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 18 genes and 11 clinical features across 194 patients, 9 significant findings detected with Q value < 0.25.

TP53 mutation correlated to 'GENDER' and 'RACE'.
CTNNB1 mutation correlated to 'GENDER'.
RB1 mutation correlated to 'YEARS_TO_BIRTH' and 'RACE'.
BAP1 mutation correlated to 'GENDER'.
IL6ST mutation correlated to 'Time to Death'.
ALB mutation correlated to 'RACE'.
HIST1H1C mutation correlated to 'PATHOLOGY_T_STAGE'.

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, 9 significant findings detected.


		Clinical Features	Time to Death	YEARS TO BIRTH	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	61 (31%)	133	0.338 (0.985)	0.087 (0.749)	0.136 (0.887)	0.0549 (0.561)	0.559 (1.00)	0.308 (0.962)	0.000623 (0.0308)	0.694 (1.00)	0.961 (1.00)	0.00014 (0.0139)	1 (1.00)
RB1	15 (8%)	179	0.89 (1.00)	0.00267 (0.106)	0.454 (1.00)	0.18 (0.887)	1 (1.00)	1 (1.00)	0.404 (1.00)	1 (1.00)	0.528 (1.00)	0.00706 (0.175)	0.358 (1.00)
CTNNB1	50 (26%)	144	0.99 (1.00)	0.208 (0.887)	0.75 (1.00)	0.598 (1.00)	0.161 (0.887)	1 (1.00)	3.08e-05 (0.0061)	0.683 (1.00)	0.117 (0.86)	0.287 (0.962)	1 (1.00)
BAP1	10 (5%)	184	0.185 (0.887)	0.804 (1.00)	0.0476 (0.555)	0.036 (0.509)	0.181 (0.887)	0.227 (0.892)	0.000452 (0.0298)	1 (1.00)	0.596 (1.00)	1 (1.00)	1 (1.00)
IL6ST	7 (4%)	187	0.0045 (0.133)	0.17 (0.887)	0.953 (1.00)	0.866 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.685 (1.00)	0.825 (1.00)	1 (1.00)
ALB	18 (9%)	176	0.504 (1.00)	0.028 (0.462)	0.241 (0.904)	0.112 (0.856)	0.243 (0.904)	1 (1.00)	0.0356 (0.509)	1 (1.00)	0.405 (1.00)	0.00847 (0.186)	1 (1.00)
HIST1H1C	5 (3%)	189	0.219 (0.892)	0.88 (1.00)	0.0445 (0.555)	0.00469 (0.133)	1 (1.00)	1 (1.00)	1 (1.00)	0.101 (0.799)	0.32 (0.962)	0.022 (0.436)	1 (1.00)
AXIN1	9 (5%)	185	0.141 (0.887)	0.799 (1.00)	0.0942 (0.777)	0.631 (1.00)	1 (1.00)	0.18 (0.887)	1 (1.00)	1 (1.00)	0.297 (0.962)	1 (1.00)	0.206 (0.887)
TSC2	9 (5%)	185	0.771 (1.00)	0.515 (1.00)	0.177 (0.887)	0.0765 (0.689)	1 (1.00)	1 (1.00)	0.723 (1.00)	1 (1.00)	0.547 (1.00)	0.211 (0.887)	0.183 (0.887)
ARID1A	16 (8%)	178	0.0567 (0.561)	0.295 (0.962)	0.188 (0.887)	1 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.313 (0.962)	0.467 (1.00)	1 (1.00)
HNF1A	8 (4%)	186	0.482 (1.00)	0.403 (1.00)	0.23 (0.892)	0.281 (0.962)	1 (1.00)	1 (1.00)	0.458 (1.00)	1 (1.00)	0.247 (0.904)	0.687 (1.00)	1 (1.00)
APOB	23 (12%)	171	0.695 (1.00)	0.325 (0.962)	0.21 (0.887)	0.846 (1.00)	1 (1.00)	0.413 (1.00)	0.487 (1.00)	1 (1.00)	0.741 (1.00)	0.0541 (0.561)	1 (1.00)
EEF1A1	5 (3%)	189	0.0277 (0.462)	0.326 (0.962)	0.667 (1.00)	0.385 (1.00)	1 (1.00)	1 (1.00)	0.163 (0.887)	1 (1.00)	0.559 (1.00)	0.179 (0.887)	1 (1.00)
KIF19	10 (5%)	184	0.0702 (0.662)	0.0452 (0.555)	0.919 (1.00)	0.637 (1.00)	1 (1.00)	1 (1.00)	0.499 (1.00)	1 (1.00)	0.597 (1.00)	0.507 (1.00)	1 (1.00)
GNAS	7 (4%)	187	0.158 (0.887)	0.975 (1.00)	0.684 (1.00)	0.314 (0.962)	1 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.693 (1.00)	1 (1.00)
F5	4 (2%)	190	0.473 (1.00)	0.225 (0.892)	0.62 (1.00)	0.446 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
PTEN	7 (4%)	187	0.566 (1.00)	0.26 (0.936)	0.85 (1.00)	0.761 (1.00)	1 (1.00)	1 (1.00)	0.425 (1.00)	1 (1.00)	0.202 (0.887)	0.458 (1.00)	0.158 (0.887)
DLK2	4 (2%)	190	0.37 (1.00)	0.733 (1.00)	0.475 (1.00)	0.301 (0.962)	1 (1.00)	1 (1.00)	0.299 (0.962)	1 (1.00)	1 (1.00)	0.355 (1.00)	1 (1.00)

'TP53 MUTATION STATUS' versus 'GENDER'

P value = 0.000623 (Fisher's exact test), Q value = 0.031

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

nPatients	FEMALE	MALE
ALL	69	125
TP53 MUTATED	11	50
TP53 WILD-TYPE	58	75

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

'TP53 MUTATION STATUS' versus 'RACE'

P value = 0.00014 (Fisher's exact test), Q value = 0.014

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	54	14	117
TP53 MUTATED	1	22	10	26
TP53 WILD-TYPE	0	32	4	91

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

'CTNNB1 MUTATION STATUS' versus 'GENDER'

P value = 3.08e-05 (Fisher's exact test), Q value = 0.0061

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

nPatients	FEMALE	MALE
ALL	69	125
CTNNB1 MUTATED	6	44
CTNNB1 WILD-TYPE	63	81

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

'RB1 MUTATION STATUS' versus 'YEARS_TO_BIRTH'

P value = 0.00267 (Wilcoxon-test), Q value = 0.11

Table S4. Gene #3: 'RB1 MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	192	60.1 (13.9)
RB1 MUTATED	15	51.8 (11.3)
RB1 WILD-TYPE	177	60.8 (13.9)

Figure S4. Get High-res Image Gene #3: 'RB1 MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RB1 MUTATION STATUS' versus 'RACE'

P value = 0.00706 (Fisher's exact test), Q value = 0.17

Table S5. Gene #3: 'RB1 MUTATION STATUS' versus Clinical Feature #10: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	54	14	117
RB1 MUTATED	0	10	1	4
RB1 WILD-TYPE	1	44	13	113

Figure S5. Get High-res Image Gene #3: 'RB1 MUTATION STATUS' versus Clinical Feature #10: 'RACE'

'BAP1 MUTATION STATUS' versus 'GENDER'

P value = 0.000452 (Fisher's exact test), Q value = 0.03

Table S6. Gene #5: 'BAP1 MUTATION STATUS' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	69	125
BAP1 MUTATED	9	1
BAP1 WILD-TYPE	60	124

Figure S6. Get High-res Image Gene #5: 'BAP1 MUTATION STATUS' versus Clinical Feature #7: 'GENDER'

'IL6ST MUTATION STATUS' versus 'Time to Death'

P value = 0.0045 (logrank test), Q value = 0.13

Table S7. Gene #8: 'IL6ST MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	178	75	0.0 - 114.3 (18.6)
IL6ST MUTATED	6	0	13.4 - 113.0 (66.0)
IL6ST WILD-TYPE	172	75	0.0 - 114.3 (18.2)

Figure S7. Get High-res Image Gene #8: 'IL6ST MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'ALB MUTATION STATUS' versus 'RACE'

P value = 0.00847 (Fisher's exact test), Q value = 0.19

Table S8. Gene #9: 'ALB MUTATION STATUS' versus Clinical Feature #10: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	54	14	117
ALB MUTATED	1	1	1	15
ALB WILD-TYPE	0	53	13	102

Figure S8. Get High-res Image Gene #9: 'ALB MUTATION STATUS' versus Clinical Feature #10: 'RACE'

'HIST1H1C MUTATION STATUS' versus 'PATHOLOGY_T_STAGE'

P value = 0.00469 (Fisher's exact test), Q value = 0.13

Table S9. Gene #17: 'HIST1H1C MUTATION STATUS' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	79	50	52	11
HIST1H1C MUTATED	0	4	0	1
HIST1H1C WILD-TYPE	79	46	52	10

Figure S9. Get High-res Image Gene #17: 'HIST1H1C MUTATION STATUS' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

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/LIHC-TP/15165528/transformed.cor.cli.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/LIHC-TP/15082975/LIHC-TP.merged_data.txt
Number of patients = 194
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.

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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