Correlation between RPPA expression and clinical features
Testicular Germ Cell Tumors (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C12J6BB3
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/C12J6BB3
Overview
Introduction

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

Summary

Testing the association between 192 genes and 10 clinical features across 104 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 5 clinical features related to at least one genes.

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • SFRS1|SF2 ,  GSK3A GSK3B|GSK3-ALPHA-BETA ,  PREX1|PREX1 ,  ERRFI1|MIG-6 ,  GAB2|GAB2 ,  ...

  • 30 genes correlated to 'PATHOLOGIC_STAGE'.

    • CCNE2|CYCLIN_E2 ,  SFRS1|SF2 ,  PEA15|PEA15 ,  SCD1|SCD1 ,  PDK1|PDK1_PS241 ,  ...

  • 14 genes correlated to 'PATHOLOGY_T_STAGE'.

    • BCL2L1|BCL-XL ,  NRAS|N-RAS ,  ACVRL1|ACVRL1 ,  PEA15|PEA15 ,  SERPINE1|PAI-1 ,  ...

  • 23 genes correlated to 'PATHOLOGY_N_STAGE'.

    • SFRS1|SF2 ,  MAP2K1|MEK1 ,  PDK1|PDK1_PS241 ,  SCD1|SCD1 ,  TP53BP1|53BP1 ,  ...

  • 30 genes correlated to 'RADIATION_THERAPY'.

    • PDCD4|PDCD4 ,  SCD1|SCD1 ,  EEF2|EEF2 ,  SFRS1|SF2 ,  SMAD3|SMAD3 ,  ...

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'PATHOLOGY_M_STAGE', 'KARNOFSKY_PERFORMANCE_SCORE', '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=0        
YEARS_TO_BIRTH Spearman correlation test N=30 older N=18 younger N=12
PATHOLOGIC_STAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=14 higher stage N=9 lower stage N=5
PATHOLOGY_N_STAGE Spearman correlation test N=23 higher stage N=12 lower stage N=11
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
RADIATION_THERAPY Wilcoxon test N=30 yes N=30 no N=0
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test   N=0        
RACE Kruskal-Wallis test   N=0        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

No gene 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) 0.1-244.5 (median=44.5)
  censored N = 99
  death N = 4
     
  Significant markers N = 0
Clinical variable #2: 'YEARS_TO_BIRTH'

30 genes related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 31.99 (9)
  Significant markers N = 30
  pos. correlated 18
  neg. correlated 12
List of top 10 genes differentially expressed by 'YEARS_TO_BIRTH'

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

SpearmanCorr corrP Q
SFRS1|SF2 0.3494 0.0002777 0.0275
GSK3A GSK3B|GSK3-ALPHA-BETA 0.3394 0.0004242 0.0275
PREX1|PREX1 0.3343 0.0005231 0.0275
ERRFI1|MIG-6 -0.3321 0.0005722 0.0275
GAB2|GAB2 0.3257 0.0007425 0.0285
ERBB2|HER2 -0.3051 0.001637 0.0524
FN1|FIBRONECTIN -0.2937 0.00248 0.068
YBX1|YB-1_PS102 -0.2847 0.003403 0.0817
CDKN1A|P21 0.2726 0.005109 0.104
CDH1|E-CADHERIN -0.2704 0.005495 0.104
Clinical variable #3: 'PATHOLOGIC_STAGE'

30 genes related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  IS 38
  STAGE I 13
  STAGE IA 21
  STAGE IB 8
  STAGE II 4
  STAGE IIA 3
  STAGE IIC 1
  STAGE III 1
  STAGE IIIA 1
  STAGE IIIB 5
  STAGE IIIC 4
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'PATHOLOGIC_STAGE'

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

kruskal_wallis_P Q
CCNE2|CYCLIN_E2 0.0004291 0.0824
SFRS1|SF2 0.001149 0.0941
PEA15|PEA15 0.001471 0.0941
SCD1|SCD1 0.002608 0.125
PDK1|PDK1_PS241 0.004366 0.161
PDK1|PDK1 0.005177 0.161
MS4A1|CD20 0.006091 0.161
MAPK14|P38 0.006725 0.161
GAB2|GAB2 0.008036 0.171
MAPK9|JNK2 0.009377 0.178
Clinical variable #4: 'PATHOLOGY_T_STAGE'

14 genes related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 1.44 (0.55)
  N
  T1 61
  T2 40
  T3 3
     
  Significant markers N = 14
  pos. correlated 9
  neg. correlated 5
List of top 10 genes differentially expressed by 'PATHOLOGY_T_STAGE'

Table S7.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
BCL2L1|BCL-XL 0.3715 0.0001035 0.0199
NRAS|N-RAS -0.3154 0.001111 0.107
ACVRL1|ACVRL1 -0.2887 0.002954 0.186
PEA15|PEA15 0.2808 0.003885 0.186
SERPINE1|PAI-1 0.2692 0.005728 0.201
YBX1|YB-1 0.2663 0.006297 0.201
MAPK9|JNK2 0.2522 0.009798 0.228
PCNA|PCNA 0.2495 0.01063 0.228
CLDN7|CLAUDIN-7 0.2475 0.0113 0.228
CCNE2|CYCLIN_E2 -0.2459 0.01186 0.228
Clinical variable #5: 'PATHOLOGY_N_STAGE'

23 genes related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 0.2 (0.46)
  N
  N0 40
  N1 8
  N2 1
     
  Significant markers N = 23
  pos. correlated 12
  neg. correlated 11
List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

Table S9.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY_N_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
SFRS1|SF2 -0.4809 0.0004692 0.0895
MAP2K1|MEK1 0.4581 0.000932 0.0895
PDK1|PDK1_PS241 0.4269 0.002218 0.11
SCD1|SCD1 -0.4121 0.003255 0.11
TP53BP1|53BP1 -0.4062 0.003777 0.11
NRAS|N-RAS -0.4053 0.003864 0.11
CCNE2|CYCLIN_E2 -0.4037 0.004019 0.11
PEA15|PEA15 0.3985 0.00457 0.11
PDK1|PDK1 0.3782 0.007373 0.142
RPS6KA1|P90RSK 0.3782 0.007373 0.142
Clinical variable #6: 'PATHOLOGY_M_STAGE'

No gene related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 90
  class1 4
     
  Significant markers N = 0
Clinical variable #7: 'RADIATION_THERAPY'

30 genes related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 86
  YES 16
     
  Significant markers N = 30
  Higher in YES 30
  Higher in NO 0
List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

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

W(pos if higher in 'YES') wilcoxontestP Q AUC
PDCD4|PDCD4 1151 2.084e-05 0.004 0.8365
SCD1|SCD1 1110 0.0001051 0.0101 0.8067
EEF2|EEF2 1084 0.0002735 0.0129 0.7878
SFRS1|SF2 1076 0.000363 0.0129 0.782
SMAD3|SMAD3 1069 0.0004632 0.0129 0.7769
CDKN1B|P27_PT198 1068 0.0004794 0.0129 0.7762
NRAS|N-RAS 1062 0.0005886 0.0129 0.7718
KIT|C-KIT 1062 0.0005886 0.0129 0.7718
PREX1|PREX1 1056 0.0007207 0.0129 0.7674
XRCC5|KU80 1051 0.0008512 0.0129 0.7638
Clinical variable #8: 'KARNOFSKY_PERFORMANCE_SCORE'

No gene related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 94.62 (5.7)
  Score N
  80 3
  90 37
  100 40
     
  Significant markers N = 0
Clinical variable #9: 'RACE'

No gene related to 'RACE'.

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

RACE Labels N
  ASIAN 4
  BLACK OR AFRICAN AMERICAN 4
  WHITE 92
     
  Significant markers N = 0
Clinical variable #10: 'ETHNICITY'

No gene related to 'ETHNICITY'.

Table S15.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 10
  NOT HISPANIC OR LATINO 85
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 104

  • Number of genes = 192

  • Number of clinical features = 10

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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