Correlation between gene methylation status and clinical features
Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1QF8S86
Overview
Introduction

This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features. The input file "DLBC-TP.meth.by_min_clin_corr.data.txt" is generated in the pipeline Methylation_Preprocess in stddata run.

Summary

Testing the association between 16579 genes and 8 clinical features across 48 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one genes.

  • 2 genes correlated to 'YEARS_TO_BIRTH'.

    • LY75 ,  BRIX1

  • 1 gene correlated to 'RACE'.

    • AHRR

  • 30 genes correlated to 'ETHNICITY'.

    • KIAA1737 ,  CHD6 ,  MAML2 ,  C14ORF21 ,  ADAMTS6 ,  ...

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'TUMOR_TISSUE_SITE', 'GENDER', 'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.

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 genes that are significantly associated with each clinical feature at P value < 0.05 and Q value < 0.3.

Clinical feature Statistical test Significant genes Associated with                 Associated with
DAYS_TO_DEATH_OR_LAST_FUP Cox regression test   N=0        
YEARS_TO_BIRTH Spearman correlation test N=2 older N=1 younger N=1
TUMOR_TISSUE_SITE Kruskal-Wallis test   N=0        
GENDER Wilcoxon test   N=0        
RADIATION_THERAPY Wilcoxon test   N=0        
HISTOLOGICAL_TYPE Kruskal-Wallis test   N=0        
RACE Kruskal-Wallis test N=1        
ETHNICITY Wilcoxon test N=30 not hispanic or latino N=30 hispanic or latino N=0
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

No gene 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) 0-211.2 (median=29.2)
  censored N = 38
  death N = 9
     
  Significant markers N = 0
Clinical variable #2: 'YEARS_TO_BIRTH'

2 genes related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 56.27 (14)
  Significant markers N = 2
  pos. correlated 1
  neg. correlated 1
List of 2 genes differentially expressed by 'YEARS_TO_BIRTH'

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

SpearmanCorr corrP Q
LY75 0.5874 1.137e-05 0.189
BRIX1 -0.5653 2.837e-05 0.235
Clinical variable #3: 'TUMOR_TISSUE_SITE'

No gene related to 'TUMOR_TISSUE_SITE'.

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

TUMOR_TISSUE_SITE Labels N
  ADRENAL 2
  ASCITES/PERITONEUM 2
  BONE 4
  BRAIN 3
  BREAST 1
  COLON 2
  LIVER 1
  OTHER EXTRANODAL SITE 1
  PAROTID GLAND 1
  SMALL INTESTINE 3
  SOFT TISSUE (MUSCLE LIGAMENTS SUBCUTANEOUS) 1
  STOMACH 2
  THYROID 2
     
  Significant markers N = 0
Clinical variable #4: 'GENDER'

No gene related to 'GENDER'.

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

GENDER Labels N
  FEMALE 26
  MALE 22
     
  Significant markers N = 0
Clinical variable #5: 'RADIATION_THERAPY'

No gene related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 40
  YES 7
     
  Significant markers N = 0
Clinical variable #6: 'HISTOLOGICAL_TYPE'

No gene related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  DIFFUSE LARGE B-CELL LYMPHOMA (DLBCL) NOS (ANY ANATOMIC SITE NODAL OR EXTRANODAL) 41
  PRIMARY DLBCL OF THE CNS 3
  PRIMARY MEDIASTINAL (THYMIC) DLBCL 4
     
  Significant markers N = 0
Clinical variable #7: 'RACE'

One gene related to 'RACE'.

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

RACE Labels N
  ASIAN 18
  BLACK OR AFRICAN AMERICAN 1
  WHITE 29
     
  Significant markers N = 1
List of one gene differentially expressed by 'RACE'

Table S9.  Get Full Table List of one gene differentially expressed by 'RACE'

kruskal_wallis_P Q
AHRR 7.339e-06 0.122
Clinical variable #8: 'ETHNICITY'

30 genes related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 12
  NOT HISPANIC OR LATINO 36
     
  Significant markers N = 30
  Higher in NOT HISPANIC OR LATINO 30
  Higher in HISPANIC OR LATINO 0
List of top 10 genes differentially expressed by 'ETHNICITY'

Methods & Data
Input
  • Expresson data file = DLBC-TP.meth.by_min_clin_corr.data.txt

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

  • Number of patients = 48

  • Number of genes = 16579

  • Number of clinical features = 8

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)