Correlation between RPPA expression and clinical features
Ovarian Serous Cystadenocarcinoma (Primary solid tumor)
16 April 2014 | analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1930RTR
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/C1930RTR
Overview
Introduction

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

Summary

Testing the association between 165 genes and 5 clinical features across 407 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.

- 2 genes correlated to 'Time to Death'.

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

- 4 genes correlated to 'AGE'.

- PGR|PR-R-V , ESR1|ER-ALPHA-R-V , ERBB2|HER2-M-V , ERBB2|HER2_PY1248-R-V

- 1 gene correlated to 'COMPLETENESS.OF.RESECTION'.

- MET|C-MET-M-C

- No genes correlated to 'PRIMARY.SITE.OF.DISEASE', and 'KARNOFSKY.PERFORMANCE.SCORE'.

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=2 | shorter survival | N=2 | longer survival | N=0
AGE | Spearman correlation test | N=4 | older | N=3 | younger | N=1
PRIMARY SITE OF DISEASE | ANOVA test | N=0 |  |  |  |
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 |  |  |  |
COMPLETENESS OF RESECTION | ANOVA test | N=1 |  |  |  |

Clinical variable #1: 'Time to Death'

2 genes related to 'Time to Death'.

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

Time to Death | Duration (Months) | 0.3-180.2 (median=28.7)
| censored | N = 188
| death | N = 213
| Significant markers | N = 2
| associated with shorter survival | 2
| associated with longer survival | 0

List of 2 genes significantly associated with 'Time to Death' by Cox regression test

Table S2. Get Full Table List of 2 genes significantly associated with 'Time to Death' by Cox regression test

| HazardRatio | Wald_P | Q | C_index
--- | --- | --- | --- | ---
MAPK1 MAPK3|MAPK_PT202_Y204-R-V | 1.28 | 9.224e-05 | 0.015 | 0.575
MAP2K1|MEK1_PS217_S221-R-V | 1.91 | 0.0001133 | 0.019 | 0.582

Figure S1. Get High-res Image As an example, this figure shows the association of MAPK1 MAPK3|MAPK_PT202_Y204-R-V to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 9.22e-05 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

4 genes related to 'AGE'.

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

AGE | Mean (SD) | 59.67 (12)
| Significant markers | N = 4
| pos. correlated | 3
| neg. correlated | 1

List of 4 genes significantly correlated to 'AGE' by Spearman correlation test

Table S4. Get Full Table List of 4 genes significantly correlated to 'AGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
PGR|PR-R-V | -0.1951 | 8.575e-05 | 0.0141
ESR1|ER-ALPHA-R-V | 0.1899 | 0.0001324 | 0.0217
ERBB2|HER2-M-V | 0.1873 | 0.0001642 | 0.0268
ERBB2|HER2_PY1248-R-V | 0.1815 | 0.0002642 | 0.0428

Figure S2. Get High-res Image As an example, this figure shows the association of PGR|PR-R-V to 'AGE'. P value = 8.58e-05 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 S5. Basic characteristics of clinical feature: 'PRIMARY.SITE.OF.DISEASE'

PRIMARY.SITE.OF.DISEASE | Labels | N
| OMENTUM | 2
| OVARY | 403
| PERITONEUM OVARY | 2
| Significant markers | N = 0

Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S6. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 74.9 (12)
| Score | N
| 40 | 1
| 60 | 14
| 80 | 33
| 100 | 3
| Significant markers | N = 0

Clinical variable #5: 'COMPLETENESS.OF.RESECTION'

One gene related to 'COMPLETENESS.OF.RESECTION'.

Table S7. Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION | Labels | N
| R0 | 13
| R1 | 28
| R2 | 2
| Significant markers | N = 1

List of one gene differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S8. Get Full Table List of one gene differentially expressed by 'COMPLETENESS.OF.RESECTION'

| ANOVA_P | Q
--- | --- | ---
MET|C-MET-M-C | 9.338e-05 | 0.0154

Figure S3. Get High-res Image As an example, this figure shows the association of MET|C-MET-M-C to 'COMPLETENESS.OF.RESECTION'. P value = 9.34e-05 with ANOVA analysis.

Methods & Data
Input

- Expresson data file = OV-TP.rppa.txt

- Clinical data file = OV-TP.merged_data.txt

- Number of patients = 407

- Number of genes = 165

- 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

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.

- 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)
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