Correlation between mutation rate and clinical features
Stomach and Esophageal carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1MP52R4
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/C1MP52R4
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 580 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_SILENT ,  MUTATIONRATE_NONSYNONYMOUS

  • 2 variables correlated to 'AGE_mutation.rate'.

    • MUTATIONRATE_SILENT ,  MUTATIONRATE_NONSYNONYMOUS

  • 2 variables correlated to 'PATHOLOGIC_STAGE'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • 2 variables correlated to 'PATHOLOGY_T_STAGE'.

    • MUTATIONRATE_SILENT ,  MUTATIONRATE_NONSYNONYMOUS

  • 2 variables correlated to 'PATHOLOGY_N_STAGE'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • No variables correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'PATHOLOGY_M_STAGE', 'GENDER', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', 'NUMBER_PACK_YEARS_SMOKED', '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=2 higher stage N=0 lower stage N=2
PATHOLOGY_N_STAGE Spearman correlation test N=2 higher stage N=0 lower stage N=2
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test   N=0        
RADIATION_THERAPY Wilcoxon test   N=0        
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test   N=0        
NUMBER_PACK_YEARS_SMOKED Spearman correlation 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) 0-122.3 (median=13.6)
  censored N = 358
  death N = 221
     
  Significant variables N = 0
Clinical variable #2: 'AGE'

2 variables related to 'AGE'.

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

AGE Mean (SD) 64.62 (11)
  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_SILENT 0.2215 8.48e-08 1.7e-07
MUTATIONRATE_NONSYNONYMOUS 0.1982 1.744e-06 1.74e-06
Clinical variable #3: 'AGE'

2 variables related to 'AGE'.

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

AGE Mean (SD) 64.62 (11)
  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 0.0183 11.7 0.000682 4.32e-06 571 5.51e-08 ( -1.43e-06 ) 0.000682 ( 0.177 )
MUTATIONRATE_NONSYNONYMOUS 0.0167 10.7 0.00114 1.59e-05 571 1.94e-07 ( -4.54e-06 ) 0.00114 ( 0.243 )
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 10
  STAGE IA 20
  STAGE IB 46
  STAGE II 31
  STAGE IIA 85
  STAGE IIB 83
  STAGE III 29
  STAGE IIIA 84
  STAGE IIIB 62
  STAGE IIIC 40
  STAGE IV 43
  STAGE IVA 4
     
  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 0.002164 0.00433
MUTATIONRATE_SILENT 0.01206 0.0121
Clinical variable #5: 'PATHOLOGY_T_STAGE'

2 variables related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 2.75 (0.87)
  N
  T0 1
  T1 53
  T2 135
  T3 262
  T4 103
     
  Significant variables N = 2
  pos. correlated 0
  neg. correlated 2
List of 2 variables associated with 'PATHOLOGY_T_STAGE'

Table S9.  Get Full Table List of 2 variables significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
MUTATIONRATE_SILENT -0.1088 0.01036 0.0205
MUTATIONRATE_NONSYNONYMOUS -0.0984 0.02052 0.0205
Clinical variable #6: 'PATHOLOGY_N_STAGE'

2 variables related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 1.12 (1.1)
  N
  N0 196
  N1 173
  N2 90
  N3 85
     
  Significant variables N = 2
  pos. correlated 0
  neg. correlated 2
List of 2 variables associated with 'PATHOLOGY_N_STAGE'

Table S11.  Get Full Table List of 2 variables significantly correlated to 'PATHOLOGY_N_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
MUTATIONRATE_NONSYNONYMOUS -0.0863 0.0442 0.0463
MUTATIONRATE_SILENT -0.0855 0.04628 0.0463
Clinical variable #7: 'PATHOLOGY_M_STAGE'

No variable related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 484
  class1 38
     
  Significant variables N = 0
Clinical variable #8: 'GENDER'

No variable related to 'GENDER'.

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

GENDER Labels N
  FEMALE 173
  MALE 407
     
  Significant variables N = 0
Clinical variable #9: 'RADIATION_THERAPY'

No variable related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 418
  YES 102
     
  Significant variables N = 0
Clinical variable #10: 'KARNOFSKY_PERFORMANCE_SCORE'

No variable related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 73.82 (16)
  Significant variables N = 0
Clinical variable #11: 'NUMBER_PACK_YEARS_SMOKED'

No variable related to 'NUMBER_PACK_YEARS_SMOKED'.

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

NUMBER_PACK_YEARS_SMOKED Mean (SD) 34.48 (22)
  Significant variables N = 0
Clinical variable #12: 'RACE'

No variable related to 'RACE'.

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

RACE Labels N
  ASIAN 134
  BLACK OR AFRICAN AMERICAN 14
  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER 1
  WHITE 358
     
  Significant variables N = 0
Clinical variable #13: 'ETHNICITY'

No variable related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 10
  NOT HISPANIC OR LATINO 400
     
  Significant variables N = 0
Methods & Data
Input

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

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

  • Number of patients = 580

  • 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)
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