Correlation between mutation rate and clinical features

Prostate Adenocarcinoma (Primary solid tumor)

17 October 2014 | analyses__2014_10_17

Maintainer Information

Citation Information

Maintained by Juok Cho (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between mutation rate and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1Z60MZT

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 256 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 8 clinical features related to at least one variables.

2 variables correlated to 'AGE'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

1 variable correlated to 'PATHOLOGY.T.STAGE'.

MUTATIONRATE_NONSYNONYMOUS

1 variable correlated to 'HISTOLOGICAL.TYPE'.

MUTATIONRATE_NONSYNONYMOUS

2 variables correlated to 'GLEASON_SCORE_COMBINED'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

2 variables correlated to 'GLEASON_SCORE_PRIMARY'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

2 variables correlated to 'GLEASON_SCORE'.

MUTATIONRATE_SILENT , MUTATIONRATE_NONSYNONYMOUS

2 variables correlated to 'PSA_RESULT_PREOP'.

MUTATIONRATE_SILENT , MUTATIONRATE_NONSYNONYMOUS

2 variables correlated to 'PSA_VALUE'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

No variables correlated to 'AGE_mutation.rate', 'PATHOLOGY.N.STAGE', 'COMPLETENESS.OF.RESECTION', 'NUMBER.OF.LYMPH.NODES', 'GLEASON_SCORE_SECONDARY', 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
AGE	Spearman correlation test	N=2	older	N=2	younger	N=0
AGE	Linear Regression Analysis	N=0
PATHOLOGY T STAGE	Spearman correlation test	N=1	higher stage	N=1	lower stage	N=0
PATHOLOGY N STAGE	Wilcoxon test	N=0
HISTOLOGICAL TYPE	Wilcoxon test	N=1	prostate adenocarcinoma acinar type	N=1	prostate adenocarcinoma other subtype	N=0
COMPLETENESS OF RESECTION	Kruskal-Wallis test	N=0
NUMBER OF LYMPH NODES	Spearman correlation test	N=0
GLEASON_SCORE_COMBINED	Spearman correlation test	N=2	higher score	N=2	lower score	N=0
GLEASON_SCORE_PRIMARY	Spearman correlation test	N=2	higher score	N=2	lower score	N=0
GLEASON_SCORE_SECONDARY	Spearman correlation test	N=0
GLEASON_SCORE	Spearman correlation test	N=2	higher score	N=2	lower score	N=0
PSA_RESULT_PREOP	Spearman correlation test	N=2	higher psa_result_preop	N=2	lower psa_result_preop	N=0
PSA_VALUE	Spearman correlation test	N=2	higher psa_value	N=2	lower psa_value	N=0
RACE	Kruskal-Wallis test	N=0

Clinical variable #1: 'AGE'

2 variables related to 'AGE'.

Table S1. Basic characteristics of clinical feature: 'AGE'


AGE	Mean (SD)	60.38 (7.1)
	Significant variables	N = 2
	pos. correlated	2
	neg. correlated	0

List of 2 variables associated with 'AGE'

Table S2. Get Full Table List of 2 variables significantly correlated to 'AGE' by Spearman correlation test

	SpearmanCorr	corrP	Q
MUTATIONRATE_NONSYNONYMOUS	0.2697	1.364e-05	2.73e-05
MUTATIONRATE_SILENT	0.1859	0.003001	0.003

Clinical variable #2: 'AGE'

No variable related to 'AGE'.

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


AGE	Mean (SD)	60.38 (7.1)
	Significant variables	N = 0

Clinical variable #3: 'PATHOLOGY.T.STAGE'

One variable related to 'PATHOLOGY.T.STAGE'.

Table S4. Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'


PATHOLOGY.T.STAGE	Mean (SD)	2.57 (0.53)
		N
	2	115
	3	134
	4	5

	Significant variables	N = 1
	pos. correlated	1
	neg. correlated	0

List of one variable associated with 'PATHOLOGY.T.STAGE'

Table S5. Get Full Table List of one variable significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

	SpearmanCorr	corrP	Q
MUTATIONRATE_NONSYNONYMOUS	0.2047	0.001032	0.00206

Clinical variable #4: 'PATHOLOGY.N.STAGE'

No variable related to 'PATHOLOGY.N.STAGE'.

Table S6. Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'


PATHOLOGY.N.STAGE	Labels	N
	class0	190
	class1	23

	Significant variables	N = 0

Clinical variable #5: 'HISTOLOGICAL.TYPE'

One variable related to 'HISTOLOGICAL.TYPE'.

Table S7. Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'


HISTOLOGICAL.TYPE	Labels	N
	PROSTATE ADENOCARCINOMA OTHER SUBTYPE	6
	PROSTATE ADENOCARCINOMA ACINAR TYPE	250

	Significant variables	N = 1
	Higher in PROSTATE ADENOCARCINOMA ACINAR TYPE	1
	Higher in PROSTATE ADENOCARCINOMA OTHER SUBTYPE	0

List of one variable associated with 'HISTOLOGICAL.TYPE'

Table S8. Get Full Table List of one variable differentially expressed by 'HISTOLOGICAL.TYPE'

	W(pos if higher in 'PROSTATE ADENOCARCINOMA ACINAR TYPE')	wilcoxontestP	Q	AUC
MUTATIONRATE_NONSYNONYMOUS	c("395", "0.04794")	c("395", "0.04794")	0.0959	0.7367

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

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

Table S9. Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'


COMPLETENESS.OF.RESECTION	Labels	N
	R0	181
	R1	52
	R2	2
	RX	8

	Significant variables	N = 0

Clinical variable #7: 'NUMBER.OF.LYMPH.NODES'

No variable related to 'NUMBER.OF.LYMPH.NODES'.

Table S10. Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'


NUMBER.OF.LYMPH.NODES	Mean (SD)	0.2 (0.72)
	Significant variables	N = 0

Clinical variable #8: 'GLEASON_SCORE_COMBINED'

2 variables related to 'GLEASON_SCORE_COMBINED'.

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


GLEASON_SCORE_COMBINED	Mean (SD)	7.35 (0.86)
	Significant variables	N = 2
	pos. correlated	2
	neg. correlated	0

List of 2 variables associated with 'GLEASON_SCORE_COMBINED'

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

	SpearmanCorr	corrP	Q
MUTATIONRATE_NONSYNONYMOUS	0.2247	0.0002903	0.000581
MUTATIONRATE_SILENT	0.2027	0.001112	0.00111

Clinical variable #9: 'GLEASON_SCORE_PRIMARY'

2 variables related to 'GLEASON_SCORE_PRIMARY'.

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


GLEASON_SCORE_PRIMARY	Mean (SD)	3.5 (0.57)
	Score	N
	2	1
	3	135
	4	111
	5	9

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

List of 2 variables associated with 'GLEASON_SCORE_PRIMARY'

Table S14. Get Full Table List of 2 variables significantly correlated to 'GLEASON_SCORE_PRIMARY' by Spearman correlation test

	SpearmanCorr	corrP	Q
MUTATIONRATE_NONSYNONYMOUS	0.2689	1.285e-05	2.57e-05
MUTATIONRATE_SILENT	0.2309	0.0001942	0.000194

Clinical variable #10: 'GLEASON_SCORE_SECONDARY'

No variable related to 'GLEASON_SCORE_SECONDARY'.

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


GLEASON_SCORE_SECONDARY	Mean (SD)	3.85 (0.65)
	Score	N
	3	76
	4	142
	5	38

	Significant variables	N = 0

Clinical variable #11: 'GLEASON_SCORE'

2 variables related to 'GLEASON_SCORE'.

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


GLEASON_SCORE	Mean (SD)	7.39 (0.88)
	Score	N
	6	20
	7	163
	8	28
	9	43
	10	2

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

List of 2 variables associated with 'GLEASON_SCORE'

Table S17. Get Full Table List of 2 variables significantly correlated to 'GLEASON_SCORE' by Spearman correlation test

	SpearmanCorr	corrP	Q
MUTATIONRATE_SILENT	0.2168	0.0004761	0.000952
MUTATIONRATE_NONSYNONYMOUS	0.2163	0.0004911	0.000952

Clinical variable #12: 'PSA_RESULT_PREOP'

2 variables related to 'PSA_RESULT_PREOP'.

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


PSA_RESULT_PREOP	Mean (SD)	10.41 (10)
	Significant variables	N = 2
	pos. correlated	2
	neg. correlated	0

List of 2 variables associated with 'PSA_RESULT_PREOP'

Table S19. Get Full Table List of 2 variables significantly correlated to 'PSA_RESULT_PREOP' by Spearman correlation test

	SpearmanCorr	corrP	Q
MUTATIONRATE_SILENT	0.1855	0.003	0.006
MUTATIONRATE_NONSYNONYMOUS	0.1466	0.01943	0.0194

Clinical variable #13: 'PSA_VALUE'

2 variables related to 'PSA_VALUE'.

Table S20. Basic characteristics of clinical feature: 'PSA_VALUE'


PSA_VALUE	Mean (SD)	1.12 (3.9)
	Significant variables	N = 2
	pos. correlated	2
	neg. correlated	0

List of 2 variables associated with 'PSA_VALUE'

Table S21. Get Full Table List of 2 variables significantly correlated to 'PSA_VALUE' by Spearman correlation test

	SpearmanCorr	corrP	Q
MUTATIONRATE_NONSYNONYMOUS	0.1916	0.004436	0.00887
MUTATIONRATE_SILENT	0.1466	0.03006	0.0301

Clinical variable #14: 'RACE'

No variable related to 'RACE'.

Table S22. Basic characteristics of clinical feature: 'RACE'


RACE	Labels	N
	ASIAN	2
	BLACK OR AFRICAN AMERICAN	7
	WHITE	147

	Significant variables	N = 0

Methods & Data

Input

Expresson data file = PRAD-TP.patients.counts_and_rates.txt
Clinical data file = PRAD-TP.merged_data.txt
Number of patients = 256
Number of variables = 2
Number of clinical features = 14

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

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

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

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] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)

[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[3] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)

[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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