Correlation between RPPA expression and clinical features
Prostate Adenocarcinoma (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/C17H1J29
Overview
Introduction

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

Summary

Testing the association between 195 genes and 11 clinical features across 352 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 9 clinical features related to at least one genes.

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • RB1|RB_PS807_S811 ,  MAP2K1|MEK1 ,  JUN|C-JUN_PS73 ,  ANXA1|ANNEXIN-1 ,  MAPK1 MAPK3|MAPK_PT202_Y204 ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • EIF4G1|EIF4G ,  XRCC5|KU80 ,  PRKCA |PKC-ALPHA ,  SQSTM1|P62-LCK-LIGAND ,  DVL3|DVL3 ,  ...

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • SQSTM1|P62-LCK-LIGAND ,  NF2|NF2 ,  ITGA2|CD49B ,  TFRC|TFRC ,  CDH3|P-CADHERIN ,  ...

  • 30 genes correlated to 'RADIATION_THERAPY'.

    • ASNS|ASNS ,  COL6A1|COLLAGEN_VI ,  CAV1|CAVEOLIN-1 ,  MAPK14|P38 ,  PRKCA |PKC-ALPHA ,  ...

  • 12 genes correlated to 'HISTOLOGICAL_TYPE'.

    • BAD|BAD_PS112 ,  MAPK14|P38_PT180_Y182 ,  SRC|SRC_PY527 ,  MAPK14|P38 ,  RAD51|RAD51 ,  ...

  • 30 genes correlated to 'RESIDUAL_TUMOR'.

    • CCNB1|CYCLIN_B1 ,  ASNS|ASNS ,  TFRC|TFRC ,  MAP2K1|MEK1_PS217_S221 ,  SQSTM1|P62-LCK-LIGAND ,  ...

  • 30 genes correlated to 'NUMBER_OF_LYMPH_NODES'.

    • SQSTM1|P62-LCK-LIGAND ,  NF2|NF2 ,  ITGA2|CD49B ,  TFRC|TFRC ,  CDH3|P-CADHERIN ,  ...

  • 30 genes correlated to 'GLEASON_SCORE'.

    • EIF4G1|EIF4G ,  BCL2L11|BIM ,  GAB2|GAB2 ,  SQSTM1|P62-LCK-LIGAND ,  CAV1|CAVEOLIN-1 ,  ...

  • 30 genes correlated to 'PSA_VALUE'.

    • BRCA2|BRCA2 ,  ACVRL1|ACVRL1 ,  MYH9|MYOSIN-IIA_PS1943 ,  ERBB3|HER3_PY1289 ,  PRKCD|PKC-DELTA_PS664 ,  ...

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 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=0        
YEARS_TO_BIRTH Spearman correlation test N=30 older N=16 younger N=14
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=16 lower stage N=14
PATHOLOGY_N_STAGE Wilcoxon test N=30 n1 N=30 n0 N=0
RADIATION_THERAPY Wilcoxon test N=30 yes N=30 no N=0
HISTOLOGICAL_TYPE Wilcoxon test N=12 prostate adenocarcinoma, other subtype N=12 prostate adenocarcinoma acinar type N=0
RESIDUAL_TUMOR Kruskal-Wallis test N=30        
NUMBER_OF_LYMPH_NODES Spearman correlation test N=30 higher number_of_lymph_nodes N=12 lower number_of_lymph_nodes N=18
GLEASON_SCORE Spearman correlation test N=30 higher score N=17 lower score N=13
PSA_VALUE Spearman correlation test N=30 higher psa_value N=12 lower psa_value N=18
RACE Kruskal-Wallis 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) 0.9-165.2 (median=32.5)
  censored N = 344
  death N = 7
     
  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) 61.44 (6.6)
  Significant markers N = 30
  pos. correlated 16
  neg. correlated 14
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
RB1|RB_PS807_S811 0.2558 1.277e-06 0.000249
MAP2K1|MEK1 -0.2273 1.804e-05 0.00176
JUN|C-JUN_PS73 0.2068 9.934e-05 0.00591
ANXA1|ANNEXIN-1 -0.2043 0.0001213 0.00591
MAPK1 MAPK3|MAPK_PT202_Y204 0.2007 0.0001598 0.00623
MAPK8|JNK_PT183_PY185 0.1931 0.0002858 0.00823
EGFR|EGFR_PY1068 0.1921 0.0003065 0.00823
EIF4EBP1|4E-BP1_PT37_T46 0.1908 0.0003375 0.00823
GSK3A GSK3B|GSK3-ALPHA-BETA -0.1882 0.0004093 0.00842
FOXM1|FOXM1 0.1873 0.0004343 0.00842
Clinical variable #3: 'PATHOLOGY_T_STAGE'

30 genes related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 2.71 (0.51)
  N
  T2 110
  T3 227
  T4 10
     
  Significant markers N = 30
  pos. correlated 16
  neg. correlated 14
List of top 10 genes differentially expressed by 'PATHOLOGY_T_STAGE'

Table S5.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
EIF4G1|EIF4G 0.2814 9.723e-08 1.9e-05
XRCC5|KU80 0.2715 2.794e-07 2.72e-05
PRKCA |PKC-ALPHA -0.2522 1.961e-06 0.000114
SQSTM1|P62-LCK-LIGAND 0.2492 2.611e-06 0.000114
DVL3|DVL3 0.2473 3.135e-06 0.000114
PRKCA |PKC-ALPHA_PS657 -0.2461 3.507e-06 0.000114
GAB2|GAB2 0.2392 6.606e-06 0.000184
CCNB1|CYCLIN_B1 0.2286 1.705e-05 0.000415
COL6A1|COLLAGEN_VI -0.2269 1.986e-05 0.00043
PKC|PKC-PAN_BETAII_PS660 -0.2251 2.32e-05 0.000435
Clinical variable #4: 'PATHOLOGY_N_STAGE'

30 genes related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Labels N
  N0 246
  N1 63
     
  Significant markers N = 30
  Higher in N1 30
  Higher in N0 0
List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

Table S7.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

W(pos if higher in 'N1') wilcoxontestP Q AUC
SQSTM1|P62-LCK-LIGAND 10492 1.462e-05 0.00285 0.677
NF2|NF2 5348 0.0001484 0.0145 0.6549
ITGA2|CD49B 5443 0.0002688 0.0175 0.6488
TFRC|TFRC 9915 0.0006207 0.0303 0.6398
CDH3|P-CADHERIN 5668 0.001009 0.0393 0.6343
PGR|PR 5839 0.002546 0.0827 0.6232
ASNS|ASNS 9609.5 0.003287 0.0916 0.62
RAF1|C-RAF_PS338 5916 0.003778 0.0921 0.6183
EGFR|EGFR_PY1173 6012.5 0.006077 0.132 0.612
SYK|SYK 9456 0.006997 0.136 0.6101
Clinical variable #5: 'RADIATION_THERAPY'

30 genes related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 283
  YES 41
     
  Significant markers N = 30
  Higher in YES 30
  Higher in NO 0
List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

Table S9.  Get Full Table List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

W(pos if higher in 'YES') wilcoxontestP Q AUC
ASNS|ASNS 7985 9.853e-05 0.0192 0.6882
COL6A1|COLLAGEN_VI 3795 0.0003456 0.0337 0.6729
CAV1|CAVEOLIN-1 3976 0.001132 0.0495 0.6573
MAPK14|P38 2957 0.001317 0.0495 0.689
PRKCA |PKC-ALPHA 4029 0.001572 0.0495 0.6528
PRKCD|PKC-DELTA_PS664 4039 0.001671 0.0495 0.6519
NF2|NF2 4092 0.002299 0.0495 0.6473
PGR|PR 4107 0.002512 0.0495 0.646
TFRC|TFRC 7489 0.002618 0.0495 0.6454
RICTOR|RICTOR 4120 0.002711 0.0495 0.6449
Clinical variable #6: 'HISTOLOGICAL_TYPE'

12 genes related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  PROSTATE ADENOCARCINOMA ACINAR TYPE 341
  PROSTATE ADENOCARCINOMA, OTHER SUBTYPE 11
     
  Significant markers N = 12
  Higher in PROSTATE ADENOCARCINOMA, OTHER SUBTYPE 12
  Higher in PROSTATE ADENOCARCINOMA ACINAR TYPE 0
List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

Clinical variable #7: 'RESIDUAL_TUMOR'

30 genes related to 'RESIDUAL_TUMOR'.

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

RESIDUAL_TUMOR Labels N
  R0 221
  R1 108
  R2 3
  RX 10
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RESIDUAL_TUMOR'

Table S13.  Get Full Table List of top 10 genes differentially expressed by 'RESIDUAL_TUMOR'

kruskal_wallis_P Q
CCNB1|CYCLIN_B1 3.502e-05 0.00683
ASNS|ASNS 7.982e-05 0.00778
TFRC|TFRC 0.0002444 0.0131
MAP2K1|MEK1_PS217_S221 0.0002689 0.0131
SQSTM1|P62-LCK-LIGAND 0.0005582 0.0158
PRKCA |PKC-ALPHA_PS657 0.0006044 0.0158
PRKCA |PKC-ALPHA 0.0006433 0.0158
FOXM1|FOXM1 0.0006491 0.0158
CCNE1|CYCLIN_E1 0.0008534 0.0185
RICTOR|RICTOR_PT1135 0.001284 0.0216
Clinical variable #8: 'NUMBER_OF_LYMPH_NODES'

30 genes related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 0.51 (1.5)
  Significant markers N = 30
  pos. correlated 12
  neg. correlated 18
List of top 10 genes differentially expressed by 'NUMBER_OF_LYMPH_NODES'

Table S15.  Get Full Table List of top 10 genes significantly correlated to 'NUMBER_OF_LYMPH_NODES' by Spearman correlation test

SpearmanCorr corrP Q
SQSTM1|P62-LCK-LIGAND 0.2568 6.662e-06 0.0013
NF2|NF2 -0.2326 4.75e-05 0.00463
ITGA2|CD49B -0.2128 0.0002052 0.0126
TFRC|TFRC 0.2095 0.0002583 0.0126
CDH3|P-CADHERIN -0.2011 0.0004587 0.0179
PGR|PR -0.193 0.0007799 0.0253
RAF1|C-RAF_PS338 -0.1802 0.001725 0.0481
ASNS|ASNS 0.1739 0.002501 0.061
EGFR|EGFR_PY1173 -0.1696 0.003215 0.0697
SYK|SYK 0.1597 0.005565 0.0888
Clinical variable #9: 'GLEASON_SCORE'

30 genes related to 'GLEASON_SCORE'.

Table S16.  Basic characteristics of clinical feature: 'GLEASON_SCORE'

GLEASON_SCORE Mean (SD) 7.64 (1)
  Score N
  6 30
  7 174
  8 41
  9 105
  10 2
     
  Significant markers N = 30
  pos. correlated 17
  neg. correlated 13
List of top 10 genes differentially expressed by 'GLEASON_SCORE'

Table S17.  Get Full Table List of top 10 genes significantly correlated to 'GLEASON_SCORE' by Spearman correlation test

SpearmanCorr corrP Q
EIF4G1|EIF4G 0.4011 4.904e-15 9.56e-13
BCL2L11|BIM 0.382 1.129e-13 1.1e-11
GAB2|GAB2 0.3779 2.145e-13 1.39e-11
SQSTM1|P62-LCK-LIGAND 0.3401 5.59e-11 2.73e-09
CAV1|CAVEOLIN-1 -0.328 2.856e-10 1.11e-08
XRCC5|KU80 0.3248 4.291e-10 1.39e-08
PGR|PR -0.3173 1.132e-09 3.15e-08
ACVRL1|ACVRL1 -0.3149 1.535e-09 3.74e-08
NF2|NF2 -0.3126 2.03e-09 4.23e-08
CCNB1|CYCLIN_B1 0.3116 2.312e-09 4.23e-08
Clinical variable #10: 'PSA_VALUE'

30 genes related to 'PSA_VALUE'.

Table S18.  Basic characteristics of clinical feature: 'PSA_VALUE'

PSA_VALUE Mean (SD) 1.24 (4.6)
  Significant markers N = 30
  pos. correlated 12
  neg. correlated 18
List of top 10 genes differentially expressed by 'PSA_VALUE'

Table S19.  Get Full Table List of top 10 genes significantly correlated to 'PSA_VALUE' by Spearman correlation test

SpearmanCorr corrP Q
BRCA2|BRCA2 -0.2439 1.124e-05 0.00219
ACVRL1|ACVRL1 -0.2119 0.0001439 0.014
MYH9|MYOSIN-IIA_PS1943 0.1837 0.00102 0.0663
ERBB3|HER3_PY1289 -0.1754 0.001716 0.0714
PRKCD|PKC-DELTA_PS664 -0.1736 0.001916 0.0714
SYK|SYK 0.1714 0.002197 0.0714
PRDX1|PRDX1 -0.1675 0.002767 0.072
MAPK14|P38 0.2246 0.003522 0.072
CAV1|CAVEOLIN-1 -0.1631 0.003593 0.072
PRKCA |PKC-ALPHA -0.1626 0.003694 0.072
Clinical variable #11: 'RACE'

No gene related to 'RACE'.

Table S20.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  ASIAN 2
  BLACK OR AFRICAN AMERICAN 6
  WHITE 121
     
  Significant markers N = 0
Methods & Data
Input
  • Expresson data file = PRAD-TP.rppa.txt

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

  • Number of patients = 352

  • Number of genes = 195

  • Number of clinical features = 11

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)