Correlation between mutation rate and clinical features
Colorectal Adenocarcinoma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C1GX4990
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between mutation rate and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1GX4990
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 12 clinical features across 223 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 6 clinical features related to at least one variables.

  • 2 variables correlated to 'AGE'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • 1 variable correlated to 'AGE_mutation.rate'.

    • MUTATIONRATE_NONSYNONYMOUS

  • 2 variables correlated to 'PRIMARY.SITE.OF.DISEASE'.

    • MUTATIONRATE_SILENT ,  MUTATIONRATE_NONSYNONYMOUS

  • 1 variable correlated to 'NEOPLASM.DISEASESTAGE'.

    • MUTATIONRATE_NONSYNONYMOUS

  • 1 variable correlated to 'PATHOLOGY.T.STAGE'.

    • MUTATIONRATE_SILENT

  • 2 variables correlated to 'HISTOLOGICAL.TYPE'.

    • MUTATIONRATE_NONSYNONYMOUS ,  MUTATIONRATE_SILENT

  • No variables correlated to 'Time to Death', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'GENDER', 'COMPLETENESS.OF.RESECTION', and 'NUMBER.OF.LYMPH.NODES'.

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
Time to Death Cox regression test   N=0        
AGE Spearman correlation test N=2 older N=2 younger 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=1        
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 Kruskal-Wallis test   N=0        
GENDER Wilcoxon test   N=0        
HISTOLOGICAL TYPE Kruskal-Wallis test N=2        
COMPLETENESS OF RESECTION Kruskal-Wallis test   N=0        
NUMBER OF LYMPH NODES Spearman correlation test   N=0        
Clinical variable #1: 'Time to Death'

No variable related to 'Time to Death'.

Table S1.  Basic characteristics of clinical feature: 'Time to Death'

Time to Death Duration (Months) 0.9-117.1 (median=20.5)
  censored N = 169
  death N = 35
     
  Significant variables N = 0
Clinical variable #2: 'AGE'

2 variables related to 'AGE'.

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

AGE Mean (SD) 69.19 (12)
  Significant variables N = 2
  pos. correlated 2
  neg. correlated 0
List of 2 variables associated with 'AGE'

Table S3.  Get Full Table List of 2 variables significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
MUTATIONRATE_NONSYNONYMOUS 0.19 0.004408 0.00882
MUTATIONRATE_SILENT 0.1561 0.01965 0.0196
Clinical variable #3: 'AGE'

One variable related to 'AGE'.

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

AGE Mean (SD) 69.19 (12)
  Significant variables N = 1
List of one variable associated with 'AGE'

Table S5.  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 0.0205 5.65 0.0183 2.84e-05 221 -3.87e-07 ( 3.65e-05 ) 0.0183 ( 0.00161 )
Clinical variable #4: 'PRIMARY.SITE.OF.DISEASE'

2 variables related to 'PRIMARY.SITE.OF.DISEASE'.

Table S6.  Basic characteristics of clinical feature: 'PRIMARY.SITE.OF.DISEASE'

PRIMARY.SITE.OF.DISEASE Labels N
  COLON 153
  RECTUM 68
     
  Significant variables N = 2
  Higher in RECTUM 2
  Higher in COLON 0
List of 2 variables associated with 'PRIMARY.SITE.OF.DISEASE'

Table S7.  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 4023 0.007219 0.0144 0.6133
MUTATIONRATE_NONSYNONYMOUS 4070 0.009904 0.0144 0.6088
Clinical variable #5: 'NEOPLASM.DISEASESTAGE'

One variable related to 'NEOPLASM.DISEASESTAGE'.

Table S8.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 49
  STAGE II 20
  STAGE IIA 57
  STAGE IIB 4
  STAGE III 18
  STAGE IIIA 2
  STAGE IIIB 20
  STAGE IIIC 20
  STAGE IV 31
  STAGE IVA 1
     
  Significant variables N = 1
List of one variable associated with 'NEOPLASM.DISEASESTAGE'

Table S9.  Get Full Table List of one variable differentially expressed by 'NEOPLASM.DISEASESTAGE'

ANOVA_P Q
MUTATIONRATE_NONSYNONYMOUS 0.04156 0.0831
Clinical variable #6: 'PATHOLOGY.T.STAGE'

One variable related to 'PATHOLOGY.T.STAGE'.

Table S10.  Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE Mean (SD) 2.79 (0.64)
  N
  1 9
  2 47
  3 149
  4 18
     
  Significant variables N = 1
  pos. correlated 1
  neg. correlated 0
List of one variable associated with 'PATHOLOGY.T.STAGE'

Table S11.  Get Full Table List of one variable significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
MUTATIONRATE_SILENT 0.1456 0.02973 0.0595
Clinical variable #7: 'PATHOLOGY.N.STAGE'

No variable related to 'PATHOLOGY.N.STAGE'.

Table S12.  Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE Mean (SD) 0.61 (0.81)
  N
  0 133
  1 44
  2 46
     
  Significant variables N = 0
Clinical variable #8: 'PATHOLOGY.M.STAGE'

No variable related to 'PATHOLOGY.M.STAGE'.

Table S13.  Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'

PATHOLOGY.M.STAGE Labels N
  M0 189
  M1 31
  M1A 1
     
  Significant variables N = 0
Clinical variable #9: 'GENDER'

No variable related to 'GENDER'.

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

GENDER Labels N
  FEMALE 107
  MALE 116
     
  Significant variables N = 0
Clinical variable #10: 'HISTOLOGICAL.TYPE'

2 variables related to 'HISTOLOGICAL.TYPE'.

Table S15.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  COLON ADENOCARCINOMA 130
  COLON MUCINOUS ADENOCARCINOMA 22
  RECTAL ADENOCARCINOMA 57
  RECTAL MUCINOUS ADENOCARCINOMA 8
     
  Significant variables N = 2
List of 2 variables associated with 'HISTOLOGICAL.TYPE'

Table S16.  Get Full Table List of 2 variables differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
MUTATIONRATE_NONSYNONYMOUS 6.192e-06 1.24e-05
MUTATIONRATE_SILENT 1.025e-05 1.24e-05
Clinical variable #11: 'COMPLETENESS.OF.RESECTION'

No variable related to 'COMPLETENESS.OF.RESECTION'.

Table S17.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 187
  R1 2
  R2 27
  RX 1
     
  Significant variables N = 0
Clinical variable #12: 'NUMBER.OF.LYMPH.NODES'

No variable related to 'NUMBER.OF.LYMPH.NODES'.

Table S18.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 2.25 (4.8)
  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 = 223

  • Number of variables = 2

  • Number of clinical features = 12

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

Student's t-test analysis

For two-class clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the log2-expression levels between the two clinical classes using 't.test' function in R

ANOVA analysis

For multi-class clinical features (ordinal or nominal), one-way analysis of variance (Howell 2002) was applied to compare the log2-expression levels between different clinical classes using 'anova' function in R

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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] 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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