Correlation between mutation rate and clinical features
Thyroid Adenocarcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C11C1WC7
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/C11C1WC7
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 18 clinical features across 492 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 'AGE'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • 2 variables correlated to 'AGE_mutation.rate'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • 2 variables correlated to 'PATHOLOGIC_STAGE'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • 1 variable correlated to 'PATHOLOGY_T_STAGE'.

    • MUTATIONRATE_NONSYNONYMOUS

  • 2 variables correlated to 'EXTRATHYROIDAL_EXTENSION'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • 2 variables correlated to 'TUMOR_SIZE'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • No variables correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', 'RADIATION_THERAPY', 'HISTOLOGICAL_TYPE', 'RADIATION_EXPOSURE', 'RESIDUAL_TUMOR', 'NUMBER_OF_LYMPH_NODES', 'MULTIFOCALITY', '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=2        
PATHOLOGIC_STAGE Kruskal-Wallis test N=2        
PATHOLOGY_T_STAGE Spearman correlation test N=1 higher stage N=1 lower stage N=0
PATHOLOGY_N_STAGE Wilcoxon test   N=0        
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test   N=0        
RADIATION_THERAPY Wilcoxon test   N=0        
HISTOLOGICAL_TYPE Kruskal-Wallis test   N=0        
RADIATION_EXPOSURE Wilcoxon test   N=0        
EXTRATHYROIDAL_EXTENSION Kruskal-Wallis test N=2        
RESIDUAL_TUMOR Kruskal-Wallis test   N=0        
NUMBER_OF_LYMPH_NODES Spearman correlation test   N=0        
MULTIFOCALITY Wilcoxon test   N=0        
TUMOR_SIZE Spearman correlation test N=2 higher tumor_size N=2 lower tumor_size 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) 0.2-178.3 (median=31.2)
  censored N = 477
  death N = 14
     
  Significant variables N = 0
Clinical variable #2: 'AGE'

2 variables related to 'AGE'.

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

AGE Mean (SD) 47.23 (16)
  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 0.4221 1.105e-22 2.21e-22
MUTATIONRATE_SILENT 0.3887 3.441e-19 3.44e-19
Clinical variable #3: 'AGE'

2 variables related to 'AGE'.

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

AGE Mean (SD) 47.23 (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 0.174 105 2.16e-22 2.38e-07 490 7e-09 ( 5.72e-08 ) 2.16e-22 ( 0.0937 )
MUTATIONRATE_SILENT 0.14 81 5.01e-18 9.74e-08 490 2.52e-09 ( 4.33e-09 ) 5.01e-18 ( 0.756 )
Clinical variable #4: 'PATHOLOGIC_STAGE'

2 variables related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  STAGE I 278
  STAGE II 50
  STAGE III 108
  STAGE IV 2
  STAGE IVA 46
  STAGE IVC 6
     
  Significant variables N = 2
List of 2 variables associated with 'PATHOLOGIC_STAGE'

Table S7.  Get Full Table List of 2 variables differentially expressed by 'PATHOLOGIC_STAGE'

kruskal_wallis_P Q
MUTATIONRATE_NONSYNONYMOUS 1.437e-12 2.87e-12
MUTATIONRATE_SILENT 2.751e-07 2.75e-07
Clinical variable #5: 'PATHOLOGY_T_STAGE'

One variable related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 2.14 (0.89)
  N
  T1 141
  T2 162
  T3 164
  T4 23
     
  Significant variables N = 1
  pos. correlated 1
  neg. correlated 0
List of one variable associated with 'PATHOLOGY_T_STAGE'

Table S9.  Get Full Table List of one variable significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
MUTATIONRATE_NONSYNONYMOUS 0.1904 2.214e-05 4.43e-05
Clinical variable #6: 'PATHOLOGY_N_STAGE'

No variable related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Labels N
  N0 220
  N1 222
     
  Significant variables N = 0
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 276
  class1 9
     
  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 362
  MALE 130
     
  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 179
  YES 298
     
  Significant variables N = 0
Clinical variable #10: 'HISTOLOGICAL_TYPE'

No variable related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  OTHER, SPECIFY 7
  THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL 348
  THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) 102
  THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES) 35
     
  Significant variables N = 0
Clinical variable #11: 'RADIATION_EXPOSURE'

No variable related to 'RADIATION_EXPOSURE'.

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

RADIATION_EXPOSURE Labels N
  NO 415
  YES 17
     
  Significant variables N = 0
Clinical variable #12: 'EXTRATHYROIDAL_EXTENSION'

2 variables related to 'EXTRATHYROIDAL_EXTENSION'.

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

EXTRATHYROIDAL_EXTENSION Labels N
  MINIMAL (T3) 130
  MODERATE/ADVANCED (T4A) 18
  NONE 328
  VERY ADVANCED (T4B) 1
     
  Significant variables N = 2
List of 2 variables associated with 'EXTRATHYROIDAL_EXTENSION'

Table S17.  Get Full Table List of 2 variables differentially expressed by 'EXTRATHYROIDAL_EXTENSION'

kruskal_wallis_P Q
MUTATIONRATE_NONSYNONYMOUS 5.946e-06 1.19e-05
MUTATIONRATE_SILENT 0.0002648 0.000265
Clinical variable #13: 'RESIDUAL_TUMOR'

No variable related to 'RESIDUAL_TUMOR'.

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

RESIDUAL_TUMOR Labels N
  R0 379
  R1 51
  R2 4
  RX 30
     
  Significant variables N = 0
Clinical variable #14: 'NUMBER_OF_LYMPH_NODES'

No variable related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 3.74 (6.2)
  Significant variables N = 0
Clinical variable #15: 'MULTIFOCALITY'

No variable related to 'MULTIFOCALITY'.

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

MULTIFOCALITY Labels N
  MULTIFOCAL 221
  UNIFOCAL 262
     
  Significant variables N = 0
Clinical variable #16: 'TUMOR_SIZE'

2 variables related to 'TUMOR_SIZE'.

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

TUMOR_SIZE Mean (SD) 2.99 (1.6)
  Significant variables N = 2
  pos. correlated 2
  neg. correlated 0
List of 2 variables associated with 'TUMOR_SIZE'

Table S22.  Get Full Table List of 2 variables significantly correlated to 'TUMOR_SIZE' by Spearman correlation test

SpearmanCorr corrP Q
MUTATIONRATE_NONSYNONYMOUS 0.1099 0.02933 0.0302
MUTATIONRATE_SILENT 0.1094 0.03017 0.0302
Clinical variable #17: 'RACE'

No variable related to 'RACE'.

Table S23.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 51
  BLACK OR AFRICAN AMERICAN 26
  WHITE 322
     
  Significant variables N = 0
Clinical variable #18: 'ETHNICITY'

No variable related to 'ETHNICITY'.

Table S24.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 38
  NOT HISPANIC OR LATINO 351
     
  Significant variables N = 0
Methods & Data
Input

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

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

  • Number of patients = 492

  • Number of variables = 2

  • Number of clinical features = 18

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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