Correlation between mutation rate and clinical features
Skin Cutaneous Melanoma (Metastatic)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1BK1BT9
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between mutation rate and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1BK1BT9
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 290 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 6 clinical features related to at least one variables.

  • 2 variables correlated to 'TIME_FROM_SPECIMEN_DX_TO_DEATH_OR_LAST_FUP'.

    • MUTATIONRATE_SILENT ,  MUTATIONRATE_NONSYNONYMOUS

  • 2 variables correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • 1 variable correlated to 'AGE_mutation.rate'.

    • MUTATIONRATE_NONSYNONYMOUS

  • 2 variables correlated to 'MELANOMA_ULCERATION'.

    • MUTATIONRATE_SILENT ,  MUTATIONRATE_NONSYNONYMOUS

  • 2 variables correlated to 'GENDER'.

    • MUTATIONRATE_SILENT ,  MUTATIONRATE_NONSYNONYMOUS

  • 2 variables correlated to 'RACE'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • No variables correlated to 'AGE', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'MELANOMA_PRIMARY_KNOWN', 'BRESLOW_THICKNESS', 'RADIATION_THERAPY', 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=2   N=NA   N=NA
DAYS_TO_DEATH_OR_LAST_FUP Cox regression test N=2   N=NA   N=NA
AGE Spearman correlation test   N=0        
AGE Linear Regression Analysis N=1        
PATHOLOGIC_STAGE 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=0        
BRESLOW_THICKNESS Spearman correlation test   N=0        
GENDER Wilcoxon test N=2 male N=2 female N=0
RADIATION_THERAPY Wilcoxon test   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'

2 variables 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=47.3)
  censored N = 97
  death N = 103
     
  Significant variables N = 2
  associated with shorter survival NA
  associated with longer survival NA
List of 2 variables associated with 'TIME_FROM_SPECIMEN_DX_TO_DEATH_OR_LAST_FUP'

Table S2.  Get Full Table List of 2 variables significantly associated with 'Time from Specimen Diagnosis to Death' by Cox regression test. For the survival curves, it compared quantile intervals at c(0, 0.25, 0.50, 0.75, 1) and did not try survival analysis if there is only one interval.

logrank_P Q C_index
MUTATIONRATE_SILENT 1.82e-05 3.1e-05 0.418
MUTATIONRATE_NONSYNONYMOUS 3.06e-05 3.1e-05 0.417
Clinical variable #2: 'DAYS_TO_DEATH_OR_LAST_FUP'

2 variables related to 'DAYS_TO_DEATH_OR_LAST_FUP'.

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

DAYS_TO_DEATH_OR_LAST_FUP Duration (Months) 0.2-369.9 (median=53.9)
  censored N = 128
  death N = 161
     
  Significant variables N = 2
  associated with shorter survival NA
  associated with longer survival NA
List of 2 variables associated with 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S4.  Get Full Table List of 2 variables significantly associated with 'Time to Death' by Cox regression test. For the survival curves, it compared quantile intervals at c(0, 0.25, 0.50, 0.75, 1) and did not try survival analysis if there is only one interval.

logrank_P Q C_index
MUTATIONRATE_NONSYNONYMOUS 6.52e-06 1.3e-05 0.42
MUTATIONRATE_SILENT 5.07e-05 5.1e-05 0.421
Clinical variable #3: 'AGE'

No variable related to 'AGE'.

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

AGE Mean (SD) 55.92 (16)
  Significant variables N = 0
Clinical variable #4: 'AGE'

One variable related to 'AGE'.

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

AGE Mean (SD) 55.92 (16)
  Significant variables N = 1
List of one variable associated with 'AGE'

Table S7.  Get Full Table List of one variable 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 0.0107 4.08 0.0443 3.07e-05 283 2.37e-07 ( 2.5e-06 ) 0.0443 ( 0.714 )
Clinical variable #5: 'PATHOLOGIC_STAGE'

No variable related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  I/II NOS 10
  STAGE 0 6
  STAGE I 25
  STAGE IA 10
  STAGE IB 25
  STAGE II 18
  STAGE IIA 10
  STAGE IIB 14
  STAGE IIC 10
  STAGE III 36
  STAGE IIIA 14
  STAGE IIIB 26
  STAGE IIIC 43
  STAGE IV 16
     
  Significant variables N = 0
Clinical variable #6: 'PATHOLOGY_T_STAGE'

No variable related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 2.41 (1.3)
  N
  T0 23
  T1 30
  T2 62
  T3 60
  T4 55
     
  Significant variables N = 0
Clinical variable #7: 'PATHOLOGY_N_STAGE'

No variable related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 0.85 (1.1)
  N
  N0 143
  N1 53
  N2 34
  N3 35
     
  Significant variables N = 0
Clinical variable #8: 'PATHOLOGY_M_STAGE'

No variable related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 256
  class1 17
     
  Significant variables N = 0
Clinical variable #9: 'MELANOMA_ULCERATION'

2 variables related to 'MELANOMA_ULCERATION'.

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

MELANOMA_ULCERATION Labels N
  NO 107
  YES 73
     
  Significant variables N = 2
  Higher in YES 2
  Higher in NO 0
List of 2 variables associated with 'MELANOMA_ULCERATION'

Table S13.  Get Full Table List of 2 variables differentially expressed by 'MELANOMA_ULCERATION'

W(pos if higher in 'YES') wilcoxontestP Q AUC
MUTATIONRATE_SILENT 3082 0.0165 0.0176 0.6054
MUTATIONRATE_NONSYNONYMOUS 3090 0.01758 0.0176 0.6044
Clinical variable #10: 'MELANOMA_PRIMARY_KNOWN'

No variable related to 'MELANOMA_PRIMARY_KNOWN'.

Table S14.  Basic characteristics of clinical feature: 'MELANOMA_PRIMARY_KNOWN'

MELANOMA_PRIMARY_KNOWN Labels N
  NO 37
  YES 253
     
  Significant variables N = 0
Clinical variable #11: 'BRESLOW_THICKNESS'

No variable related to 'BRESLOW_THICKNESS'.

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

BRESLOW_THICKNESS Mean (SD) 3.57 (4.9)
  Significant variables N = 0
Clinical variable #12: 'GENDER'

2 variables related to 'GENDER'.

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

GENDER Labels N
  FEMALE 109
  MALE 181
     
  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_SILENT 11906 0.00317 0.00328 0.6035
MUTATIONRATE_NONSYNONYMOUS 11899 0.003275 0.00328 0.6031
Clinical variable #13: 'RADIATION_THERAPY'

No variable related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 254
  YES 35
     
  Significant variables N = 0
Clinical variable #14: 'RACE'

2 variables related to 'RACE'.

Table S19.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  ASIAN 5
  BLACK OR AFRICAN AMERICAN 1
  WHITE 284
     
  Significant variables N = 2
List of 2 variables associated with 'RACE'

Table S20.  Get Full Table List of 2 variables differentially expressed by 'RACE'

kruskal_wallis_P Q
MUTATIONRATE_NONSYNONYMOUS 0.002749 0.00333
MUTATIONRATE_SILENT 0.003329 0.00333
Clinical variable #15: 'ETHNICITY'

No variable related to 'ETHNICITY'.

Table S21.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 3
  NOT HISPANIC OR LATINO 283
     
  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 = 290

  • Number of variables = 2

  • Number of clinical features = 15

Selected clinical features

  • Further details on clinical features selected for this analysis, please find a documentation on selected CDEs (Clinical Data Elements). The first column of the file is a formula to convert values and the second column is a clinical parameter name.

  • 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, logrank test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values comparing quantile intervals using the 'coxph' function in R. Kaplan-Meier survival curves were plotted using quantile intervals at c(0, 0.25, 0.50, 0.75, 1). If there is only one interval group, it will not try survival analysis.

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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