Correlation between mutation rate and clinical features

Ovarian Serous Cystadenocarcinoma (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/C13B5Z6M

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 9 clinical features across 465 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one variables.

1 variable correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

MUTATIONRATE_NONSYNONYMOUS

1 variable correlated to 'AGE_mutation.rate'.

MUTATIONRATE_SILENT

1 variable correlated to 'RADIATIONS_RADIATION_REGIMENINDICATION'.

MUTATIONRATE_NONSYNONYMOUS

No variables correlated to 'AGE', 'PRIMARY_SITE_OF_DISEASE', 'KARNOFSKY_PERFORMANCE_SCORE', 'COMPLETENESS_OF_RESECTION', '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=1	shorter survival	N=0	longer survival	N=1
AGE	Spearman correlation test	N=0
AGE	Linear Regression Analysis	N=1
PRIMARY_SITE_OF_DISEASE	Kruskal-Wallis test	N=0
KARNOFSKY_PERFORMANCE_SCORE	Spearman correlation test	N=0
RADIATIONS_RADIATION_REGIMENINDICATION	Wilcoxon test	N=1	yes	N=1	no	N=0
COMPLETENESS_OF_RESECTION	Kruskal-Wallis test	N=0
RACE	Kruskal-Wallis test	N=0
ETHNICITY	Wilcoxon test	N=0

Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

One 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)	0.3-180.2 (median=28.6)
	censored	N = 231
	death	N = 233

	Significant variables	N = 1
	associated with shorter survival	0
	associated with longer survival	1

List of one variable associated with 'DAYS_TO_DEATH_OR_LAST_FUP'

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

	HazardRatio	Wald_P	Q	C_index
MUTATIONRATE_NONSYNONYMOUS	0	0.01416	0.028	0.438

Clinical variable #2: 'AGE'

No variable related to 'AGE'.

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


AGE	Mean (SD)	59.66 (12)
	Significant variables	N = 0

Clinical variable #3: 'AGE'

One variable related to 'AGE'.

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


AGE	Mean (SD)	59.66 (12)
	Significant variables	N = 1

List of one variable associated with 'AGE'

Table S5. Get Full Table List of one variable 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	0.00678	4.1	0.0434	2.86e-07	454	2.32e-09 ( 2.99e-07 )	0.0434 ( 2.15e-05 )

Clinical variable #4: '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
	OMENTUM	2
	OVARY	462
	PERITONEUM OVARY	1

	Significant variables	N = 0

Clinical variable #5: 'KARNOFSKY_PERFORMANCE_SCORE'

No variable related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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


KARNOFSKY_PERFORMANCE_SCORE	Mean (SD)	76 (13)
	Score	N
	40	2
	60	15
	80	42
	100	6

	Significant variables	N = 0

Clinical variable #6: 'RADIATIONS_RADIATION_REGIMENINDICATION'

One variable related to 'RADIATIONS_RADIATION_REGIMENINDICATION'.

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


RADIATIONS_RADIATION_REGIMENINDICATION	Labels	N
	NO	3
	YES	462

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

List of one variable associated with 'RADIATIONS_RADIATION_REGIMENINDICATION'

Table S9. Get Full Table List of one variable differentially expressed by 'RADIATIONS_RADIATION_REGIMENINDICATION'

	W(pos if higher in 'YES')	wilcoxontestP	Q	AUC
MUTATIONRATE_NONSYNONYMOUS	221.5	0.04234	0.0847	0.8402

Clinical variable #7: 'COMPLETENESS_OF_RESECTION'

No variable related to 'COMPLETENESS_OF_RESECTION'.

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


COMPLETENESS_OF_RESECTION	Labels	N
	R0	13
	R1	22
	R2	1

	Significant variables	N = 0

Clinical variable #8: 'RACE'

No variable related to 'RACE'.

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


RACE	Labels	N
	AMERICAN INDIAN OR ALASKA NATIVE	2
	ASIAN	18
	BLACK OR AFRICAN AMERICAN	20
	WHITE	403

	Significant variables	N = 0

Clinical variable #9: 'ETHNICITY'

No variable related to 'ETHNICITY'.

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


ETHNICITY	Labels	N
	HISPANIC OR LATINO	9
	NOT HISPANIC OR LATINO	271

	Significant variables	N = 0

Methods & Data

Input

Expresson data file = OV-TP.patients.counts_and_rates.txt
Clinical data file = OV-TP.merged_data.txt
Number of patients = 465
Number of variables = 2
Number of clinical features = 9

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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