Correlation between RPPA expression and clinical features
Brain Lower Grade Glioma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C15B01W4
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/C15B01W4
Overview
Introduction

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

Summary

Testing the association between 201 genes and 8 clinical features across 428 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 7 clinical features related to at least one genes.

  • 30 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • IGFBP2|IGFBP2 ,  TFRC|TFRC ,  SCD1|SCD1 ,  BAD|BAD_PS112 ,  ERBB2|HER2 ,  ...

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • IGFBP2|IGFBP2 ,  PEA15|PEA15 ,  SERPINE1|PAI-1 ,  CDH1|E-CADHERIN ,  ASNS|ASNS ,  ...

  • 28 genes correlated to 'GENDER'.

    • SHC1|SHC_PY317 ,  PRKAA1|AMPK_ALPHA ,  PREX1|PREX1 ,  GSK3A GSK3B|GSK3-ALPHA-BETA ,  TSC1|TSC1 ,  ...

  • 30 genes correlated to 'RADIATION_THERAPY'.

    • AR|AR ,  SYK|SYK ,  KIT|C-KIT ,  EIF4E|EIF4E ,  PRKCD|PKC-DELTA_PS664 ,  ...

  • 30 genes correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

    • MSH2|MSH2 ,  ARHI|ARHI ,  RAB25|RAB25 ,  COPS5|JAB1 ,  IGFBP2|IGFBP2 ,  ...

  • 30 genes correlated to 'HISTOLOGICAL_TYPE'.

    • SYK|SYK ,  AR|AR ,  ANXA1|ANNEXIN-1 ,  BAX|BAX ,  ERBB3|HER3_PY1289 ,  ...

  • 6 genes correlated to 'ETHNICITY'.

    • GSK3A GSK3B|GSK3-ALPHA-BETA_PS21_S9 ,  GSK3A GSK3B|GSK3_PS9 ,  SRC|SRC_PY527 ,  TSC2|TUBERIN_PT1462 ,  EIF4EBP1|4E-BP1_PT37_T46 ,  ...

  • No genes correlated to '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=30   N=NA   N=NA
YEARS_TO_BIRTH Spearman correlation test N=30 older N=16 younger N=14
GENDER Wilcoxon test N=28 male N=28 female N=0
RADIATION_THERAPY Wilcoxon test N=30 yes N=30 no N=0
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test N=30 higher score N=19 lower score N=11
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
RACE Kruskal-Wallis test   N=0        
ETHNICITY Wilcoxon test N=6 not hispanic or latino N=6 hispanic or latino N=0
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

30 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) 0-211.2 (median=20.7)
  censored N = 328
  death N = 99
     
  Significant markers N = 30
  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
IGFBP2|IGFBP2 4.26e-10 8.6e-08 0.764
TFRC|TFRC 3.25e-08 3.3e-06 0.687
SCD1|SCD1 1.37e-07 9.2e-06 0.27
BAD|BAD_PS112 5.04e-07 2.5e-05 0.677
ERBB2|HER2 1.46e-06 5.7e-05 0.59
RAB25|RAB25 1.79e-06 5.7e-05 0.318
PGR|PR 1.98e-06 5.7e-05 0.34
ESR1|ER-ALPHA 2.95e-06 7.4e-05 0.312
ERBB2|HER2_PY1248 5.94e-06 0.00012 0.627
BAX|BAX 6.13e-06 0.00012 0.708
Clinical variable #2: 'YEARS_TO_BIRTH'

30 genes related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 42.7 (13)
  Significant markers N = 30
  pos. correlated 16
  neg. correlated 14
List of top 10 genes differentially expressed by 'YEARS_TO_BIRTH'

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

SpearmanCorr corrP Q
IGFBP2|IGFBP2 0.2649 2.752e-08 5.53e-06
PEA15|PEA15 -0.251 1.478e-07 1.49e-05
SERPINE1|PAI-1 0.2193 4.806e-06 0.000273
CDH1|E-CADHERIN -0.2166 6.269e-06 0.000273
ASNS|ASNS 0.2158 6.789e-06 0.000273
EGFR|EGFR 0.2113 1.067e-05 0.000358
EGFR|EGFR_PY1173 0.2015 2.738e-05 0.000786
SRC|SRC_PY527 -0.1965 4.335e-05 0.00108
YBX1|YB-1 0.1953 4.826e-05 0.00108
ERBB2|HER2_PY1248 0.1847 0.0001235 0.00248
Clinical variable #3: 'GENDER'

28 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 191
  MALE 237
     
  Significant markers N = 28
  Higher in MALE 28
  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'. 3 significant gene(s) located in sex chromosomes is(are) filtered out.

W(pos if higher in 'MALE') wilcoxontestP Q AUC
SHC1|SHC_PY317 27314 0.0002342 0.0288 0.6034
PRKAA1|AMPK_ALPHA 18058.5 0.0003232 0.0288 0.6011
PREX1|PREX1 26961 0.0006704 0.0288 0.5956
GSK3A GSK3B|GSK3-ALPHA-BETA 18327 0.0007121 0.0288 0.5951
TSC1|TSC1 18331 0.0007203 0.0288 0.595
TSC2|TUBERIN 18432.5 0.0009601 0.0288 0.5928
EIF4EBP1|4E-BP1_PS65 18502 0.001165 0.0288 0.5913
BRAF|B-RAF 18516 0.001211 0.0288 0.591
STAT5A|STAT5-ALPHA 18546 0.001315 0.0288 0.5903
AKT1 AKT2 AKT3|AKT 18577 0.001431 0.0288 0.5896
Clinical variable #4: 'RADIATION_THERAPY'

30 genes related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 152
  YES 244
     
  Significant markers N = 30
  Higher in YES 30
  Higher in NO 0
List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

Table S8.  Get Full Table List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

W(pos if higher in 'YES') wilcoxontestP Q AUC
AR|AR 22299 0.0007003 0.049 0.6012
SYK|SYK 22250 0.0008222 0.049 0.5999
KIT|C-KIT 14916 0.001057 0.049 0.5978
EIF4E|EIF4E 22157 0.001109 0.049 0.5974
PRKCD|PKC-DELTA_PS664 15016 0.00145 0.049 0.5951
PRKAA1|AMPK_ALPHA 22069 0.001464 0.049 0.595
BAX|BAX 21980 0.001926 0.049 0.5926
PRKCA |PKC-ALPHA 15112 0.001949 0.049 0.5925
RPS6KA1|P90RSK 15358 0.00403 0.0785 0.5859
SCD1|SCD1 15406 0.004619 0.0785 0.5846
Clinical variable #5: 'KARNOFSKY_PERFORMANCE_SCORE'

30 genes related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 85.73 (13)
  Significant markers N = 30
  pos. correlated 19
  neg. correlated 11
List of top 10 genes differentially expressed by 'KARNOFSKY_PERFORMANCE_SCORE'

Table S10.  Get Full Table List of top 10 genes significantly correlated to 'KARNOFSKY_PERFORMANCE_SCORE' by Spearman correlation test

SpearmanCorr corrP Q
MSH2|MSH2 0.3493 0.0001497 0.0301
ARHI|ARHI 0.3118 0.0007741 0.0764
RAB25|RAB25 0.2009 0.001796 0.0764
COPS5|JAB1 0.2905 0.001799 0.0764
IGFBP2|IGFBP2 -0.1998 0.001911 0.0764
CTNNB1|ALPHA-CATENIN 0.196 0.002332 0.0764
SCD1|SCD1 0.1911 0.00301 0.0764
CHEK1|CHK1 0.1909 0.003043 0.0764
EGFR|EGFR_PY1173 -0.1865 0.003802 0.0787
C12ORF5|TIGAR -0.1847 0.004162 0.0787
Clinical variable #6: 'HISTOLOGICAL_TYPE'

30 genes related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  ASTROCYTOMA 147
  OLIGOASTROCYTOMA 114
  OLIGODENDROGLIOMA 167
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

Table S12.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

kruskal_wallis_P Q
SYK|SYK 1.511e-20 3.04e-18
AR|AR 2.601e-12 2.61e-10
ANXA1|ANNEXIN-1 2.39e-10 1.6e-08
BAX|BAX 3.434e-10 1.73e-08
ERBB3|HER3_PY1289 1.733e-08 5.31e-07
KIT|C-KIT 1.765e-08 5.31e-07
YAP1|YAP_PS127 1.851e-08 5.31e-07
FASN|FASN 4.854e-08 1.22e-06
STAT3|STAT3_PY705 8.605e-08 1.92e-06
ACACA|ACC1 1.088e-07 2.19e-06
Clinical variable #7: 'RACE'

No gene related to 'RACE'.

Table S13.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 8
  BLACK OR AFRICAN AMERICAN 18
  WHITE 393
     
  Significant markers N = 0
Clinical variable #8: 'ETHNICITY'

6 genes related to 'ETHNICITY'.

Table S14.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 26
  NOT HISPANIC OR LATINO 377
     
  Significant markers N = 6
  Higher in NOT HISPANIC OR LATINO 6
  Higher in HISPANIC OR LATINO 0
List of 6 genes differentially expressed by 'ETHNICITY'

Methods & Data
Input

  • Expresson data file = LGG-TP.rppa.txt

  • Clinical data file = LGG-TP.merged_data.txt

  • Number of patients = 428

  • Number of genes = 201

  • 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.

  • 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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