Correlation between mutation rate and clinical features

Adrenocortical Carcinoma (Primary solid tumor)

15 July 2014 | analyses__2014_07_15

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/C1MK6BKJ

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 8 clinical features across 81 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'.

MUTATIONRATE_SILENT , MUTATIONRATE_NONSYNONYMOUS

2 variables correlated to 'NEOPLASM.DISEASESTAGE'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

2 variables correlated to 'PATHOLOGY.T.STAGE'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

1 variable correlated to 'ETHNICITY'.

MUTATIONRATE_NONSYNONYMOUS

No variables correlated to 'Time to Death', 'AGE_mutation.rate', 'PATHOLOGY.N.STAGE', and 'GENDER'.

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=0
AGE	Spearman correlation test	N=2	older	N=2	younger	N=0
AGE	Linear Regression Analysis	N=0
NEOPLASM DISEASESTAGE	Kruskal-Wallis test	N=2
PATHOLOGY T STAGE	Spearman correlation test	N=2	higher stage	N=2	lower stage	N=0
PATHOLOGY N STAGE	Wilcoxon test	N=0
GENDER	Wilcoxon test	N=0
ETHNICITY	Wilcoxon test	N=1	not hispanic or latino	N=1	hispanic or latino	N=0

Clinical variable #1: 'Time to Death'

No variable related to 'Time to Death'.

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


Time to Death	Duration (Months)	4.1-153.6 (median=30.8)
	censored	N = 52
	death	N = 28

	Significant variables	N = 0

Clinical variable #2: 'AGE'

2 variables related to 'AGE'.

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


AGE	Mean (SD)	47.19 (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_SILENT	0.3056	0.005529	0.0111
MUTATIONRATE_NONSYNONYMOUS	0.266	0.01639	0.0164

Clinical variable #3: 'AGE'

No variable related to 'AGE'.

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


AGE	Mean (SD)	47.19 (16)
	Significant variables	N = 0

Clinical variable #4: 'NEOPLASM.DISEASESTAGE'

2 variables related to 'NEOPLASM.DISEASESTAGE'.

Table S5. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'


NEOPLASM.DISEASESTAGE	Labels	N
	STAGE I	8
	STAGE II	33
	STAGE III	18
	STAGE IV	17

	Significant variables	N = 2

List of 2 variables associated with 'NEOPLASM.DISEASESTAGE'

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

	ANOVA_P	Q
MUTATIONRATE_NONSYNONYMOUS	0.0001071	0.000214
MUTATIONRATE_SILENT	0.001255	0.00126

Clinical variable #5: 'PATHOLOGY.T.STAGE'

2 variables related to 'PATHOLOGY.T.STAGE'.

Table S7. Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'


PATHOLOGY.T.STAGE	Mean (SD)	2.54 (0.99)
		N
	1	8
	2	38
	3	11
	4	19

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

List of 2 variables associated with 'PATHOLOGY.T.STAGE'

Table S8. Get Full Table List of 2 variables significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

	SpearmanCorr	corrP	Q
MUTATIONRATE_NONSYNONYMOUS	0.4836	9.644e-06	1.93e-05
MUTATIONRATE_SILENT	0.3975	0.000377	0.000377

Clinical variable #6: 'PATHOLOGY.N.STAGE'

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

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


PATHOLOGY.N.STAGE	Labels	N
	class0	67
	class1	10

	Significant variables	N = 0

Clinical variable #7: 'GENDER'

No variable related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	53
	MALE	28

	Significant variables	N = 0

Clinical variable #8: 'ETHNICITY'

One variable related to 'ETHNICITY'.

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


ETHNICITY	Labels	N
	HISPANIC OR LATINO	8
	NOT HISPANIC OR LATINO	29

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

	W(pos if higher in 'NOT HISPANIC OR LATINO')	wilcoxontestP	Q	AUC
MUTATIONRATE_NONSYNONYMOUS	c("60", "0.0406")	c("60", "0.0406")	0.0812	0.7414

Methods & Data

Input

Expresson data file = ACC-TP.patients.counts_and_rates.txt
Clinical data file = ACC-TP.merged_data.txt
Number of patients = 81
Number of variables = 2
Number of clinical features = 8

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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