Correlation between RPPA expression and clinical features
Breast Invasive Carcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1QC02GG
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between RPPA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1QC02GG
Overview
Introduction

This pipeline uses various statistical tests to identify RPPAs whose expression levels correlated to selected clinical features.

Summary

Testing the association between 142 genes and 12 clinical features across 410 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 9 clinical features related to at least one genes.

  • 1 gene correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • PIK3CA |PI3K-P110-ALPHA

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • ESR1|ER-ALPHA ,  STMN1|STATHMIN ,  CDC2|CDK1 ,  KIT|C-KIT ,  AR|AR ,  ...

  • 30 genes correlated to 'NEOPLASM_DISEASESTAGE'.

    • COL6A1|COLLAGEN_VI ,  ASNS|ASNS ,  MAPK14|P38_PT180_Y182 ,  BCL2L1|BCL-XL ,  CDKN1B|P27_PT198 ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • MET|C-MET_PY1235 ,  MAPK14|P38_PT180_Y182 ,  CHEK2|CHK2_PT68 ,  COL6A1|COLLAGEN_VI ,  PRKCD|PKC-DELTA_PS664 ,  ...

  • 4 genes correlated to 'PATHOLOGY_N_STAGE'.

    • EIF4E|EIF4E ,  NF2|NF2 ,  MAPK14|P38_PT180_Y182 ,  PRKCD|PKC-DELTA_PS664

  • 21 genes correlated to 'GENDER'.

    • ACACA|ACC1 ,  RPS6KB1|P70S6K ,  BCL2L11|BIM ,  ACACA ACACB|ACC_PS79 ,  AKT1 AKT2 AKT3|AKT_PT308 ,  ...

  • 30 genes correlated to 'HISTOLOGICAL_TYPE'.

    • CDH1|E-CADHERIN ,  CTNNB1|BETA-CATENIN ,  CTNNA1|ALPHA-CATENIN ,  ERBB3|HER3_PY1289 ,  TP53|P53 ,  ...

  • 27 genes correlated to 'RADIATIONS_RADIATION_REGIMENINDICATION'.

    • MAPK14|P38_PT180_Y182 ,  MET|C-MET_PY1235 ,  MRE11A|MRE11 ,  BAX|BAX ,  CHEK2|CHK2_PT68 ,  ...

  • 22 genes correlated to 'RACE'.

    • SCD1|SCD1 ,  PECAM1|CD31 ,  ERBB3|HER3 ,  CHEK2|CHK2_PT68 ,  COL6A1|COLLAGEN_VI ,  ...

  • No genes correlated to 'PATHOLOGY_M_STAGE', '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=1 shorter survival N=1 longer survival N=0
YEARS_TO_BIRTH Spearman correlation test N=30 older N=13 younger N=17
NEOPLASM_DISEASESTAGE 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=4 higher stage N=1 lower stage N=3
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test N=21 male N=21 female N=0
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
RADIATIONS_RADIATION_REGIMENINDICATION Wilcoxon test N=27 yes N=27 no N=0
NUMBER_OF_LYMPH_NODES Spearman correlation test   N=0        
RACE Kruskal-Wallis test N=22        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

One 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) 0.2-189 (median=31.7)
  censored N = 355
  death N = 54
     
  Significant markers N = 1
  associated with shorter survival 1
  associated with longer survival 0
List of one gene differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2.  Get Full Table List of one gene significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
PIK3CA |PI3K-P110-ALPHA 2.9 0.0008594 0.12 0.595
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) 57.9 (13)
  Significant markers N = 30
  pos. correlated 13
  neg. correlated 17
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 0.385 7.326e-16 1.04e-13
STMN1|STATHMIN -0.2288 3.02e-06 0.000214
CDC2|CDK1 -0.2221 5.91e-06 0.00028
KIT|C-KIT -0.2178 9.028e-06 0.000321
AR|AR 0.2104 1.833e-05 0.000429
EGFR|EGFR -0.21 1.907e-05 0.000429
CDH3|P-CADHERIN -0.208 2.289e-05 0.000429
MET|C-MET_PY1235 -0.2074 2.417e-05 0.000429
PDK1|PDK1_PS241 0.1939 8.053e-05 0.00127
NOTCH1|NOTCH1 -0.1914 9.975e-05 0.00142
Clinical variable #3: 'NEOPLASM_DISEASESTAGE'

30 genes related to 'NEOPLASM_DISEASESTAGE'.

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

NEOPLASM_DISEASESTAGE Labels N
  STAGE I 36
  STAGE IA 27
  STAGE IB 4
  STAGE IIA 136
  STAGE IIB 95
  STAGE IIIA 62
  STAGE IIIB 13
  STAGE IIIC 15
  STAGE IV 14
  STAGE X 6
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'NEOPLASM_DISEASESTAGE'

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

kruskal_wallis_P Q
COL6A1|COLLAGEN_VI 0.0002865 0.0407
ASNS|ASNS 0.0007343 0.0521
MAPK14|P38_PT180_Y182 0.001588 0.0752
BCL2L1|BCL-XL 0.002136 0.0758
CDKN1B|P27_PT198 0.003243 0.08
BAK1|BAK 0.003767 0.08
GSK3A GSK3B|GSK3-ALPHA-BETA 0.004098 0.08
BID|BID 0.004505 0.08
BRAF|B-RAF 0.005531 0.0873
CAV1|CAVEOLIN-1 0.006877 0.0977
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 (0.73)
  N
  T1 95
  T2 246
  T3 50
  T4 18
     
  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 0.1795 0.0002627 0.0243
MAPK14|P38_PT180_Y182 -0.1762 0.0003418 0.0243
CHEK2|CHK2_PT68 0.161 0.001083 0.0327
COL6A1|COLLAGEN_VI -0.1603 0.001145 0.0327
PRKCD|PKC-DELTA_PS664 0.1588 0.00127 0.0327
BCL2|BCL-2 -0.1558 0.001579 0.0327
CAV1|CAVEOLIN-1 -0.1555 0.001613 0.0327
YBX1|YB-1_PS102 -0.1453 0.003237 0.0575
PECAM1|CD31 0.1401 0.004517 0.0623
SRC|SRC_PY527 -0.1395 0.00472 0.0623
Clinical variable #5: 'PATHOLOGY_N_STAGE'

4 genes related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 0.77 (0.9)
  N
  N0 194
  N1 132
  N2 52
  N3 25
     
  Significant markers N = 4
  pos. correlated 1
  neg. correlated 3
List of 4 genes differentially expressed by 'PATHOLOGY_N_STAGE'

Table S10.  Get Full Table List of 4 genes significantly correlated to 'PATHOLOGY_N_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
EIF4E|EIF4E -0.156 0.001688 0.138
NF2|NF2 -0.1539 0.001944 0.138
MAPK14|P38_PT180_Y182 -0.1405 0.00472 0.223
PRKCD|PKC-DELTA_PS664 0.1336 0.007254 0.258
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 387
  class1 15
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

21 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 405
  MALE 5
     
  Significant markers N = 21
  Higher in MALE 21
  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'. 1 significant gene(s) located in sex chromosomes is(are) filtered out.

W(pos if higher in 'MALE') wilcoxontestP Q AUC
ACACA|ACC1 1829 0.001945 0.111 0.9032
RPS6KB1|P70S6K 1825 0.002047 0.111 0.9012
BCL2L11|BIM 1798 0.002875 0.111 0.8879
ACACA ACACB|ACC_PS79 1791 0.003135 0.111 0.8844
AKT1 AKT2 AKT3|AKT_PT308 256 0.004096 0.116 0.8736
AKT1 AKT2 AKT3|AKT_PS473 273 0.005015 0.119 0.8652
SMAD3|SMAD3 1693 0.009821 0.182 0.836
CDH3|P-CADHERIN 336 0.01026 0.182 0.8341
EGFR|EGFR 389 0.018 0.264 0.8079
INPP4B|INPP4B 1615 0.02226 0.264 0.7975
Clinical variable #8: 'HISTOLOGICAL_TYPE'

30 genes related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  INFILTRATING DUCTAL CARCINOMA 355
  INFILTRATING LOBULAR CARCINOMA 30
  MEDULLARY CARCINOMA 1
  MIXED HISTOLOGY (PLEASE SPECIFY) 8
  MUCINOUS CARCINOMA 2
  OTHER SPECIFY 14
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

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

kruskal_wallis_P Q
CDH1|E-CADHERIN 5.99e-15 8.51e-13
CTNNB1|BETA-CATENIN 3.283e-13 2.33e-11
CTNNA1|ALPHA-CATENIN 2.691e-09 1.27e-07
ERBB3|HER3_PY1289 3.221e-05 0.00114
TP53|P53 0.0002128 0.00604
DVL3|DVL3 0.0002745 0.0065
YBX1|YB-1 0.0005154 0.0105
COL6A1|COLLAGEN_VI 0.000615 0.0109
SCD1|SCD1 0.0007407 0.0112
CAV1|CAVEOLIN-1 0.0007872 0.0112
Clinical variable #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

27 genes related to 'RADIATIONS_RADIATION_REGIMENINDICATION'.

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

RADIATIONS_RADIATION_REGIMENINDICATION Labels N
  NO 145
  YES 265
     
  Significant markers N = 27
  Higher in YES 27
  Higher in NO 0
List of top 10 genes differentially expressed by 'RADIATIONS_RADIATION_REGIMENINDICATION'

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

W(pos if higher in 'YES') wilcoxontestP Q AUC
MAPK14|P38_PT180_Y182 14540 4.65e-05 0.0066 0.6216
MET|C-MET_PY1235 23428 0.0002386 0.0169 0.6097
MRE11A|MRE11 23293 0.0003758 0.0178 0.6062
BAX|BAX 15591 0.001597 0.0567 0.5942
CHEK2|CHK2_PT68 22741 0.002103 0.0597 0.5918
SCD1|SCD1 22549 0.003638 0.0857 0.5868
RAD50|RAD50 15930 0.004225 0.0857 0.5854
RAF1|C-RAF_PS338 22377 0.005815 0.0938 0.5824
INPP4B|INPP4B 22347 0.006297 0.0938 0.5816
BECN1|BECLIN 22329 0.006604 0.0938 0.5811
Clinical variable #10: 'NUMBER_OF_LYMPH_NODES'

No gene related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 1.84 (3.5)
  Significant markers N = 0
Clinical variable #11: 'RACE'

22 genes related to 'RACE'.

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

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 27
  BLACK OR AFRICAN AMERICAN 29
  WHITE 295
     
  Significant markers N = 22
List of top 10 genes differentially expressed by 'RACE'

Table S20.  Get Full Table List of top 10 genes differentially expressed by 'RACE'

kruskal_wallis_P Q
SCD1|SCD1 0.0001397 0.0198
PECAM1|CD31 0.001728 0.111
ERBB3|HER3 0.002344 0.111
CHEK2|CHK2_PT68 0.003856 0.122
COL6A1|COLLAGEN_VI 0.005112 0.122
MRE11A|MRE11 0.005443 0.122
MAPK14|P38_PT180_Y182 0.006008 0.122
HSPA1A|HSP70 0.007311 0.127
CDKN1B|P27_PT198 0.008059 0.127
EIF4EBP1|4E-BP1_PS65 0.01006 0.132
Clinical variable #12: 'ETHNICITY'

No gene related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 6
  NOT HISPANIC OR LATINO 298
     
  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 = 410

  • Number of genes = 142

  • Number of clinical features = 12

Selected clinical features

  • For clinical features selected for this analysis and their value conozzle.versions, please find a documentation on selected CDEs .

  • 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, Wald's test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values using the 'coxph' function in R. Kaplan-Meier survival curves were plot using the four quartile subgroups of patients based on expression levels

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 © 2015 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle