Correlation between APOBEC signature variables and clinical features
Uterine Corpus Endometrioid Carcinoma (Primary solid tumor)
04 October 2018  |  None
Maintainer Information
Maintained by Broad Institute GDAC (Broad Institute of MIT & Harvard)
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 17 clinical features across 100 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 'HISTOLOGICAL_TYPE'.

    • [TCW_TO_G+TCW_TO_T]_PER_MUT

  • 1 variable correlated to 'MSI'.

    • [TCW_TO_G+TCW_TO_T]_PER_MUT

  • 1 variable correlated to 'RACE'.

    • [TCW_TO_G+TCW_TO_T]_PER_MUT

  • 2 variables correlated to 'BMI'.

    • [TCW_TO_G+TCW_TO_T]_PER_MUT ,  TCW_TO_G+TCW_TO_T

  • No variables correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'FIGO_GRADE', 'KARNOFSKY_PERFORMANCE_SCORE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGIC_STAGE', 'YEARS_TO_BIRTH', 'ETHNICITY', 'RADIATION_THERAPY', 'DIABETES', 'NUMBER_PACK_YEARS_SMOKED', 'SMOKER', and 'COUNTRY_OF_ORIGIN'.

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        
HISTOLOGICAL_TYPE Kruskal-Wallis test N=1        
FIGO_GRADE Kruskal-Wallis test   N=0        
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test   N=0        
MSI Kruskal-Wallis test N=1        
PATHOLOGY_T_STAGE Spearman correlation test   N=0        
PATHOLOGY_N_STAGE Spearman correlation test   N=0        
PATHOLOGIC_STAGE Kruskal-Wallis test   N=0        
YEARS_TO_BIRTH Spearman correlation test   N=0        
ETHNICITY Wilcoxon test   N=0        
RACE Kruskal-Wallis test N=1        
RADIATION_THERAPY Wilcoxon test   N=0        
DIABETES Wilcoxon test   N=0        
BMI Kruskal-Wallis test N=2        
NUMBER_PACK_YEARS_SMOKED Spearman correlation test   N=0        
SMOKER Wilcoxon test   N=0        
COUNTRY_OF_ORIGIN Kruskal-Wallis 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) 0-130.4 (median=11.6)
  censored N = 95
  death N = 3
     
  Significant markers N = 0
Clinical variable #2: 'HISTOLOGICAL_TYPE'

One variable related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  CLEAR CELL CARCINOMA 1
  ENDOMETRIOID CARCINOMA 77
  MIXED CELL ADENOCARCINOMA 1
  SEROUS CARCINOMA 21
     
  Significant variables N = 1
List of one variable associated with 'HISTOLOGICAL_TYPE'

Table S3.  Get Full Table List of one variable differentially expressed by 'HISTOLOGICAL_TYPE'

kruskal_wallis_P Q
[TCW_TO_G+TCW_TO_T]_PER_MUT 0.02162 0.0648
Clinical variable #3: 'FIGO_GRADE'

No variable related to 'FIGO_GRADE'.

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

FIGO_GRADE Labels N
  FIGO GRADE 1 32
  FIGO GRADE 2 34
  FIGO GRADE 3 7
     
  Significant variables N = 0
Clinical variable #4: 'KARNOFSKY_PERFORMANCE_SCORE'

No variable related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 91.9 (8.5)
  Score N
  60 1
  70 3
  80 1
  90 32
  100 21
     
  Significant variables N = 0
Clinical variable #5: 'MSI'

One variable related to 'MSI'.

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

MSI Labels N
  MSI-H 4
  MSI-L 5
  MSS 23
     
  Significant variables N = 1
List of one variable associated with 'MSI'

Table S7.  Get Full Table List of one variable differentially expressed by 'MSI'

kruskal_wallis_P Q
[TCW_TO_G+TCW_TO_T]_PER_MUT 0.001105 0.00332
Clinical variable #6: 'PATHOLOGY_T_STAGE'

No variable related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 1.31 (0.65)
  N
  T1 78
  T2 11
  T3 10
     
  Significant variables N = 0
Clinical variable #7: 'PATHOLOGY_N_STAGE'

No variable related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 0.22 (0.53)
  N
  N0 46
  N1 6
  N2 3
     
  Significant variables N = 0
Clinical variable #8: 'PATHOLOGIC_STAGE'

No variable related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  STAGE I 72
  STAGE IA 1
  STAGE IB 1
  STAGE II 8
  STAGE III 15
  STAGE IV 2
  STAGE IVB 1
     
  Significant variables N = 0
Clinical variable #9: 'YEARS_TO_BIRTH'

No variable related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 63.56 (10)
  Significant variables N = 0
Clinical variable #10: 'ETHNICITY'

No variable related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 4
  NOT HISPANIC OR LATINO 41
     
  Significant variables N = 0
Clinical variable #11: 'RACE'

One variable related to 'RACE'.

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

RACE Labels N
  ASIAN 1
  BLACK OR AFRICAN AMERICAN 3
  WHITE 58
     
  Significant variables N = 1
List of one variable associated with 'RACE'

Table S14.  Get Full Table List of one variable differentially expressed by 'RACE'

kruskal_wallis_P Q
[TCW_TO_G+TCW_TO_T]_PER_MUT 0.03614 0.108
Clinical variable #12: 'RADIATION_THERAPY'

No variable related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 43
  YES 54
     
  Significant variables N = 0
Clinical variable #13: 'DIABETES'

No variable related to 'DIABETES'.

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

DIABETES Labels N
  NO 70
  YES 28
     
  Significant variables N = 0
Clinical variable #14: 'BMI'

2 variables related to 'BMI'.

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

BMI Labels N
  NORMAL 8
  OBESE 47
  OVERWEIGHT 21
  SEVERELY OBESE 21
  UNDERWEIGHT 3
     
  Significant variables N = 2
List of 2 variables associated with 'BMI'

Table S18.  Get Full Table List of 2 variables differentially expressed by 'BMI'

kruskal_wallis_P Q
[TCW_TO_G+TCW_TO_T]_PER_MUT 0.004533 0.0136
TCW_TO_G+TCW_TO_T 0.03504 0.0526
Clinical variable #15: 'NUMBER_PACK_YEARS_SMOKED'

No variable related to 'NUMBER_PACK_YEARS_SMOKED'.

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

NUMBER_PACK_YEARS_SMOKED Mean (SD) 14.48 (12)
  Significant variables N = 0
Clinical variable #16: 'SMOKER'

No variable related to 'SMOKER'.

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

SMOKER Labels N
  NON-SMOKER 73
  SMOKER 22
     
  Significant variables N = 0
Clinical variable #17: 'COUNTRY_OF_ORIGIN'

No variable related to 'COUNTRY_OF_ORIGIN'.

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

COUNTRY_OF_ORIGIN Labels N
  MEXICO 2
  POLAND 5
  UKRAINE 32
  UNITED STATES 36
     
  Significant variables N = 0
Methods & Data
Input

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

  • Clinical data file = CPTAC3-UCEC-TP.clin.merged.picked.txt

  • Number of patients = 100

  • Number of variables = 3

  • Number of clinical features = 17

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.

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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