Correlation between RPPA expression and clinical features

Glioblastoma Multiforme (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 RPPA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1M61JN7

Overview

Introduction

This pipeline uses various statistical tests to identify RPPAs whose expression levels correlated to selected clinical features. The input file "GBM-TP.rppa.txt" is generated in the pipeline RPPA_AnnotateWithGene in the stddata run.

Summary

Testing the association between 208 genes and 8 clinical features across 232 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one genes.

24 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

CDKN1A|P21 , CHEK2|CHK2 , GAB2|GAB2 , SERPINE1|PAI-1 , NDRG1|NDRG1_PT346 , ...

6 genes correlated to 'YEARS_TO_BIRTH'.

TFRC|TFRC , ASNS|ASNS , EEF2K|EEF2K , ANXA1|ANNEXIN-1 , PDCD4|PDCD4 , ...

30 genes correlated to 'GENDER'.

SHC1|SHC_PY317 , TP53BP1|53BP1 , RBM15|RBM15 , RPS6KA1|P90RSK_PT359_S363 , PRKAA1|AMPK_PT172 , ...

1 gene correlated to 'RADIATION_THERAPY'.

CDH1|E-CADHERIN

No genes correlated to 'KARNOFSKY_PERFORMANCE_SCORE', 'HISTOLOGICAL_TYPE', 'RACE', and 'ETHNICITY'.

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=24		N=NA		N=NA
YEARS_TO_BIRTH	Spearman correlation test	N=6	older	N=4	younger	N=2
GENDER	Wilcoxon test	N=30	male	N=30	female	N=0
RADIATION_THERAPY	Wilcoxon test	N=1	yes	N=1	no	N=0
KARNOFSKY_PERFORMANCE_SCORE	Spearman correlation test	N=0
HISTOLOGICAL_TYPE	Kruskal-Wallis test	N=0
RACE	Kruskal-Wallis test	N=0
ETHNICITY	Wilcoxon test	N=0

Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

24 genes 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)	0.1-120.6 (median=10.9)
	censored	N = 60
	death	N = 171

	Significant markers	N = 24
	associated with shorter survival	NA
	associated with longer survival	NA

List of top 10 genes differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2. Get Full Table List of top 10 genes significantly associated with 'Time to Death' by Cox regression test. For the survival curves, it compared quantile intervals at c(0, 0.25, 0.50, 0.75, 1) and did not try survival analysis if there is only one interval.

	logrank_P	Q	C_index
CDKN1A\|P21	3.98e-05	0.0083	0.529
CHEK2\|CHK2	0.000118	0.011	0.43
GAB2\|GAB2	0.000153	0.011	0.435
SERPINE1\|PAI-1	0.000563	0.029	0.574
NDRG1\|NDRG1_PT346	0.00127	0.05	0.589
WWTR1\|TAZ	0.00145	0.05	0.574
ANXA1\|ANNEXIN-1	0.00261	0.078	0.539
CTNNA1\|ALPHA-CATENIN	0.00389	0.1	0.688
TGM2\|TRANSGLUTAMINASE	0.00684	0.16	0.574
ERBB2\|HER2_PY1248	0.00938	0.18	0.486

Clinical variable #2: 'YEARS_TO_BIRTH'

6 genes related to 'YEARS_TO_BIRTH'.

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


YEARS_TO_BIRTH	Mean (SD)	59.55 (14)
	Significant markers	N = 6
	pos. correlated	4
	neg. correlated	2

List of 6 genes differentially expressed by 'YEARS_TO_BIRTH'

Table S4. Get Full Table List of 6 genes significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

	SpearmanCorr	corrP	Q
TFRC\|TFRC	0.2912	6.475e-06	0.00135
ASNS\|ASNS	0.2166	0.0008991	0.0794
EEF2K\|EEF2K	-0.2122	0.001145	0.0794
ANXA1\|ANNEXIN-1	0.1869	0.004283	0.223
PDCD4\|PDCD4	-0.1776	0.006673	0.244
IGFBP2\|IGFBP2	0.1765	0.007038	0.244

Clinical variable #3: 'GENDER'

30 genes related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	92
	MALE	140

	Significant markers	N = 30
	Higher in MALE	30
	Higher in FEMALE	0

List of top 10 genes differentially expressed by 'GENDER'

Table S6. Get Full Table List of top 10 genes differentially expressed by 'GENDER'. 0 significant gene(s) located in sex chromosomes is(are) filtered out.

	W(pos if higher in 'MALE')	wilcoxontestP	Q	AUC
SHC1\|SHC_PY317	8751	3.834e-06	0.000797	0.6794
TP53BP1\|53BP1	4827	0.001262	0.131	0.6252
RBM15\|RBM15	4970	0.003298	0.138	0.6141
RPS6KA1\|P90RSK_PT359_S363	7909	0.003319	0.138	0.6141
PRKAA1\|AMPK_PT172	5016	0.00442	0.138	0.6106
RAF1\|C-RAF	5043	0.00523	0.138	0.6085
NRG1\|HEREGULIN	7834.5	0.005311	0.138	0.6083
MSH6\|MSH6	5061	0.005842	0.138	0.6071
GSK3A GSK3B\|GSK3-ALPHA-BETA	5067	0.00606	0.138	0.6066
MTOR\|MTOR	5082	0.006637	0.138	0.6054

Clinical variable #4: 'RADIATION_THERAPY'

One gene related to 'RADIATION_THERAPY'.

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


RADIATION_THERAPY	Labels	N
	NO	29
	YES	187

	Significant markers	N = 1
	Higher in YES	1
	Higher in NO	0

List of one gene differentially expressed by 'RADIATION_THERAPY'

Table S8. Get Full Table List of one gene differentially expressed by 'RADIATION_THERAPY'

	W(pos if higher in 'YES')	wilcoxontestP	Q	AUC
CDH1\|E-CADHERIN	3742	0.001005	0.209	0.69

Clinical variable #5: 'KARNOFSKY_PERFORMANCE_SCORE'

No gene related to 'KARNOFSKY_PERFORMANCE_SCORE'.

Table S9. Basic characteristics of clinical feature: 'KARNOFSKY_PERFORMANCE_SCORE'


KARNOFSKY_PERFORMANCE_SCORE	Mean (SD)	74.61 (18)
	Significant markers	N = 0

Clinical variable #6: 'HISTOLOGICAL_TYPE'

No gene related to 'HISTOLOGICAL_TYPE'.

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


HISTOLOGICAL_TYPE	Labels	N
	GLIOBLASTOMA MULTIFORME (GBM)	19
	TREATED PRIMARY GBM	3
	UNTREATED PRIMARY (DE NOVO) GBM	210

	Significant markers	N = 0

Clinical variable #7: 'RACE'

No gene related to 'RACE'.

Table S11. Basic characteristics of clinical feature: 'RACE'


RACE	Labels	N
	ASIAN	4
	BLACK OR AFRICAN AMERICAN	27
	WHITE	185

	Significant markers	N = 0

Clinical variable #8: 'ETHNICITY'

No gene related to 'ETHNICITY'.

Table S12. Basic characteristics of clinical feature: 'ETHNICITY'


ETHNICITY	Labels	N
	HISPANIC OR LATINO	3
	NOT HISPANIC OR LATINO	196

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = GBM-TP.rppa.txt
Clinical data file = GBM-TP.merged_data.txt
Number of patients = 232
Number of genes = 208
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.

There are also useful links about clinical features.

CDE Browser

Data dictionary for clinical data

NCI wiki - Biospecimen and Clinical XSD Files Specification

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)

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