Correlation between mutation rate and clinical features

Rectum 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/C14F1PT6

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

2 variables correlated to 'AGE_mutation.rate'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

2 variables correlated to 'HISTOLOGICAL_TYPE'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

2 variables correlated to 'COMPLETENESS_OF_RESECTION'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

No variables correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'AGE', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', 'RADIATIONS_RADIATION_REGIMENINDICATION', '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=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	Wilcoxon test	N=2	rectal mucinous adenocarcinoma	N=2	rectal adenocarcinoma	N=0
RADIATIONS_RADIATION_REGIMENINDICATION	Wilcoxon test	N=0
COMPLETENESS_OF_RESECTION	Kruskal-Wallis test	N=2
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)	0.5-126.4 (median=17)
	censored	N = 103
	death	N = 18

	Significant variables	N = 0

Clinical variable #2: 'AGE'

No variable related to 'AGE'.

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


AGE	Mean (SD)	65.89 (11)
	Significant variables	N = 0

Clinical variable #3: 'AGE'

2 variables related to 'AGE'.

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


AGE	Mean (SD)	65.89 (11)
	Significant variables	N = 2

List of 2 variables associated with 'AGE'

Table S4. 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	0.0556	8.12	0.00515	3.18e-05	120	-7.17e-07 ( 5.47e-05 )	0.00515 ( 0.0015 )
MUTATIONRATE_SILENT	0.0538	7.88	0.00582	7.97e-06	120	-1.77e-07 ( 1.39e-05 )	0.00582 ( 0.00131 )

Clinical variable #4: 'NEOPLASM_DISEASESTAGE'

No variable related to 'NEOPLASM_DISEASESTAGE'.

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


NEOPLASM_DISEASESTAGE	Labels	N
	STAGE I	27
	STAGE II	7
	STAGE IIA	29
	STAGE IIB	2
	STAGE III	5
	STAGE IIIA	4
	STAGE IIIB	15
	STAGE IIIC	10
	STAGE IV	13
	STAGE IVA	6

	Significant variables	N = 0

Clinical variable #5: 'PATHOLOGY_T_STAGE'

No variable related to 'PATHOLOGY_T_STAGE'.

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


PATHOLOGY_T_STAGE	Mean (SD)	2.74 (0.69)
		N
	T1	8
	T2	25
	T3	80
	T4	9

	Significant variables	N = 0

Clinical variable #6: 'PATHOLOGY_N_STAGE'

No variable related to 'PATHOLOGY_N_STAGE'.

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


PATHOLOGY_N_STAGE	Mean (SD)	0.62 (0.77)
		N
	N0	67
	N1	32
	N2	21

	Significant variables	N = 0

Clinical variable #7: 'PATHOLOGY_M_STAGE'

No variable related to 'PATHOLOGY_M_STAGE'.

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


PATHOLOGY_M_STAGE	Labels	N
	class0	92
	class1	17

	Significant variables	N = 0

Clinical variable #8: 'GENDER'

No variable related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	55
	MALE	67

	Significant variables	N = 0

Clinical variable #9: 'HISTOLOGICAL_TYPE'

2 variables related to 'HISTOLOGICAL_TYPE'.

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


HISTOLOGICAL_TYPE	Labels	N
	RECTAL ADENOCARCINOMA	106
	RECTAL MUCINOUS ADENOCARCINOMA	10

	Significant variables	N = 2
	Higher in RECTAL MUCINOUS ADENOCARCINOMA	2
	Higher in RECTAL ADENOCARCINOMA	0

List of 2 variables associated with 'HISTOLOGICAL_TYPE'

Table S11. Get Full Table List of 2 variables differentially expressed by 'HISTOLOGICAL_TYPE'

	W(pos if higher in 'RECTAL MUCINOUS ADENOCARCINOMA')	wilcoxontestP	Q	AUC
MUTATIONRATE_NONSYNONYMOUS	152	0.0002045	0.000409	0.8566
MUTATIONRATE_SILENT	188	0.0007813	0.000781	0.8226

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	4
	YES	118

	Significant variables	N = 0

Clinical variable #11: 'COMPLETENESS_OF_RESECTION'

2 variables related to 'COMPLETENESS_OF_RESECTION'.

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


COMPLETENESS_OF_RESECTION	Labels	N
	R0	88
	R1	2
	R2	9
	RX	4

	Significant variables	N = 2

List of 2 variables associated with 'COMPLETENESS_OF_RESECTION'

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

	kruskal_wallis_P	Q
MUTATIONRATE_NONSYNONYMOUS	0.02487	0.0429
MUTATIONRATE_SILENT	0.04295	0.0429

Clinical variable #12: 'NUMBER_OF_LYMPH_NODES'

No variable related to 'NUMBER_OF_LYMPH_NODES'.

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


NUMBER_OF_LYMPH_NODES	Mean (SD)	2.36 (5.1)
	Significant variables	N = 0

Clinical variable #13: 'RACE'

No variable related to 'RACE'.

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


RACE	Labels	N
	ASIAN	1
	BLACK OR AFRICAN AMERICAN	3
	WHITE	50

	Significant variables	N = 0

Methods & Data

Input

Expresson data file = READ-TP.patients.counts_and_rates.txt
Clinical data file = READ-TP.merged_data.txt
Number of patients = 122
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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