Correlation between RPPA expression and clinical features
Adrenocortical Carcinoma (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/C1GF0SVM
Overview
Introduction

This pipeline uses various statistical tests to identify RPPAs whose expression levels correlated to selected clinical features. The input file "ACC-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 46 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 6 clinical features related to at least one genes.

  • 30 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • CCNB1|CYCLIN_B1 ,  TFRC|TFRC ,  MAPK1|ERK2 ,  TSC1|TSC1 ,  FASN|FASN ,  ...

  • 2 genes correlated to 'YEARS_TO_BIRTH'.

    • BECN1|BECLIN ,  WWTR1|TAZ

  • 30 genes correlated to 'PATHOLOGIC_STAGE'.

    • MAPK1|ERK2 ,  FN1|FIBRONECTIN ,  CCNB1|CYCLIN_B1 ,  YAP1|YAP_PS127 ,  FOXO3|FOXO3A ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • CCNB1|CYCLIN_B1 ,  YAP1|YAP_PS127 ,  TFRC|TFRC ,  GSK3A GSK3B|GSK3-ALPHA-BETA ,  MAPK1|ERK2 ,  ...

  • 13 genes correlated to 'GENDER'.

    • CHEK1|CHK1_PS345 ,  MAPK1|ERK2 ,  ADAR|ADAR1 ,  FOXO3|FOXO3A ,  PRKCD|PKC-DELTA_PS664 ,  ...

  • 14 genes correlated to 'RADIATION_THERAPY'.

    • BECN1|BECLIN ,  ESR1|ER-ALPHA ,  COL6A1|COLLAGEN_VI ,  XRCC1|XRCC1 ,  CDH1|E-CADHERIN ,  ...

  • No genes correlated to 'PATHOLOGY_N_STAGE', 'RESIDUAL_TUMOR', 'NUMBER_OF_LYMPH_NODES', 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=30   N=NA   N=NA
YEARS_TO_BIRTH Spearman correlation test N=2 older N=1 younger N=1
PATHOLOGIC_STAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=15 lower stage N=15
PATHOLOGY_N_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test N=13 male N=13 female N=0
RADIATION_THERAPY Wilcoxon test N=14 yes N=14 no N=0
RESIDUAL_TUMOR Kruskal-Wallis test   N=0        
NUMBER_OF_LYMPH_NODES Spearman correlation test   N=0        
ETHNICITY Wilcoxon test   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) 4.1-153.6 (median=44.5)
  censored N = 31
  death N = 14
     
  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
CCNB1|CYCLIN_B1 8.9e-08 1.7e-05 0.846
TFRC|TFRC 6.75e-06 0.00065 0.826
MAPK1|ERK2 0.000494 0.025 0.782
TSC1|TSC1 0.000516 0.025 0.774
FASN|FASN 0.000772 0.03 0.7
ESR1|ER-ALPHA_PS118 0.000951 0.03 0.256
FN1|FIBRONECTIN 0.00201 0.055 0.804
PRKCA |PKC-ALPHA 0.00533 0.12 0.248
RBM15|RBM15 0.0058 0.12 0.73
PRDX1|PRDX1 0.00611 0.12 0.253
Clinical variable #2: 'YEARS_TO_BIRTH'

2 genes related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 47.17 (14)
  Significant markers N = 2
  pos. correlated 1
  neg. correlated 1
List of 2 genes differentially expressed by 'YEARS_TO_BIRTH'

Table S4.  Get Full Table List of 2 genes significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

SpearmanCorr corrP Q
BECN1|BECLIN -0.4663 0.001089 0.159
WWTR1|TAZ 0.451 0.00166 0.159
Clinical variable #3: 'PATHOLOGIC_STAGE'

30 genes related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  STAGE I 2
  STAGE II 26
  STAGE III 10
  STAGE IV 8
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'PATHOLOGIC_STAGE'

Table S6.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGIC_STAGE'

kruskal_wallis_P Q
MAPK1|ERK2 0.003051 0.263
FN1|FIBRONECTIN 0.003294 0.263
CCNB1|CYCLIN_B1 0.004113 0.263
YAP1|YAP_PS127 0.007744 0.285
FOXO3|FOXO3A 0.008405 0.285
TFRC|TFRC 0.01144 0.285
RAD50|RAD50 0.01372 0.285
PRKCA |PKC-ALPHA_PS657 0.014 0.285
CDKN1B|P27_PT157 0.01486 0.285
PRKCA |PKC-ALPHA 0.01547 0.285
Clinical variable #4: 'PATHOLOGY_T_STAGE'

30 genes related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 2.41 (0.8)
  N
  T1 2
  T2 30
  T3 7
  T4 7
     
  Significant markers N = 30
  pos. correlated 15
  neg. correlated 15
List of top 10 genes differentially expressed by 'PATHOLOGY_T_STAGE'

Table S8.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
CCNB1|CYCLIN_B1 0.5436 9.468e-05 0.0182
YAP1|YAP_PS127 0.4972 0.0004394 0.0422
TFRC|TFRC 0.4673 0.001059 0.0678
GSK3A GSK3B|GSK3-ALPHA-BETA 0.4559 0.001453 0.068
MAPK1|ERK2 0.4485 0.001771 0.068
FASN|FASN 0.4306 0.002812 0.09
FOXO3|FOXO3A -0.4148 0.004151 0.113
ESR1|ER-ALPHA -0.4077 0.004919 0.113
TSC1|TSC1 0.3966 0.006357 0.113
RAB25|RAB25 -0.3935 0.006824 0.113
Clinical variable #5: 'PATHOLOGY_N_STAGE'

No gene related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Labels N
  N0 40
  N1 6
     
  Significant markers N = 0
Clinical variable #6: 'GENDER'

13 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 28
  MALE 18
     
  Significant markers N = 13
  Higher in MALE 13
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S11.  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
CHEK1|CHK1_PS345 386 0.002658 0.263 0.7659
MAPK1|ERK2 123 0.003825 0.263 0.756
ADAR|ADAR1 124 0.004109 0.263 0.754
FOXO3|FOXO3A 372 0.007153 0.293 0.7381
PRKCD|PKC-DELTA_PS664 365 0.01134 0.293 0.7242
RPS6KB1|P70S6K_PT389 365 0.01134 0.293 0.7242
CAV1|CAVEOLIN-1 146 0.01757 0.293 0.7103
RAD50|RAD50 146 0.01757 0.293 0.7103
TP53|P53 358 0.01757 0.293 0.7103
RPS6KA1|P90RSK 146 0.01757 0.293 0.7103
Clinical variable #7: 'RADIATION_THERAPY'

14 genes related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 37
  YES 9
     
  Significant markers N = 14
  Higher in YES 14
  Higher in NO 0
List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

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

W(pos if higher in 'YES') wilcoxontestP Q AUC
BECN1|BECLIN 42 0.0005958 0.114 0.8739
ESR1|ER-ALPHA 67 0.00612 0.276 0.7988
COL6A1|COLLAGEN_VI 262 0.008525 0.276 0.7868
XRCC1|XRCC1 71 0.008525 0.276 0.7868
CDH1|E-CADHERIN 75 0.01174 0.276 0.7748
MAP2K1|MEK1_PS217_S221 256 0.01372 0.276 0.7688
RICTOR|RICTOR_PT1135 256 0.01372 0.276 0.7688
MYH11|MYH11 78 0.01482 0.276 0.7658
RAB25|RAB25 78 0.01482 0.276 0.7658
RAF1|C-RAF_PS338 254 0.016 0.276 0.7628
Clinical variable #8: 'RESIDUAL_TUMOR'

No gene related to 'RESIDUAL_TUMOR'.

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

RESIDUAL_TUMOR Labels N
  R0 33
  R1 3
  R2 5
  RX 4
     
  Significant markers N = 0
Clinical variable #9: 'NUMBER_OF_LYMPH_NODES'

No gene related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 3.05 (11)
  Value N
  0 17
  1 1
  3 1
  8 1
  52 1
     
  Significant markers N = 0
Clinical variable #10: 'ETHNICITY'

No gene related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 6
  NOT HISPANIC OR LATINO 17
     
  Significant markers N = 0
Methods & Data
Input
  • Expresson data file = ACC-TP.rppa.txt

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

  • Number of patients = 46

  • 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)