Correlation between RPPA expression and clinical features
Mesothelioma (Primary solid tumor)
28 January 2016 | analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1S75FRR
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between RPPA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1S75FRR
Overview
Introduction

This pipeline uses various statistical tests to identify RPPAs whose expression levels correlated to selected clinical features. The input file "MESO-TP.rppa.txt" is generated in the pipeline RPPA_AnnotateWithGene in the stddata run.

Summary

Testing the association between 193 genes and 10 clinical features across 63 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one genes.

- 17 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

- NFKB1|NF-KB-P65_PS536 , CDKN1B|P27 , SERPINE1|PAI-1 , IGFBP2|IGFBP2 , FN1|FIBRONECTIN , ...

- 1 gene correlated to 'PATHOLOGY_N_STAGE'.

- RPTOR|RAPTOR

- 10 genes correlated to 'RADIATION_THERAPY'.

- TGM2|TRANSGLUTAMINASE , SHC1|SHC_PY317 , EGFR|EGFR_PY1068 , PRKAA1|AMPK_PT172 , EIF4EBP1|4E-BP1_PS65 , ...

- No genes correlated to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'GENDER', 'KARNOFSKY_PERFORMANCE_SCORE', 'HISTOLOGICAL_TYPE', and 'RESIDUAL_TUMOR'.

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 P value < 0.05 and Q value < 0.3.

Clinical feature | Statistical test | Significant genes | Associated with |  | Associated with |
--- | --- | --- | --- | --- | --- | ---
DAYS_TO_DEATH_OR_LAST_FUP | Cox regression test | N=17 |  | N=NA |  | N=NA
YEARS_TO_BIRTH | Spearman correlation test | N=0 |  |  |  |
PATHOLOGIC_STAGE | Kruskal-Wallis test | N=0 |  |  |  |
PATHOLOGY_T_STAGE | Spearman correlation test | N=0 |  |  |  |
PATHOLOGY_N_STAGE | Spearman correlation test | N=1 | higher stage | N=0 | lower stage | N=1
GENDER | Wilcoxon test | N=0 |  |  |  |
RADIATION_THERAPY | Wilcoxon test | N=10 | yes | N=10 | no | N=0
KARNOFSKY_PERFORMANCE_SCORE | Spearman correlation test | N=0 |  |  |  |
HISTOLOGICAL_TYPE | Kruskal-Wallis test | N=0 |  |  |  |
RESIDUAL_TUMOR | Kruskal-Wallis test | N=0 |  |  |  |

Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

17 genes related to 'DAYS_TO_DEATH_OR_LAST_FUP'.

Table S1. Basic characteristics of clinical feature: 'DAYS_TO_DEATH_OR_LAST_FUP'

DAYS_TO_DEATH_OR_LAST_FUP | Duration (Months) | 1.3-80.8 (median=17.6)
| censored | N = 5
| death | N = 57
| Significant markers | N = 17
| associated with shorter survival | NA
| associated with longer survival | NA

List of top 10 genes differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2. Get Full Table List of top 10 genes significantly associated with 'Time to Death' by Cox regression test. For the survival curves, it compared quantile intervals at c(0, 0.25, 0.50, 0.75, 1) and did not try survival analysis if there is only one interval.

| logrank_P | Q | C_index
--- | --- | --- | ---
NFKB1|NF-KB-P65_PS536 | 0.000414 | 0.08 | 0.499
CDKN1B|P27 | 0.0019 | 0.14 | 0.365
SERPINE1|PAI-1 | 0.00255 | 0.14 | 0.662
IGFBP2|IGFBP2 | 0.00378 | 0.14 | 0.604
FN1|FIBRONECTIN | 0.00405 | 0.14 | 0.614
PRKAA1|AMPK_PT172 | 0.00503 | 0.14 | 0.525
CCNB1|CYCLIN_B1 | 0.00525 | 0.14 | 0.629
PKC|PKC-PAN_BETAII_PS660 | 0.00917 | 0.22 | 0.581
EIF4EBP1|4E-BP1 | 0.012 | 0.26 | 0.653
TFRC|TFRC | 0.0137 | 0.26 | 0.615

Clinical variable #2: 'YEARS_TO_BIRTH'

No gene related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH | Mean (SD) | 62.48 (10)
| Significant markers | N = 0

Clinical variable #3: 'PATHOLOGIC_STAGE'

No gene related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE | Labels | N
| STAGE I | 5
| STAGE IA | 1
| STAGE II | 9
| STAGE III | 35
| STAGE IV | 13
| Significant markers | N = 0

Clinical variable #4: 'PATHOLOGY_T_STAGE'

No gene related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE | Mean (SD) | 2.66 (0.95)
|  | N
| T1 | 8
| T2 | 17
| T3 | 24
| T4 | 12
| Significant markers | N = 0

Clinical variable #5: 'PATHOLOGY_N_STAGE'

One gene related to 'PATHOLOGY_N_STAGE'.

Table S6. Basic characteristics of clinical feature: 'PATHOLOGY_N_STAGE'

PATHOLOGY_N_STAGE | Mean (SD) | 1 (1)
|  | N
| N0 | 28
| N1 | 8
| N2 | 22
| N3 | 3
| Significant markers | N = 1
| pos. correlated | 0
| neg. correlated | 1

List of one gene differentially expressed by 'PATHOLOGY_N_STAGE'

Table S7. Get Full Table List of one gene significantly correlated to 'PATHOLOGY_N_STAGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
RPTOR|RAPTOR | -0.474 | 0.0001139 | 0.022

Clinical variable #6: 'GENDER'

No gene related to 'GENDER'.

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

GENDER | Labels | N
| FEMALE | 13
| MALE | 50
| Significant markers | N = 0

Clinical variable #7: 'RADIATION_THERAPY'

10 genes related to 'RADIATION_THERAPY'.

Table S9. Basic characteristics of clinical feature: 'RADIATION_THERAPY'

RADIATION_THERAPY | Labels | N
| NO | 47
| YES | 16
| Significant markers | N = 10
| Higher in YES | 10
| Higher in NO | 0

List of 10 genes differentially expressed by 'RADIATION_THERAPY'

Table S10. Get Full Table List of 10 genes differentially expressed by 'RADIATION_THERAPY'

| W(pos if higher in 'YES') | wilcoxontestP | Q | AUC
--- | --- | --- | --- | ---
TGM2|TRANSGLUTAMINASE | 180 | 0.002022 | 0.197 | 0.7606
SHC1|SHC_PY317 | 568 | 0.002496 | 0.197 | 0.7553
EGFR|EGFR_PY1068 | 564 | 0.00307 | 0.197 | 0.75
PRKAA1|AMPK_PT172 | 204 | 0.006768 | 0.296 | 0.7287
EIF4EBP1|4E-BP1_PS65 | 543 | 0.008561 | 0.296 | 0.7221
G6PD|G6PD | 218 | 0.01288 | 0.296 | 0.7101
C12ORF5|TIGAR | 218.5 | 0.01317 | 0.296 | 0.7094
FOXO3|FOXO3A_PS318_S321 | 219 | 0.01347 | 0.296 | 0.7088
RB1|RB_PS807_S811 | 532 | 0.01407 | 0.296 | 0.7074
NF2|NF2 | 222 | 0.01536 | 0.296 | 0.7048

Clinical variable #8: 'KARNOFSKY_PERFORMANCE_SCORE'

No gene related to 'KARNOFSKY_PERFORMANCE_SCORE'.

Table S11. Basic characteristics of clinical feature: 'KARNOFSKY_PERFORMANCE_SCORE'

KARNOFSKY_PERFORMANCE_SCORE | Mean (SD) | 72.22 (30)
| Score | N
| 0 | 1
| 50 | 1
| 80 | 3
| 90 | 4
| Significant markers | N = 0

Clinical variable #9: 'HISTOLOGICAL_TYPE'

No gene related to 'HISTOLOGICAL_TYPE'.

Table S12. Basic characteristics of clinical feature: 'HISTOLOGICAL_TYPE'

HISTOLOGICAL_TYPE | Labels | N
| BIPHASIC MESOTHELIOMA | 17
| DIFFUSE MALIGNANT MESOTHELIOMA - NOS | 4
| EPITHELIOID MESOTHELIOMA | 40
| SARCOMATOID MESOTHELIOMA | 2
| Significant markers | N = 0

Clinical variable #10: 'RESIDUAL_TUMOR'

No gene related to 'RESIDUAL_TUMOR'.

Table S13. Basic characteristics of clinical feature: 'RESIDUAL_TUMOR'

RESIDUAL_TUMOR | Labels | N
| R0 | 12
| R1 | 2
| R2 | 11
| RX | 6
| Significant markers | N = 0

Methods & Data
Input

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

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

- Number of patients = 63

- Number of genes = 193

- Number of clinical features = 10

Selected clinical features

- Further details on clinical features selected for this analysis, please find a documentation on selected CDEs (Clinical Data Elements). The first column of the file is a formula to convert values and the second column is a clinical parameter name.

- There are also useful links about clinical features.

- CDE Browser

- Data dictionary for clinical data

- NCI wiki - Biospecimen and Clinical XSD Files Specification

- Survival time data

- Survival time data is a combined value of days_to_death and days_to_last_followup. For each patient, it creates a combined value 'days_to_death_or_last_fup' using conversion process below.

- if 'vital_status'==1(dead), 'days_to_last_followup' is always NA. Thus, uses 'days_to_death' value for 'days_to_death_or_fup'

- if 'vital_status'==0(alive),

- if 'days_to_death'==NA & 'days_to_last_followup'!=NA, uses 'days_to_last_followup' value for 'days_to_death_or_fup'

- if 'days_to_death'!=NA, excludes this case in survival analysis and report the case.

- if 'vital_status'==NA,excludes this case in survival analysis and report the case.

- cf. In certain diesase types such as SKCM, days_to_death parameter is replaced with time_from_specimen_dx or time_from_specimen_procurement_to_death .

- This analysis excluded clinical variables that has only NA values.

Survival analysis

For survival clinical features, logrank test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values comparing quantile intervals using the 'coxph' function in R. Kaplan-Meier survival curves were plotted using quantile intervals at c(0, 0.25, 0.50, 0.75, 1). If there is only one interval group, it will not try survival analysis.

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

Wilcoxon rank sum test (Mann-Whitney U test)

For two groups (mutant or wild-type) of continuous type of clinical data, wilcoxon rank sum test (Mann and Whitney, 1947) was applied to compare their mean difference using 'wilcox.test(continuous.clinical ~ as.factor(group), exact=FALSE)' function in R. This test is equivalent to the Mann-Whitney test.

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] Mann and Whitney, On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other, Annals of Mathematical Statistics 18 (1), 50-60 (1947)
[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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