Breast Invasive Carcinoma: Correlation between RPPA expression and clinical features
Maintained by Juok Cho (Broad Institute)

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


Testing the association between 165 genes and 4 clinical features across 408 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.

  • 19 genes correlated to 'AGE'.


  • No genes correlated to 'Time to Death', 'GENDER', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.

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=19 older N=6 younger N=13
GENDER t 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=24.5)
  censored N = 336
  death N = 44
  Significant markers N = 0
Clinical variable #2: 'AGE'

19 genes related to 'AGE'.

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

AGE Mean (SD) 57.85 (13)
  Significant markers N = 19
  pos. correlated 6
  neg. correlated 13
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-R-V 0.384 8.695e-16 1.43e-13
NOTCH3|NOTCH3-R-C -0.276 1.442e-08 2.36e-06
SETD2|SETD2-R-NA -0.2441 6.005e-07 9.79e-05
STMN1|STATHMIN-R-V -0.2352 1.552e-06 0.000251
MET|C-MET_PY1235-R-C -0.2158 1.09e-05 0.00175
KIT|C-KIT-R-V -0.2144 1.249e-05 0.002
CDC2|CDK1-R-V -0.2094 2.016e-05 0.0032
AR|AR-R-V 0.2091 2.055e-05 0.00325
CDH3|P-CADHERIN-R-C -0.2017 4.058e-05 0.00637
PDK1|PDK1_PS241-R-V 0.1972 6.056e-05 0.00945

Figure S1.  Get High-res Image As an example, this figure shows the association of ESR1|ER-ALPHA-R-V to 'AGE'. P value = 8.7e-16 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

No gene related to 'GENDER'.

Table S4.  Basic characteristics of clinical feature: 'GENDER'

  FEMALE 403
  MALE 5
  Significant markers N = 0


Table S5.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

  NO 124
  YES 284
  Significant markers N = 0
Methods & Data
  • Expresson data file = BRCA-TP.rppa.txt

  • Clinical data file = BRCA-TP.clin.merged.picked.txt

  • Number of patients = 408

  • Number of genes = 165

  • Number of clinical features = 4

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

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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

[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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[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)