Correlation between mutation rate and clinical features
Uterine Carcinosarcoma (Primary solid tumor)
02 April 2015 | analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1HT2NFF
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between mutation rate and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1HT2NFF
Overview
Introduction

This pipeline uses various statistical tests to identify selected clinical features related to mutation rate.

Summary

Testing the association between 2 variables and 4 clinical features across 57 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 1 clinical feature related to at least one variables.

- 2 variables correlated to 'AGE'.

- MUTATIONRATE_SILENT , MUTATIONRATE_NONSYNONYMOUS

- No variables correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'AGE_mutation.rate', and 'RACE'.

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 variables that are significantly associated with each clinical feature at P value < 0.05 and Q value < 0.3.

Clinical feature | Statistical test | Significant variables | Associated with |  | Associated with |
--- | --- | --- | --- | --- | --- | ---
DAYS_TO_DEATH_OR_LAST_FUP | Cox regression test | N=0 |  |  |  |
AGE | Spearman correlation test | N=2 | older | N=2 | younger | N=0
AGE | Linear Regression Analysis | N=0 |  |  |  |
RACE | Kruskal-Wallis test | N=0 |  |  |  |

Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

No variable 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) | 0.3-140.3 (median=19.6)
| censored | N = 22
| death | N = 34
| Significant variables | N = 0

Clinical variable #2: 'AGE'

2 variables related to 'AGE'.

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

AGE | Mean (SD) | 69.72 (9.3)
| Significant variables | N = 2
| pos. correlated | 2
| neg. correlated | 0

List of 2 variables associated with 'AGE'

Table S3. Get Full Table List of 2 variables significantly correlated to 'AGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
MUTATIONRATE_SILENT | 0.3015 | 0.02266 | 0.0331
MUTATIONRATE_NONSYNONYMOUS | 0.2828 | 0.03307 | 0.0331

Clinical variable #3: 'AGE'

No variable related to 'AGE'.

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

AGE | Mean (SD) | 69.72 (9.3)
| Significant variables | N = 0

Clinical variable #4: 'RACE'

No variable related to 'RACE'.

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

RACE | Labels | N
| ASIAN | 3
| BLACK OR AFRICAN AMERICAN | 9
| WHITE | 44
| Significant variables | N = 0

Methods & Data
Input

- Expresson data file = UCS-TP.patients.counts_and_rates.txt

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

- Number of patients = 57

- Number of variables = 2

- Number of clinical features = 4

Selected clinical features

- For clinical features selected for this analysis and their value conozzle.versions, please find a documentation on selected CDEs .

- 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, 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

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] 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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