Correlation between RPPA expression and clinical features
Breast Invasive Carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1H131CJ
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/C1H131CJ
Overview
Introduction

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

Summary

Testing the association between 226 genes and 12 clinical features across 886 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 10 clinical features related to at least one genes.

  • 10 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • EIF4G1|EIF4G ,  LCK|LCK ,  AKT1S1|PRAS40_PT246 ,  BCL2|BCL-2 ,  DVL3|DVL3 ,  ...

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • ESR1|ER-ALPHA ,  KIT|C-KIT ,  PDK1|PDK1_PS241 ,  HSPA1A|HSP70 ,  STMN1|STATHMIN ,  ...

  • 30 genes correlated to 'PATHOLOGIC_STAGE'.

    • MAPK14|P38_PT180_Y182 ,  EIF4EBP1|4E-BP1_PT70 ,  CTNNB1|BETA-CATENIN ,  FOXM1|FOXM1 ,  MET|C-MET_PY1235 ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • MET|C-MET_PY1235 ,  CHEK2|CHK2_PT68 ,  SRC|SRC_PY527 ,  CAV1|CAVEOLIN-1 ,  COL6A1|COLLAGEN_VI ,  ...

  • 9 genes correlated to 'PATHOLOGY_N_STAGE'.

    • CTNNB1|BETA-CATENIN ,  ERCC5|ERCC5 ,  FASN|FASN ,  ERRFI1|MIG-6 ,  NDRG1|NDRG1_PT346 ,  ...

  • 30 genes correlated to 'GENDER'.

    • ACACA ACACB|ACC_PS79 ,  ACACA|ACC1 ,  CDH3|P-CADHERIN ,  FASN|FASN ,  PREX1|PREX1 ,  ...

  • 30 genes correlated to 'RADIATION_THERAPY'.

    • MAPK14|P38_PT180_Y182 ,  MAPK8|JNK_PT183_PY185 ,  MAP2K1|MEK1_PS217_S221 ,  YBX1|YB-1_PS102 ,  MAPK1 MAPK3|MAPK_PT202_Y204 ,  ...

  • 30 genes correlated to 'HISTOLOGICAL_TYPE'.

    • CDH1|E-CADHERIN ,  CTNNB1|BETA-CATENIN ,  SLC1A5|SLC1A5 ,  CLDN7|CLAUDIN-7 ,  DVL3|DVL3 ,  ...

  • 2 genes correlated to 'NUMBER_OF_LYMPH_NODES'.

    • NDRG1|NDRG1_PT346 ,  PTPN11|SHP-2_PY542

  • 30 genes correlated to 'RACE'.

    • ASNS|ASNS ,  RB1|RB ,  EGFR|EGFR_PY1173 ,  CASP8|CASPASE-8 ,  CCNE1|CYCLIN_E1 ,  ...

  • No genes correlated to 'PATHOLOGY_M_STAGE', 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=10   N=NA   N=NA
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=30 higher stage N=14 lower stage N=16
PATHOLOGY_N_STAGE Spearman correlation test N=9 higher stage N=2 lower stage N=7
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test N=30 male N=30 female N=0
RADIATION_THERAPY Wilcoxon test N=30 yes N=30 no N=0
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
NUMBER_OF_LYMPH_NODES Spearman correlation test N=2 higher number_of_lymph_nodes N=0 lower number_of_lymph_nodes N=2
RACE Kruskal-Wallis test N=30        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

10 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) 0-282.9 (median=27.6)
  censored N = 762
  death N = 123
     
  Significant markers N = 10
  associated with shorter survival NA
  associated with longer survival NA
List of 10 genes differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2.  Get Full Table List of 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
EIF4G1|EIF4G 0.000172 0.039 0.637
LCK|LCK 0.00201 0.12 0.414
AKT1S1|PRAS40_PT246 0.00206 0.12 0.591
BCL2|BCL-2 0.00252 0.12 0.397
DVL3|DVL3 0.00256 0.12 0.621
COL6A1|COLLAGEN_VI 0.00451 0.16 0.378
STAT5A|STAT5-ALPHA 0.00496 0.16 0.511
TGM2|TRANSGLUTAMINASE 0.00975 0.27 0.44
SETD2|SETD2 0.0107 0.27 0.443
RB1|RB_PS807_S811 0.0125 0.28 0.614
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) 58.38 (13)
  Significant markers N = 30
  pos. correlated 18
  neg. correlated 12
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
ESR1|ER-ALPHA 0.3037 4.665e-20 1.05e-17
KIT|C-KIT -0.2267 1.256e-11 1.42e-09
PDK1|PDK1_PS241 0.1651 9.469e-07 7.13e-05
HSPA1A|HSP70 -0.1596 2.174e-06 0.000112
STMN1|STATHMIN -0.1587 2.47e-06 0.000112
COG3|COG3 0.1517 6.833e-06 0.000257
PDK1|PDK1 0.144 1.96e-05 0.000633
TSC2|TUBERIN_PT1462 0.1385 4.06e-05 0.00115
EEF2K|EEF2K 0.1368 5.027e-05 0.00126
IGFBP2|IGFBP2 0.1346 6.654e-05 0.0015
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 62
  STAGE IA 64
  STAGE IB 4
  STAGE II 4
  STAGE IIA 296
  STAGE IIB 213
  STAGE III 2
  STAGE IIIA 134
  STAGE IIIB 23
  STAGE IIIC 53
  STAGE IV 18
  STAGE X 6
     
  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
MAPK14|P38_PT180_Y182 3.829e-05 0.00591
EIF4EBP1|4E-BP1_PT70 5.416e-05 0.00591
CTNNB1|BETA-CATENIN 8.811e-05 0.00591
FOXM1|FOXM1 0.0001046 0.00591
MET|C-MET_PY1235 0.0001341 0.00606
JUN|C-JUN_PS73 0.0002338 0.00881
RAD51|RAD51 0.0006819 0.022
CCNB1|CYCLIN_B1 0.001309 0.037
BID|BID 0.001565 0.0393
PTPN11|SHP-2_PY542 0.002149 0.0438
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) 1.98 (0.72)
  N
  T1 201
  T2 533
  T3 115
  T4 36
     
  Significant markers N = 30
  pos. correlated 14
  neg. correlated 16
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
MET|C-MET_PY1235 0.1843 3.336e-08 7.54e-06
CHEK2|CHK2_PT68 0.1704 3.392e-07 3.83e-05
SRC|SRC_PY527 -0.1653 7.668e-07 5.78e-05
CAV1|CAVEOLIN-1 -0.1373 4.157e-05 0.00166
COL6A1|COLLAGEN_VI -0.1372 4.204e-05 0.00166
CCNB1|CYCLIN_B1 0.1368 4.419e-05 0.00166
MRE11A|MRE11 0.1313 8.996e-05 0.00271
TP53|P53 0.1302 0.0001031 0.00271
PDCD4|PDCD4 -0.1298 0.0001079 0.00271
PRKCD|PKC-DELTA_PS664 0.1265 0.0001619 0.00364
Clinical variable #5: 'PATHOLOGY_N_STAGE'

9 genes related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 0.8 (0.92)
  N
  N0 412
  N1 291
  N2 104
  N3 65
     
  Significant markers N = 9
  pos. correlated 2
  neg. correlated 7
List of 9 genes differentially expressed by 'PATHOLOGY_N_STAGE'

Table S10.  Get Full Table List of 9 genes significantly correlated to 'PATHOLOGY_N_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
CTNNB1|BETA-CATENIN -0.1071 0.00154 0.151
ERCC5|ERCC5 -0.1043 0.002048 0.151
FASN|FASN 0.1035 0.002203 0.151
ERRFI1|MIG-6 -0.1013 0.002743 0.151
NDRG1|NDRG1_PT346 -0.099 0.003434 0.151
INPP4B|INPP4B 0.0971 0.004105 0.151
YBX1|YB-1_PS102 -0.0957 0.004676 0.151
MAPK14|P38_PT180_Y182 -0.0907 0.007336 0.207
PDCD4|PDCD4 -0.0852 0.0118 0.296
Clinical variable #6: 'PATHOLOGY_M_STAGE'

No gene related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 739
  class1 20
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

30 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 876
  MALE 10
     
  Significant markers N = 30
  Higher in MALE 30
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S13.  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
ACACA ACACB|ACC_PS79 7775 2.46e-05 0.003 0.8876
ACACA|ACC1 7761 2.657e-05 0.003 0.886
CDH3|P-CADHERIN 1458 0.0002827 0.0213 0.8336
FASN|FASN 7134 0.000622 0.0351 0.8144
PREX1|PREX1 6926 0.00156 0.0705 0.7906
RPS6KB1|P70S6K 6834 0.002296 0.0837 0.7801
XBP1|XBP1 6767 0.003019 0.0837 0.7725
RAF1|C-RAF_PS338 2018 0.003339 0.0837 0.7696
ERBB3|HER3_PY1289 2058 0.003914 0.0837 0.7651
MRE11A|MRE11 2064 0.004008 0.0837 0.7644
Clinical variable #8: 'RADIATION_THERAPY'

30 genes related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 365
  YES 448
     
  Significant markers N = 30
  Higher in YES 30
  Higher in NO 0
List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

Table S15.  Get Full Table List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

W(pos if higher in 'YES') wilcoxontestP Q AUC
MAPK14|P38_PT180_Y182 98794 3.147e-07 7.11e-05 0.6042
MAPK8|JNK_PT183_PY185 97353 2.844e-06 0.000321 0.5954
MAP2K1|MEK1_PS217_S221 94920 7.775e-05 0.00586 0.5805
YBX1|YB-1_PS102 94614 0.0001137 0.00642 0.5786
MAPK1 MAPK3|MAPK_PT202_Y204 94023 0.0002315 0.00864 0.575
EEF2K|EEF2K 69536 0.0002424 0.00864 0.5748
PARP1|PARP_CLEAVED 69757 0.0003136 0.00864 0.5734
PIK3R1 PIK3R2|PI3K-P85 93698 0.000338 0.00864 0.573
BAD|BAD_PS112 93640 0.0003612 0.00864 0.5727
SCD|SCD 70045.5 0.0004361 0.00864 0.5716
Clinical variable #9: 'HISTOLOGICAL_TYPE'

30 genes related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  INFILTRATING CARCINOMA NOS 1
  INFILTRATING DUCTAL CARCINOMA 642
  INFILTRATING LOBULAR CARCINOMA 155
  MEDULLARY CARCINOMA 6
  METAPLASTIC CARCINOMA 7
  MIXED HISTOLOGY (PLEASE SPECIFY) 23
  MUCINOUS CARCINOMA 14
  OTHER, SPECIFY 38
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

Table S17.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

kruskal_wallis_P Q
CDH1|E-CADHERIN 1.475e-44 3.33e-42
CTNNB1|BETA-CATENIN 1.992e-36 2.25e-34
SLC1A5|SLC1A5 9.182e-13 6.92e-11
CLDN7|CLAUDIN-7 2.919e-12 1.65e-10
DVL3|DVL3 4.039e-12 1.83e-10
CAV1|CAVEOLIN-1 3.749e-11 1.41e-09
EIF4G1|EIF4G 1.261e-10 4.07e-09
RBM15|RBM15 1.526e-10 4.31e-09
RAB11A RAB11B|RAB11 2.997e-10 7.53e-09
MYH9|MYOSIN-IIA 1.787e-09 4.04e-08
Clinical variable #10: 'NUMBER_OF_LYMPH_NODES'

2 genes related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 2.43 (4.7)
  Significant markers N = 2
  pos. correlated 0
  neg. correlated 2
List of 2 genes differentially expressed by 'NUMBER_OF_LYMPH_NODES'

Table S19.  Get Full Table List of 2 genes significantly correlated to 'NUMBER_OF_LYMPH_NODES' by Spearman correlation test

SpearmanCorr corrP Q
NDRG1|NDRG1_PT346 -0.1202 0.0009178 0.157
PTPN11|SHP-2_PY542 -0.1159 0.001394 0.157
Clinical variable #11: 'RACE'

30 genes related to 'RACE'.

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

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 59
  BLACK OR AFRICAN AMERICAN 137
  WHITE 629
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RACE'

Table S21.  Get Full Table List of top 10 genes differentially expressed by 'RACE'

kruskal_wallis_P Q
ASNS|ASNS 3.748e-13 8.47e-11
RB1|RB 1.024e-12 1.16e-10
EGFR|EGFR_PY1173 9.956e-11 7.5e-09
CASP8|CASPASE-8 2.29e-09 1.29e-07
CCNE1|CYCLIN_E1 5.38e-09 2.43e-07
CHEK1|CHK1_PS345 1.29e-08 4.28e-07
CCNB1|CYCLIN_B1 1.326e-08 4.28e-07
PGR|PR 1.905e-08 5.38e-07
PRKAA1|AMPK_ALPHA 2.193e-08 5.51e-07
RPS6|S6 4.793e-08 1.08e-06
Clinical variable #12: 'ETHNICITY'

No gene related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 28
  NOT HISPANIC OR LATINO 730
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 886

  • Number of genes = 226

  • Number of clinical features = 12

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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