Correlation between APOBEC signature variables and clinical features
Bladder Urothelial Carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C15T3JTD
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between APOBEC signature variables and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C15T3JTD
Overview
Introduction

This pipeline uses various statistical tests to identify selected clinical features related to APOBEC signature variables.

Summary

Testing the association between 3 variables and 13 clinical features across 395 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one variables.

  • 3 variables correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • TCW_TO_G+TCW_TO_T ,  APOBEC_MUTLOAD_MINESTIMATE ,  [TCW_TO_G+TCW_TO_T]_PER_MUT

  • 3 variables correlated to 'GENDER'.

    • TCW_TO_G+TCW_TO_T ,  APOBEC_MUTLOAD_MINESTIMATE ,  [TCW_TO_G+TCW_TO_T]_PER_MUT

  • 2 variables correlated to 'RADIATION_THERAPY'.

    • TCW_TO_G+TCW_TO_T ,  APOBEC_MUTLOAD_MINESTIMATE

  • 3 variables correlated to 'RACE'.

    • TCW_TO_G+TCW_TO_T ,  APOBEC_MUTLOAD_MINESTIMATE ,  [TCW_TO_G+TCW_TO_T]_PER_MUT

  • No variables correlated to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'KARNOFSKY_PERFORMANCE_SCORE', 'NUMBER_PACK_YEARS_SMOKED', 'NUMBER_OF_LYMPH_NODES', 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=3   N=NA   N=NA
YEARS_TO_BIRTH Spearman correlation test   N=0        
PATHOLOGIC_STAGE Kruskal-Wallis test   N=0        
PATHOLOGY_T_STAGE Spearman correlation test   N=0        
PATHOLOGY_N_STAGE Spearman correlation test   N=0        
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test N=3 male N=3 female N=0
RADIATION_THERAPY Wilcoxon test N=2 yes N=2 no N=0
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test   N=0        
NUMBER_PACK_YEARS_SMOKED Spearman correlation test   N=0        
NUMBER_OF_LYMPH_NODES Spearman correlation test   N=0        
RACE Kruskal-Wallis test N=3        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

3 variables 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) 0.6-166 (median=17.6)
  censored N = 217
  death N = 177
     
  Significant markers N = 3
  associated with shorter survival NA
  associated with longer survival NA
List of 3 variables associated with 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2.  Get Full Table List of 3 variables significantly associated with 'Time to Death' by Cox regression test. For the survival curves, it compared quantile intervals at c(0, 0.25, 0.50, 0.75, 1) and did not try survival analysis if there is only one interval.

logrank_P Q C_index
TCW_TO_G+TCW_TO_T 0.00027 0.00081 0.417
APOBEC_MUTLOAD_MINESTIMATE 0.000952 0.0014 0.419
[TCW_TO_G+TCW_TO_T]_PER_MUT 0.00269 0.0027 0.423
Clinical variable #2: 'YEARS_TO_BIRTH'

No variable related to 'YEARS_TO_BIRTH'.

Table S3.  Basic characteristics of clinical feature: 'YEARS_TO_BIRTH'

YEARS_TO_BIRTH Mean (SD) 68.11 (11)
  Significant variables N = 0
Clinical variable #3: 'PATHOLOGIC_STAGE'

No variable related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  STAGE I 2
  STAGE II 122
  STAGE III 135
  STAGE IV 134
     
  Significant variables N = 0
Clinical variable #4: 'PATHOLOGY_T_STAGE'

No variable related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 2.82 (0.7)
  N
  T0 1
  T1 3
  T2 112
  T3 190
  T4 57
     
  Significant variables N = 0
Clinical variable #5: 'PATHOLOGY_N_STAGE'

No variable related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 0.62 (0.89)
  N
  N0 225
  N1 46
  N2 75
  N3 8
     
  Significant variables N = 0
Clinical variable #6: 'PATHOLOGY_M_STAGE'

No variable related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 189
  class1 11
     
  Significant variables N = 0
Clinical variable #7: 'GENDER'

3 variables related to 'GENDER'.

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

GENDER Labels N
  FEMALE 102
  MALE 293
     
  Significant variables N = 3
  Higher in MALE 3
  Higher in FEMALE 0
List of 3 variables associated with 'GENDER'

Table S9.  Get Full Table List of 3 variables differentially expressed by 'GENDER'. 0 significant gene(s) located in sex chromosomes is(are) filtered out.

W(pos if higher in 'MALE') wilcoxontestP Q AUC
TCW_TO_G+TCW_TO_T 17414.5 0.01284 0.025 0.5827
APOBEC_MUTLOAD_MINESTIMATE 17301.5 0.01748 0.025 0.5789
[TCW_TO_G+TCW_TO_T]_PER_MUT 17169.5 0.025 0.025 0.5745
Clinical variable #8: 'RADIATION_THERAPY'

2 variables related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 350
  YES 20
     
  Significant variables N = 2
  Higher in YES 2
  Higher in NO 0
List of 2 variables associated with 'RADIATION_THERAPY'

Table S11.  Get Full Table List of 2 variables differentially expressed by 'RADIATION_THERAPY'

W(pos if higher in 'YES') wilcoxontestP Q AUC
TCW_TO_G+TCW_TO_T 2277 0.00859 0.0148 0.6747
APOBEC_MUTLOAD_MINESTIMATE 2300 0.009872 0.0148 0.6714
Clinical variable #9: 'KARNOFSKY_PERFORMANCE_SCORE'

No variable related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 83.23 (13)
  Significant variables N = 0
Clinical variable #10: 'NUMBER_PACK_YEARS_SMOKED'

No variable related to 'NUMBER_PACK_YEARS_SMOKED'.

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

NUMBER_PACK_YEARS_SMOKED Mean (SD) 39.62 (54)
  Significant variables N = 0
Clinical variable #11: 'NUMBER_OF_LYMPH_NODES'

No variable related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 1.82 (4.4)
  Significant variables N = 0
Clinical variable #12: 'RACE'

3 variables related to 'RACE'.

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

RACE Labels N
  ASIAN 39
  BLACK OR AFRICAN AMERICAN 22
  WHITE 317
     
  Significant variables N = 3
List of 3 variables associated with 'RACE'

Table S16.  Get Full Table List of 3 variables differentially expressed by 'RACE'

kruskal_wallis_P Q
TCW_TO_G+TCW_TO_T 0.0001006 0.000239
APOBEC_MUTLOAD_MINESTIMATE 0.0001596 0.000239
[TCW_TO_G+TCW_TO_T]_PER_MUT 0.0007275 0.000728
Clinical variable #13: 'ETHNICITY'

No variable related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 8
  NOT HISPANIC OR LATINO 355
     
  Significant variables N = 0
Methods & Data
Input

  • Expresson data file = APOBEC_for_clinical.correlaion.input.continuous.txt

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

  • Number of patients = 395

  • Number of variables = 3

  • 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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