Correlation between mutation rate and clinical features
Prostate Adenocarcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1C53K45
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/C1C53K45
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 12 clinical features across 332 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'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • 2 variables correlated to 'PATHOLOGY_T_STAGE'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • 1 variable correlated to 'RADIATION_THERAPY'.

    • MUTATIONRATE_SILENT

  • 2 variables correlated to 'GLEASON_SCORE'.

    • MUTATIONRATE_SILENT ,  MUTATIONRATE_NONSYNONYMOUS

  • 2 variables correlated to 'PSA_VALUE'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • No variables correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'AGE_mutation.rate', 'PATHOLOGY_N_STAGE', 'HISTOLOGICAL_TYPE', 'RESIDUAL_TUMOR', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.

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        
PATHOLOGY_T_STAGE Spearman correlation test N=2 higher stage N=2 lower stage N=0
PATHOLOGY_N_STAGE Wilcoxon test   N=0        
RADIATION_THERAPY Wilcoxon test N=1 yes N=1 no N=0
HISTOLOGICAL_TYPE Wilcoxon test   N=0        
RESIDUAL_TUMOR Kruskal-Wallis test   N=0        
NUMBER_OF_LYMPH_NODES Spearman correlation test   N=0        
GLEASON_SCORE Spearman correlation test N=2 higher score N=2 lower score N=0
PSA_VALUE Spearman correlation test N=2 higher psa_value N=2 lower psa_value N=0
RACE Kruskal-Wallis 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) 0.7-165.2 (median=29)
  censored N = 324
  death N = 7
     
  Significant variables N = 0
Clinical variable #2: 'AGE'

2 variables related to 'AGE'.

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

AGE Mean (SD) 60.63 (6.9)
  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 0.2934 7.452e-08 1.49e-07
MUTATIONRATE_SILENT 0.2121 0.0001195 0.000119
Clinical variable #3: 'AGE'

No variable related to 'AGE'.

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

AGE Mean (SD) 60.63 (6.9)
  Significant variables N = 0
Clinical variable #4: 'PATHOLOGY_T_STAGE'

2 variables related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 2.63 (0.52)
  N
  T2 128
  T3 194
  T4 6
     
  Significant variables N = 2
  pos. correlated 2
  neg. correlated 0
List of 2 variables associated with 'PATHOLOGY_T_STAGE'

Table S6.  Get Full Table List of 2 variables significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
MUTATIONRATE_NONSYNONYMOUS 0.1434 0.009314 0.0186
MUTATIONRATE_SILENT 0.1122 0.0422 0.0422
Clinical variable #5: 'PATHOLOGY_N_STAGE'

No variable related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Labels N
  N0 233
  N1 52
     
  Significant variables N = 0
Clinical variable #6: 'RADIATION_THERAPY'

One variable related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 252
  YES 36
     
  Significant variables N = 1
  Higher in YES 1
  Higher in NO 0
List of one variable associated with 'RADIATION_THERAPY'

Table S9.  Get Full Table List of one variable differentially expressed by 'RADIATION_THERAPY'

W(pos if higher in 'YES') wilcoxontestP Q AUC
MUTATIONRATE_SILENT 5699 0.01288 0.0258 0.6282
Clinical variable #7: 'HISTOLOGICAL_TYPE'

No variable related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  PROSTATE ADENOCARCINOMA OTHER SUBTYPE 10
  PROSTATE ADENOCARCINOMA ACINAR TYPE 322
     
  Significant variables N = 0
Clinical variable #8: 'RESIDUAL_TUMOR'

No variable related to 'RESIDUAL_TUMOR'.

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

RESIDUAL_TUMOR Labels N
  R0 221
  R1 85
  R2 4
  RX 9
     
  Significant variables N = 0
Clinical variable #9: 'NUMBER_OF_LYMPH_NODES'

No variable related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 0.4 (1.3)
  Significant variables N = 0
Clinical variable #10: 'GLEASON_SCORE'

2 variables related to 'GLEASON_SCORE'.

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

GLEASON_SCORE Mean (SD) 7.58 (0.98)
  Score N
  6 27
  7 176
  8 42
  9 84
  10 3
     
  Significant variables N = 2
  pos. correlated 2
  neg. correlated 0
List of 2 variables associated with 'GLEASON_SCORE'

Table S14.  Get Full Table List of 2 variables significantly correlated to 'GLEASON_SCORE' by Spearman correlation test

SpearmanCorr corrP Q
MUTATIONRATE_SILENT 0.1538 0.004977 0.00995
MUTATIONRATE_NONSYNONYMOUS 0.1192 0.02985 0.0298
Clinical variable #11: 'PSA_VALUE'

2 variables related to 'PSA_VALUE'.

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

PSA_VALUE Mean (SD) 1.98 (19)
  Significant variables N = 2
  pos. correlated 2
  neg. correlated 0
List of 2 variables associated with 'PSA_VALUE'

Table S16.  Get Full Table List of 2 variables significantly correlated to 'PSA_VALUE' by Spearman correlation test

SpearmanCorr corrP Q
MUTATIONRATE_NONSYNONYMOUS 0.164 0.004609 0.00922
MUTATIONRATE_SILENT 0.1323 0.02261 0.0226
Clinical variable #12: 'RACE'

No variable related to 'RACE'.

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

RACE Labels N
  ASIAN 2
  BLACK OR AFRICAN AMERICAN 7
  WHITE 133
     
  Significant variables N = 0
Methods & Data
Input

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

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

  • Number of patients = 332

  • Number of variables = 2

  • Number of clinical features = 12

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, 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)
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