Skin Cutaneous Melanoma: Correlation between RPPA expression and clinical features
(All_Primary cohort)
Maintained by Juok Cho (Broad Institute)
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 5 clinical features across 12 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.

- 1 gene correlated to 'AGE'.

- MAP2K1|MEK1_PS217_S221-R-V

- No genes correlated to 'Time to Death', 'PRIMARY.SITE.OF.DISEASE', 'LYMPH.NODE.METASTASIS', 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=0 |  |  |  |
AGE | Spearman correlation test | N=1 | older | N=0 | younger | N=1
PRIMARY SITE OF DISEASE | ANOVA test | N=0 |  |  |  |
LYMPH NODE METASTASIS | ANOVA test | N=0 |  |  |  |
NEOPLASM DISEASESTAGE | ANOVA 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) | 0.4-24 (median=5.6)
| censored | N = 3
| death | N = 4
| Significant markers | N = 0

Clinical variable #2: 'AGE'

One gene related to 'AGE'.

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

AGE | Mean (SD) | 56.4 (16)
| Significant markers | N = 1
| pos. correlated | 0
| neg. correlated | 1

List of one gene significantly correlated to 'AGE' by Spearman correlation test

Table S3. Get Full Table List of one gene significantly correlated to 'AGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
MAP2K1|MEK1_PS217_S221-R-V | -0.9147 | 0.0002092 | 0.0366

Figure S1. Get High-res Image As an example, this figure shows the association of MAP2K1|MEK1_PS217_S221-R-V to 'AGE'. P value = 0.000209 with Spearman correlation analysis. The straight line presents the best linear regression.

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 | 2
| PRIMARY TUMOR | 8
| REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE (INCLUDES SATELLITE AND IN-TRANSIT METASTASIS) | 2
| Significant markers | N = 0

Clinical variable #4: 'LYMPH.NODE.METASTASIS'

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

Table S5. Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'

LYMPH.NODE.METASTASIS | Labels | N
| N0 | 3
| N1A | 1
| N2A | 1
| N2B | 3
| N3 | 2
| NX | 1
| Significant markers | N = 0

Clinical variable #5: 'NEOPLASM.DISEASESTAGE'

No gene related to 'NEOPLASM.DISEASESTAGE'.

Table S6. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE | Labels | N
| STAGE IB | 1
| STAGE IIC | 3
| STAGE IIIA | 1
| STAGE IIIB | 2
| STAGE IIIC | 2
| STAGE IV | 1
| Significant markers | N = 0

Methods & Data
Input

- Expresson data file = SKCM-All_Primary.rppa.txt

- Clinical data file = SKCM-All_Primary.clin.merged.picked.txt

- Number of patients = 12

- Number of genes = 175

- Number of clinical features = 5

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

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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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] 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)
Copyright © 2013 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle