Correlation between mutation rate and clinical features

Stomach Adenocarcinoma (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/C1RJ4HKR

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 289 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 2 clinical features related to at least one variables.

2 variables correlated to 'AGE'.

MUTATIONRATE_SILENT , MUTATIONRATE_NONSYNONYMOUS

2 variables correlated to 'AGE_mutation.rate'.

MUTATIONRATE_SILENT , MUTATIONRATE_NONSYNONYMOUS

No variables correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', 'HISTOLOGICAL_TYPE', 'RADIATIONS_RADIATION_REGIMENINDICATION', 'COMPLETENESS_OF_RESECTION', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.

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=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=0
PATHOLOGY_N_STAGE	Spearman correlation test	N=0
PATHOLOGY_M_STAGE	Wilcoxon test	N=0
GENDER	Wilcoxon test	N=0
HISTOLOGICAL_TYPE	Kruskal-Wallis test	N=0
RADIATIONS_RADIATION_REGIMENINDICATION	Wilcoxon test	N=0
COMPLETENESS_OF_RESECTION	Kruskal-Wallis test	N=0
NUMBER_OF_LYMPH_NODES	Spearman correlation test	N=0
RACE	Kruskal-Wallis test	N=0

Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

No 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)	0-105.1 (median=12.6)
	censored	N = 206
	death	N = 82

	Significant variables	N = 0

Clinical variable #2: 'AGE'

2 variables related to 'AGE'.

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


AGE	Mean (SD)	66.09 (11)
	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	0.2654	5.771e-06	9.04e-06
MUTATIONRATE_NONSYNONYMOUS	0.26	9.04e-06	9.04e-06

Clinical variable #3: 'AGE'

2 variables related to 'AGE'.

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


AGE	Mean (SD)	66.09 (11)
	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_SILENT	0.017	5.9	0.0157	7.29e-06	282	9.86e-08 ( -2.6e-06 )	0.0157 ( 0.339 )
MUTATIONRATE_NONSYNONYMOUS	0.0144	5.13	0.0243	2.26e-05	282	2.85e-07 ( -6.62e-06 )	0.0243 ( 0.433 )

Clinical variable #4: 'NEOPLASM_DISEASESTAGE'

No variable related to 'NEOPLASM_DISEASESTAGE'.

Table S6. Basic characteristics of clinical feature: 'NEOPLASM_DISEASESTAGE'


NEOPLASM_DISEASESTAGE	Labels	N
	STAGE I	1
	STAGE IA	9
	STAGE IB	27
	STAGE II	26
	STAGE IIA	31
	STAGE IIB	44
	STAGE III	3
	STAGE IIIA	49
	STAGE IIIB	36
	STAGE IIIC	24
	STAGE IV	24

	Significant variables	N = 0

Clinical variable #5: 'PATHOLOGY_T_STAGE'

No variable related to 'PATHOLOGY_T_STAGE'.

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


PATHOLOGY_T_STAGE	Mean (SD)	2.93 (0.82)
		N
	T1	11
	T2	72
	T3	122
	T4	75

	Significant variables	N = 0

Clinical variable #6: 'PATHOLOGY_N_STAGE'

No variable related to 'PATHOLOGY_N_STAGE'.

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


PATHOLOGY_N_STAGE	Mean (SD)	1.23 (1.1)
		N
	N0	93
	N1	81
	N2	50
	N3	53

	Significant variables	N = 0

Clinical variable #7: 'PATHOLOGY_M_STAGE'

No variable related to 'PATHOLOGY_M_STAGE'.

Table S9. Basic characteristics of clinical feature: 'PATHOLOGY_M_STAGE'


PATHOLOGY_M_STAGE	Labels	N
	class0	257
	class1	18

	Significant variables	N = 0

Clinical variable #8: 'GENDER'

No variable related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	110
	MALE	179

	Significant variables	N = 0

Clinical variable #9: 'HISTOLOGICAL_TYPE'

No variable related to 'HISTOLOGICAL_TYPE'.

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


HISTOLOGICAL_TYPE	Labels	N
	STOMACH ADENOCARCINOMA DIFFUSE TYPE	50
	STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	135
	STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	40
	STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE	38
	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	16
	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	6
	STOMACH ADENOCARCINOMA SIGNET RING TYPE	3

	Significant variables	N = 0

Clinical variable #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

No variable related to 'RADIATIONS_RADIATION_REGIMENINDICATION'.

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


RADIATIONS_RADIATION_REGIMENINDICATION	Labels	N
	NO	5
	YES	284

	Significant variables	N = 0

Clinical variable #11: 'COMPLETENESS_OF_RESECTION'

No variable related to 'COMPLETENESS_OF_RESECTION'.

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


COMPLETENESS_OF_RESECTION	Labels	N
	R0	227
	R1	10
	R2	10
	RX	24

	Significant variables	N = 0

Clinical variable #12: 'NUMBER_OF_LYMPH_NODES'

No variable related to 'NUMBER_OF_LYMPH_NODES'.

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


NUMBER_OF_LYMPH_NODES	Mean (SD)	5.03 (7.6)
	Significant variables	N = 0

Clinical variable #13: 'RACE'

No variable related to 'RACE'.

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


RACE	Labels	N
	ASIAN	76
	BLACK OR AFRICAN AMERICAN	4
	WHITE	168

	Significant variables	N = 0

Methods & Data

Input

Expresson data file = STAD-TP.patients.counts_and_rates.txt
Clinical data file = STAD-TP.merged_data.txt
Number of patients = 289
Number of variables = 2
Number of clinical features = 13

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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