STAGE	GENDER	HISTOLOGICAL TYPE	COMPLETENESS OF RESECTION	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	Fisher's exact test	Fisher's exact test
TP53	58 (31%)	130	0.383 (1.00)	0.0967 (1.00)	0.225 (1.00)	0.0546 (1.00)	0.562 (1.00)	0.553 (1.00)	0.000995 (0.194)	0.69 (1.00)	1 (1.00)	0.00018 (0.0355)	1 (1.00)
CTNNB1	49 (26%)	139	0.989 (1.00)	0.171 (1.00)	0.77 (1.00)	0.561 (1.00)	0.159 (1.00)	0.45 (1.00)	2.8e-05 (0.00554)	0.681 (1.00)	0.125 (1.00)	0.251 (1.00)	1 (1.00)
BAP1	10 (5%)	178	0.208 (1.00)	0.844 (1.00)	0.0486 (1.00)	0.0367 (1.00)	0.186 (1.00)	0.112 (1.00)	0.000521 (0.102)	1 (1.00)	0.466 (1.00)	1 (1.00)	1 (1.00)
RB1	15 (8%)	173	0.904 (1.00)	0.00212 (0.412)	0.443 (1.00)	0.18 (1.00)	1 (1.00)	0.543 (1.00)	0.409 (1.00)	1 (1.00)	0.534 (1.00)	0.00654 (1.00)	0.366 (1.00)
AXIN1	9 (5%)	179	0.094 (1.00)	0.773 (1.00)	0.0657 (1.00)	0.632 (1.00)	1 (1.00)	0.216 (1.00)	1 (1.00)	1 (1.00)	0.29 (1.00)	1 (1.00)	0.212 (1.00)
TSC2	9 (5%)	179	0.832 (1.00)	0.485 (1.00)	0.175 (1.00)	0.0777 (1.00)	1 (1.00)	0.554 (1.00)	0.725 (1.00)	1 (1.00)	0.549 (1.00)	0.217 (1.00)	0.188 (1.00)
ARID1A	14 (7%)	174	0.0597 (1.00)	0.718 (1.00)	0.323 (1.00)	0.683 (1.00)	1 (1.00)	0.521 (1.00)	1 (1.00)	1 (1.00)	0.206 (1.00)	0.454 (1.00)	1 (1.00)
IL6ST	7 (4%)	181	0.00279 (0.538)	0.159 (1.00)	0.953 (1.00)	0.869 (1.00)	1 (1.00)	0.306 (1.00)	1 (1.00)	1 (1.00)	0.681 (1.00)	0.828 (1.00)	1 (1.00)
ALB	18 (10%)	170	0.512 (1.00)	0.0327 (1.00)	0.252 (1.00)	0.126 (1.00)	0.249 (1.00)	0.261 (1.00)	0.0204 (1.00)	1 (1.00)	0.403 (1.00)	0.00871 (1.00)	1 (1.00)
HNF1A	6 (3%)	182	0.335 (1.00)	0.198 (1.00)	0.532 (1.00)	0.377 (1.00)	1 (1.00)	1 (1.00)	0.669 (1.00)	1 (1.00)	0.624 (1.00)	1 (1.00)	1 (1.00)
APOB	23 (12%)	165	0.819 (1.00)	0.288 (1.00)	0.172 (1.00)	0.858 (1.00)	1 (1.00)	0.587 (1.00)	0.49 (1.00)	1 (1.00)	0.676 (1.00)	0.0586 (1.00)	1 (1.00)
EEF1A1	5 (3%)	183	0.0519 (1.00)	0.339 (1.00)	0.664 (1.00)	0.389 (1.00)	1 (1.00)	1 (1.00)	0.161 (1.00)	1 (1.00)	0.554 (1.00)	0.184 (1.00)	1 (1.00)
KIF19	9 (5%)	179	0.0841 (1.00)	0.0882 (1.00)	0.961 (1.00)	0.634 (1.00)	1 (1.00)	0.0829 (1.00)	0.492 (1.00)	1 (1.00)	0.41 (1.00)	0.657 (1.00)	1 (1.00)
GNAS	7 (4%)	181	0.151 (1.00)	1 (1.00)	0.678 (1.00)	0.315 (1.00)	1 (1.00)	0.461 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.696 (1.00)	1 (1.00)
F5	4 (2%)	184	0.464 (1.00)	0.217 (1.00)	0.615 (1.00)	0.452 (1.00)	1 (1.00)	0.127 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
PTEN	5 (3%)	183	0.239 (1.00)	0.128 (1.00)	1 (1.00)	0.9 (1.00)	1 (1.00)	0.641 (1.00)	0.655 (1.00)	1 (1.00)	0.111 (1.00)	0.184 (1.00)	0.137 (1.00)
HIST1H1C	5 (3%)	183	0.21 (1.00)	0.853 (1.00)	0.0371 (1.00)	0.00618 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.102 (1.00)	0.323 (1.00)	0.0227 (1.00)	1 (1.00)
DLK2	4 (2%)	184	0.391 (1.00)	0.707 (1.00)	0.469 (1.00)	0.272 (1.00)	1 (1.00)	0.321 (1.00)	0.298 (1.00)	1 (1.00)	1 (1.00)	0.362 (1.00)	1 (1.00)

'TP53 MUTATION STATUS' versus 'GENDER'

P value = 0.000995 (Fisher's exact test), Q value = 0.19

Table S1. Gene #1: 'TP53 MUTATION STATUS' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	68	120
TP53 MUTATED	11	47
TP53 WILD-TYPE	57	73

Figure S1. Get High-res Image Gene #1: 'TP53 MUTATION STATUS' versus Clinical Feature #7: 'GENDER'

'TP53 MUTATION STATUS' versus 'RACE'

P value = 0.00018 (Fisher's exact test), Q value = 0.035

Table S2. Gene #1: 'TP53 MUTATION STATUS' versus Clinical Feature #10: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	52	14	113
TP53 MUTATED	1	20	10	25
TP53 WILD-TYPE	0	32	4	88

Figure S2. Get High-res Image Gene #1: 'TP53 MUTATION STATUS' versus Clinical Feature #10: 'RACE'

'CTNNB1 MUTATION STATUS' versus 'GENDER'

P value = 2.8e-05 (Fisher's exact test), Q value = 0.0055

Table S3. Gene #2: 'CTNNB1 MUTATION STATUS' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	68	120
CTNNB1 MUTATED	6	43
CTNNB1 WILD-TYPE	62	77

Figure S3. Get High-res Image Gene #2: 'CTNNB1 MUTATION STATUS' versus Clinical Feature #7: 'GENDER'

'BAP1 MUTATION STATUS' versus 'GENDER'

P value = 0.000521 (Fisher's exact test), Q value = 0.1

Table S4. Gene #5: 'BAP1 MUTATION STATUS' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	68	120
BAP1 MUTATED	9	1
BAP1 WILD-TYPE	59	119

Figure S4. Get High-res Image Gene #5: 'BAP1 MUTATION STATUS' versus Clinical Feature #7: 'GENDER'

Methods & Data

Input

Mutation data file = transformed.cor.cli.txt
Clinical data file = LIHC-TP.merged_data.txt
Number of patients = 188
Number of significantly mutated genes = 18
Number of selected clinical features = 11
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)

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