Correlation between RPPA expression and clinical features
Breast Invasive Carcinoma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1MC8XMQ
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between RPPA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1MC8XMQ
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 10 clinical features across 408 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

  • 16 genes correlated to 'AGE'.

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

  • 2 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • BCL2L1|BCL-XL ,  COL6A1|COLLAGEN_VI

  • 2 genes correlated to 'PATHOLOGY.T.STAGE'.

    • MAPK14|P38_PT180_Y182 ,  MET|C-MET_PY1235

  • 1 gene correlated to 'PATHOLOGY.M.STAGE'.

    • BCL2L1|BCL-XL

  • 4 genes correlated to 'HISTOLOGICAL.TYPE'.

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

  • 1 gene correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

    • MAPK14|P38_PT180_Y182

  • No genes correlated to 'Time to Death', 'PATHOLOGY.N.STAGE', 'GENDER', and 'NUMBER.OF.LYMPH.NODES'.

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 Q value < 0.05.

Clinical feature Statistical test Significant genes Associated with                 Associated with
Time to Death Cox regression test   N=0        
AGE Spearman correlation test N=16 older N=6 younger N=10
NEOPLASM DISEASESTAGE ANOVA test N=2        
PATHOLOGY T STAGE Spearman correlation test N=2 higher stage N=1 lower stage N=1
PATHOLOGY N STAGE Spearman correlation test   N=0        
PATHOLOGY M STAGE ANOVA test N=1        
GENDER t test   N=0        
HISTOLOGICAL TYPE ANOVA test N=4        
RADIATIONS RADIATION REGIMENINDICATION t test N=1 yes N=0 no N=1
NUMBER OF LYMPH NODES Spearman correlation test   N=0        
Clinical variable #1: 'Time to Death'

No gene related to 'Time to Death'.

Table S1.  Basic characteristics of clinical feature: 'Time to Death'

Time to Death Duration (Months) 0.1-189 (median=28.6)
  censored N = 344
  death N = 51
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

16 genes related to 'AGE'.

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

AGE Mean (SD) 57.92 (13)
  Significant markers N = 16
  pos. correlated 6
  neg. correlated 10
List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
ESR1|ER-ALPHA 0.3852 8.314e-16 1.18e-13
STMN1|STATHMIN -0.2321 2.291e-06 0.000323
CDC2|CDK1 -0.2181 9.249e-06 0.00129
KIT|C-KIT -0.216 1.134e-05 0.00158
AR|AR 0.2137 1.412e-05 0.00195
CDH3|P-CADHERIN -0.2116 1.708e-05 0.00234
MET|C-MET_PY1235 -0.2116 1.713e-05 0.00234
EGFR|EGFR -0.2106 1.887e-05 0.00255
NOTCH1|NOTCH1 -0.1974 6.23e-05 0.00835
PDK1|PDK1_PS241 0.1913 0.0001047 0.0139

Figure S1.  Get High-res Image As an example, this figure shows the association of ESR1|ER-ALPHA to 'AGE'. P value = 8.31e-16 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

2 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S4.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 31
  STAGE IA 30
  STAGE IB 4
  STAGE IIA 137
  STAGE IIB 94
  STAGE IIIA 62
  STAGE IIIB 13
  STAGE IIIC 15
  STAGE IV 14
  STAGE X 8
     
  Significant markers N = 2
List of 2 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S5.  Get Full Table List of 2 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

ANOVA_P Q
BCL2L1|BCL-XL 1.412e-05 0.00201
COL6A1|COLLAGEN_VI 0.0001396 0.0197

Figure S2.  Get High-res Image As an example, this figure shows the association of BCL2L1|BCL-XL to 'NEOPLASM.DISEASESTAGE'. P value = 1.41e-05 with ANOVA analysis.

Clinical variable #4: 'PATHOLOGY.T.STAGE'

2 genes related to 'PATHOLOGY.T.STAGE'.

Table S6.  Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE Mean (SD) 1.98 (0.73)
  N
  1 93
  2 246
  3 50
  4 18
     
  Significant markers N = 2
  pos. correlated 1
  neg. correlated 1
List of 2 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S7.  Get Full Table List of 2 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
MAPK14|P38_PT180_Y182 -0.1821 0.0002219 0.0315
MET|C-MET_PY1235 0.1779 0.0003091 0.0436

Figure S3.  Get High-res Image As an example, this figure shows the association of MAPK14|P38_PT180_Y182 to 'PATHOLOGY.T.STAGE'. P value = 0.000222 with Spearman correlation analysis.

Clinical variable #5: 'PATHOLOGY.N.STAGE'

No gene related to 'PATHOLOGY.N.STAGE'.

Table S8.  Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE Mean (SD) 0.78 (0.9)
  N
  0 192
  1 132
  2 52
  3 25
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY.M.STAGE'

One gene related to 'PATHOLOGY.M.STAGE'.

Table S9.  Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'

PATHOLOGY.M.STAGE Labels N
  CM0 (I+) 1
  M0 386
  M1 14
  MX 7
     
  Significant markers N = 1
List of one gene differentially expressed by 'PATHOLOGY.M.STAGE'

Table S10.  Get Full Table List of one gene differentially expressed by 'PATHOLOGY.M.STAGE'

ANOVA_P Q
BCL2L1|BCL-XL 0.0002307 0.0328

Figure S4.  Get High-res Image As an example, this figure shows the association of BCL2L1|BCL-XL to 'PATHOLOGY.M.STAGE'. P value = 0.000231 with ANOVA analysis.

Clinical variable #7: 'GENDER'

No gene related to 'GENDER'.

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

GENDER Labels N
  FEMALE 403
  MALE 5
     
  Significant markers N = 0
Clinical variable #8: 'HISTOLOGICAL.TYPE'

4 genes related to 'HISTOLOGICAL.TYPE'.

Table S12.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  INFILTRATING DUCTAL CARCINOMA 353
  INFILTRATING LOBULAR CARCINOMA 30
  MEDULLARY CARCINOMA 1
  MIXED HISTOLOGY (PLEASE SPECIFY) 8
  MUCINOUS CARCINOMA 2
  OTHER SPECIFY 14
     
  Significant markers N = 4
List of 4 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S13.  Get Full Table List of 4 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
CDH1|E-CADHERIN 4.541e-37 6.45e-35
CTNNB1|BETA-CATENIN 5.547e-18 7.82e-16
CTNNA1|ALPHA-CATENIN 2.328e-14 3.26e-12
TP53|P53 0.0001191 0.0166

Figure S5.  Get High-res Image As an example, this figure shows the association of CDH1|E-CADHERIN to 'HISTOLOGICAL.TYPE'. P value = 4.54e-37 with ANOVA analysis.

Clinical variable #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

One gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S14.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 144
  YES 264
     
  Significant markers N = 1
  Higher in YES 0
  Higher in NO 1
List of one gene differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S15.  Get Full Table List of one gene differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
MAPK14|P38_PT180_Y182 -4.18 3.831e-05 0.00544 0.6264

Figure S6.  Get High-res Image As an example, this figure shows the association of MAPK14|P38_PT180_Y182 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 3.83e-05 with T-test analysis.

Clinical variable #10: 'NUMBER.OF.LYMPH.NODES'

No gene related to 'NUMBER.OF.LYMPH.NODES'.

Table S16.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 1.86 (3.5)
  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 = 408

  • Number of genes = 142

  • Number of clinical features = 10

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

ANOVA analysis

For multi-class clinical features (ordinal or nominal), one-way analysis of variance (Howell 2002) was applied to compare the log2-expression levels between different clinical classes using 'anova' function in R

Student's t-test analysis

For two-class clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the log2-expression levels between the two clinical classes using 't.test' function in R

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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[4] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[5] 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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