Correlation between RPPA expression and clinical features
Glioblastoma Multiforme (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C18S4NZD
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between RPPA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C18S4NZD
Overview
Introduction

This pipeline uses various statistical tests to identify RPPAs whose expression levels correlated to selected clinical features.

Summary

Testing the association between 171 genes and 7 clinical features across 211 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 2 clinical features related to at least one genes.

  • 11 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • PTGS2|COX-2-R-C ,  WWTR1|TAZ-R-C ,  IGFBP2|IGFBP2-R-V ,  ANXA1|ANNEXIN_I-R-V ,  CHEK2|CHK2-M-C ,  ...

  • 30 genes correlated to 'GENDER'.

    • SHC1|SHC_PY317-R-NA ,  RPS6KA1|P90RSK_PT359_S363-R-C ,  TP53BP1|53BP1-R-C ,  FRAP1|MTOR-R-V ,  XRCC5|KU80-R-C ,  ...

  • No genes correlated to 'YEARS_TO_BIRTH', 'KARNOFSKY_PERFORMANCE_SCORE', 'HISTOLOGICAL_TYPE', 'RADIATIONS_RADIATION_REGIMENINDICATION', and 'RACE'.

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=11 shorter survival N=7 longer survival N=4
YEARS_TO_BIRTH Spearman correlation test   N=0        
GENDER Wilcoxon test N=30 male N=30 female N=0
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test   N=0        
HISTOLOGICAL_TYPE Kruskal-Wallis test   N=0        
RADIATIONS_RADIATION_REGIMENINDICATION Wilcoxon test   N=0        
RACE Kruskal-Wallis test   N=0        
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

11 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=10.2)
  censored N = 52
  death N = 158
     
  Significant markers N = 11
  associated with shorter survival 7
  associated with longer survival 4
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

HazardRatio Wald_P Q C_index
PTGS2|COX-2-R-C 1.52 3.289e-05 0.0056 0.595
WWTR1|TAZ-R-C 1.73 0.0003876 0.033 0.575
IGFBP2|IGFBP2-R-V 1.3 0.000698 0.034 0.577
ANXA1|ANNEXIN_I-R-V 1.26 0.0008058 0.034 0.571
CHEK2|CHK2-M-C 0.55 0.003477 0.12 0.423
TGM2|TRANSGLUTAMINASE-M-V 2.8 0.004907 0.14 0.578
ERRFI1|MIG-6-M-V 2 0.007114 0.15 0.56
CTNNB1|BETA-CATENIN-R-V 0.71 0.007499 0.15 0.431
BCL2L11|BIM-R-V 0.65 0.008469 0.15 0.412
SRC|SRC_PY527-R-V 0.66 0.008737 0.15 0.451
Clinical variable #2: 'YEARS_TO_BIRTH'

No gene related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 59.88 (14)
  Significant markers N = 0
Clinical variable #3: 'GENDER'

30 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 85
  MALE 126
     
  Significant markers N = 30
  Higher in MALE 30
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S5.  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-R-NA 7151 3.663e-05 0.00626 0.6677
RPS6KA1|P90RSK_PT359_S363-R-C 6708 0.001875 0.12 0.6263
TP53BP1|53BP1-R-C 4064 0.003009 0.12 0.6205
FRAP1|MTOR-R-V 4108 0.004162 0.12 0.6164
XRCC5|KU80-R-C 4144 0.005388 0.12 0.6131
MSH6|MSH6-R-C 4158.5 0.005968 0.12 0.6117
PRKAA1|AMPK_PT172-R-V 4170 0.006467 0.12 0.6106
NCOA3|AIB1-M-V 4182 0.007028 0.12 0.6095
GATA3|GATA3-M-V 6516 0.007632 0.12 0.6084
CASP7|CASPASE-7_CLEAVEDD198-R-C 6514 0.007737 0.12 0.6082
Clinical variable #4: 'KARNOFSKY_PERFORMANCE_SCORE'

No gene related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 75.68 (15)
  Significant markers N = 0
Clinical variable #5: 'HISTOLOGICAL_TYPE'

No gene related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  GLIOBLASTOMA MULTIFORME (GBM) 1
  TREATED PRIMARY GBM 3
  UNTREATED PRIMARY (DE NOVO) GBM 207
     
  Significant markers N = 0
Clinical variable #6: 'RADIATIONS_RADIATION_REGIMENINDICATION'

No gene related to 'RADIATIONS_RADIATION_REGIMENINDICATION'.

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

RADIATIONS_RADIATION_REGIMENINDICATION Labels N
  NO 156
  YES 55
     
  Significant markers N = 0
Clinical variable #7: 'RACE'

No gene related to 'RACE'.

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

RACE Labels N
  ASIAN 4
  BLACK OR AFRICAN AMERICAN 11
  WHITE 180
     
  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 = 211

  • Number of genes = 171

  • Number of clinical features = 7

Selected clinical features

  • For clinical features selected for this analysis and their value conozzle.versions, please find a documentation on selected CDEs .

  • 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, 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

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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