Correlation between mutation rate and clinical features

Breast Invasive Carcinoma (Primary solid tumor)

21 August 2015 | analyses__2015_08_21

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/C15H7FFN

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 13 clinical features across 977 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 9 clinical features related to at least one variables.

2 variables correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

2 variables correlated to 'AGE'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

2 variables correlated to 'AGE_mutation.rate'.

MUTATIONRATE_SILENT , MUTATIONRATE_NONSYNONYMOUS

2 variables correlated to 'PATHOLOGY_T_STAGE'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

1 variable correlated to 'PATHOLOGY_N_STAGE'.

MUTATIONRATE_NONSYNONYMOUS

2 variables correlated to 'HISTOLOGICAL_TYPE'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

2 variables correlated to 'NUMBER_OF_LYMPH_NODES'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

2 variables correlated to 'RACE'.

MUTATIONRATE_SILENT , MUTATIONRATE_NONSYNONYMOUS

1 variable correlated to 'ETHNICITY'.

MUTATIONRATE_SILENT

No variables correlated to 'PATHOLOGIC_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', and 'RADIATION_THERAPY'.

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=2	shorter survival	N=2	longer survival	N=0
AGE	Spearman correlation test	N=2	older	N=2	younger	N=0
AGE	Linear Regression Analysis	N=2
PATHOLOGIC_STAGE	Kruskal-Wallis test	N=0
PATHOLOGY_T_STAGE	Spearman correlation test	N=2	higher stage	N=2	lower stage	N=0
PATHOLOGY_N_STAGE	Spearman correlation test	N=1	higher stage	N=0	lower stage	N=1
PATHOLOGY_M_STAGE	Wilcoxon test	N=0
GENDER	Wilcoxon test	N=0
RADIATION_THERAPY	Wilcoxon test	N=0
HISTOLOGICAL_TYPE	Kruskal-Wallis test	N=2
NUMBER_OF_LYMPH_NODES	Spearman correlation test	N=2	higher number_of_lymph_nodes	N=0	lower number_of_lymph_nodes	N=2
RACE	Kruskal-Wallis test	N=2
ETHNICITY	Wilcoxon test	N=1	not hispanic or latino	N=1	hispanic or latino	N=0

Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

2 variables 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)	0-282.9 (median=30.3)
	censored	N = 838
	death	N = 138

	Significant variables	N = 2
	associated with shorter survival	2
	associated with longer survival	0

List of 2 variables associated with 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2. Get Full Table List of 2 variables significantly associated with 'Time to Death' by Cox regression test

	HazardRatio	Wald_P	Q	C_index
MUTATIONRATE_NONSYNONYMOUS	Inf	0.00103	0.0021	0.571
MUTATIONRATE_SILENT	Inf	0.004328	0.0043	0.595

Clinical variable #2: 'AGE'

2 variables related to 'AGE'.

Table S3. Basic characteristics of clinical feature: 'AGE'


AGE	Mean (SD)	58.74 (13)
	Significant variables	N = 2
	pos. correlated	2
	neg. correlated	0

List of 2 variables associated with 'AGE'

Table S4. Get Full Table List of 2 variables significantly correlated to 'AGE' by Spearman correlation test

	SpearmanCorr	corrP	Q
MUTATIONRATE_NONSYNONYMOUS	0.1248	0.0001023	0.000205
MUTATIONRATE_SILENT	0.1124	0.0004737	0.000474

Clinical variable #3: 'AGE'

2 variables related to 'AGE'.

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


AGE	Mean (SD)	58.74 (13)
	Significant variables	N = 2

List of 2 variables associated with 'AGE'

Table S6. Get Full Table List of 2 variables significantly correlated to 'AGE' by Linear regression analysis [lm (mutation rate ~ age)]. Compared to a correlation analysis testing for interdependence of the variables, a regression model attempts to describe the dependence of a variable on one (or more) explanatory variables assuming that there is a one-way causal effect from the explanatory variable(s) to the response variable. If 'Residuals vs Fitted' plot (a standard residual plot) shows a random pattern indicating a good fit for a linear model, it explains linear regression relationship between Mutation rate and age factor. Adj.R-squared (= Explained variation / Total variation) indicates regression model's explanatory power.

	Adj.R.squared	F	P	Residual.std.err	DF	coef(intercept)	coef.p(intercept)
MUTATIONRATE_SILENT	0.00916	9.91	0.0017	1.77e-06	962	1.37e-08 ( -2.83e-07 )	0.0017 ( 0.278 )
MUTATIONRATE_NONSYNONYMOUS	0.00882	9.57	0.00204	5.97e-06	962	4.53e-08 ( -8.81e-07 )	0.00204 ( 0.318 )

Clinical variable #4: 'PATHOLOGIC_STAGE'

No variable related to 'PATHOLOGIC_STAGE'.

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


PATHOLOGIC_STAGE	Labels	N
	STAGE I	87
	STAGE IA	73
	STAGE IB	6
	STAGE II	4
	STAGE IIA	327
	STAGE IIB	226
	STAGE III	2
	STAGE IIIA	136
	STAGE IIIB	26
	STAGE IIIC	57
	STAGE IV	15
	STAGE TIS	1
	STAGE X	12

	Significant variables	N = 0

Clinical variable #5: 'PATHOLOGY_T_STAGE'

2 variables related to 'PATHOLOGY_T_STAGE'.

Table S8. Basic characteristics of clinical feature: 'PATHOLOGY_T_STAGE'


PATHOLOGY_T_STAGE	Mean (SD)	1.93 (0.73)
		N
	T1	258
	T2	563
	T3	119
	T4	35

	Significant variables	N = 2
	pos. correlated	2
	neg. correlated	0

List of 2 variables associated with 'PATHOLOGY_T_STAGE'

Table S9. Get Full Table List of 2 variables significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

	SpearmanCorr	corrP	Q
MUTATIONRATE_NONSYNONYMOUS	0.1296	4.961e-05	5.44e-05
MUTATIONRATE_SILENT	0.1289	5.443e-05	5.44e-05

Clinical variable #6: 'PATHOLOGY_N_STAGE'

One variable related to 'PATHOLOGY_N_STAGE'.

Table S10. Basic characteristics of clinical feature: 'PATHOLOGY_N_STAGE'


PATHOLOGY_N_STAGE	Mean (SD)	0.77 (0.91)
		N
	N0	462
	N1	325
	N2	107
	N3	68

	Significant variables	N = 1
	pos. correlated	0
	neg. correlated	1

List of one variable associated with 'PATHOLOGY_N_STAGE'

Table S11. Get Full Table List of one variable significantly correlated to 'PATHOLOGY_N_STAGE' by Spearman correlation test

	SpearmanCorr	corrP	Q
MUTATIONRATE_NONSYNONYMOUS	-0.0827	0.01029	0.0206

Clinical variable #7: 'PATHOLOGY_M_STAGE'

No variable related to 'PATHOLOGY_M_STAGE'.

Table S12. Basic characteristics of clinical feature: 'PATHOLOGY_M_STAGE'


PATHOLOGY_M_STAGE	Labels	N
	class0	841
	class1	16

	Significant variables	N = 0

Clinical variable #8: 'GENDER'

No variable related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	968
	MALE	9

	Significant variables	N = 0

Clinical variable #9: 'RADIATION_THERAPY'

No variable related to 'RADIATION_THERAPY'.

Table S14. Basic characteristics of clinical feature: 'RADIATION_THERAPY'


RADIATION_THERAPY	Labels	N
	NO	377
	YES	472

	Significant variables	N = 0

Clinical variable #10: 'HISTOLOGICAL_TYPE'

2 variables related to 'HISTOLOGICAL_TYPE'.

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


HISTOLOGICAL_TYPE	Labels	N
	INFILTRATING CARCINOMA NOS	1
	INFILTRATING DUCTAL CARCINOMA	712
	INFILTRATING LOBULAR CARCINOMA	171
	MEDULLARY CARCINOMA	5
	METAPLASTIC CARCINOMA	6
	MIXED HISTOLOGY (PLEASE SPECIFY)	26
	MUCINOUS CARCINOMA	14
	OTHER SPECIFY	41

	Significant variables	N = 2

List of 2 variables associated with 'HISTOLOGICAL_TYPE'

Table S16. Get Full Table List of 2 variables differentially expressed by 'HISTOLOGICAL_TYPE'

	kruskal_wallis_P	Q
MUTATIONRATE_NONSYNONYMOUS	7.917e-05	0.000158
MUTATIONRATE_SILENT	0.006195	0.0062

Clinical variable #11: 'NUMBER_OF_LYMPH_NODES'

2 variables related to 'NUMBER_OF_LYMPH_NODES'.

Table S17. Basic characteristics of clinical feature: 'NUMBER_OF_LYMPH_NODES'


NUMBER_OF_LYMPH_NODES	Mean (SD)	2.35 (4.6)
	Significant variables	N = 2
	pos. correlated	0
	neg. correlated	2

List of 2 variables associated with 'NUMBER_OF_LYMPH_NODES'

Table S18. Get Full Table List of 2 variables significantly correlated to 'NUMBER_OF_LYMPH_NODES' by Spearman correlation test

	SpearmanCorr	corrP	Q
MUTATIONRATE_NONSYNONYMOUS	-0.1151	0.0009198	0.00184
MUTATIONRATE_SILENT	-0.0815	0.0191	0.0191

Clinical variable #12: 'RACE'

2 variables related to 'RACE'.

Table S19. Basic characteristics of clinical feature: 'RACE'


RACE	Labels	N
	AMERICAN INDIAN OR ALASKA NATIVE	1
	ASIAN	57
	BLACK OR AFRICAN AMERICAN	121
	WHITE	705

	Significant variables	N = 2

List of 2 variables associated with 'RACE'

Table S20. Get Full Table List of 2 variables differentially expressed by 'RACE'

	kruskal_wallis_P	Q
MUTATIONRATE_SILENT	0.01841	0.0217
MUTATIONRATE_NONSYNONYMOUS	0.02174	0.0217

Clinical variable #13: 'ETHNICITY'

One variable related to 'ETHNICITY'.

Table S21. Basic characteristics of clinical feature: 'ETHNICITY'


ETHNICITY	Labels	N
	HISPANIC OR LATINO	34
	NOT HISPANIC OR LATINO	772

	Significant variables	N = 1
	Higher in NOT HISPANIC OR LATINO	1
	Higher in HISPANIC OR LATINO	0

List of one variable associated with 'ETHNICITY'

Table S22. Get Full Table List of one variable differentially expressed by 'ETHNICITY'

	W(pos if higher in 'NOT HISPANIC OR LATINO')	wilcoxontestP	Q	AUC
MUTATIONRATE_SILENT	c("15785.5", "0.04519")	c("15785.5", "0.04519")	0.0904	0.6014

Methods & Data

Input

Expresson data file = BRCA-TP.patients.counts_and_rates.txt
Clinical data file = BRCA-TP.merged_data.txt
Number of patients = 977
Number of variables = 2
Number of clinical features = 13

Selected clinical features

Further details on clinical features selected for this analysis, please find a documentation on selected CDEs (Clinical Data Elements). The first column of the file is a formula to convert values and the second column is a clinical parameter name.

There are also useful links about clinical features.

CDE Browser

Data dictionary for clinical data

NCI wiki - Biospecimen and Clinical XSD Files Specification

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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