Correlation between gene mutation status and selected clinical features

Kidney Chromophobe (Primary solid tumor)

02 April 2015 | analyses__2015_04_02

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 (2015): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1FQ9VNM

Overview

Introduction

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

Summary

Testing the association between mutation status of 4 genes and 12 clinical features across 66 patients, 7 significant findings detected with Q value < 0.25.

TP53 mutation correlated to 'Time to Death' and 'PATHOLOGY_N_STAGE'.
PTEN mutation correlated to 'Time to Death' and 'PATHOLOGY_T_STAGE'.
PABPC1 mutation correlated to 'Time to Death', 'NEOPLASM_DISEASESTAGE', and 'RACE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 4 genes and 12 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 7 significant findings detected.


		Clinical Features	Time to Death	YEARS TO BIRTH	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	PATHOLOGY M STAGE	GENDER	KARNOFSKY PERFORMANCE SCORE	NUMBER PACK YEARS SMOKED	YEAR OF TOBACCO SMOKING ONSET	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	Wilcoxon-test	Wilcoxon-test	Wilcoxon-test	Fisher's exact test	Fisher's exact test
PABPC1	7 (11%)	59	0.0126 (0.163)	0.252 (0.711)	0.00447 (0.163)	0.0469 (0.271)	0.0874 (0.381)	1 (1.00)	0.226 (0.677)				0.0136 (0.163)	1 (1.00)
TP53	22 (33%)	44	0.0214 (0.205)	0.935 (1.00)	0.306 (0.816)	0.369 (0.932)	0.036 (0.247)	0.524 (1.00)	1 (1.00)		0.394 (0.946)	0.885 (1.00)	0.195 (0.677)	1 (1.00)
PTEN	6 (9%)	60	0.00755 (0.163)	0.422 (0.964)	0.0508 (0.271)	0.0323 (0.247)	0.0874 (0.381)	1 (1.00)	0.217 (0.677)				0.216 (0.677)	0.213 (0.677)
URGCP	3 (5%)	63	0.504 (1.00)	0.841 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)				1 (1.00)	1 (1.00)

'TP53 MUTATION STATUS' versus 'Time to Death'

P value = 0.0214 (logrank test), Q value = 0.21

Table S1. Gene #1: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	65	9	1.0 - 152.0 (65.2)
TP53 MUTATED	22	6	10.7 - 141.7 (55.7)
TP53 WILD-TYPE	43	3	1.0 - 152.0 (73.9)

Figure S1. Get High-res Image Gene #1: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'TP53 MUTATION STATUS' versus 'PATHOLOGY_N_STAGE'

P value = 0.036 (Fisher's exact test), Q value = 0.25

Table S2. Gene #1: 'TP53 MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1+N2
ALL	40	5
TP53 MUTATED	11	4
TP53 WILD-TYPE	29	1

Figure S2. Get High-res Image Gene #1: 'TP53 MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'PTEN MUTATION STATUS' versus 'Time to Death'

P value = 0.00755 (logrank test), Q value = 0.16

Table S3. Gene #2: 'PTEN MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	65	9	1.0 - 152.0 (65.2)
PTEN MUTATED	6	3	16.7 - 90.5 (46.4)
PTEN WILD-TYPE	59	6	1.0 - 152.0 (71.4)

Figure S3. Get High-res Image Gene #2: 'PTEN MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'PTEN MUTATION STATUS' versus 'PATHOLOGY_T_STAGE'

P value = 0.0323 (Fisher's exact test), Q value = 0.25

Table S4. Gene #2: 'PTEN MUTATION STATUS' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3+T4
ALL	21	25	20
PTEN MUTATED	2	0	4
PTEN WILD-TYPE	19	25	16

Figure S4. Get High-res Image Gene #2: 'PTEN MUTATION STATUS' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'PABPC1 MUTATION STATUS' versus 'Time to Death'

P value = 0.0126 (logrank test), Q value = 0.16

Table S5. Gene #3: 'PABPC1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	65	9	1.0 - 152.0 (65.2)
PABPC1 MUTATED	7	3	1.0 - 123.1 (71.4)
PABPC1 WILD-TYPE	58	6	2.5 - 152.0 (64.6)

Figure S5. Get High-res Image Gene #3: 'PABPC1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'PABPC1 MUTATION STATUS' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00447 (Fisher's exact test), Q value = 0.16

Table S6. Gene #3: 'PABPC1 MUTATION STATUS' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	21	25	14	6
PABPC1 MUTATED	3	0	1	3
PABPC1 WILD-TYPE	18	25	13	3

Figure S6. Get High-res Image Gene #3: 'PABPC1 MUTATION STATUS' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'PABPC1 MUTATION STATUS' versus 'RACE'

P value = 0.0136 (Fisher's exact test), Q value = 0.16

Table S7. Gene #3: 'PABPC1 MUTATION STATUS' versus Clinical Feature #11: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	4	58
PABPC1 MUTATED	2	0	5
PABPC1 WILD-TYPE	0	4	53

Figure S7. Get High-res Image Gene #3: 'PABPC1 MUTATION STATUS' versus Clinical Feature #11: 'RACE'

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/KICH-TP/15174156/transformed.cor.cli.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/KICH-TP/15080874/KICH-TP.merged_data.txt
Number of patients = 66
Number of significantly mutated genes = 4
Number of selected clinical features = 12
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)

Made with Nozzle