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 17315 genes and 5 clinical features across 506 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

1 gene correlated to 'Time to Death'.

CDC73

145 genes correlated to 'AGE'.

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

186 genes correlated to 'GENDER'.

ALDOC , ZNF486 , CRIP1 , NMNAT3 , LOC400043 , ...

242 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

CCDC86 , TUBA4B , MAP1LC3B2 , TAF6 , HS1BP3 , ...

433 genes correlated to 'NEOADJUVANT.THERAPY'.

TAF6 , DKFZP779M0652 , HS1BP3 , SYNGR4 , RPS15 , ...

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=1	shorter survival	N=0	longer survival	N=1
AGE	Spearman correlation test	N=145	older	N=131	younger	N=14
GENDER	t test	N=186	male	N=41	female	N=145
RADIATIONS RADIATION REGIMENINDICATION	t test	N=242	yes	N=182	no	N=60
NEOADJUVANT THERAPY	t test	N=433	yes	N=326	no	N=107

Clinical variable #1: 'Time to Death'

One 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 = 1
	associated with shorter survival	0
	associated with longer survival	1

List of one gene significantly associated with 'Time to Death' by Cox regression test

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

	HazardRatio	Wald_P	Q	C_index
CDC73	0	2.414e-06	0.042	0.351

Figure S1. Get High-res Image As an example, this figure shows the association of CDC73 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 2.41e-06 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

145 genes related to 'AGE'.

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


AGE	Mean (SD)	57.47 (13)
	Significant markers	N = 145
	pos. correlated	131
	neg. correlated	14

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
KIF15	0.2973	9.117e-12	1.58e-07
MEX3C	0.2874	4.627e-11	8.01e-07
EGR2	0.2856	6.172e-11	1.07e-06
LGALS8	-0.2844	7.45e-11	1.29e-06
C10ORF35	0.2838	8.295e-11	1.44e-06
PAPSS1	0.2786	1.855e-10	3.21e-06
RPS2	0.2758	2.887e-10	5e-06
RPL13A	0.2738	3.932e-10	6.81e-06
MTMR7	0.2731	4.344e-10	7.52e-06
RPL7A	0.272	5.104e-10	8.83e-06

Figure S2. Get High-res Image As an example, this figure shows the association of KIF15 to 'AGE'. P value = 9.12e-12 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

186 genes related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	500
	MALE	6

	Significant markers	N = 186
	Higher in MALE	41
	Higher in FEMALE	145

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	-25.02	2.713e-89	4.7e-85	0.8637
ZNF486	-18.01	3.185e-56	5.51e-52	0.819
CRIP1	-16.27	3.944e-48	6.83e-44	0.8667
NMNAT3	-13.04	2.914e-33	5.05e-29	0.687
LOC400043	-12.99	7.319e-31	1.27e-26	0.6022
RND2	-12.86	9.539e-28	1.65e-23	0.7923
EML1	-11.17	7.152e-26	1.24e-21	0.6103
SPC25	-11.85	1.759e-25	3.05e-21	0.7523
HSPC157	-12.76	7.425e-25	1.29e-20	0.6363
ADCY5	12.08	2.017e-24	3.49e-20	0.7297

Figure S3. Get High-res Image As an example, this figure shows the association of ALDOC to 'GENDER'. P value = 2.71e-89 with T-test analysis.

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

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

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


RADIATIONS.RADIATION.REGIMENINDICATION	Labels	N
	NO	124
	YES	382

	Significant markers	N = 242
	Higher in YES	182
	Higher in NO	60

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
CCDC86	7.7	2.621e-13	4.54e-09	0.7089
TUBA4B	7.29	1.756e-12	3.04e-08	0.6682
MAP1LC3B2	-7.16	1.157e-11	2e-07	0.695
TAF6	7.05	1.256e-11	2.17e-07	0.6702
HS1BP3	7.02	1.372e-11	2.38e-07	0.6885
C12ORF52	7.04	1.413e-11	2.45e-07	0.6783
DDX54	7.04	1.413e-11	2.45e-07	0.6783
CCDC85B	7.04	1.486e-11	2.57e-07	0.6834
MBD3	6.99	2.301e-11	3.98e-07	0.6819
SYNGR4	6.91	4.995e-11	8.64e-07	0.7098

Figure S4. Get High-res Image As an example, this figure shows the association of CCDC86 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 2.62e-13 with T-test analysis.

Clinical variable #5: 'NEOADJUVANT.THERAPY'

433 genes related to 'NEOADJUVANT.THERAPY'.

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


NEOADJUVANT.THERAPY	Labels	N
	NO	190
	YES	316

	Significant markers	N = 433
	Higher in YES	326
	Higher in NO	107

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
TAF6	8.94	7.834e-18	1.36e-13	0.7046
DKFZP779M0652	8.4	4.816e-16	8.34e-12	0.7038
HS1BP3	8.22	1.7e-15	2.94e-11	0.694
SYNGR4	8.26	1.861e-15	3.22e-11	0.7152
RPS15	8.2	2.066e-15	3.58e-11	0.7072
CATSPER2	-8.33	2.624e-15	4.54e-11	0.7185
DLEU2L	-8.17	2.733e-15	4.73e-11	0.7058
C12ORF52	8.08	4.909e-15	8.5e-11	0.686
DDX54	8.08	4.909e-15	8.5e-11	0.686
MAP1LC3B2	-8.13	5.013e-15	8.68e-11	0.7126

Figure S5. Get High-res Image As an example, this figure shows the association of TAF6 to 'NEOADJUVANT.THERAPY'. P value = 7.83e-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 = 17315
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)

Made with Nozzle