Correlation between mutation rate and clinical features

Colorectal 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/C1QF8RVD

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 14 clinical features across 488 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 9 clinical features related to at least one variables.

1 variable correlated to 'AGE_mutation.rate'.

MUTATIONRATE_NONSYNONYMOUS

2 variables correlated to 'PRIMARY_SITE_OF_DISEASE'.

MUTATIONRATE_SILENT , MUTATIONRATE_NONSYNONYMOUS

2 variables correlated to 'NEOPLASM_DISEASESTAGE'.

MUTATIONRATE_SILENT , MUTATIONRATE_NONSYNONYMOUS

2 variables correlated to 'PATHOLOGY_T_STAGE'.

MUTATIONRATE_SILENT , MUTATIONRATE_NONSYNONYMOUS

2 variables correlated to 'PATHOLOGY_N_STAGE'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

2 variables correlated to 'PATHOLOGY_M_STAGE'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

2 variables correlated to 'HISTOLOGICAL_TYPE'.

MUTATIONRATE_SILENT , MUTATIONRATE_NONSYNONYMOUS

2 variables correlated to 'COMPLETENESS_OF_RESECTION'.

MUTATIONRATE_SILENT , MUTATIONRATE_NONSYNONYMOUS

2 variables correlated to 'NUMBER_OF_LYMPH_NODES'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

No variables correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'AGE', 'GENDER', 'RADIATIONS_RADIATION_REGIMENINDICATION', 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=0
AGE	Linear Regression Analysis	N=1
PRIMARY_SITE_OF_DISEASE	Wilcoxon test	N=2	rectum	N=2	colon	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	Spearman correlation test	N=2	higher stage	N=0	lower stage	N=2
PATHOLOGY_M_STAGE	Wilcoxon test	N=2	class1	N=2	class0	N=0
GENDER	Wilcoxon test	N=0
HISTOLOGICAL_TYPE	Kruskal-Wallis test	N=2
RADIATIONS_RADIATION_REGIMENINDICATION	Wilcoxon test	N=0
COMPLETENESS_OF_RESECTION	Kruskal-Wallis test	N=2
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=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)	0-140.4 (median=20)
	censored	N = 398
	death	N = 89

	Significant variables	N = 0

Clinical variable #2: 'AGE'

No variable related to 'AGE'.

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


AGE	Mean (SD)	67.11 (13)
	Significant variables	N = 0

Clinical variable #3: 'AGE'

One variable related to 'AGE'.

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


AGE	Mean (SD)	67.11 (13)
	Significant variables	N = 1

List of one variable associated with 'AGE'

Table S4. 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_NONSYNONYMOUS	0.00719	4.52	0.034	2.81e-05	485	-2.16e-07 ( 2.69e-05 )	0.034 ( 0.000123 )

Clinical variable #4: 'PRIMARY_SITE_OF_DISEASE'

2 variables related to 'PRIMARY_SITE_OF_DISEASE'.

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


PRIMARY_SITE_OF_DISEASE	Labels	N
	COLON	365
	RECTUM	119

	Significant variables	N = 2
	Higher in RECTUM	2
	Higher in COLON	0

List of 2 variables associated with 'PRIMARY_SITE_OF_DISEASE'

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

	W(pos if higher in 'RECTUM')	wilcoxontestP	Q	AUC
MUTATIONRATE_SILENT	12995	4.612e-11	9.22e-11	0.7008
MUTATIONRATE_NONSYNONYMOUS	13191.5	1.238e-10	1.24e-10	0.6963

Clinical variable #5: 'NEOPLASM_DISEASESTAGE'

2 variables related to 'NEOPLASM_DISEASESTAGE'.

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


NEOPLASM_DISEASESTAGE	Labels	N
	STAGE I	91
	STAGE IA	1
	STAGE II	32
	STAGE IIA	138
	STAGE IIB	10
	STAGE IIC	1
	STAGE III	24
	STAGE IIIA	14
	STAGE IIIB	56
	STAGE IIIC	40
	STAGE IV	48
	STAGE IVA	19
	STAGE IVB	1

	Significant variables	N = 2

List of 2 variables associated with 'NEOPLASM_DISEASESTAGE'

Table S8. Get Full Table List of 2 variables differentially expressed by 'NEOPLASM_DISEASESTAGE'

	kruskal_wallis_P	Q
MUTATIONRATE_SILENT	0.0002956	0.000301
MUTATIONRATE_NONSYNONYMOUS	0.0003014	0.000301

Clinical variable #6: 'PATHOLOGY_T_STAGE'

2 variables related to 'PATHOLOGY_T_STAGE'.

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


PATHOLOGY_T_STAGE	Mean (SD)	2.84 (0.64)
		N
	T1	17
	T2	93
	T3	327
	T4	50

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

List of 2 variables associated with 'PATHOLOGY_T_STAGE'

Table S10. Get Full Table List of 2 variables significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

	SpearmanCorr	corrP	Q
MUTATIONRATE_SILENT	0.1053	0.02017	0.0403
MUTATIONRATE_NONSYNONYMOUS	0.0925	0.04124	0.0412

Clinical variable #7: 'PATHOLOGY_N_STAGE'

2 variables related to 'PATHOLOGY_N_STAGE'.

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


PATHOLOGY_N_STAGE	Mean (SD)	0.59 (0.77)
		N
	N0	287
	N1	112
	N2	86

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

List of 2 variables associated with 'PATHOLOGY_N_STAGE'

Table S12. Get Full Table List of 2 variables significantly correlated to 'PATHOLOGY_N_STAGE' by Spearman correlation test

	SpearmanCorr	corrP	Q
MUTATIONRATE_NONSYNONYMOUS	-0.1432	0.001566	0.00313
MUTATIONRATE_SILENT	-0.1282	0.004673	0.00467

Clinical variable #8: 'PATHOLOGY_M_STAGE'

2 variables related to 'PATHOLOGY_M_STAGE'.

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


PATHOLOGY_M_STAGE	Labels	N
	class0	374
	class1	65

	Significant variables	N = 2
	Higher in class1	2
	Higher in class0	0

List of 2 variables associated with 'PATHOLOGY_M_STAGE'

Table S14. Get Full Table List of 2 variables differentially expressed by 'PATHOLOGY_M_STAGE'

	W(pos if higher in 'class1')	wilcoxontestP	Q	AUC
MUTATIONRATE_NONSYNONYMOUS	9039	0.0009672	0.00193	0.6282
MUTATIONRATE_SILENT	9385	0.003352	0.00335	0.6139

Clinical variable #9: 'GENDER'

No variable related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	224
	MALE	264

	Significant variables	N = 0

Clinical variable #10: 'HISTOLOGICAL_TYPE'

2 variables related to 'HISTOLOGICAL_TYPE'.

Table S16. Basic characteristics of clinical feature: 'HISTOLOGICAL_TYPE'


HISTOLOGICAL_TYPE	Labels	N
	COLON ADENOCARCINOMA	316
	COLON MUCINOUS ADENOCARCINOMA	48
	RECTAL ADENOCARCINOMA	106
	RECTAL MUCINOUS ADENOCARCINOMA	10

	Significant variables	N = 2

List of 2 variables associated with 'HISTOLOGICAL_TYPE'

Table S17. Get Full Table List of 2 variables differentially expressed by 'HISTOLOGICAL_TYPE'

	kruskal_wallis_P	Q
MUTATIONRATE_SILENT	1.76e-12	3.36e-12
MUTATIONRATE_NONSYNONYMOUS	3.356e-12	3.36e-12

Clinical variable #11: 'RADIATIONS_RADIATION_REGIMENINDICATION'

No variable related to 'RADIATIONS_RADIATION_REGIMENINDICATION'.

Table S18. Basic characteristics of clinical feature: 'RADIATIONS_RADIATION_REGIMENINDICATION'


RADIATIONS_RADIATION_REGIMENINDICATION	Labels	N
	NO	6
	YES	482

	Significant variables	N = 0

Clinical variable #12: 'COMPLETENESS_OF_RESECTION'

2 variables related to 'COMPLETENESS_OF_RESECTION'.

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


COMPLETENESS_OF_RESECTION	Labels	N
	R0	345
	R1	3
	R2	31
	RX	25

	Significant variables	N = 2

List of 2 variables associated with 'COMPLETENESS_OF_RESECTION'

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

	kruskal_wallis_P	Q
MUTATIONRATE_SILENT	0.0002761	0.000368
MUTATIONRATE_NONSYNONYMOUS	0.0003679	0.000368

Clinical variable #13: 'NUMBER_OF_LYMPH_NODES'

2 variables related to 'NUMBER_OF_LYMPH_NODES'.

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


NUMBER_OF_LYMPH_NODES	Mean (SD)	2.03 (4.2)
	Significant variables	N = 2
	pos. correlated	0
	neg. correlated	2

List of 2 variables associated with 'NUMBER_OF_LYMPH_NODES'

Table S22. Get Full Table List of 2 variables significantly correlated to 'NUMBER_OF_LYMPH_NODES' by Spearman correlation test

	SpearmanCorr	corrP	Q
MUTATIONRATE_NONSYNONYMOUS	-0.151	0.001204	0.00241
MUTATIONRATE_SILENT	-0.1344	0.004011	0.00401

Clinical variable #14: 'RACE'

No variable related to 'RACE'.

Table S23. Basic characteristics of clinical feature: 'RACE'


RACE	Labels	N
	AMERICAN INDIAN OR ALASKA NATIVE	1
	ASIAN	11
	BLACK OR AFRICAN AMERICAN	21
	WHITE	226

	Significant variables	N = 0

Methods & Data

Input

Expresson data file = COADREAD-TP.patients.counts_and_rates.txt
Clinical data file = COADREAD-TP.merged_data.txt
Number of patients = 488
Number of variables = 2
Number of clinical features = 14

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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