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 17362 genes and 5 clinical features across 506 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

- 140 genes correlated to 'AGE'.

- KIF15 , EGR2 , MEX3C , LGALS8 , C10ORF35 , ...

- 169 genes correlated to 'GENDER'.

- ALDOC , ZNF486 , CRIP1 , RAD51AP2 , DNAJC15 , ...

- 229 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

- CCDC86 , CCDC85B , NDUFB4 , HS1BP3 , SGSH , ...

- 421 genes correlated to 'NEOADJUVANT.THERAPY'.

- TAF6 , PIN1 , HS1BP3 , RPS15 , SGSH , ...

- No genes correlated to 'Time to Death'

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=0 |  |  |  |
AGE | Spearman correlation test | N=140 | older | N=126 | younger | N=14
GENDER | t test | N=169 | male | N=39 | female | N=130
RADIATIONS RADIATION REGIMENINDICATION | t test | N=229 | yes | N=170 | no | N=59
NEOADJUVANT THERAPY | t test | N=421 | yes | N=323 | no | N=98

Clinical variable #1: 'Time to Death'

No gene related to 'Time to Death'.

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

Time to Death | Duration (Months) | 0-223.4 (median=18.3)
| censored | N = 422
| death | N = 56
| Significant markers | N = 0

Clinical variable #2: 'AGE'

140 genes related to 'AGE'.

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

AGE | Mean (SD) | 57.47 (13)
| Significant markers | N = 140
| pos. correlated | 126
| neg. correlated | 14

List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

Table S3. Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
KIF15 | 0.295 | 1.336e-11 | 2.32e-07
EGR2 | 0.2895 | 3.331e-11 | 5.78e-07
MEX3C | 0.2874 | 4.644e-11 | 8.06e-07
LGALS8 | -0.2843 | 7.59e-11 | 1.32e-06
C10ORF35 | 0.2782 | 1.995e-10 | 3.46e-06
PAPSS1 | 0.2766 | 2.559e-10 | 4.44e-06
RPS2 | 0.2757 | 2.92e-10 | 5.07e-06
RPL13A | 0.2726 | 4.684e-10 | 8.13e-06
RPL7A | 0.2706 | 6.378e-10 | 1.11e-05
MTMR7 | 0.2705 | 6.44e-10 | 1.12e-05

Figure S1. Get High-res Image As an example, this figure shows the association of KIF15 to 'AGE'. P value = 1.34e-11 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

169 genes related to 'GENDER'.

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

GENDER | Labels | N
| FEMALE | 500
| MALE | 6
| Significant markers | N = 169
| Higher in MALE | 39
| Higher in FEMALE | 130

List of top 10 genes differentially expressed by 'GENDER'

Table S5. Get Full Table List of top 10 genes differentially expressed by 'GENDER'

| T(pos if higher in 'MALE') | ttestP | Q | AUC
--- | --- | --- | --- | ---
ALDOC | -25.05 | 2.072e-89 | 3.6e-85 | 0.8637
ZNF486 | -18 | 3.5e-56 | 6.08e-52 | 0.8187
CRIP1 | -16.27 | 3.831e-48 | 6.65e-44 | 0.8647
RAD51AP2 | -14.02 | 2.555e-37 | 4.43e-33 | 0.6927
DNAJC15 | -13.66 | 3.497e-35 | 6.07e-31 | 0.7287
RIMS4 | -17.55 | 8.669e-34 | 1.5e-29 | 0.7963
NMNAT3 | -13.06 | 2.507e-33 | 4.35e-29 | 0.6853
LOC400043 | -13 | 7.144e-31 | 1.24e-26 | 0.6022
RND2 | -12.85 | 1.011e-27 | 1.75e-23 | 0.791
EML1 | -11.16 | 7.642e-26 | 1.33e-21 | 0.608

Figure S2. Get High-res Image As an example, this figure shows the association of ALDOC to 'GENDER'. P value = 2.07e-89 with T-test analysis.

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

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

Table S6. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N
| NO | 124
| YES | 382
| Significant markers | N = 229
| Higher in YES | 170
| Higher in NO | 59

List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S7. Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

| T(pos if higher in 'YES') | ttestP | Q | AUC
--- | --- | --- | --- | ---
CCDC86 | 7.5 | 1.024e-12 | 1.78e-08 | 0.7064
CCDC85B | 7.2 | 5.52e-12 | 9.58e-08 | 0.6853
NDUFB4 | 7.15 | 8.444e-12 | 1.47e-07 | 0.6847
HS1BP3 | 7.03 | 1.296e-11 | 2.25e-07 | 0.6874
SGSH | 6.96 | 1.96e-11 | 3.4e-07 | 0.6698
MAP3K10 | 6.96 | 2.279e-11 | 3.96e-07 | 0.6672
CAPNS1 | 6.96 | 2.6e-11 | 4.51e-07 | 0.6825
TAF6 | 6.9 | 3.187e-11 | 5.53e-07 | 0.6677
MAP1LC3B2 | -6.97 | 3.543e-11 | 6.15e-07 | 0.6913
ERP29 | 6.78 | 4.002e-11 | 6.95e-07 | 0.7045

Figure S3. Get High-res Image As an example, this figure shows the association of CCDC86 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 1.02e-12 with T-test analysis.

Clinical variable #5: 'NEOADJUVANT.THERAPY'

421 genes related to 'NEOADJUVANT.THERAPY'.

Table S8. Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY | Labels | N
| NO | 190
| YES | 316
| Significant markers | N = 421
| Higher in YES | 323
| Higher in NO | 98

List of top 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

Table S9. Get Full Table List of top 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

| T(pos if higher in 'YES') | ttestP | Q | AUC
--- | --- | --- | --- | ---
TAF6 | 9 | 4.894e-18 | 8.5e-14 | 0.705
PIN1 | 8.87 | 1.301e-17 | 2.26e-13 | 0.7055
HS1BP3 | 8.34 | 7.221e-16 | 1.25e-11 | 0.6969
RPS15 | 8.25 | 1.397e-15 | 2.43e-11 | 0.7075
SGSH | 8.23 | 1.624e-15 | 2.82e-11 | 0.6892
CDC20B | 8.09 | 4.862e-15 | 8.44e-11 | 0.6985
CATSPER2 | -8.17 | 7.68e-15 | 1.33e-10 | 0.715
DLEU2L | -8.02 | 7.707e-15 | 1.34e-10 | 0.7041
CCDC86 | 7.99 | 1.197e-14 | 2.08e-10 | 0.7076
UCK1 | 7.96 | 1.205e-14 | 2.09e-10 | 0.6756

Figure S4. Get High-res Image As an example, this figure shows the association of TAF6 to 'NEOADJUVANT.THERAPY'. P value = 4.89e-18 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 = 506

- Number of genes = 17362

- 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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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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