Correlation between mutation rate and clinical features
Uterine Corpus Endometrioid Carcinoma (Primary solid tumor)
04 October 2018 | None
Maintainer Information
Maintained by Broad Institute GDAC (Broad Institute of MIT & Harvard)
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 18 clinical features across 100 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 'PATHOLOGY_N_STAGE'.

- MUTATIONRATE_SILENT , MUTATIONRATE_NONSYNONYMOUS

- No variables correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'AGE', 'AGE_mutation.rate', 'HISTOLOGICAL_TYPE', 'FIGO_GRADE', 'KARNOFSKY_PERFORMANCE_SCORE', 'MSI', 'PATHOLOGY_T_STAGE', 'PATHOLOGIC_STAGE', 'ETHNICITY', 'RACE', 'RADIATION_THERAPY', 'DIABETES', 'BMI', 'NUMBER_PACK_YEARS_SMOKED', 'SMOKER', and 'COUNTRY_OF_ORIGIN'.

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=0 |  |  |  |
AGE | Linear Regression Analysis | N=0 |  |  |  |
HISTOLOGICAL_TYPE | Kruskal-Wallis test | N=0 |  |  |  |
FIGO_GRADE | Kruskal-Wallis test | N=0 |  |  |  |
KARNOFSKY_PERFORMANCE_SCORE | Spearman correlation test | N=0 |  |  |  |
MSI | Kruskal-Wallis test | N=0 |  |  |  |
PATHOLOGY_T_STAGE | Spearman correlation test | N=0 |  |  |  |
PATHOLOGY_N_STAGE | Spearman correlation test | N=2 | higher stage | N=0 | lower stage | N=2
PATHOLOGIC_STAGE | Kruskal-Wallis test | N=0 |  |  |  |
ETHNICITY | Wilcoxon test | N=0 |  |  |  |
RACE | Kruskal-Wallis test | N=0 |  |  |  |
RADIATION_THERAPY | Wilcoxon test | N=0 |  |  |  |
DIABETES | Wilcoxon test | N=0 |  |  |  |
BMI | Kruskal-Wallis test | N=0 |  |  |  |
NUMBER_PACK_YEARS_SMOKED | Spearman correlation test | N=0 |  |  |  |
SMOKER | Wilcoxon test | N=0 |  |  |  |
COUNTRY_OF_ORIGIN | 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) | 0-130.4 (median=11.6)
| censored | N = 95
| death | N = 3
| Significant variables | N = 0

Clinical variable #2: 'AGE'

No variable related to 'AGE'.

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

AGE | Mean (SD) | 63.56 (10)
| Significant variables | N = 0

Clinical variable #3: 'AGE'

No variable related to 'AGE'.

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

AGE | Mean (SD) | 63.56 (10)
| Significant variables | N = 0

Clinical variable #4: 'HISTOLOGICAL_TYPE'

No variable related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE | Labels | N
| CLEAR CELL CARCINOMA | 1
| ENDOMETRIOID CARCINOMA | 77
| MIXED CELL ADENOCARCINOMA | 1
| SEROUS CARCINOMA | 21
| Significant variables | N = 0

Clinical variable #5: 'FIGO_GRADE'

No variable related to 'FIGO_GRADE'.

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

FIGO_GRADE | Labels | N
| FIGO GRADE 1 | 32
| FIGO GRADE 2 | 34
| FIGO GRADE 3 | 7
| Significant variables | N = 0

Clinical variable #6: 'KARNOFSKY_PERFORMANCE_SCORE'

No variable related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE | Mean (SD) | 91.9 (8.5)
| Score | N
| 60 | 1
| 70 | 3
| 80 | 1
| 90 | 32
| 100 | 21
| Significant variables | N = 0

Clinical variable #7: 'MSI'

No variable related to 'MSI'.

Table S7. Basic characteristics of clinical feature: 'MSI'

MSI | Labels | N
| MSI-H | 4
| MSI-L | 5
| MSS | 23
| Significant variables | N = 0

Clinical variable #8: 'PATHOLOGY_T_STAGE'

No variable related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE | Mean (SD) | 1.31 (0.65)
|  | N
| T1 | 78
| T2 | 11
| T3 | 10
| Significant variables | N = 0

Clinical variable #9: 'PATHOLOGY_N_STAGE'

2 variables related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE | Mean (SD) | 0.22 (0.53)
|  | N
| N0 | 46
| N1 | 6
| N2 | 3
| Significant variables | N = 2
| pos. correlated | 0
| neg. correlated | 2

List of 2 variables associated with 'PATHOLOGY_N_STAGE'

Table S10. Get Full Table List of 2 variables significantly correlated to 'PATHOLOGY_N_STAGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
MUTATIONRATE_SILENT | -0.3398 | 0.01113 | 0.0223
MUTATIONRATE_NONSYNONYMOUS | -0.305 | 0.02354 | 0.0235

Clinical variable #10: 'PATHOLOGIC_STAGE'

No variable related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE | Labels | N
| STAGE I | 72
| STAGE IA | 1
| STAGE IB | 1
| STAGE II | 8
| STAGE III | 15
| STAGE IV | 2
| STAGE IVB | 1
| Significant variables | N = 0

Clinical variable #11: 'ETHNICITY'

No variable related to 'ETHNICITY'.

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

ETHNICITY | Labels | N
| HISPANIC OR LATINO | 4
| NOT HISPANIC OR LATINO | 41
| Significant variables | N = 0

Clinical variable #12: 'RACE'

No variable related to 'RACE'.

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

RACE | Labels | N
| ASIAN | 1
| BLACK OR AFRICAN AMERICAN | 3
| WHITE | 58
| Significant variables | N = 0

Clinical variable #13: 'RADIATION_THERAPY'

No variable related to 'RADIATION_THERAPY'.

Table S14. Basic characteristics of clinical feature: 'RADIATION_THERAPY'

RADIATION_THERAPY | Labels | N
| NO | 43
| YES | 54
| Significant variables | N = 0

Clinical variable #14: 'DIABETES'

No variable related to 'DIABETES'.

Table S15. Basic characteristics of clinical feature: 'DIABETES'

DIABETES | Labels | N
| NO | 70
| YES | 28
| Significant variables | N = 0

Clinical variable #15: 'BMI'

No variable related to 'BMI'.

Table S16. Basic characteristics of clinical feature: 'BMI'

BMI | Labels | N
| NORMAL | 8
| OBESE | 47
| OVERWEIGHT | 21
| SEVERELY OBESE | 21
| UNDERWEIGHT | 3
| Significant variables | N = 0

Clinical variable #16: 'NUMBER_PACK_YEARS_SMOKED'

No variable related to 'NUMBER_PACK_YEARS_SMOKED'.

Table S17. Basic characteristics of clinical feature: 'NUMBER_PACK_YEARS_SMOKED'

NUMBER_PACK_YEARS_SMOKED | Mean (SD) | 14.48 (12)
| Significant variables | N = 0

Clinical variable #17: 'SMOKER'

No variable related to 'SMOKER'.

Table S18. Basic characteristics of clinical feature: 'SMOKER'

SMOKER | Labels | N
| NON-SMOKER | 73
| SMOKER | 22
| Significant variables | N = 0

Clinical variable #18: 'COUNTRY_OF_ORIGIN'

No variable related to 'COUNTRY_OF_ORIGIN'.

Table S19. Basic characteristics of clinical feature: 'COUNTRY_OF_ORIGIN'

COUNTRY_OF_ORIGIN | Labels | N
| MEXICO | 2
| POLAND | 5
| UKRAINE | 32
| UNITED STATES | 36
| Significant variables | N = 0

Methods & Data
Input

- Expresson data file = patient_counts_and_rates.txt

- Clinical data file = CPTAC3-UCEC-TP.clin.merged.picked.txt

- Number of patients = 100

- Number of variables = 2

- Number of clinical features = 18

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.

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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