Correlation between RPPA expression and clinical features
Pan-kidney cohort (KICH+KIRC+KIRP) (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/C1GH9HC4
Overview
Introduction

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

Summary

Testing the association between 227 genes and 14 clinical features across 756 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 10 clinical features related to at least one genes.

  • 30 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • AR|AR ,  ERBB3|HER3 ,  GAB2|GAB2 ,  ERRFI1|MIG-6 ,  TFRC|TFRC ,  ...

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • IGFBP2|IGFBP2 ,  SCD1|SCD1 ,  MYH9|MYOSIN-IIA-PS1943 ,  GSK3A GSK3B|GSK3-ALPHA-BETA ,  PDCD4|PDCD4 ,  ...

  • 30 genes correlated to 'PATHOLOGIC_STAGE'.

    • SRC|SRC_PY527 ,  TFRC|TFRC ,  MAPK1 MAPK3|MAPK_PT202_Y204 ,  CCNB1|CYCLIN_B1 ,  TP53BP1|53BP1 ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • TFRC|TFRC ,  SRC|SRC_PY527 ,  CTNNB1|ALPHA-CATENIN ,  MAPK1 MAPK3|MAPK_PT202_Y204 ,  CDH3|P-CADHERIN ,  ...

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • FN1|FIBRONECTIN ,  CCNB1|CYCLIN_B1 ,  FASN|FASN ,  CTNNB1|ALPHA-CATENIN ,  MSH6|MSH6 ,  ...

  • 30 genes correlated to 'PATHOLOGY_M_STAGE'.

    • CCNB1|CYCLIN_B1 ,  TP53BP1|53BP1 ,  TFRC|TFRC ,  ERBB3|HER3_PY1289 ,  MAPK1 MAPK3|MAPK_PT202_Y204 ,  ...

  • 29 genes correlated to 'GENDER'.

    • CLDN7|CLAUDIN-7 ,  EIF4EBP1|4E-BP1_PS65 ,  PDK1|PDK1 ,  SERPINE1|PAI-1 ,  DIABLO|SMAC ,  ...

  • 2 genes correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

    • RPS6KB1|P70S6K_PT389 ,  MAP2K1|MEK1

  • 30 genes correlated to 'HISTOLOGICAL_TYPE'.

    • CDKN1B|P27_PT198 ,  ERCC1|ERCC1 ,  IRS1|IRS1 ,  RPS6KA1|P90RSK_PT359_S363 ,  TP53|P53 ,  ...

  • 30 genes correlated to 'RACE'.

    • RAB25|RAB25 ,  FOXO3|FOXO3A_PS318_S321 ,  BRCA2|BRCA2 ,  ITGA2|CD49B ,  PDK1|PDK1_PS241 ,  ...

  • No genes correlated to 'RADIATION_THERAPY', 'NUMBER_PACK_YEARS_SMOKED', 'YEAR_OF_TOBACCO_SMOKING_ONSET', 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=30 older N=17 younger N=13
PATHOLOGIC_STAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=17 lower stage N=13
PATHOLOGY_N_STAGE Spearman correlation test N=30 higher stage N=20 lower stage N=10
PATHOLOGY_M_STAGE Wilcoxon test N=30 class1 N=30 class0 N=0
GENDER Wilcoxon test N=29 male N=29 female N=0
RADIATION_THERAPY Wilcoxon test   N=0        
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test N=2 higher score N=1 lower score N=1
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
NUMBER_PACK_YEARS_SMOKED Spearman correlation test   N=0        
YEAR_OF_TOBACCO_SMOKING_ONSET Spearman correlation test   N=0        
RACE Kruskal-Wallis test N=30        
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) 0.1-194.8 (median=36.9)
  censored N = 548
  death N = 207
     
  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
AR|AR 2.66e-15 6e-13 0.356
ERBB3|HER3 4.21e-11 4.8e-09 0.357
GAB2|GAB2 1.38e-10 1e-08 0.356
ERRFI1|MIG-6 4.74e-10 2.7e-08 0.364
TFRC|TFRC 8.27e-10 3.8e-08 0.641
CCNB1|CYCLIN_B1 2.32e-09 8.8e-08 0.604
SERPINE1|PAI-1 8.5e-09 2.8e-07 0.611
MAPK1 MAPK3|MAPK_PT202_Y204 1.67e-08 4.7e-07 0.36
ACACA|ACC1 2.85e-07 7.2e-06 0.63
EGFR|EGFR_PY1068 3.62e-07 8.2e-06 0.383
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) 60.03 (13)
  Significant markers N = 30
  pos. correlated 17
  neg. correlated 13
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 0.2017 2.462e-08 5.59e-06
SCD1|SCD1 0.2988 4.696e-07 5.33e-05
MYH9|MYOSIN-IIA-PS1943 -0.2927 2.255e-05 0.00135
GSK3A GSK3B|GSK3-ALPHA-BETA -0.1535 2.386e-05 0.00135
PDCD4|PDCD4 -0.1371 0.0001638 0.00744
AR|AR -0.1342 0.000225 0.00815
MTCO2|MITOCHONDRIA 0.1711 0.0002528 0.00815
ARHI|ARHI 0.1327 0.0002872 0.00815
CDH3|P-CADHERIN 0.1295 0.0003751 0.00922
MYH11|MYH11 -0.1287 0.0004063 0.00922
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 382
  STAGE II 91
  STAGE III 172
  STAGE IV 101
     
  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
SRC|SRC_PY527 2.101e-14 4.77e-12
TFRC|TFRC 8.659e-14 9.83e-12
MAPK1 MAPK3|MAPK_PT202_Y204 1.142e-11 8.64e-10
CCNB1|CYCLIN_B1 7.883e-11 3.27e-09
TP53BP1|53BP1 8.263e-11 3.27e-09
ERBB2|HER2_PY1248 8.635e-11 3.27e-09
PECAM1|CD31 1.86e-10 6.03e-09
RAF1|C-RAF_PS338 3.618e-10 1.03e-08
ERBB3|HER3_PY1289 1.595e-09 4.02e-08
YBX1|YB-1 2.637e-09 5.99e-08
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) 1.83 (0.94)
  N
  T1 396
  T2 109
  T3 236
  T4 15
     
  Significant markers N = 30
  pos. correlated 17
  neg. correlated 13
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
TFRC|TFRC 0.2775 7.87e-15 1.22e-12
SRC|SRC_PY527 -0.2761 1.074e-14 1.22e-12
CTNNB1|ALPHA-CATENIN -0.3565 1.758e-10 1.07e-08
MAPK1 MAPK3|MAPK_PT202_Y204 -0.229 1.887e-10 1.07e-08
CDH3|P-CADHERIN 0.2228 5.872e-10 2.67e-08
CCNB1|CYCLIN_B1 0.2188 1.201e-09 4.54e-08
RAF1|C-RAF_PS338 -0.2172 1.608e-09 5.21e-08
PEA15|PEA15 0.2144 2.589e-09 7.35e-08
TP53BP1|53BP1 0.2117 4.166e-09 1.05e-07
YBX1|YB-1 0.2101 5.434e-09 1.23e-07
Clinical variable #5: 'PATHOLOGY_N_STAGE'

30 genes related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 0.14 (0.39)
  N
  N0 300
  N1 39
  N2 5
     
  Significant markers N = 30
  pos. correlated 20
  neg. correlated 10
List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

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

SpearmanCorr corrP Q
FN1|FIBRONECTIN 0.343 6.277e-11 1.42e-08
CCNB1|CYCLIN_B1 0.3252 6.477e-10 7.35e-08
FASN|FASN 0.2786 1.492e-07 9.64e-06
CTNNB1|ALPHA-CATENIN -0.4557 1.698e-07 9.64e-06
MSH6|MSH6 0.2676 4.712e-07 2.14e-05
PDK1|PDK1_PS241 -0.2486 3.062e-06 0.000116
DVL3|DVL3 0.2392 7.263e-06 0.000218
YBX1|YB-1 0.2386 7.696e-06 0.000218
ASNS|ASNS 0.2352 1.038e-05 0.000262
EIF4EBP1|4E-BP1 0.2332 1.248e-05 0.000272
Clinical variable #6: 'PATHOLOGY_M_STAGE'

30 genes related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 487
  class1 86
     
  Significant markers N = 30
  Higher in class1 30
  Higher in class0 0
List of top 10 genes differentially expressed by 'PATHOLOGY_M_STAGE'

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

W(pos if higher in 'class1') wilcoxontestP Q AUC
CCNB1|CYCLIN_B1 29109 7.906e-09 1.79e-06 0.695
TP53BP1|53BP1 28478 1.012e-07 9.22e-06 0.68
TFRC|TFRC 28430 1.218e-07 9.22e-06 0.6788
ERBB3|HER3_PY1289 13531 1.651e-07 9.37e-06 0.6769
MAPK1 MAPK3|MAPK_PT202_Y204 13921 7.072e-07 2.86e-05 0.6676
PECAM1|CD31 13939 7.55e-07 2.86e-05 0.6672
EEF2|EEF2 27618 2.393e-06 7.76e-05 0.6594
ERBB2|HER2_PY1248 14354 3.264e-06 8.35e-05 0.6573
PIK3R1|PI3K-P85 27524 3.309e-06 8.35e-05 0.6572
YBX1|YB-1 27476 3.899e-06 8.85e-05 0.656
Clinical variable #7: 'GENDER'

29 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 243
  MALE 513
     
  Significant markers N = 29
  Higher in MALE 29
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S14.  Get Full Table List of top 10 genes differentially expressed by 'GENDER'. 1 significant gene(s) located in sex chromosomes is(are) filtered out.

W(pos if higher in 'MALE') wilcoxontestP Q AUC
CLDN7|CLAUDIN-7 75123 5.067e-06 0.000575 0.6026
EIF4EBP1|4E-BP1_PS65 52242 0.0003219 0.0244 0.5809
PDK1|PDK1 71787 0.0007453 0.0304 0.5759
SERPINE1|PAI-1 71549 0.001011 0.0304 0.574
DIABLO|SMAC 30274 0.001067 0.0304 0.5914
NOTCH1|NOTCH1 53209 0.001145 0.0304 0.5732
ANXA1|ANNEXIN-1 59716 0.001175 0.0304 0.5767
VHL|VHL 59676 0.001245 0.0304 0.5763
MAPK14|P38_MAPK 21010 0.001338 0.0304 0.5896
EEF2|EEF2 70826 0.002448 0.0505 0.5682
Clinical variable #8: 'RADIATION_THERAPY'

No gene related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 331
  YES 3
     
  Significant markers N = 0
Clinical variable #9: 'KARNOFSKY_PERFORMANCE_SCORE'

2 genes related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 89.83 (20)
  Significant markers N = 2
  pos. correlated 1
  neg. correlated 1
List of 2 genes differentially expressed by 'KARNOFSKY_PERFORMANCE_SCORE'

Table S17.  Get Full Table List of 2 genes significantly correlated to 'KARNOFSKY_PERFORMANCE_SCORE' by Spearman correlation test

SpearmanCorr corrP Q
RPS6KB1|P70S6K_PT389 0.3249 0.0003319 0.0695
MAP2K1|MEK1 -0.3108 0.0006126 0.0695
Clinical variable #10: 'HISTOLOGICAL_TYPE'

30 genes related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  KIDNEY CHROMOPHOBE 63
  KIDNEY CLEAR CELL RENAL CARCINOMA 478
  KIDNEY PAPILLARY RENAL CELL CARCINOMA 215
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

Table S19.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

kruskal_wallis_P Q
CDKN1B|P27_PT198 2.779e-11 6.31e-09
ERCC1|ERCC1 7.089e-10 8.05e-08
IRS1|IRS1 7.874e-09 5.96e-07
RPS6KA1|P90RSK_PT359_S363 1.987e-08 1.13e-06
TP53|P53 2.565e-06 0.000116
ESR1|ER-ALPHA_PS118 3.889e-06 0.000137
CDC2|CDK1 4.224e-06 0.000137
EGFR|EGFR 5.323e-06 0.000151
PDK1|PDK1 1.961e-05 0.000495
EIF4EBP1|4E-BP1_PS65 8.487e-05 0.00193
Clinical variable #11: 'NUMBER_PACK_YEARS_SMOKED'

No gene related to 'NUMBER_PACK_YEARS_SMOKED'.

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

NUMBER_PACK_YEARS_SMOKED Mean (SD) 29.79 (24)
  Significant markers N = 0
Clinical variable #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

No gene related to 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

Table S21.  Basic characteristics of clinical feature: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

YEAR_OF_TOBACCO_SMOKING_ONSET Mean (SD) 1971.68 (15)
  Significant markers N = 0
Clinical variable #13: 'RACE'

30 genes related to 'RACE'.

Table S22.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 16
  BLACK OR AFRICAN AMERICAN 93
  WHITE 625
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RACE'

Table S23.  Get Full Table List of top 10 genes differentially expressed by 'RACE'

kruskal_wallis_P Q
RAB25|RAB25 4.417e-05 0.01
FOXO3|FOXO3A_PS318_S321 0.0001333 0.0151
BRCA2|BRCA2 0.000464 0.0351
ITGA2|CD49B 0.001092 0.057
PDK1|PDK1_PS241 0.001256 0.057
IRS1|IRS1 0.002896 0.106
FOXO3|FOXO3A 0.003431 0.106
ESR1|ER-ALPHA_PS118 0.00375 0.106
STMN1|STATHMIN 0.005375 0.126
C12ORF5|TIGAR 0.006037 0.126
Clinical variable #14: 'ETHNICITY'

No gene related to 'ETHNICITY'.

Table S24.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 32
  NOT HISPANIC OR LATINO 523
     
  Significant markers N = 0
Methods & Data
Input
  • Expresson data file = KIPAN-TP.rppa.txt

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

  • Number of patients = 756

  • Number of genes = 227

  • Number of clinical features = 14

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)