This pipeline uses various statistical tests to identify selected clinical features related to mutation rate.
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.
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2 variables correlated to 'PATHOLOGY_N_STAGE'.
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MUTATIONRATE_SILENT , MUTATIONRATE_NONSYNONYMOUS
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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'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant variables | Associated with | Associated with | ||
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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 |
No variable related to '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 |
AGE | Mean (SD) | 63.56 (10) |
Significant variables | N = 0 |
AGE | Mean (SD) | 63.56 (10) |
Significant variables | N = 0 |
HISTOLOGICAL_TYPE | Labels | N |
CLEAR CELL CARCINOMA | 1 | |
ENDOMETRIOID CARCINOMA | 77 | |
MIXED CELL ADENOCARCINOMA | 1 | |
SEROUS CARCINOMA | 21 | |
Significant variables | N = 0 |
FIGO_GRADE | Labels | N |
FIGO GRADE 1 | 32 | |
FIGO GRADE 2 | 34 | |
FIGO GRADE 3 | 7 | |
Significant variables | N = 0 |
No variable related to '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 |
MSI | Labels | N |
MSI-H | 4 | |
MSI-L | 5 | |
MSS | 23 | |
Significant variables | N = 0 |
PATHOLOGY_T_STAGE | Mean (SD) | 1.31 (0.65) |
N | ||
T1 | 78 | |
T2 | 11 | |
T3 | 10 | |
Significant variables | N = 0 |
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 |
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 |
ETHNICITY | Labels | N |
HISPANIC OR LATINO | 4 | |
NOT HISPANIC OR LATINO | 41 | |
Significant variables | N = 0 |
RACE | Labels | N |
ASIAN | 1 | |
BLACK OR AFRICAN AMERICAN | 3 | |
WHITE | 58 | |
Significant variables | N = 0 |
RADIATION_THERAPY | Labels | N |
NO | 43 | |
YES | 54 | |
Significant variables | N = 0 |
DIABETES | Labels | N |
NO | 70 | |
YES | 28 | |
Significant variables | N = 0 |
BMI | Labels | N |
NORMAL | 8 | |
OBESE | 47 | |
OVERWEIGHT | 21 | |
SEVERELY OBESE | 21 | |
UNDERWEIGHT | 3 | |
Significant variables | N = 0 |
No variable related to 'NUMBER_PACK_YEARS_SMOKED'.
NUMBER_PACK_YEARS_SMOKED | Mean (SD) | 14.48 (12) |
Significant variables | N = 0 |
SMOKER | Labels | N |
NON-SMOKER | 73 | |
SMOKER | 22 | |
Significant variables | N = 0 |
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Expresson data file = patient_counts_and_rates.txt
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Clinical data file = CPTAC3-UCEC-TP.clin.merged.picked.txt
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Number of patients = 100
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Number of variables = 2
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Number of clinical features = 18
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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.
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There are also useful links about clinical features.
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Survival time data
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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.
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if 'vital_status'==1(dead), 'days_to_last_followup' is always NA. Thus, uses 'days_to_death' value for 'days_to_death_or_fup'
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if 'vital_status'==0(alive),
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if 'days_to_death'==NA & 'days_to_last_followup'!=NA, uses 'days_to_last_followup' value for 'days_to_death_or_fup'
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if 'days_to_death'!=NA, excludes this case in survival analysis and report the case.
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if 'vital_status'==NA,excludes this case in survival analysis and report the case.
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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 .
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This analysis excluded clinical variables that has only NA values.
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.
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
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.
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.