This pipeline uses various statistical tests to identify selected clinical features related to mutation rate.
Testing the association between 2 variables and 9 clinical features across 123 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one variables.

1 variable correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

MUTATIONRATE_SILENT

2 variables correlated to 'AGE'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

2 variables correlated to 'AGE_mutation.rate'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

2 variables correlated to 'HISTOLOGICAL_TYPE'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

No variables correlated to 'TUMOR_TISSUE_SITE', 'GENDER', 'RADIATION_THERAPY', 'RACE', and 'ETHNICITY'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature  Statistical test  Significant variables  Associated with  Associated with  

DAYS_TO_DEATH_OR_LAST_FUP  Cox regression test  N=1  N=NA  N=NA  
AGE  Spearman correlation test  N=2  older  N=2  younger  N=0 
AGE  Linear Regression Analysis  N=2  
TUMOR_TISSUE_SITE  Wilcoxon test  N=0  
GENDER  Wilcoxon test  N=0  
RADIATION_THERAPY  Wilcoxon test  N=0  
HISTOLOGICAL_TYPE  KruskalWallis test  N=2  
RACE  KruskalWallis test  N=0  
ETHNICITY  Wilcoxon test  N=0 
One variable related to 'DAYS_TO_DEATH_OR_LAST_FUP'.
DAYS_TO_DEATH_OR_LAST_FUP  Duration (Months)  0.5150.4 (median=40.1) 
censored  N = 114  
death  N = 8  
Significant variables  N = 1  
associated with shorter survival  NA  
associated with longer survival  NA 
logrank_P  Q  C_index  

MUTATIONRATE_SILENT  0.0475  0.095  0.766 
AGE  Mean (SD)  58.26 (13) 
Significant variables  N = 2  
pos. correlated  2  
neg. correlated  0 
AGE  Mean (SD)  58.26 (13) 
Significant variables  N = 2 
TUMOR_TISSUE_SITE  Labels  N 
ANTERIOR MEDIASTINUM  27  
THYMUS  96  
Significant variables  N = 0 
GENDER  Labels  N 
FEMALE  59  
MALE  64  
Significant variables  N = 0 
RADIATION_THERAPY  Labels  N 
NO  81  
YES  42  
Significant variables  N = 0 
HISTOLOGICAL_TYPE  Labels  N 
THYMOMA; TYPE A  17  
THYMOMA; TYPE AB  38  
THYMOMA; TYPE B1  15  
THYMOMA; TYPE B2  30  
THYMOMA; TYPE B3  12  
THYMOMA; TYPE C  11  
Significant variables  N = 2 
RACE  Labels  N 
ASIAN  13  
BLACK OR AFRICAN AMERICAN  6  
WHITE  102  
Significant variables  N = 0 

Expresson data file = THYMTP.patients.counts_and_rates.txt

Clinical data file = THYMTP.merged_data.txt

Number of patients = 123

Number of variables = 2

Number of clinical features = 9

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.

There are also useful links about clinical features.

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.
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. KaplanMeier 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.
For continuous numerical clinical features, Spearman's rank correlation coefficients (Spearman 1904) and twotailed P values were estimated using 'cor.test' function in R
For two groups (mutant or wildtype) 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 MannWhitney test.
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.
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.