Correlation between mutation rate 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 mutation rate and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1FF3RG3
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 15 clinical features across 288 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 5 clinical features related to at least one variables.

  • 2 variables correlated to 'AGE_mutation.rate'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • 2 variables correlated to 'MELANOMA_ULCERATION'.

    • MUTATIONRATE_SILENT ,  MUTATIONRATE_NONSYNONYMOUS

  • 1 variable correlated to 'MELANOMA_PRIMARY_KNOWN'.

    • MUTATIONRATE_NONSYNONYMOUS

  • 2 variables correlated to 'GENDER'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • 2 variables correlated to 'RACE'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • No variables correlated to 'TIME_FROM_SPECIMEN_DX_TO_DEATH_OR_LAST_FUP', 'DAYS_TO_DEATH_OR_LAST_FUP', 'AGE', 'PRIMARY_SITE_OF_DISEASE', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', '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        
AGE Spearman correlation test   N=0        
AGE Linear Regression Analysis N=2        
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=2 yes N=2 no N=0
MELANOMA_PRIMARY_KNOWN Wilcoxon test N=1 yes N=1 no 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=2        
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 variables 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 variables N = 0
Clinical variable #3: 'AGE'

No variable related to 'AGE'.

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

AGE Mean (SD) 55.9 (16)
  Significant variables N = 0
Clinical variable #4: 'AGE'

2 variables related to 'AGE'.

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

AGE Mean (SD) 55.9 (16)
  Significant variables N = 2
List of 2 variables associated with 'AGE'

Table S5.  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 0.011 4.14 0.0428 3.43e-05 281 2.67e-07 ( 2.56e-06 ) 0.0428 ( 0.737 )
MUTATIONRATE_SILENT 0.0102 3.89 0.0494 1.9e-05 281 1.43e-07 ( 8.1e-07 ) 0.0494 ( 0.847 )
Clinical variable #5: 'PRIMARY_SITE_OF_DISEASE'

No variable related to 'PRIMARY_SITE_OF_DISEASE'.

Table S6.  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 #6: 'NEOPLASM_DISEASESTAGE'

No variable related to 'NEOPLASM_DISEASESTAGE'.

Table S7.  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 #7: 'PATHOLOGY_T_STAGE'

No variable related to 'PATHOLOGY_T_STAGE'.

Table S8.  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 #8: 'PATHOLOGY_N_STAGE'

No variable related to 'PATHOLOGY_N_STAGE'.

Table S9.  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 #9: 'PATHOLOGY_M_STAGE'

No variable related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 255
  class1 17
     
  Significant variables N = 0
Clinical variable #10: 'MELANOMA_ULCERATION'

2 variables related to 'MELANOMA_ULCERATION'.

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

MELANOMA_ULCERATION Labels N
  NO 106
  YES 73
     
  Significant variables N = 2
  Higher in YES 2
  Higher in NO 0
List of 2 variables associated with 'MELANOMA_ULCERATION'

Table S12.  Get Full Table List of 2 variables differentially expressed by 'MELANOMA_ULCERATION'

W(pos if higher in 'YES') wilcoxontestP Q AUC
MUTATIONRATE_SILENT 3080 0.02064 0.021 0.602
MUTATIONRATE_NONSYNONYMOUS 3082 0.02097 0.021 0.6017
Clinical variable #11: 'MELANOMA_PRIMARY_KNOWN'

One variable related to 'MELANOMA_PRIMARY_KNOWN'.

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

MELANOMA_PRIMARY_KNOWN Labels N
  NO 36
  YES 251
     
  Significant variables N = 1
  Higher in YES 1
  Higher in NO 0
List of one variable associated with 'MELANOMA_PRIMARY_KNOWN'

Table S14.  Get Full Table List of one variable differentially expressed by 'MELANOMA_PRIMARY_KNOWN'

W(pos if higher in 'YES') wilcoxontestP Q AUC
MUTATIONRATE_NONSYNONYMOUS 3571 0.0421 0.0711 0.6048
Clinical variable #12: 'BRESLOW_THICKNESS'

No variable related to 'BRESLOW_THICKNESS'.

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

BRESLOW_THICKNESS Mean (SD) 3.58 (4.9)
  Significant variables N = 0
Clinical variable #13: 'GENDER'

2 variables related to 'GENDER'.

Table S16.  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 S17.  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
MUTATIONRATE_NONSYNONYMOUS 11651 0.004782 0.00517 0.5993
MUTATIONRATE_SILENT 11634 0.005165 0.00517 0.5985
Clinical variable #14: 'RACE'

2 variables related to 'RACE'.

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

RACE Labels N
  ASIAN 5
  BLACK OR AFRICAN AMERICAN 1
  WHITE 282
     
  Significant variables N = 2
List of 2 variables associated with 'RACE'

Table S19.  Get Full Table List of 2 variables differentially expressed by 'RACE'

kruskal_wallis_P Q
MUTATIONRATE_NONSYNONYMOUS 0.002802 0.00302
MUTATIONRATE_SILENT 0.003023 0.00302
Clinical variable #15: 'ETHNICITY'

No variable related to 'ETHNICITY'.

Table S20.  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 = SKCM-TM.patients.counts_and_rates.txt

  • Clinical data file = SKCM-TM.merged_data.txt

  • Number of patients = 288

  • Number of variables = 2

  • Number of clinical features = 15

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)