Correlation between mutation rate and clinical features

Breast Invasive Carcinoma (Primary solid tumor)

17 October 2014 | analyses__2014_10_17

Maintainer Information

Citation Information

Maintained by Juok Cho (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between mutation rate and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C19G5KMP

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 959 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 8 clinical features related to at least one variables.

2 variables correlated to 'Time to Death'.

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_NONSYNONYMOUS , MUTATIONRATE_SILENT

No variables correlated to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.M.STAGE', 'GENDER', 'RADIATIONS.RADIATION.REGIMENINDICATION', 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
Time to Death	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
NEOPLASM DISEASESTAGE	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	Kruskal-Wallis test	N=0
GENDER	Wilcoxon test	N=0
HISTOLOGICAL TYPE	Kruskal-Wallis test	N=2
RADIATIONS RADIATION REGIMENINDICATION	Wilcoxon test	N=0
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=0

Clinical variable #1: 'Time to Death'

2 variables related to 'Time to Death'.

Table S1. Basic characteristics of clinical feature: 'Time to Death'


Time to Death	Duration (Months)	0-234.3 (median=23.9)
	censored	N = 824
	death	N = 115

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

List of 2 variables associated with 'Time to Death'

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	0.001253	0.0025	0.585
MUTATIONRATE_SILENT	Inf	0.005766	0.0058	0.609

Clinical variable #2: 'AGE'

2 variables related to 'AGE'.

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


AGE	Mean (SD)	58.72 (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	0.1307	5.509e-05	0.00011
MUTATIONRATE_SILENT	0.1302	5.904e-05	0.00011

Clinical variable #3: 'AGE'

2 variables related to 'AGE'.

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


AGE	Mean (SD)	58.72 (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	0.00991	10.5	0.00126	1.82e-06	944	1.46e-08 ( -3.03e-07 )	0.00126 ( 0.266 )
MUTATIONRATE_NONSYNONYMOUS	0.00944	10	0.00161	6.86e-06	944	5.38e-08 ( -1.04e-06 )	0.00161 ( 0.31 )

Clinical variable #4: 'NEOPLASM.DISEASESTAGE'

No variable related to 'NEOPLASM.DISEASESTAGE'.

Table S7. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'


NEOPLASM.DISEASESTAGE	Labels	N
	STAGE I	81
	STAGE IA	74
	STAGE IB	8
	STAGE II	5
	STAGE IIA	327
	STAGE IIB	217
	STAGE III	2
	STAGE IIIA	131
	STAGE IIIB	26
	STAGE IIIC	55
	STAGE IV	15
	STAGE TIS	1
	STAGE X	16

	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)	1.92 (0.73)
		N
	1	256
	2	553
	3	113
	4	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	0.1481	4.214e-06	8.43e-06
MUTATIONRATE_SILENT	0.1288	6.482e-05	6.48e-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
	0	456
	1	317
	2	105
	3	66

	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	-0.0858	0.008385	0.0168

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
	CM0 (I+)	2
	M0	830
	M1	15
	MX	112

	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	950
	MALE	9

	Significant variables	N = 0

Clinical variable #9: 'HISTOLOGICAL.TYPE'

2 variables related to 'HISTOLOGICAL.TYPE'.

Table S14. Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'


HISTOLOGICAL.TYPE	Labels	N
	INFILTRATING CARCINOMA NOS	1
	INFILTRATING DUCTAL CARCINOMA	708
	INFILTRATING LOBULAR CARCINOMA	159
	MEDULLARY CARCINOMA	5
	MIXED HISTOLOGY (PLEASE SPECIFY)	28
	MUCINOUS CARCINOMA	14
	OTHER SPECIFY	43

	Significant variables	N = 2

List of 2 variables associated with 'HISTOLOGICAL.TYPE'

Table S15. Get Full Table List of 2 variables differentially expressed by 'HISTOLOGICAL.TYPE'

	ANOVA_P	Q
MUTATIONRATE_NONSYNONYMOUS	0.0001087	0.000217
MUTATIONRATE_SILENT	0.01849	0.0185

Clinical variable #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

No variable related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S16. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'


RADIATIONS.RADIATION.REGIMENINDICATION	Labels	N
	NO	276
	YES	683

	Significant variables	N = 0

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)	2.34 (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	-0.117	0.0008643	0.00173
MUTATIONRATE_SILENT	-0.0853	0.01526	0.0153

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	56
	BLACK OR AFRICAN AMERICAN	114
	WHITE	695

	Significant variables	N = 2

List of 2 variables associated with 'RACE'

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

	ANOVA_P	Q
MUTATIONRATE_NONSYNONYMOUS	0.02556	0.0382
MUTATIONRATE_SILENT	0.0191	0.0382

Clinical variable #13: 'ETHNICITY'

No variable related to 'ETHNICITY'.

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


ETHNICITY	Labels	N
	HISPANIC OR LATINO	34
	NOT HISPANIC OR LATINO	757

	Significant variables	N = 0

Methods & Data

Input

Expresson data file = BRCA-TP.patients.counts_and_rates.txt
Clinical data file = BRCA-TP.merged_data.txt
Number of patients = 959
Number of variables = 2
Number of clinical features = 13

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

ANOVA analysis

For multi-class clinical features (ordinal or nominal), one-way analysis of variance (Howell 2002) was applied to compare the log2-expression levels between different clinical classes using 'anova' function in R

Student's t-test analysis

For two-class clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the log2-expression levels between the two clinical classes using 't.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] 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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)

[4] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[5] 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