Correlation between mutation rate and clinical features

Skin Cutaneous Melanoma (Metastatic)

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/C1CR5S9H

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 15 clinical features across 276 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one variables.

2 variables correlated to 'AGE_mutation.rate'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

1 variable correlated to 'MELANOMA.ULCERATION'.

MUTATIONRATE_SILENT

2 variables correlated to 'GENDER'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

2 variables correlated to 'RACE'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

No variables correlated to 'Time from Specimen Diagnosis to Death', 'Time to Death', 'AGE', 'PRIMARY.SITE.OF.DISEASE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'MELANOMA.PRIMARY.KNOWN', 'BRESLOW.THICKNESS', 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 from Specimen Diagnosis to Death	Cox regression test	N=0
Time to Death	Cox regression test	N=0
AGE	Spearman correlation test	N=0
AGE	Linear Regression Analysis	N=2
PRIMARY SITE OF DISEASE	Kruskal-Wallis test	N=0
NEOPLASM DISEASESTAGE	Kruskal-Wallis test	N=0
PATHOLOGY T STAGE	Spearman correlation test	N=0
PATHOLOGY N STAGE	Spearman correlation test	N=0
PATHOLOGY M STAGE	Kruskal-Wallis test	N=0
MELANOMA ULCERATION	Wilcoxon test	N=1	yes	N=1	no	N=0
MELANOMA PRIMARY KNOWN	Wilcoxon test	N=0
BRESLOW THICKNESS	Spearman correlation test	N=0
GENDER	Wilcoxon test	N=2	male	N=2	female	N=0
RACE	Kruskal-Wallis test	N=2
ETHNICITY	Wilcoxon test	N=0

Clinical variable #1: 'Time from Specimen Diagnosis to Death'

No variable related to 'Time from Specimen Diagnosis to Death'.

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


Time from Specimen Diagnosis to Death	Duration (Months)	0.1-125.7 (median=16.9)
	censored	N = 126
	death	N = 140

	Significant variables	N = 0

Clinical variable #2: 'Time to Death'

No variable related to 'Time to Death'.

Table S2. Basic characteristics of clinical feature: 'Time to Death'


Time to Death	Duration (Months)	0.2-357.4 (median=51.8)
	censored	N = 129
	death	N = 141

	Significant variables	N = 0

Clinical variable #3: 'AGE'

No variable related to 'AGE'.

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


AGE	Mean (SD)	55.82 (16)
	Significant variables	N = 0

Clinical variable #4: 'AGE'

2 variables related to 'AGE'.

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


AGE	Mean (SD)	55.82 (16)
	Significant variables	N = 2

List of 2 variables associated with 'AGE'

Table S5. 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_NONSYNONYMOUS	0.0126	4.45	0.0358	3.45e-05	269	2.82e-07 ( 1.98e-06 )	0.0358 ( 0.799 )
MUTATIONRATE_SILENT	0.011	4.01	0.0464	1.87e-05	269	1.45e-07 ( 5.11e-07 )	0.0464 ( 0.903 )

Clinical variable #5: 'PRIMARY.SITE.OF.DISEASE'

No variable related to 'PRIMARY.SITE.OF.DISEASE'.

Table S6. Basic characteristics of clinical feature: 'PRIMARY.SITE.OF.DISEASE'


PRIMARY.SITE.OF.DISEASE	Labels	N
	DISTANT METASTASIS	39
	PRIMARY TUMOR	5
	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE	60
	REGIONAL LYMPH NODE	171

	Significant variables	N = 0

Clinical variable #6: 'NEOPLASM.DISEASESTAGE'

No variable related to 'NEOPLASM.DISEASESTAGE'.

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


NEOPLASM.DISEASESTAGE	Labels	N
	I OR II NOS	10
	STAGE 0	6
	STAGE I	24
	STAGE IA	10
	STAGE IB	24
	STAGE II	16
	STAGE IIA	10
	STAGE IIB	14
	STAGE IIC	9
	STAGE III	32
	STAGE IIIA	13
	STAGE IIIB	23
	STAGE IIIC	46
	STAGE IV	16

	Significant variables	N = 0

Clinical variable #7: 'PATHOLOGY.T.STAGE'

No variable related to 'PATHOLOGY.T.STAGE'.

Table S8. Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'


PATHOLOGY.T.STAGE	Mean (SD)	2.42 (1.3)
		N
	0	22
	1	30
	2	59
	3	54
	4	57

	Significant variables	N = 0

Clinical variable #8: 'PATHOLOGY.N.STAGE'

No variable related to 'PATHOLOGY.N.STAGE'.

Table S9. Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'


PATHOLOGY.N.STAGE	Mean (SD)	0.87 (1.1)
		N
	0	139
	1	48
	2	33
	3	36

	Significant variables	N = 0

Clinical variable #9: 'PATHOLOGY.M.STAGE'

No variable related to 'PATHOLOGY.M.STAGE'.

Table S10. Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'


PATHOLOGY.M.STAGE	Labels	N
	M0	244
	M1	4
	M1A	3
	M1B	2
	M1C	8

	Significant variables	N = 0

Clinical variable #10: 'MELANOMA.ULCERATION'

One variable related to 'MELANOMA.ULCERATION'.

Table S11. Basic characteristics of clinical feature: 'MELANOMA.ULCERATION'


MELANOMA.ULCERATION	Labels	N
	NO	103
	YES	68

	Significant variables	N = 1
	Higher in YES	1
	Higher in NO	0

List of one variable associated with 'MELANOMA.ULCERATION'

Table S12. Get Full Table List of one variable differentially expressed by 'MELANOMA.ULCERATION'

	W(pos if higher in 'YES')	wilcoxontestP	Q	AUC
MUTATIONRATE_SILENT	2871	0.0466	0.0932	0.5901

Clinical variable #11: 'MELANOMA.PRIMARY.KNOWN'

No variable related to 'MELANOMA.PRIMARY.KNOWN'.

Table S13. Basic characteristics of clinical feature: 'MELANOMA.PRIMARY.KNOWN'


MELANOMA.PRIMARY.KNOWN	Labels	N
	NO	36
	YES	239

	Significant variables	N = 0

Clinical variable #12: 'BRESLOW.THICKNESS'

No variable related to 'BRESLOW.THICKNESS'.

Table S14. Basic characteristics of clinical feature: 'BRESLOW.THICKNESS'


BRESLOW.THICKNESS	Mean (SD)	3.61 (5)
	Significant variables	N = 0

Clinical variable #13: 'GENDER'

2 variables related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	106
	MALE	170

	Significant variables	N = 2
	Higher in MALE	2
	Higher in FEMALE	0

List of 2 variables associated with 'GENDER'

Table S16. Get Full Table List of 2 variables 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_NONSYNONYMOUS	10725	0.007853	0.0157	0.5952
MUTATIONRATE_SILENT	10715	0.008221	0.0157	0.5946

Clinical variable #14: 'RACE'

2 variables related to 'RACE'.

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


RACE	Labels	N
	ASIAN	3
	BLACK OR AFRICAN AMERICAN	1
	WHITE	272

	Significant variables	N = 2

List of 2 variables associated with 'RACE'

Table S18. Get Full Table List of 2 variables differentially expressed by 'RACE'

	ANOVA_P	Q
MUTATIONRATE_NONSYNONYMOUS	0.03031	0.0543
MUTATIONRATE_SILENT	0.02715	0.0543

Clinical variable #15: 'ETHNICITY'

No variable related to 'ETHNICITY'.

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


ETHNICITY	Labels	N
	HISPANIC OR LATINO	3
	NOT HISPANIC OR LATINO	269

	Significant variables	N = 0

Methods & Data

Input

Expresson data file = SKCM-TM.patients.counts_and_rates.txt
Clinical data file = SKCM-TM.merged_data.txt
Number of patients = 276
Number of variables = 2
Number of clinical features = 15

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)

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