Correlation between RPPA expression and clinical features
Head and Neck Squamous Cell Carcinoma (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C18S4MXJ
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between RPPA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C18S4MXJ
Overview
Introduction

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

Summary

Testing the association between 174 genes and 9 clinical features across 212 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

  • 1 gene correlated to 'Time to Death'.

    • SMAD3|SMAD3-R-V

  • 1 gene correlated to 'GENDER'.

    • MAPK14|P38_PT180_Y182-R-V

  • 2 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

    • BCL2|BCL-2-R-C ,  COL6A1|COLLAGEN_VI-R-V

  • 1 gene correlated to 'NUMBERPACKYEARSSMOKED'.

    • CHEK2|CHK2-M-C

  • 3 genes correlated to 'NUMBER.OF.LYMPH.NODES'.

    • ANXA1|ANNEXIN_I-R-V ,  SRC|SRC_PY416-R-C ,  YAP1|YAP_PS127-R-C

  • 2 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • MAPK1 MAPK3|MAPK_PT202_Y204-R-V ,  MAP2K1|MEK1_PS217_S221-R-V

  • No genes correlated to 'AGE', 'YEAROFTOBACCOSMOKINGONSET', and 'LYMPH.NODE.METASTASIS'.

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=1 shorter survival N=1 longer survival N=0
AGE Spearman correlation test   N=0        
GENDER t test N=1 male N=0 female N=1
RADIATIONS RADIATION REGIMENINDICATION t test N=2 yes N=0 no N=2
NUMBERPACKYEARSSMOKED Spearman correlation test N=1 higher numberpackyearssmoked N=1 lower numberpackyearssmoked N=0
YEAROFTOBACCOSMOKINGONSET Spearman correlation test   N=0        
LYMPH NODE METASTASIS ANOVA test   N=0        
NUMBER OF LYMPH NODES Spearman correlation test N=3 higher number.of.lymph.nodes N=0 lower number.of.lymph.nodes N=3
NEOPLASM DISEASESTAGE ANOVA test N=2        
Clinical variable #1: 'Time to Death'

One gene related to 'Time to Death'.

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

Time to Death Duration (Months) 0.1-210.9 (median=13.2)
  censored N = 105
  death N = 107
     
  Significant markers N = 1
  associated with shorter survival 1
  associated with longer survival 0
List of one gene significantly associated with 'Time to Death' by Cox regression test

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
SMAD3|SMAD3-R-V 3.5 0.0001412 0.025 0.581

Figure S1.  Get High-res Image As an example, this figure shows the association of SMAD3|SMAD3-R-V to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 0.000141 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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

AGE Mean (SD) 62.12 (12)
  Significant markers N = 0
Clinical variable #3: 'GENDER'

One gene related to 'GENDER'.

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

GENDER Labels N
  FEMALE 62
  MALE 150
     
  Significant markers N = 1
  Higher in MALE 0
  Higher in FEMALE 1
List of one gene differentially expressed by 'GENDER'

Table S5.  Get Full Table List of one gene differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
MAPK14|P38_PT180_Y182-R-V -3.86 0.0001831 0.0319 0.6624

Figure S2.  Get High-res Image As an example, this figure shows the association of MAPK14|P38_PT180_Y182-R-V to 'GENDER'. P value = 0.000183 with T-test analysis.

Clinical variable #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

2 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S6.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 58
  YES 154
     
  Significant markers N = 2
  Higher in YES 0
  Higher in NO 2
List of 2 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S7.  Get Full Table List of 2 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
BCL2|BCL-2-R-C -4.31 4.8e-05 0.00835 0.7071
COL6A1|COLLAGEN_VI-R-V -3.99 0.0001411 0.0244 0.6773

Figure S3.  Get High-res Image As an example, this figure shows the association of BCL2|BCL-2-R-C to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 4.8e-05 with T-test analysis.

Clinical variable #5: 'NUMBERPACKYEARSSMOKED'

One gene related to 'NUMBERPACKYEARSSMOKED'.

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

NUMBERPACKYEARSSMOKED Mean (SD) 48.65 (38)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'NUMBERPACKYEARSSMOKED' by Spearman correlation test

Table S9.  Get Full Table List of one gene significantly correlated to 'NUMBERPACKYEARSSMOKED' by Spearman correlation test

SpearmanCorr corrP Q
CHEK2|CHK2-M-C 0.3495 0.0002242 0.039

Figure S4.  Get High-res Image As an example, this figure shows the association of CHEK2|CHK2-M-C to 'NUMBERPACKYEARSSMOKED'. P value = 0.000224 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #6: 'YEAROFTOBACCOSMOKINGONSET'

No gene related to 'YEAROFTOBACCOSMOKINGONSET'.

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

YEAROFTOBACCOSMOKINGONSET Mean (SD) 1964.27 (12)
  Significant markers N = 0
Clinical variable #7: 'LYMPH.NODE.METASTASIS'

No gene related to 'LYMPH.NODE.METASTASIS'.

Table S11.  Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'

LYMPH.NODE.METASTASIS Labels N
  N0 72
  N1 21
  N2 4
  N2A 4
  N2B 45
  N2C 26
  N3 4
  NX 34
     
  Significant markers N = 0
Clinical variable #8: 'NUMBER.OF.LYMPH.NODES'

3 genes related to 'NUMBER.OF.LYMPH.NODES'.

Table S12.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 2.86 (5.1)
  Significant markers N = 3
  pos. correlated 0
  neg. correlated 3
List of 3 genes significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

Table S13.  Get Full Table List of 3 genes significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

SpearmanCorr corrP Q
ANXA1|ANNEXIN_I-R-V -0.3328 9.218e-06 0.0016
SRC|SRC_PY416-R-C -0.2985 7.711e-05 0.0133
YAP1|YAP_PS127-R-C -0.2805 0.0002115 0.0364

Figure S5.  Get High-res Image As an example, this figure shows the association of ANXA1|ANNEXIN_I-R-V to 'NUMBER.OF.LYMPH.NODES'. P value = 9.22e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #9: 'NEOPLASM.DISEASESTAGE'

2 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S14.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 9
  STAGE II 39
  STAGE III 31
  STAGE IVA 117
  STAGE IVB 4
     
  Significant markers N = 2
List of 2 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S15.  Get Full Table List of 2 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

ANOVA_P Q
MAPK1 MAPK3|MAPK_PT202_Y204-R-V 6.74e-05 0.0117
MAP2K1|MEK1_PS217_S221-R-V 0.0001008 0.0174

Figure S6.  Get High-res Image As an example, this figure shows the association of MAPK1 MAPK3|MAPK_PT202_Y204-R-V to 'NEOPLASM.DISEASESTAGE'. P value = 6.74e-05 with ANOVA analysis.

Methods & Data
Input

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

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

  • Number of patients = 212

  • Number of genes = 174

  • Number of clinical features = 9

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

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

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.

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