Correlation between RPPA expression and clinical features
Overview
Introduction

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

Summary

Testing the association between 175 genes and 7 clinical features across 109 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.

  • 1 gene correlated to 'Time to Death'.

    • GSK3A GSK3B|GSK3-ALPHA-BETA-M-V

  • 1 gene correlated to 'DISTANT.METASTASIS'.

    • C12ORF5|TIGAR-R-V

  • 2 genes correlated to 'LYMPH.NODE.METASTASIS'.

    • PGR|PR-R-V ,  ESR1|ER-ALPHA-R-V

  • No genes correlated to 'AGE', 'PRIMARY.SITE.OF.DISEASE', 'GENDER', and 'NEOPLASM.DISEASESTAGE'.

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        
PRIMARY SITE OF DISEASE ANOVA test   N=0        
GENDER t test   N=0        
DISTANT METASTASIS ANOVA test N=1        
LYMPH NODE METASTASIS ANOVA test N=2        
NEOPLASM DISEASESTAGE ANOVA test   N=0        
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) 2.6-357.4 (median=47.5)
  censored N = 50
  death N = 58
     
  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
GSK3A GSK3B|GSK3-ALPHA-BETA-M-V 9.1 0.0002674 0.047 0.624

Figure S1.  Get High-res Image As an example, this figure shows the association of GSK3A GSK3B|GSK3-ALPHA-BETA-M-V to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 0.000267 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) 56.4 (17)
  Significant markers N = 0
Clinical variable #3: 'PRIMARY.SITE.OF.DISEASE'

No gene related to 'PRIMARY.SITE.OF.DISEASE'.

Table S4.  Basic characteristics of clinical feature: 'PRIMARY.SITE.OF.DISEASE'

PRIMARY.SITE.OF.DISEASE Labels N
  DISTANT METASTASIS 13
  PRIMARY TUMOR 1
  REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE (INCLUDES SATELLITE AND IN-TRANSIT METASTASIS) 15
  REGIONAL LYMPH NODE 80
     
  Significant markers N = 0
Clinical variable #4: 'GENDER'

No gene related to 'GENDER'.

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

GENDER Labels N
  FEMALE 44
  MALE 65
     
  Significant markers N = 0
Clinical variable #5: 'DISTANT.METASTASIS'

One gene related to 'DISTANT.METASTASIS'.

Table S6.  Basic characteristics of clinical feature: 'DISTANT.METASTASIS'

DISTANT.METASTASIS Labels N
  M0 90
  M1 1
  M1B 2
  M1C 2
     
  Significant markers N = 1
List of one gene differentially expressed by 'DISTANT.METASTASIS'

Table S7.  Get Full Table List of one gene differentially expressed by 'DISTANT.METASTASIS'

ANOVA_P Q
C12ORF5|TIGAR-R-V 0.0001783 0.0312

Figure S2.  Get High-res Image As an example, this figure shows the association of C12ORF5|TIGAR-R-V to 'DISTANT.METASTASIS'. P value = 0.000178 with ANOVA analysis.

Clinical variable #6: 'LYMPH.NODE.METASTASIS'

2 genes related to 'LYMPH.NODE.METASTASIS'.

Table S8.  Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'

LYMPH.NODE.METASTASIS Labels N
  N0 56
  N1 2
  N1A 3
  N1B 13
  N2A 2
  N2B 5
  N2C 4
  N3 9
  NX 2
     
  Significant markers N = 2
List of 2 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

Table S9.  Get Full Table List of 2 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

ANOVA_P Q
PGR|PR-R-V 3.618e-08 6.33e-06
ESR1|ER-ALPHA-R-V 8.673e-08 1.51e-05

Figure S3.  Get High-res Image As an example, this figure shows the association of PGR|PR-R-V to 'LYMPH.NODE.METASTASIS'. P value = 3.62e-08 with ANOVA analysis.

Clinical variable #7: 'NEOPLASM.DISEASESTAGE'

No gene related to 'NEOPLASM.DISEASESTAGE'.

Table S10.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  I OR II NOS 3
  STAGE I 11
  STAGE IA 8
  STAGE IB 8
  STAGE II 10
  STAGE IIA 5
  STAGE IIB 5
  STAGE IIC 3
  STAGE III 6
  STAGE IIIA 2
  STAGE IIIB 11
  STAGE IIIC 13
  STAGE IV 4
     
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = SKCM-TM.rppa.txt

  • Clinical data file = SKCM-TM.clin.merged.picked.txt

  • Number of patients = 109

  • Number of genes = 175

  • Number of clinical features = 7

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.

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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