Correlation between mutation rate and clinical features

Glioma (Primary solid tumor)

02 April 2015 | analyses__2015_04_02

Maintainer Information

Citation Information

Maintained by Juok Cho (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between mutation rate and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1BG2N0M

Overview

Introduction

This pipeline uses various statistical tests to identify selected clinical features related to mutation rate.

Summary

Testing the association between 2 variables and 10 clinical features across 751 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 6 clinical features related to at least one variables.

2 variables correlated to 'AGE'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

2 variables correlated to 'PRIMARY_SITE_OF_DISEASE'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

1 variable correlated to 'GENDER'.

MUTATIONRATE_SILENT

2 variables correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

MUTATIONRATE_SILENT , MUTATIONRATE_NONSYNONYMOUS

2 variables correlated to 'HISTOLOGICAL_TYPE'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

2 variables correlated to 'RADIATIONS_RADIATION_REGIMENINDICATION'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

No variables correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'AGE_mutation.rate', 'RACE', and 'ETHNICITY'.

Results

Overview of the results

Complete statistical result table is provided in Supplement Table 1

Table 1. Get Full Table This table shows the clinical features, statistical methods used, and the number of variables that are significantly associated with each clinical feature at P value < 0.05 and Q value < 0.3.

Clinical feature	Statistical test	Significant variables	Associated with		Associated with
DAYS_TO_DEATH_OR_LAST_FUP	Cox regression test	N=0
AGE	Spearman correlation test	N=2	older	N=2	younger	N=0
AGE	Linear Regression Analysis	N=0
PRIMARY_SITE_OF_DISEASE	Wilcoxon test	N=2	central nervous system	N=2	brain	N=0
GENDER	Wilcoxon test	N=1	male	N=1	female	N=0
KARNOFSKY_PERFORMANCE_SCORE	Spearman correlation test	N=2	higher score	N=0	lower score	N=2
HISTOLOGICAL_TYPE	Kruskal-Wallis test	N=2
RADIATIONS_RADIATION_REGIMENINDICATION	Wilcoxon test	N=2	yes	N=2	no	N=0
RACE	Kruskal-Wallis test	N=0
ETHNICITY	Wilcoxon test	N=0

Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

No variable related to 'DAYS_TO_DEATH_OR_LAST_FUP'.

Table S1. Basic characteristics of clinical feature: 'DAYS_TO_DEATH_OR_LAST_FUP'


DAYS_TO_DEATH_OR_LAST_FUP	Duration (Months)	0-211.2 (median=15.2)
	censored	N = 447
	death	N = 303

	Significant variables	N = 0

Clinical variable #2: 'AGE'

2 variables related to 'AGE'.

Table S2. Basic characteristics of clinical feature: 'AGE'


AGE	Mean (SD)	49.67 (16)
	Significant variables	N = 2
	pos. correlated	2
	neg. correlated	0

List of 2 variables associated with 'AGE'

Table S3. Get Full Table List of 2 variables significantly correlated to 'AGE' by Spearman correlation test

	SpearmanCorr	corrP	Q
MUTATIONRATE_NONSYNONYMOUS	0.6248	1.985e-82	3.97e-82
MUTATIONRATE_SILENT	0.6192	1.392e-80	1.39e-80

Clinical variable #3: 'AGE'

No variable related to 'AGE'.

Table S4. Basic characteristics of clinical feature: 'AGE'


AGE	Mean (SD)	49.67 (16)
	Significant variables	N = 0

Clinical variable #4: 'PRIMARY_SITE_OF_DISEASE'

2 variables related to 'PRIMARY_SITE_OF_DISEASE'.

Table S5. Basic characteristics of clinical feature: 'PRIMARY_SITE_OF_DISEASE'


PRIMARY_SITE_OF_DISEASE	Labels	N
	BRAIN	278
	CENTRAL NERVOUS SYSTEM	473

	Significant variables	N = 2
	Higher in CENTRAL NERVOUS SYSTEM	2
	Higher in BRAIN	0

List of 2 variables associated with 'PRIMARY_SITE_OF_DISEASE'

Table S6. Get Full Table List of 2 variables differentially expressed by 'PRIMARY_SITE_OF_DISEASE'

	W(pos if higher in 'CENTRAL NERVOUS SYSTEM')	wilcoxontestP	Q	AUC
MUTATIONRATE_NONSYNONYMOUS	26606	2.48e-42	4.96e-42	0.7977
MUTATIONRATE_SILENT	27899	1.076e-39	1.08e-39	0.7878

Clinical variable #5: 'GENDER'

One variable related to 'GENDER'.

Table S7. Basic characteristics of clinical feature: 'GENDER'


GENDER	Labels	N
	FEMALE	307
	MALE	444

	Significant variables	N = 1
	Higher in MALE	1
	Higher in FEMALE	0

List of one variable associated with 'GENDER'

Table S8. Get Full Table List of one variable differentially expressed by 'GENDER'. 0 significant gene(s) located in sex chromosomes is(are) filtered out.

	W(pos if higher in 'MALE')	wilcoxontestP	Q	AUC
MUTATIONRATE_SILENT	59415	0.002791	0.00558	0.5641

Clinical variable #6: 'KARNOFSKY_PERFORMANCE_SCORE'

2 variables related to 'KARNOFSKY_PERFORMANCE_SCORE'.

Table S9. Basic characteristics of clinical feature: 'KARNOFSKY_PERFORMANCE_SCORE'


KARNOFSKY_PERFORMANCE_SCORE	Mean (SD)	82.77 (15)
	Significant variables	N = 2
	pos. correlated	0
	neg. correlated	2

List of 2 variables associated with 'KARNOFSKY_PERFORMANCE_SCORE'

Table S10. Get Full Table List of 2 variables significantly correlated to 'KARNOFSKY_PERFORMANCE_SCORE' by Spearman correlation test

	SpearmanCorr	corrP	Q
MUTATIONRATE_SILENT	-0.2865	2.614e-10	5.23e-10
MUTATIONRATE_NONSYNONYMOUS	-0.2643	6.186e-09	6.19e-09

Clinical variable #7: 'HISTOLOGICAL_TYPE'

2 variables related to 'HISTOLOGICAL_TYPE'.

Table S11. Basic characteristics of clinical feature: 'HISTOLOGICAL_TYPE'


HISTOLOGICAL_TYPE	Labels	N
	ASTROCYTOMA	175
	GLIOBLASTOMA MULTIFORME (GBM)	9
	OLIGOASTROCYTOMA	119
	OLIGODENDROGLIOMA	179
	TREATED PRIMARY GBM	4
	UNTREATED PRIMARY (DE NOVO) GBM	265

	Significant variables	N = 2

List of 2 variables associated with 'HISTOLOGICAL_TYPE'

Table S12. Get Full Table List of 2 variables differentially expressed by 'HISTOLOGICAL_TYPE'

	kruskal_wallis_P	Q
MUTATIONRATE_NONSYNONYMOUS	5.856e-43	1.17e-42
MUTATIONRATE_SILENT	1.3e-40	1.3e-40

Clinical variable #8: 'RADIATIONS_RADIATION_REGIMENINDICATION'

2 variables related to 'RADIATIONS_RADIATION_REGIMENINDICATION'.

Table S13. Basic characteristics of clinical feature: 'RADIATIONS_RADIATION_REGIMENINDICATION'


RADIATIONS_RADIATION_REGIMENINDICATION	Labels	N
	NO	278
	YES	473

	Significant variables	N = 2
	Higher in YES	2
	Higher in NO	0

List of 2 variables associated with 'RADIATIONS_RADIATION_REGIMENINDICATION'

Table S14. Get Full Table List of 2 variables differentially expressed by 'RADIATIONS_RADIATION_REGIMENINDICATION'

	W(pos if higher in 'YES')	wilcoxontestP	Q	AUC
MUTATIONRATE_NONSYNONYMOUS	42646	8.464e-16	1.69e-15	0.6757
MUTATIONRATE_SILENT	45525	1.863e-12	1.86e-12	0.6538

Clinical variable #9: 'RACE'

No variable related to 'RACE'.

Table S15. Basic characteristics of clinical feature: 'RACE'


RACE	Labels	N
	AMERICAN INDIAN OR ALASKA NATIVE	1
	ASIAN	13
	BLACK OR AFRICAN AMERICAN	32
	WHITE	688

	Significant variables	N = 0

Clinical variable #10: 'ETHNICITY'

No variable related to 'ETHNICITY'.

Table S16. Basic characteristics of clinical feature: 'ETHNICITY'


ETHNICITY	Labels	N
	HISPANIC OR LATINO	21
	NOT HISPANIC OR LATINO	640

	Significant variables	N = 0

Methods & Data

Input

Expresson data file = GBMLGG-TP.patients.counts_and_rates.txt
Clinical data file = GBMLGG-TP.merged_data.txt
Number of patients = 751
Number of variables = 2
Number of clinical features = 10

Selected clinical features

For clinical features selected for this analysis and their value conozzle.versions, please find a documentation on selected CDEs .
Survival time data

Survival time data is a combined value of days_to_death and days_to_last_followup. For each patient, it creates a combined value 'days_to_death_or_last_fup' using conversion process below.

if 'vital_status'==1(dead), 'days_to_last_followup' is always NA. Thus, uses 'days_to_death' value for 'days_to_death_or_fup'

if 'vital_status'==0(alive),

if 'days_to_death'==NA & 'days_to_last_followup'!=NA, uses 'days_to_last_followup' value for 'days_to_death_or_fup'

if 'days_to_death'!=NA, excludes this case in survival analysis and report the case.

if 'vital_status'==NA,excludes this case in survival analysis and report the case.

cf. In certain diesase types such as SKCM, days_to_death parameter is replaced with time_from_specimen_dx or time_from_specimen_procurement_to_death .

This analysis excluded clinical variables that has only NA values.

Survival analysis

For survival clinical features, Wald's test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values using the 'coxph' function in R. Kaplan-Meier survival curves were plot using the four quartile subgroups of patients based on expression levels

Correlation analysis

For continuous numerical clinical features, Spearman's rank correlation coefficients (Spearman 1904) and two-tailed P values were estimated using 'cor.test' function in R

Wilcoxon rank sum test (Mann-Whitney U test)

For two groups (mutant or wild-type) of continuous type of clinical data, wilcoxon rank sum test (Mann and Whitney, 1947) was applied to compare their mean difference using 'wilcox.test(continuous.clinical ~ as.factor(group), exact=FALSE)' function in R. This test is equivalent to the Mann-Whitney test.

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] Andersen and Gill, Cox's regression model for counting processes, a large sample study, Annals of Statistics 10(4):1100-1120 (1982)

[2] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)

[3] Mann and Whitney, On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other, Annals of Mathematical Statistics 18 (1), 50-60 (1947)

[4] 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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