Correlation between APOBEC signature variables and clinical features
Breast Invasive Carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1MS3S3K
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/C1MS3S3K
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 12 clinical features across 977 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 'YEARS_TO_BIRTH'.

    • TCW_TO_G+TCW_TO_T

  • 1 variable correlated to 'PATHOLOGY_T_STAGE'.

    • TCW_TO_G+TCW_TO_T

  • 3 variables correlated to 'HISTOLOGICAL_TYPE'.

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

  • 2 variables correlated to 'RACE'.

    • APOBEC_MUTLOAD_MINESTIMATE ,  TCW_TO_G+TCW_TO_T

  • No variables correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'PATHOLOGIC_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', 'RADIATION_THERAPY', '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=0        
YEARS_TO_BIRTH Spearman correlation test N=1 older N=1 younger N=0
PATHOLOGIC_STAGE Kruskal-Wallis test   N=0        
PATHOLOGY_T_STAGE Spearman correlation test N=1 higher stage N=1 lower stage N=0
PATHOLOGY_N_STAGE Spearman correlation 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=3        
NUMBER_OF_LYMPH_NODES Spearman correlation test   N=0        
RACE Kruskal-Wallis test N=2        
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) 0-282.9 (median=31)
  censored N = 837
  death N = 139
     
  Significant markers N = 0
Clinical variable #2: 'YEARS_TO_BIRTH'

One variable related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 58.74 (13)
  Significant variables N = 1
  pos. correlated 1
  neg. correlated 0
List of one variable associated with 'YEARS_TO_BIRTH'

Table S3.  Get Full Table List of one variable significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

SpearmanCorr corrP Q
TCW_TO_G+TCW_TO_T 0.0634 0.04919 0.148
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 88
  STAGE IA 71
  STAGE IB 6
  STAGE II 4
  STAGE IIA 326
  STAGE IIB 227
  STAGE III 2
  STAGE IIIA 137
  STAGE IIIB 24
  STAGE IIIC 57
  STAGE IV 15
  STAGE X 12
     
  Significant variables N = 0
Clinical variable #4: 'PATHOLOGY_T_STAGE'

One variable related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 1.93 (0.72)
  N
  T1 257
  T2 564
  T3 120
  T4 35
     
  Significant variables N = 1
  pos. correlated 1
  neg. correlated 0
List of one variable associated with 'PATHOLOGY_T_STAGE'

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

SpearmanCorr corrP Q
TCW_TO_G+TCW_TO_T 0.1028 0.001296 0.00389
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 Mean (SD) 0.77 (0.91)
  N
  N0 462
  N1 325
  N2 107
  N3 68
     
  Significant variables N = 0
Clinical variable #6: 'PATHOLOGY_M_STAGE'

No variable related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 842
  class1 16
     
  Significant variables N = 0
Clinical variable #7: 'GENDER'

No variable related to 'GENDER'.

Table S9.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 968
  MALE 9
     
  Significant variables N = 0
Clinical variable #8: 'RADIATION_THERAPY'

No variable related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 387
  YES 502
     
  Significant variables N = 0
Clinical variable #9: 'HISTOLOGICAL_TYPE'

3 variables related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  INFILTRATING CARCINOMA NOS 1
  INFILTRATING DUCTAL CARCINOMA 711
  INFILTRATING LOBULAR CARCINOMA 172
  MEDULLARY CARCINOMA 5
  METAPLASTIC CARCINOMA 6
  MIXED HISTOLOGY (PLEASE SPECIFY) 27
  MUCINOUS CARCINOMA 14
  OTHER, SPECIFY 40
     
  Significant variables N = 3
List of 3 variables associated with 'HISTOLOGICAL_TYPE'

Table S12.  Get Full Table List of 3 variables differentially expressed by 'HISTOLOGICAL_TYPE'

kruskal_wallis_P Q
TCW_TO_G+TCW_TO_T 0.0001532 0.000374
APOBEC_MUTLOAD_MINESTIMATE 0.0002491 0.000374
[TCW_TO_G+TCW_TO_T]_PER_MUT 0.001558 0.00156
Clinical variable #10: 'NUMBER_OF_LYMPH_NODES'

No variable related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 2.34 (4.6)
  Significant variables N = 0
Clinical variable #11: 'RACE'

2 variables related to 'RACE'.

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

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 57
  BLACK OR AFRICAN AMERICAN 121
  WHITE 705
     
  Significant variables N = 2
List of 2 variables associated with 'RACE'

Table S15.  Get Full Table List of 2 variables differentially expressed by 'RACE'

kruskal_wallis_P Q
APOBEC_MUTLOAD_MINESTIMATE 0.0009931 0.00298
TCW_TO_G+TCW_TO_T 0.005126 0.00769
Clinical variable #12: 'ETHNICITY'

No variable related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 34
  NOT HISPANIC OR LATINO 772
     
  Significant variables N = 0
Methods & Data
Input

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

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

  • Number of patients = 977

  • Number of variables = 3

  • 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, 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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