Correlation between RPPA expression and clinical features
Pancreatic Adenocarcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1FF3RS4
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/C1FF3RS4
Overview
Introduction

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

Summary

Testing the association between 195 genes and 15 clinical features across 123 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one genes.

  • 19 genes correlated to 'PATHOLOGIC_STAGE'.

    • PXN|PAXILLIN ,  TSC2|TUBERIN ,  STAT5A|STAT5-ALPHA ,  PRDX1|PRDX1 ,  PEA15|PEA15_PS116 ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • ACVRL1|ACVRL1 ,  TP53BP1|53BP1 ,  PRDX1|PRDX1 ,  PXN|PAXILLIN ,  RB1|RB_PS807_S811 ,  ...

  • 13 genes correlated to 'GENDER'.

    • BAX|BAX ,  GAB2|GAB2 ,  MAPK14|P38 ,  PXN|PAXILLIN ,  PREX1|PREX1 ,  ...

  • 6 genes correlated to 'HISTOLOGICAL_TYPE'.

    • NOTCH1|NOTCH1 ,  MAPK9|JNK2 ,  NFKB1|NF-KB-P65_PS536 ,  FOXM1|FOXM1 ,  CCNB1|CYCLIN_B1 ,  ...

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'YEARS_TO_BIRTH', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'RADIATION_THERAPY', 'NUMBER_PACK_YEARS_SMOKED', 'YEAR_OF_TOBACCO_SMOKING_ONSET', 'RESIDUAL_TUMOR', 'NUMBER_OF_LYMPH_NODES', '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=0        
PATHOLOGIC_STAGE Kruskal-Wallis test N=19        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=13 lower stage N=17
PATHOLOGY_N_STAGE Wilcoxon test   N=0        
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test N=13 male N=13 female N=0
RADIATION_THERAPY Wilcoxon test   N=0        
HISTOLOGICAL_TYPE Kruskal-Wallis test N=6        
NUMBER_PACK_YEARS_SMOKED Spearman correlation test   N=0        
YEAR_OF_TOBACCO_SMOKING_ONSET Spearman correlation test   N=0        
RESIDUAL_TUMOR Kruskal-Wallis test   N=0        
NUMBER_OF_LYMPH_NODES 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) 0.1-71.7 (median=15.3)
  censored N = 51
  death N = 71
     
  Significant markers N = 0
Clinical variable #2: 'YEARS_TO_BIRTH'

No gene related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 65.34 (11)
  Significant markers N = 0
Clinical variable #3: 'PATHOLOGIC_STAGE'

19 genes related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  STAGE IA 5
  STAGE IB 8
  STAGE IIA 19
  STAGE IIB 83
  STAGE III 5
  STAGE IV 2
     
  Significant markers N = 19
List of top 10 genes differentially expressed by 'PATHOLOGIC_STAGE'

Table S4.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGIC_STAGE'

kruskal_wallis_P Q
PXN|PAXILLIN 0.001094 0.136
TSC2|TUBERIN 0.002595 0.136
STAT5A|STAT5-ALPHA 0.002603 0.136
PRDX1|PRDX1 0.002987 0.136
PEA15|PEA15_PS116 0.003916 0.136
BAD|BAD_PS112 0.0051 0.136
ANXA7 |ANNEXIN_VII 0.005429 0.136
PARK7|DJ-1 0.005583 0.136
TP53BP1|53BP1 0.006769 0.139
BCL2L1|BCL-XL 0.007417 0.139
Clinical variable #4: 'PATHOLOGY_T_STAGE'

30 genes related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 2.84 (0.55)
  N
  T1 6
  T2 11
  T3 101
  T4 4
     
  Significant markers N = 30
  pos. correlated 13
  neg. correlated 17
List of top 10 genes differentially expressed by 'PATHOLOGY_T_STAGE'

Table S6.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
ACVRL1|ACVRL1 0.3331 0.0001779 0.0267
TP53BP1|53BP1 -0.3239 0.0002735 0.0267
PRDX1|PRDX1 0.3025 0.0007072 0.035
PXN|PAXILLIN -0.2947 0.0009835 0.035
RB1|RB_PS807_S811 -0.2934 0.001039 0.035
CHEK2|CHK2_PT68 0.2911 0.001144 0.035
RAF1|C-RAF_PS338 0.2869 0.001357 0.035
MRE11A|MRE11 0.2836 0.001549 0.035
GSK3A GSK3B|GSK3_PS9 -0.281 0.001715 0.035
EIF4G1|EIF4G -0.2779 0.00194 0.035
Clinical variable #5: 'PATHOLOGY_N_STAGE'

No gene related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Labels N
  N0 32
  N1 90
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY_M_STAGE'

No gene related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 60
  class1 2
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

13 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 57
  MALE 66
     
  Significant markers N = 13
  Higher in MALE 13
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S10.  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
BAX|BAX 1170 0.0003139 0.0416 0.689
GAB2|GAB2 2551 0.0006847 0.0416 0.6781
MAPK14|P38 1216 0.0007509 0.0416 0.6768
PXN|PAXILLIN 2539 0.0008537 0.0416 0.6749
PREX1|PREX1 1261 0.001678 0.0654 0.6648
PEA15|PEA15_PS116 2432 0.005237 0.17 0.6465
YBX1|YB-1 2414 0.006918 0.171 0.6417
TUBA1B|ACETYL-A-TUBULIN-LYS40 2413 0.007024 0.171 0.6414
EIF4E|EIF4E 1379 0.01097 0.212 0.6334
MAPK14|P38_PT180_Y182 1382 0.01146 0.212 0.6326
Clinical variable #8: 'RADIATION_THERAPY'

No gene related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 85
  YES 29
     
  Significant markers N = 0
Clinical variable #9: 'HISTOLOGICAL_TYPE'

6 genes related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  PANCREAS-ADENOCARCINOMA DUCTAL TYPE 108
  PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE 12
  PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA 2
     
  Significant markers N = 6
List of 6 genes differentially expressed by 'HISTOLOGICAL_TYPE'

Table S13.  Get Full Table List of 6 genes differentially expressed by 'HISTOLOGICAL_TYPE'

kruskal_wallis_P Q
NOTCH1|NOTCH1 0.0009322 0.182
MAPK9|JNK2 0.002638 0.257
NFKB1|NF-KB-P65_PS536 0.005281 0.261
FOXM1|FOXM1 0.00536 0.261
CCNB1|CYCLIN_B1 0.007105 0.277
CDKN1B|P27_PT198 0.00916 0.298
Clinical variable #10: 'NUMBER_PACK_YEARS_SMOKED'

No gene related to 'NUMBER_PACK_YEARS_SMOKED'.

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

NUMBER_PACK_YEARS_SMOKED Mean (SD) 26.01 (17)
  Significant markers N = 0
Clinical variable #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

No gene related to 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

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

YEAR_OF_TOBACCO_SMOKING_ONSET Mean (SD) 1970.88 (13)
  Significant markers N = 0
Clinical variable #12: 'RESIDUAL_TUMOR'

No gene related to 'RESIDUAL_TUMOR'.

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

RESIDUAL_TUMOR Labels N
  R0 72
  R1 39
  R2 2
  RX 2
     
  Significant markers N = 0
Clinical variable #13: 'NUMBER_OF_LYMPH_NODES'

No gene related to 'NUMBER_OF_LYMPH_NODES'.

Table S17.  Basic characteristics of clinical feature: 'NUMBER_OF_LYMPH_NODES'

NUMBER_OF_LYMPH_NODES Mean (SD) 2.93 (3.2)
  Significant markers N = 0
Clinical variable #14: 'RACE'

No gene related to 'RACE'.

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

RACE Labels N
  ASIAN 2
  BLACK OR AFRICAN AMERICAN 4
  WHITE 113
     
  Significant markers N = 0
Clinical variable #15: 'ETHNICITY'

No gene related to 'ETHNICITY'.

Table S19.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 3
  NOT HISPANIC OR LATINO 95
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 123

  • Number of genes = 195

  • Number of clinical features = 15

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)
Copyright © 2016 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle