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 15 clinical features across 276 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one variables.

  • 2 variables correlated to 'AGE_mutation.rate'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • 1 variable correlated to 'MELANOMA.ULCERATION'.

    • MUTATIONRATE_SILENT

  • 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 Diagnosis to Death', 'Time to Death', 'AGE', '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 Diagnosis to Death Cox regression test   N=0        
Time to Death 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 Kruskal-Wallis test   N=0        
MELANOMA ULCERATION Wilcoxon test N=1 yes N=1 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=2        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'Time from Specimen Diagnosis to Death'

No variable related to 'Time from Specimen Diagnosis to Death'.

Table S1.  Basic characteristics of clinical feature: 'Time from Specimen Diagnosis to Death'

Time from Specimen Diagnosis to Death Duration (Months) 0.1-125.7 (median=16.9)
  censored N = 126
  death N = 140
     
  Significant variables N = 0
Clinical variable #2: 'Time to Death'

No variable related to 'Time to Death'.

Table S2.  Basic characteristics of clinical feature: 'Time to Death'

Time to Death Duration (Months) 0.2-357.4 (median=51.8)
  censored N = 129
  death N = 141
     
  Significant variables N = 0
Clinical variable #3: 'AGE'

No variable related to 'AGE'.

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

AGE Mean (SD) 55.82 (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) 55.82 (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 0.0126 4.45 0.0358 3.45e-05 269 2.82e-07 ( 1.98e-06 ) 0.0358 ( 0.799 )
MUTATIONRATE_SILENT 0.011 4.01 0.0464 1.87e-05 269 1.45e-07 ( 5.11e-07 ) 0.0464 ( 0.903 )
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 39
  PRIMARY TUMOR 5
  REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE 60
  REGIONAL LYMPH NODE 171
     
  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 24
  STAGE II 16
  STAGE IIA 10
  STAGE IIB 14
  STAGE IIC 9
  STAGE III 32
  STAGE IIIA 13
  STAGE IIIB 23
  STAGE IIIC 46
  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
  0 22
  1 30
  2 59
  3 54
  4 57
     
  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) 0.87 (1.1)
  N
  0 139
  1 48
  2 33
  3 36
     
  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
  M0 244
  M1 4
  M1A 3
  M1B 2
  M1C 8
     
  Significant variables N = 0
Clinical variable #10: 'MELANOMA.ULCERATION'

One variable related to 'MELANOMA.ULCERATION'.

Table S11.  Basic characteristics of clinical feature: 'MELANOMA.ULCERATION'

MELANOMA.ULCERATION Labels N
  NO 103
  YES 68
     
  Significant variables N = 1
  Higher in YES 1
  Higher in NO 0
List of one variable associated with 'MELANOMA.ULCERATION'

Table S12.  Get Full Table List of one variable differentially expressed by 'MELANOMA.ULCERATION'

W(pos if higher in 'YES') wilcoxontestP Q AUC
MUTATIONRATE_SILENT 2871 0.0466 0.0932 0.5901
Clinical variable #11: 'MELANOMA.PRIMARY.KNOWN'

No variable related to 'MELANOMA.PRIMARY.KNOWN'.

Table S13.  Basic characteristics of clinical feature: 'MELANOMA.PRIMARY.KNOWN'

MELANOMA.PRIMARY.KNOWN Labels N
  NO 36
  YES 239
     
  Significant variables N = 0
Clinical variable #12: 'BRESLOW.THICKNESS'

No variable related to 'BRESLOW.THICKNESS'.

Table S14.  Basic characteristics of clinical feature: 'BRESLOW.THICKNESS'

BRESLOW.THICKNESS Mean (SD) 3.61 (5)
  Significant variables N = 0
Clinical variable #13: 'GENDER'

2 variables related to 'GENDER'.

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

GENDER Labels N
  FEMALE 106
  MALE 170
     
  Significant variables N = 2
  Higher in MALE 2
  Higher in FEMALE 0
List of 2 variables associated with 'GENDER'

Table S16.  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 10725 0.007853 0.0157 0.5952
MUTATIONRATE_SILENT 10715 0.008221 0.0157 0.5946
Clinical variable #14: 'RACE'

2 variables related to 'RACE'.

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

RACE Labels N
  ASIAN 3
  BLACK OR AFRICAN AMERICAN 1
  WHITE 272
     
  Significant variables N = 2
List of 2 variables associated with 'RACE'

Table S18.  Get Full Table List of 2 variables differentially expressed by 'RACE'

ANOVA_P Q
MUTATIONRATE_NONSYNONYMOUS 0.03031 0.0543
MUTATIONRATE_SILENT 0.02715 0.0543
Clinical variable #15: 'ETHNICITY'

No variable related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 3
  NOT HISPANIC OR LATINO 269
     
  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 = 276

  • Number of variables = 2

  • Number of clinical features = 15

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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