Breast Invasive Carcinoma: Correlation between gene methylation status and clinical features
Maintained by Juok Cho (Broad Institute)
Overview
Introduction

This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.

Summary

Testing the association between 20235 genes and 5 clinical features across 539 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

  • 5 genes correlated to 'Time to Death'.

    • PLAC4 ,  PIH1D1 ,  SH3GL1 ,  OPN1SW ,  PRDM15

  • 185 genes correlated to 'AGE'.

    • FASN ,  SNORD32A ,  SNORA10 ,  SNORD33 ,  SNORD36A ,  ...

  • 125 genes correlated to 'GENDER'.

    • ALDOC ,  SCD ,  FCHSD1 ,  WDR17 ,  CRIP1 ,  ...

  • 1013 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

    • XPA ,  ABCB10 ,  GINS2 ,  SLC35A5 ,  TIMM17A ,  ...

  • 1883 genes correlated to 'NEOADJUVANT.THERAPY'.

    • XPA ,  GINS2 ,  FAHD1 ,  GNRHR2 ,  TRNT1 ,  ...

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 Q value < 0.05.

Clinical feature Statistical test Significant genes Associated with                 Associated with
Time to Death Cox regression test N=5 shorter survival N=1 longer survival N=4
AGE Spearman correlation test N=185 older N=173 younger N=12
GENDER t test N=125 male N=8 female N=117
RADIATIONS RADIATION REGIMENINDICATION t test N=1013 yes N=1000 no N=13
NEOADJUVANT THERAPY t test N=1883 yes N=1848 no N=35
Clinical variable #1: 'Time to Death'

5 genes related to 'Time to Death'.

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

Time to Death Duration (Months) 0-223.4 (median=17.9)
  censored N = 449
  death N = 61
     
  Significant markers N = 5
  associated with shorter survival 1
  associated with longer survival 4
List of 5 genes significantly associated with 'Time to Death' by Cox regression test

Table S2.  Get Full Table List of 5 genes significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
PLAC4 0.01 3.489e-07 0.0071 0.406
PIH1D1 771 6.524e-07 0.013 0.573
SH3GL1 0.01 1.661e-06 0.034 0.333
OPN1SW 0 2.273e-06 0.046 0.369
PRDM15 0 2.315e-06 0.047 0.353

Figure S1.  Get High-res Image As an example, this figure shows the association of PLAC4 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 3.49e-07 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

185 genes related to 'AGE'.

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

AGE Mean (SD) 57.58 (13)
  Significant markers N = 185
  pos. correlated 173
  neg. correlated 12
List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

Table S4.  Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
FASN 0.2901 6.876e-12 1.39e-07
SNORD32A 0.2875 1.077e-11 2.18e-07
SNORA10 0.287 1.164e-11 2.36e-07
SNORD33 0.286 1.376e-11 2.78e-07
SNORD36A 0.2849 1.653e-11 3.34e-07
SNORA64 0.2787 4.666e-11 9.44e-07
BARHL1 0.2772 6.055e-11 1.22e-06
SNORD72 0.2729 1.211e-10 2.45e-06
SNORD82 0.2727 1.243e-10 2.51e-06
SNORD42A 0.2713 1.573e-10 3.18e-06

Figure S2.  Get High-res Image As an example, this figure shows the association of FASN to 'AGE'. P value = 6.88e-12 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

125 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 533
  MALE 6
     
  Significant markers N = 125
  Higher in MALE 8
  Higher in FEMALE 117
List of top 10 genes differentially expressed by 'GENDER'

Table S6.  Get Full Table List of top 10 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
ALDOC -26.45 4.631e-58 9.37e-54 0.9162
SCD -17.54 2.596e-46 5.25e-42 0.859
FCHSD1 -14.73 3.02e-41 6.11e-37 0.6998
WDR17 -15.8 4.416e-38 8.93e-34 0.8771
CRIP1 -17.12 4.683e-32 9.47e-28 0.9174
RND2 -11.34 1.804e-21 3.65e-17 0.7502
SLCO4C1 -9.99 1.879e-20 3.8e-16 0.7639
KLHL32 -14.15 3.83e-20 7.75e-16 0.7724
RAD51AP2 -14.92 9.031e-20 1.83e-15 0.7417
TET2 -10.49 1.703e-18 3.44e-14 0.8368

Figure S3.  Get High-res Image As an example, this figure shows the association of ALDOC to 'GENDER'. P value = 4.63e-58 with T-test analysis.

Clinical variable #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

1013 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S7.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 134
  YES 405
     
  Significant markers N = 1013
  Higher in YES 1000
  Higher in NO 13
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S8.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
XPA 8.32 3.434e-15 6.95e-11 0.7077
ABCB10 8.07 7.467e-15 1.51e-10 0.6986
GINS2 7.78 1.004e-13 2.03e-09 0.6882
SLC35A5 7.45 4.33e-13 8.76e-09 0.6772
TIMM17A 7.33 9.615e-13 1.95e-08 0.6731
BRPF1 7.36 1.041e-12 2.11e-08 0.6539
MDM4 7.42 1.257e-12 2.54e-08 0.6962
KIAA0406 7.24 2.738e-12 5.54e-08 0.6664
RPRD1B 7.24 2.738e-12 5.54e-08 0.6664
NUS1 7.16 2.798e-12 5.66e-08 0.6844

Figure S4.  Get High-res Image As an example, this figure shows the association of XPA to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 3.43e-15 with T-test analysis.

Clinical variable #5: 'NEOADJUVANT.THERAPY'

1883 genes related to 'NEOADJUVANT.THERAPY'.

Table S9.  Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY Labels N
  NO 202
  YES 337
     
  Significant markers N = 1883
  Higher in YES 1848
  Higher in NO 35
List of top 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

Table S10.  Get Full Table List of top 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

T(pos if higher in 'YES') ttestP Q AUC
XPA 9.26 6.318e-19 1.28e-14 0.7145
GINS2 9.01 3.758e-18 7.6e-14 0.7106
FAHD1 8.79 2.1e-17 4.25e-13 0.6936
GNRHR2 8.7 4.847e-17 9.81e-13 0.7037
TRNT1 8.62 7.609e-17 1.54e-12 0.7073
UGCG 8.59 1.105e-16 2.24e-12 0.7065
EIF2S2 8.57 1.154e-16 2.33e-12 0.7117
ATXN3 8.57 1.212e-16 2.45e-12 0.6928
DDX41 8.42 3.74e-16 7.57e-12 0.695
NDUFS1 8.38 4.737e-16 9.58e-12 0.6875

Figure S5.  Get High-res Image As an example, this figure shows the association of XPA to 'NEOADJUVANT.THERAPY'. P value = 6.32e-19 with T-test analysis.

Methods & Data
Input

  • Expresson data file = BRCA.meth.for_correlation.filtered_data.txt

  • Clinical data file = BRCA.clin.merged.picked.txt

  • Number of patients = 539

  • Number of genes = 20235

  • Number of clinical features = 5

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

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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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] 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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