Correlation between mutation rate and clinical features
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 14 clinical features across 488 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 9 clinical features related to at least one variables.

  • 1 variable correlated to 'AGE_mutation.rate'.

    • MUTATIONRATE_NONSYNONYMOUS

  • 2 variables correlated to 'PRIMARY_SITE_OF_DISEASE'.

    • MUTATIONRATE_SILENT ,  MUTATIONRATE_NONSYNONYMOUS

  • 2 variables correlated to 'NEOPLASM_DISEASESTAGE'.

    • MUTATIONRATE_SILENT ,  MUTATIONRATE_NONSYNONYMOUS

  • 2 variables correlated to 'PATHOLOGY_T_STAGE'.

    • MUTATIONRATE_SILENT ,  MUTATIONRATE_NONSYNONYMOUS

  • 2 variables correlated to 'PATHOLOGY_N_STAGE'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • 2 variables correlated to 'PATHOLOGY_M_STAGE'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • 2 variables correlated to 'HISTOLOGICAL_TYPE'.

    • MUTATIONRATE_SILENT ,  MUTATIONRATE_NONSYNONYMOUS

  • 2 variables correlated to 'COMPLETENESS_OF_RESECTION'.

    • MUTATIONRATE_SILENT ,  MUTATIONRATE_NONSYNONYMOUS

  • 2 variables correlated to 'NUMBER_OF_LYMPH_NODES'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • No variables correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'AGE', 'GENDER', 'RADIATIONS_RADIATION_REGIMENINDICATION', and 'RACE'.

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=0        
AGE Linear Regression Analysis N=1        
PRIMARY_SITE_OF_DISEASE Wilcoxon test N=2 rectum N=2 colon N=0
NEOPLASM_DISEASESTAGE Kruskal-Wallis test N=2        
PATHOLOGY_T_STAGE Spearman correlation test N=2 higher stage N=2 lower stage N=0
PATHOLOGY_N_STAGE Spearman correlation test N=2 higher stage N=0 lower stage N=2
PATHOLOGY_M_STAGE Wilcoxon test N=2 class1 N=2 class0 N=0
GENDER Wilcoxon test   N=0        
HISTOLOGICAL_TYPE Kruskal-Wallis test N=2        
RADIATIONS_RADIATION_REGIMENINDICATION Wilcoxon test   N=0        
COMPLETENESS_OF_RESECTION Kruskal-Wallis test N=2        
NUMBER_OF_LYMPH_NODES Spearman correlation test N=2 higher number_of_lymph_nodes N=0 lower number_of_lymph_nodes N=2
RACE 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) 0-140.4 (median=20)
  censored N = 398
  death N = 89
     
  Significant variables N = 0
Clinical variable #2: 'AGE'

No variable related to 'AGE'.

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

AGE Mean (SD) 67.11 (13)
  Significant variables N = 0
Clinical variable #3: 'AGE'

One variable related to 'AGE'.

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

AGE Mean (SD) 67.11 (13)
  Significant variables N = 1
List of one variable associated with 'AGE'

Table S4.  Get Full Table List of one variable 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_NONSYNONYMOUS 0.00719 4.52 0.034 2.81e-05 485 -2.16e-07 ( 2.69e-05 ) 0.034 ( 0.000123 )
Clinical variable #4: 'PRIMARY_SITE_OF_DISEASE'

2 variables related to 'PRIMARY_SITE_OF_DISEASE'.

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

PRIMARY_SITE_OF_DISEASE Labels N
  COLON 365
  RECTUM 119
     
  Significant variables N = 2
  Higher in RECTUM 2
  Higher in COLON 0
List of 2 variables associated with 'PRIMARY_SITE_OF_DISEASE'

Table S6.  Get Full Table List of 2 variables differentially expressed by 'PRIMARY_SITE_OF_DISEASE'

W(pos if higher in 'RECTUM') wilcoxontestP Q AUC
MUTATIONRATE_SILENT 12995 4.612e-11 9.22e-11 0.7008
MUTATIONRATE_NONSYNONYMOUS 13191.5 1.238e-10 1.24e-10 0.6963
Clinical variable #5: 'NEOPLASM_DISEASESTAGE'

2 variables related to 'NEOPLASM_DISEASESTAGE'.

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

NEOPLASM_DISEASESTAGE Labels N
  STAGE I 91
  STAGE IA 1
  STAGE II 32
  STAGE IIA 138
  STAGE IIB 10
  STAGE IIC 1
  STAGE III 24
  STAGE IIIA 14
  STAGE IIIB 56
  STAGE IIIC 40
  STAGE IV 48
  STAGE IVA 19
  STAGE IVB 1
     
  Significant variables N = 2
List of 2 variables associated with 'NEOPLASM_DISEASESTAGE'

Table S8.  Get Full Table List of 2 variables differentially expressed by 'NEOPLASM_DISEASESTAGE'

kruskal_wallis_P Q
MUTATIONRATE_SILENT 0.0002956 0.000301
MUTATIONRATE_NONSYNONYMOUS 0.0003014 0.000301
Clinical variable #6: 'PATHOLOGY_T_STAGE'

2 variables related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 2.84 (0.64)
  N
  T1 17
  T2 93
  T3 327
  T4 50
     
  Significant variables N = 2
  pos. correlated 2
  neg. correlated 0
List of 2 variables associated with 'PATHOLOGY_T_STAGE'

Table S10.  Get Full Table List of 2 variables significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
MUTATIONRATE_SILENT 0.1053 0.02017 0.0403
MUTATIONRATE_NONSYNONYMOUS 0.0925 0.04124 0.0412
Clinical variable #7: 'PATHOLOGY_N_STAGE'

2 variables related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 0.59 (0.77)
  N
  N0 287
  N1 112
  N2 86
     
  Significant variables N = 2
  pos. correlated 0
  neg. correlated 2
List of 2 variables associated with 'PATHOLOGY_N_STAGE'

Table S12.  Get Full Table List of 2 variables significantly correlated to 'PATHOLOGY_N_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
MUTATIONRATE_NONSYNONYMOUS -0.1432 0.001566 0.00313
MUTATIONRATE_SILENT -0.1282 0.004673 0.00467
Clinical variable #8: 'PATHOLOGY_M_STAGE'

2 variables related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 374
  class1 65
     
  Significant variables N = 2
  Higher in class1 2
  Higher in class0 0
List of 2 variables associated with 'PATHOLOGY_M_STAGE'

Table S14.  Get Full Table List of 2 variables differentially expressed by 'PATHOLOGY_M_STAGE'

W(pos if higher in 'class1') wilcoxontestP Q AUC
MUTATIONRATE_NONSYNONYMOUS 9039 0.0009672 0.00193 0.6282
MUTATIONRATE_SILENT 9385 0.003352 0.00335 0.6139
Clinical variable #9: 'GENDER'

No variable related to 'GENDER'.

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

GENDER Labels N
  FEMALE 224
  MALE 264
     
  Significant variables N = 0
Clinical variable #10: 'HISTOLOGICAL_TYPE'

2 variables related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  COLON ADENOCARCINOMA 316
  COLON MUCINOUS ADENOCARCINOMA 48
  RECTAL ADENOCARCINOMA 106
  RECTAL MUCINOUS ADENOCARCINOMA 10
     
  Significant variables N = 2
List of 2 variables associated with 'HISTOLOGICAL_TYPE'

Table S17.  Get Full Table List of 2 variables differentially expressed by 'HISTOLOGICAL_TYPE'

kruskal_wallis_P Q
MUTATIONRATE_SILENT 1.76e-12 3.36e-12
MUTATIONRATE_NONSYNONYMOUS 3.356e-12 3.36e-12
Clinical variable #11: 'RADIATIONS_RADIATION_REGIMENINDICATION'

No variable related to 'RADIATIONS_RADIATION_REGIMENINDICATION'.

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

RADIATIONS_RADIATION_REGIMENINDICATION Labels N
  NO 6
  YES 482
     
  Significant variables N = 0
Clinical variable #12: 'COMPLETENESS_OF_RESECTION'

2 variables related to 'COMPLETENESS_OF_RESECTION'.

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

COMPLETENESS_OF_RESECTION Labels N
  R0 345
  R1 3
  R2 31
  RX 25
     
  Significant variables N = 2
List of 2 variables associated with 'COMPLETENESS_OF_RESECTION'

Table S20.  Get Full Table List of 2 variables differentially expressed by 'COMPLETENESS_OF_RESECTION'

kruskal_wallis_P Q
MUTATIONRATE_SILENT 0.0002761 0.000368
MUTATIONRATE_NONSYNONYMOUS 0.0003679 0.000368
Clinical variable #13: 'NUMBER_OF_LYMPH_NODES'

2 variables related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 2.03 (4.2)
  Significant variables N = 2
  pos. correlated 0
  neg. correlated 2
List of 2 variables associated with 'NUMBER_OF_LYMPH_NODES'

Table S22.  Get Full Table List of 2 variables significantly correlated to 'NUMBER_OF_LYMPH_NODES' by Spearman correlation test

SpearmanCorr corrP Q
MUTATIONRATE_NONSYNONYMOUS -0.151 0.001204 0.00241
MUTATIONRATE_SILENT -0.1344 0.004011 0.00401
Clinical variable #14: 'RACE'

No variable related to 'RACE'.

Table S23.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 11
  BLACK OR AFRICAN AMERICAN 21
  WHITE 226
     
  Significant variables N = 0
Methods & Data
Input

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

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

  • Number of patients = 488

  • Number of variables = 2

  • Number of clinical features = 14

Selected clinical features

  • For clinical features selected for this analysis and their value conozzle.versions, please find a documentation on selected CDEs .

  • 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, Wald's test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values using the 'coxph' function in R. Kaplan-Meier survival curves were plot using the four quartile subgroups of patients based on expression levels

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)
Made with Nozzle