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 9 clinical features across 473 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one variables.

  • 2 variables correlated to 'AGE'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • 1 variable correlated to 'GENDER'.

    • MUTATIONRATE_SILENT

  • 1 variable correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

    • MUTATIONRATE_SILENT

  • No variables correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'AGE_mutation.rate', 'HISTOLOGICAL_TYPE', 'RADIATIONS_RADIATION_REGIMENINDICATION', 'RACE', 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
DAYS_TO_DEATH_OR_LAST_FUP Cox regression test   N=0        
AGE Spearman correlation test N=2 older N=2 younger N=0
AGE Linear Regression Analysis   N=0        
GENDER Wilcoxon test N=1 male N=1 female N=0
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test N=1 higher score N=0 lower score N=1
HISTOLOGICAL_TYPE Kruskal-Wallis test   N=0        
RADIATIONS_RADIATION_REGIMENINDICATION Wilcoxon test   N=0        
RACE Kruskal-Wallis test   N=0        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

No variable related to 'DAYS_TO_DEATH_OR_LAST_FUP'.

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

DAYS_TO_DEATH_OR_LAST_FUP Duration (Months) 0-211.2 (median=18.6)
  censored N = 379
  death N = 93
     
  Significant variables N = 0
Clinical variable #2: 'AGE'

2 variables related to 'AGE'.

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

AGE Mean (SD) 42.97 (13)
  Significant variables N = 2
  pos. correlated 2
  neg. correlated 0
List of 2 variables associated with 'AGE'

Table S3.  Get Full Table List of 2 variables significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
MUTATIONRATE_NONSYNONYMOUS 0.5428 1.599e-37 3.2e-37
MUTATIONRATE_SILENT 0.517 1.297e-33 1.3e-33
Clinical variable #3: 'AGE'

No variable related to 'AGE'.

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

AGE Mean (SD) 42.97 (13)
  Significant variables N = 0
Clinical variable #4: 'GENDER'

One variable related to 'GENDER'.

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

GENDER Labels N
  FEMALE 207
  MALE 266
     
  Significant variables N = 1
  Higher in MALE 1
  Higher in FEMALE 0
List of one variable associated with 'GENDER'

Table S6.  Get Full Table List of one variable 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 22725 0.00112 0.00224 0.5873
Clinical variable #5: 'KARNOFSKY_PERFORMANCE_SCORE'

One variable related to 'KARNOFSKY_PERFORMANCE_SCORE'.

Table S7.  Basic characteristics of clinical feature: 'KARNOFSKY_PERFORMANCE_SCORE'

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 87.66 (12)
  Significant variables N = 1
  pos. correlated 0
  neg. correlated 1
List of one variable associated with 'KARNOFSKY_PERFORMANCE_SCORE'

Table S8.  Get Full Table List of one variable significantly correlated to 'KARNOFSKY_PERFORMANCE_SCORE' by Spearman correlation test

SpearmanCorr corrP Q
MUTATIONRATE_SILENT -0.122 0.04566 0.0913
Clinical variable #6: 'HISTOLOGICAL_TYPE'

No variable related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  ASTROCYTOMA 175
  OLIGOASTROCYTOMA 119
  OLIGODENDROGLIOMA 179
     
  Significant variables N = 0
Clinical variable #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

No variable related to 'RADIATIONS_RADIATION_REGIMENINDICATION'.

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

RADIATIONS_RADIATION_REGIMENINDICATION Labels N
  NO 92
  YES 381
     
  Significant variables N = 0
Clinical variable #8: 'RACE'

No variable related to 'RACE'.

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

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 8
  BLACK OR AFRICAN AMERICAN 15
  WHITE 438
     
  Significant variables N = 0
Clinical variable #9: 'ETHNICITY'

No variable related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 18
  NOT HISPANIC OR LATINO 420
     
  Significant variables N = 0
Methods & Data
Input

  • Expresson data file = LGG-TP.patients.counts_and_rates.txt

  • Clinical data file = LGG-TP.merged_data.txt

  • Number of patients = 473

  • Number of variables = 2

  • Number of clinical features = 9

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.

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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