Correlation between APOBEC signature variables and clinical features

Skin Cutaneous Melanoma (Metastatic)

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 APOBEC signature variables and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C15X281F

Overview

Introduction

This pipeline uses various statistical tests to identify selected clinical features related to APOBEC signature variables.

Summary

Testing the association between 3 variables and 14 clinical features across 288 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one variables.

3 variables correlated to 'MELANOMA_ULCERATION'.

TCW_TO_G+TCW_TO_T , APOBEC_MUTLOAD_MINESTIMATE , [TCW_TO_G+TCW_TO_T]_PER_MUT

2 variables correlated to 'GENDER'.

TCW_TO_G+TCW_TO_T , APOBEC_MUTLOAD_MINESTIMATE

3 variables correlated to 'RACE'.

TCW_TO_G+TCW_TO_T , APOBEC_MUTLOAD_MINESTIMATE , [TCW_TO_G+TCW_TO_T]_PER_MUT

No variables correlated to 'TIME_FROM_SPECIMEN_DX_TO_DEATH_OR_LAST_FUP', 'DAYS_TO_DEATH_OR_LAST_FUP', 'YEARS_TO_BIRTH', 'PRIMARY_SITE_OF_DISEASE', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'MELANOMA_PRIMARY_KNOWN', 'BRESLOW_THICKNESS', 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	Associated with		Associated with
TIME_FROM_SPECIMEN_DX_TO_DEATH_OR_LAST_FUP	Cox regression test	N=0
DAYS_TO_DEATH_OR_LAST_FUP	Cox regression test	N=0
YEARS_TO_BIRTH	Spearman correlation test	N=0
PRIMARY_SITE_OF_DISEASE	Kruskal-Wallis test	N=0
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
MELANOMA_ULCERATION	Wilcoxon test	N=3	yes	N=3	no	N=0
MELANOMA_PRIMARY_KNOWN	Wilcoxon test	N=0
BRESLOW_THICKNESS	Spearman correlation test	N=0
GENDER	Wilcoxon test	N=2	male	N=2	female	N=0
RACE	Kruskal-Wallis test	N=3
ETHNICITY	Wilcoxon test	N=0

Clinical variable #1: 'TIME_FROM_SPECIMEN_DX_TO_DEATH_OR_LAST_FUP'

No variable related to 'TIME_FROM_SPECIMEN_DX_TO_DEATH_OR_LAST_FUP'.

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


TIME_FROM_SPECIMEN_DX_TO_DEATH_OR_LAST_FUP	Duration (Months)	0-346.5 (median=47)
	censored	N = 96
	death	N = 97

	Significant markers	N = 0

Clinical variable #2: 'DAYS_TO_DEATH_OR_LAST_FUP'

No variable related to 'DAYS_TO_DEATH_OR_LAST_FUP'.

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


DAYS_TO_DEATH_OR_LAST_FUP	Duration (Months)	0.2-369.9 (median=53.2)
	censored	N = 137
	death	N = 150

	Significant markers	N = 0

Clinical variable #3: 'YEARS_TO_BIRTH'

No variable related to 'YEARS_TO_BIRTH'.

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


YEARS_TO_BIRTH	Mean (SD)	55.9 (16)
	Significant variables	N = 0

Clinical variable #4: 'PRIMARY_SITE_OF_DISEASE'

No variable related to 'PRIMARY_SITE_OF_DISEASE'.

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


PRIMARY_SITE_OF_DISEASE	Labels	N
	DISTANT METASTASIS	41
	PRIMARY TUMOR	5
	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE	61
	REGIONAL LYMPH NODE	180

	Significant variables	N = 0

Clinical variable #5: 'NEOPLASM_DISEASESTAGE'

No variable related to 'NEOPLASM_DISEASESTAGE'.

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


NEOPLASM_DISEASESTAGE	Labels	N
	I OR II NOS	10
	STAGE 0	6
	STAGE I	24
	STAGE IA	10
	STAGE IB	25
	STAGE II	17
	STAGE IIA	10
	STAGE IIB	14
	STAGE IIC	9
	STAGE III	34
	STAGE IIIA	14
	STAGE IIIB	24
	STAGE IIIC	49
	STAGE IV	16

	Significant variables	N = 0

Clinical variable #6: '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)	2.42 (1.3)
		N
	T0	23
	T1	30
	T2	62
	T3	57
	T4	58

	Significant variables	N = 0

Clinical variable #7: '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)	0.89 (1.1)
		N
	N0	140
	N1	52
	N2	33
	N3	39

	Significant variables	N = 0

Clinical variable #8: '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	255
	class1	17

	Significant variables	N = 0

Clinical variable #9: 'MELANOMA_ULCERATION'

3 variables related to 'MELANOMA_ULCERATION'.

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


MELANOMA_ULCERATION	Labels	N
	NO	106
	YES	73

	Significant variables	N = 3
	Higher in YES	3
	Higher in NO	0

List of 3 variables associated with 'MELANOMA_ULCERATION'

Table S10. Get Full Table List of 3 variables differentially expressed by 'MELANOMA_ULCERATION'

	W(pos if higher in 'YES')	wilcoxontestP	Q	AUC
TCW_TO_G+TCW_TO_T	3037	0.01465	0.0231	0.6075
APOBEC_MUTLOAD_MINESTIMATE	3052.5	0.01537	0.0231	0.6055
[TCW_TO_G+TCW_TO_T]_PER_MUT	3159	0.03729	0.0373	0.5918

Clinical variable #10: 'MELANOMA_PRIMARY_KNOWN'

No variable related to 'MELANOMA_PRIMARY_KNOWN'.

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


MELANOMA_PRIMARY_KNOWN	Labels	N
	NO	36
	YES	251

	Significant variables	N = 0

Clinical variable #11: 'BRESLOW_THICKNESS'

No variable related to 'BRESLOW_THICKNESS'.

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


BRESLOW_THICKNESS	Mean (SD)	3.58 (4.9)
	Significant variables	N = 0

Clinical variable #12: 'GENDER'

2 variables related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	108
	MALE	180

	Significant variables	N = 2
	Higher in MALE	2
	Higher in FEMALE	0

List of 2 variables associated with 'GENDER'

Table S14. Get Full Table List of 2 variables differentially expressed by 'GENDER'. 0 significant gene(s) located in sex chromosomes is(are) filtered out.

	W(pos if higher in 'MALE')	wilcoxontestP	Q	AUC
TCW_TO_G+TCW_TO_T	11769.5	0.002746	0.00804	0.6054
APOBEC_MUTLOAD_MINESTIMATE	11601	0.00536	0.00804	0.5968

Clinical variable #13: 'RACE'

3 variables related to 'RACE'.

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


RACE	Labels	N
	ASIAN	5
	BLACK OR AFRICAN AMERICAN	1
	WHITE	282

	Significant variables	N = 3

List of 3 variables associated with 'RACE'

Table S16. Get Full Table List of 3 variables differentially expressed by 'RACE'

	kruskal_wallis_P	Q
TCW_TO_G+TCW_TO_T	0.002372	0.00712
APOBEC_MUTLOAD_MINESTIMATE	0.03486	0.0463
[TCW_TO_G+TCW_TO_T]_PER_MUT	0.04626	0.0463

Clinical variable #14: 'ETHNICITY'

No variable related to 'ETHNICITY'.

Table S17. Basic characteristics of clinical feature: 'ETHNICITY'


ETHNICITY	Labels	N
	HISPANIC OR LATINO	3
	NOT HISPANIC OR LATINO	281

	Significant variables	N = 0

Methods & Data

Input

Expresson data file = APOBEC_for_clinical.correlaion.input.continuous.txt
Clinical data file = SKCM-TM.merged_data.txt
Number of patients = 288
Number of variables = 3
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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