Correlation between mutation rate and clinical features
Breast Invasive Carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
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/C1W66K3V
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 13 clinical features across 977 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 8 clinical features related to at least one variables.

  • 2 variables correlated to 'AGE'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • 2 variables correlated to 'AGE_mutation.rate'.

    • MUTATIONRATE_SILENT ,  MUTATIONRATE_NONSYNONYMOUS

  • 2 variables correlated to 'PATHOLOGY_T_STAGE'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • 1 variable correlated to 'PATHOLOGY_N_STAGE'.

    • MUTATIONRATE_NONSYNONYMOUS

  • 2 variables correlated to 'HISTOLOGICAL_TYPE'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • 2 variables correlated to 'NUMBER_OF_LYMPH_NODES'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • 2 variables correlated to 'RACE'.

    • MUTATIONRATE_SILENT ,  MUTATIONRATE_NONSYNONYMOUS

  • 1 variable correlated to 'ETHNICITY'.

    • MUTATIONRATE_SILENT

  • No variables correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'PATHOLOGIC_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', and 'RADIATION_THERAPY'.

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=2        
PATHOLOGIC_STAGE Kruskal-Wallis test   N=0        
PATHOLOGY_T_STAGE Spearman correlation test N=2 higher stage N=2 lower stage N=0
PATHOLOGY_N_STAGE Spearman correlation test N=1 higher stage N=0 lower stage N=1
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test   N=0        
RADIATION_THERAPY Wilcoxon test   N=0        
HISTOLOGICAL_TYPE Kruskal-Wallis test N=2        
NUMBER_OF_LYMPH_NODES Spearman correlation test N=2 higher number_of_lymph_nodes N=0 lower number_of_lymph_nodes N=2
RACE Kruskal-Wallis test N=2        
ETHNICITY Wilcoxon test N=1 not hispanic or latino N=1 hispanic or latino 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) 0-282.9 (median=31)
  censored N = 837
  death N = 139
     
  Significant variables N = 0
Clinical variable #2: 'AGE'

2 variables related to 'AGE'.

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

AGE Mean (SD) 58.74 (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 0.1245 0.0001058 0.000212
MUTATIONRATE_SILENT 0.112 0.0004926 0.000493
Clinical variable #3: 'AGE'

2 variables related to 'AGE'.

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

AGE Mean (SD) 58.74 (13)
  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_SILENT 0.00915 9.89 0.00171 1.77e-06 962 1.36e-08 ( -2.83e-07 ) 0.00171 ( 0.279 )
MUTATIONRATE_NONSYNONYMOUS 0.00881 9.56 0.00204 5.97e-06 962 4.53e-08 ( -8.8e-07 ) 0.00204 ( 0.318 )
Clinical variable #4: 'PATHOLOGIC_STAGE'

No variable related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  STAGE I 88
  STAGE IA 71
  STAGE IB 6
  STAGE II 4
  STAGE IIA 326
  STAGE IIB 227
  STAGE III 2
  STAGE IIIA 137
  STAGE IIIB 24
  STAGE IIIC 57
  STAGE IV 15
  STAGE X 12
     
  Significant variables N = 0
Clinical variable #5: 'PATHOLOGY_T_STAGE'

2 variables related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 1.93 (0.72)
  N
  T1 257
  T2 564
  T3 120
  T4 35
     
  Significant variables N = 2
  pos. correlated 2
  neg. correlated 0
List of 2 variables associated with 'PATHOLOGY_T_STAGE'

Table S8.  Get Full Table List of 2 variables significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
MUTATIONRATE_NONSYNONYMOUS 0.126 7.953e-05 9.54e-05
MUTATIONRATE_SILENT 0.1246 9.538e-05 9.54e-05
Clinical variable #6: 'PATHOLOGY_N_STAGE'

One variable related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 0.77 (0.91)
  N
  N0 462
  N1 325
  N2 107
  N3 68
     
  Significant variables N = 1
  pos. correlated 0
  neg. correlated 1
List of one variable associated with 'PATHOLOGY_N_STAGE'

Table S10.  Get Full Table List of one variable significantly correlated to 'PATHOLOGY_N_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
MUTATIONRATE_NONSYNONYMOUS -0.0828 0.0102 0.0204
Clinical variable #7: '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 842
  class1 16
     
  Significant variables N = 0
Clinical variable #8: 'GENDER'

No variable related to 'GENDER'.

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

GENDER Labels N
  FEMALE 968
  MALE 9
     
  Significant variables N = 0
Clinical variable #9: 'RADIATION_THERAPY'

No variable related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 387
  YES 502
     
  Significant variables N = 0
Clinical variable #10: 'HISTOLOGICAL_TYPE'

2 variables related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  INFILTRATING CARCINOMA NOS 1
  INFILTRATING DUCTAL CARCINOMA 711
  INFILTRATING LOBULAR CARCINOMA 172
  MEDULLARY CARCINOMA 5
  METAPLASTIC CARCINOMA 6
  MIXED HISTOLOGY (PLEASE SPECIFY) 27
  MUCINOUS CARCINOMA 14
  OTHER, SPECIFY 40
     
  Significant variables N = 2
List of 2 variables associated with 'HISTOLOGICAL_TYPE'

Table S15.  Get Full Table List of 2 variables differentially expressed by 'HISTOLOGICAL_TYPE'

kruskal_wallis_P Q
MUTATIONRATE_NONSYNONYMOUS 5.653e-05 0.000113
MUTATIONRATE_SILENT 0.006728 0.00673
Clinical variable #11: 'NUMBER_OF_LYMPH_NODES'

2 variables related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 2.34 (4.6)
  Significant variables N = 2
  pos. correlated 0
  neg. correlated 2
List of 2 variables associated with 'NUMBER_OF_LYMPH_NODES'

Table S17.  Get Full Table List of 2 variables significantly correlated to 'NUMBER_OF_LYMPH_NODES' by Spearman correlation test

SpearmanCorr corrP Q
MUTATIONRATE_NONSYNONYMOUS -0.1146 0.0009727 0.00195
MUTATIONRATE_SILENT -0.0813 0.01938 0.0194
Clinical variable #12: 'RACE'

2 variables related to 'RACE'.

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

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 57
  BLACK OR AFRICAN AMERICAN 121
  WHITE 705
     
  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_SILENT 0.01817 0.0218
MUTATIONRATE_NONSYNONYMOUS 0.02181 0.0218
Clinical variable #13: 'ETHNICITY'

One variable related to 'ETHNICITY'.

Table S20.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 34
  NOT HISPANIC OR LATINO 772
     
  Significant variables N = 1
  Higher in NOT HISPANIC OR LATINO 1
  Higher in HISPANIC OR LATINO 0
List of one variable associated with 'ETHNICITY'

Table S21.  Get Full Table List of one variable differentially expressed by 'ETHNICITY'

W(pos if higher in 'NOT HISPANIC OR LATINO') wilcoxontestP Q AUC
MUTATIONRATE_SILENT c("15783", "0.04539") c("15783", "0.04539") 0.0908 0.6013
Methods & Data
Input
  • Expresson data file = BRCA-TP.patients.counts_and_rates.txt

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

  • Number of patients = 977

  • Number of variables = 2

  • Number of clinical features = 13

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)