Correlation between mutation rate and clinical features

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 248 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 1 clinical feature related to at least one variables.

2 variables correlated to 'HISTOLOGICAL.TYPE'.

MUTATIONRATE_SILENT , MUTATIONRATE_NONSYNONYMOUS

No variables correlated to 'Time to Death', 'AGE', 'AGE_mutation.rate', 'RADIATIONS.RADIATION.REGIMENINDICATION', '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
Time to Death	Cox regression test	N=0
AGE	Spearman correlation test	N=0
AGE	Linear Regression Analysis	N=0
HISTOLOGICAL TYPE	Kruskal-Wallis test	N=2
RADIATIONS RADIATION REGIMENINDICATION	Wilcoxon test	N=0
COMPLETENESS OF RESECTION	Kruskal-Wallis test	N=0
RACE	Kruskal-Wallis test	N=0
ETHNICITY	Wilcoxon test	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)	0.6-185.8 (median=29.2)
	censored	N = 224
	death	N = 24

	Significant variables	N = 0

Clinical variable #2: 'AGE'

No variable related to 'AGE'.

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


AGE	Mean (SD)	63.09 (11)
	Significant variables	N = 0

Clinical variable #3: 'AGE'

No variable related to 'AGE'.

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


AGE	Mean (SD)	63.09 (11)
	Significant variables	N = 0

Clinical variable #4: 'HISTOLOGICAL.TYPE'

2 variables related to 'HISTOLOGICAL.TYPE'.

Table S4. Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'


HISTOLOGICAL.TYPE	Labels	N
	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	200
	MIXED SEROUS AND ENDOMETRIOID	4
	SEROUS ENDOMETRIAL ADENOCARCINOMA	44

	Significant variables	N = 2

List of 2 variables associated with 'HISTOLOGICAL.TYPE'

Table S5. Get Full Table List of 2 variables differentially expressed by 'HISTOLOGICAL.TYPE'

	ANOVA_P	Q
MUTATIONRATE_SILENT	0.0001076	0.000215
MUTATIONRATE_NONSYNONYMOUS	0.005868	0.00587

Clinical variable #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

No variable related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S6. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'


RADIATIONS.RADIATION.REGIMENINDICATION	Labels	N
	NO	86
	YES	162

	Significant variables	N = 0

Clinical variable #6: 'COMPLETENESS.OF.RESECTION'

No variable related to 'COMPLETENESS.OF.RESECTION'.

Table S7. Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'


COMPLETENESS.OF.RESECTION	Labels	N
	R0	171
	R1	11
	R2	7
	RX	18

	Significant variables	N = 0

Clinical variable #7: 'RACE'

No variable related to 'RACE'.

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


RACE	Labels	N
	AMERICAN INDIAN OR ALASKA NATIVE	3
	ASIAN	13
	BLACK OR AFRICAN AMERICAN	25
	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	7
	WHITE	193

	Significant variables	N = 0

Clinical variable #8: 'ETHNICITY'

No variable related to 'ETHNICITY'.

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


ETHNICITY	Labels	N
	HISPANIC OR LATINO	4
	NOT HISPANIC OR LATINO	167

	Significant variables	N = 0

Methods & Data

Input

Expresson data file = UCEC-TP.patients.counts_and_rates.txt
Clinical data file = UCEC-TP.merged_data.txt
Number of patients = 248
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.

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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