Correlation between RPPA expression and clinical features
Rectum Adenocarcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C16972VQ
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/C16972VQ
Overview
Introduction

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

Summary

Testing the association between 208 genes and 12 clinical features across 131 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 7 clinical features related to at least one genes.

  • 1 gene correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • ACVRL1|ACVRL1

  • 7 genes correlated to 'YEARS_TO_BIRTH'.

    • CCNB1|CYCLIN_B1 ,  PRKCB|PKC-PAN_BETAII_PS660 ,  CAV1|CAVEOLIN-1 ,  PRKCA |PKC-ALPHA ,  RICTOR|RICTOR ,  ...

  • 7 genes correlated to 'PATHOLOGIC_STAGE'.

    • PIK3CA |PI3K-P110-ALPHA ,  BAK1|BAK ,  RPS6|S6_PS235_S236 ,  ERCC1|ERCC1 ,  YBX1|YB-1_PS102 ,  ...

  • 12 genes correlated to 'PATHOLOGY_N_STAGE'.

    • MYC|C-MYC ,  GAB2|GAB2 ,  EIF4E|EIF4E ,  PXN|PAXILLIN ,  ERBB2|HER2 ,  ...

  • 1 gene correlated to 'PATHOLOGY_M_STAGE'.

    • SQSTM1|P62-LCK-LIGAND

  • 4 genes correlated to 'HISTOLOGICAL_TYPE'.

    • RAF1|C-RAF ,  YAP1|YAP_PS127 ,  PRDX1|PRDX1 ,  CDKN1B|P27

  • 15 genes correlated to 'NUMBER_OF_LYMPH_NODES'.

    • GAB2|GAB2 ,  EIF4E|EIF4E ,  MYC|C-MYC ,  MAPK14|P38_PT180_Y182 ,  PXN|PAXILLIN ,  ...

  • No genes correlated to 'PATHOLOGY_T_STAGE', 'GENDER', 'RADIATION_THERAPY', 'RESIDUAL_TUMOR', and 'RACE'.

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=7 older N=4 younger N=3
PATHOLOGIC_STAGE Kruskal-Wallis test N=7        
PATHOLOGY_T_STAGE Spearman correlation test   N=0        
PATHOLOGY_N_STAGE Spearman correlation test N=12 higher stage N=7 lower stage N=5
PATHOLOGY_M_STAGE Wilcoxon test N=1 class1 N=1 class0 N=0
GENDER Wilcoxon test   N=0        
RADIATION_THERAPY Wilcoxon test   N=0        
HISTOLOGICAL_TYPE Wilcoxon test N=4 rectal mucinous adenocarcinoma N=4 rectal adenocarcinoma N=0
RESIDUAL_TUMOR Kruskal-Wallis test   N=0        
NUMBER_OF_LYMPH_NODES Spearman correlation test N=15 higher number_of_lymph_nodes N=10 lower number_of_lymph_nodes N=5
RACE Kruskal-Wallis 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) 0.4-129.3 (median=17.7)
  censored N = 109
  death N = 21
     
  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
ACVRL1|ACVRL1 5.7 0.0005048 0.11 0.641
Clinical variable #2: 'YEARS_TO_BIRTH'

7 genes related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 65.43 (12)
  Significant markers N = 7
  pos. correlated 4
  neg. correlated 3
List of 7 genes differentially expressed by 'YEARS_TO_BIRTH'

Table S4.  Get Full Table List of 7 genes significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

SpearmanCorr corrP Q
CCNB1|CYCLIN_B1 -0.298 0.0005454 0.113
PRKCB|PKC-PAN_BETAII_PS660 0.2599 0.002717 0.283
CAV1|CAVEOLIN-1 0.2367 0.006489 0.288
PRKCA |PKC-ALPHA 0.2355 0.006764 0.288
RICTOR|RICTOR 0.2314 0.007819 0.288
SETD2|SETD2 -0.2254 0.009632 0.288
CCNE1|CYCLIN_E1 -0.2253 0.009679 0.288
Clinical variable #3: 'PATHOLOGIC_STAGE'

7 genes related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  STAGE I 21
  STAGE II 7
  STAGE IIA 32
  STAGE IIB 1
  STAGE IIC 1
  STAGE III 6
  STAGE IIIA 8
  STAGE IIIB 20
  STAGE IIIC 9
  STAGE IV 16
  STAGE IVA 5
     
  Significant markers N = 7
List of 7 genes differentially expressed by 'PATHOLOGIC_STAGE'

Table S6.  Get Full Table List of 7 genes differentially expressed by 'PATHOLOGIC_STAGE'

kruskal_wallis_P Q
PIK3CA |PI3K-P110-ALPHA 0.0006357 0.132
BAK1|BAK 0.005171 0.249
RPS6|S6_PS235_S236 0.005905 0.249
ERCC1|ERCC1 0.006888 0.249
YBX1|YB-1_PS102 0.006974 0.249
DIRAS3|ARHI 0.007179 0.249
ERBB2|HER2 0.008745 0.26
Clinical variable #4: 'PATHOLOGY_T_STAGE'

No gene related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 2.82 (0.62)
  N
  T1 5
  T2 23
  T3 92
  T4 10
     
  Significant markers N = 0
Clinical variable #5: 'PATHOLOGY_N_STAGE'

12 genes related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 0.7 (0.79)
  N
  N0 65
  N1 37
  N2 26
     
  Significant markers N = 12
  pos. correlated 7
  neg. correlated 5
List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

Table S9.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY_N_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
MYC|C-MYC 0.312 0.000337 0.0701
GAB2|GAB2 0.2826 0.001231 0.128
EIF4E|EIF4E -0.2522 0.004075 0.258
PXN|PAXILLIN 0.235 0.007582 0.258
ERBB2|HER2 0.2346 0.007686 0.258
DVL3|DVL3 0.2332 0.008071 0.258
CCNE1|CYCLIN_E1 -0.2311 0.008682 0.258
CASP7|CASPASE-7_CLEAVEDD198 -0.2217 0.01191 0.281
VHL|VHL 0.2169 0.01392 0.281
IRS1|IRS1 0.2151 0.01474 0.281
Clinical variable #6: 'PATHOLOGY_M_STAGE'

One gene related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 99
  class1 20
     
  Significant markers N = 1
  Higher in class1 1
  Higher in class0 0
List of one gene differentially expressed by 'PATHOLOGY_M_STAGE'

Table S11.  Get Full Table List of one gene differentially expressed by 'PATHOLOGY_M_STAGE'

W(pos if higher in 'class1') wilcoxontestP Q AUC
SQSTM1|P62-LCK-LIGAND 534 0.001208 0.251 0.7303
Clinical variable #7: 'GENDER'

No gene related to 'GENDER'.

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

GENDER Labels N
  FEMALE 61
  MALE 70
     
  Significant markers N = 0
Clinical variable #8: 'RADIATION_THERAPY'

No gene related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 72
  YES 16
     
  Significant markers N = 0
Clinical variable #9: 'HISTOLOGICAL_TYPE'

4 genes related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  RECTAL ADENOCARCINOMA 118
  RECTAL MUCINOUS ADENOCARCINOMA 10
     
  Significant markers N = 4
  Higher in RECTAL MUCINOUS ADENOCARCINOMA 4
  Higher in RECTAL ADENOCARCINOMA 0
List of 4 genes differentially expressed by 'HISTOLOGICAL_TYPE'

Table S15.  Get Full Table List of 4 genes differentially expressed by 'HISTOLOGICAL_TYPE'

W(pos if higher in 'RECTAL MUCINOUS ADENOCARCINOMA') wilcoxontestP Q AUC
RAF1|C-RAF 970 0.000753 0.157 0.822
YAP1|YAP_PS127 274 0.00509 0.295 0.7678
PRDX1|PRDX1 276 0.005377 0.295 0.7661
CDKN1B|P27 278 0.005679 0.295 0.7644
Clinical variable #10: 'RESIDUAL_TUMOR'

No gene related to 'RESIDUAL_TUMOR'.

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

RESIDUAL_TUMOR Labels N
  R0 98
  R1 2
  R2 12
  RX 3
     
  Significant markers N = 0
Clinical variable #11: 'NUMBER_OF_LYMPH_NODES'

15 genes related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 2.42 (4.9)
  Significant markers N = 15
  pos. correlated 10
  neg. correlated 5
List of top 10 genes differentially expressed by 'NUMBER_OF_LYMPH_NODES'

Table S18.  Get Full Table List of top 10 genes significantly correlated to 'NUMBER_OF_LYMPH_NODES' by Spearman correlation test

SpearmanCorr corrP Q
GAB2|GAB2 0.3109 0.000418 0.0675
EIF4E|EIF4E -0.298 0.0007387 0.0675
MYC|C-MYC 0.2915 0.0009729 0.0675
MAPK14|P38_PT180_Y182 -0.277 0.001766 0.0918
PXN|PAXILLIN 0.2618 0.003187 0.13
IRS1|IRS1 0.2575 0.003746 0.13
SRC|SRC_PY416 -0.2465 0.005582 0.147
DVL3|DVL3 0.2445 0.005991 0.147
MAP2K1|MEK1_PS217_S221 -0.2428 0.006374 0.147
ERBB2|HER2 0.2344 0.00852 0.177
Clinical variable #12: 'RACE'

No gene related to 'RACE'.

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

RACE Labels N
  ASIAN 1
  BLACK OR AFRICAN AMERICAN 4
  WHITE 65
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 131

  • Number of genes = 208

  • 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, 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)
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