This pipeline computes the correlation between APOBRC groups and selected clinical features.
Testing the association between APOBEC groups identified by 2 different apobec score and 15 clinical features across 177 patients, 4 significant findings detected with Q value < 0.25.
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3 subtypes identified in current cancer cohort by 'APOBEC MUTLOAD MINESTIMATE'. These subtypes correlate to 'KARNOFSKY_PERFORMANCE_SCORE' and 'RADIATIONS_RADIATION_REGIMENINDICATION'.
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3 subtypes identified in current cancer cohort by 'APOBEC ENRICH'. These subtypes correlate to 'KARNOFSKY_PERFORMANCE_SCORE' and 'RADIATIONS_RADIATION_REGIMENINDICATION'.
Clinical Features |
Statistical Tests |
APOBEC MUTLOAD MINESTIMATE |
APOBEC ENRICH |
Time to Death | logrank test |
0.487 (0.731) |
0.329 (0.607) |
YEARS TO BIRTH | Kruskal-Wallis (anova) |
0.465 (0.731) |
0.344 (0.607) |
NEOPLASM DISEASESTAGE | Fisher's exact test |
0.0914 (0.507) |
0.118 (0.507) |
PATHOLOGY T STAGE | Fisher's exact test |
0.207 (0.607) |
0.202 (0.607) |
PATHOLOGY N STAGE | Fisher's exact test |
0.565 (0.764) |
0.593 (0.764) |
PATHOLOGY M STAGE | Fisher's exact test |
1 (1.00) |
1 (1.00) |
GENDER | Fisher's exact test |
0.611 (0.764) |
0.522 (0.746) |
KARNOFSKY PERFORMANCE SCORE | Kruskal-Wallis (anova) |
0.0153 (0.204) |
0.0147 (0.204) |
HISTOLOGICAL TYPE | Fisher's exact test |
1 (1.00) |
0.245 (0.607) |
RADIATIONS RADIATION REGIMENINDICATION | Fisher's exact test |
0.0272 (0.204) |
0.0246 (0.204) |
NUMBER PACK YEARS SMOKED | Kruskal-Wallis (anova) |
0.846 (0.976) |
0.43 (0.717) |
YEAR OF TOBACCO SMOKING ONSET | Kruskal-Wallis (anova) |
0.297 (0.607) |
0.111 (0.507) |
COMPLETENESS OF RESECTION | Fisher's exact test |
0.147 (0.55) |
0.308 (0.607) |
RACE | Fisher's exact test |
0.936 (1.00) |
0.838 (0.976) |
ETHNICITY | Fisher's exact test |
0.228 (0.607) |
0.286 (0.607) |
Cluster Labels | 0 | HIGH | LOW |
---|---|---|---|
Number of samples | 67 | 43 | 67 |
P value = 0.0153 (Kruskal-Wallis (anova)), Q value = 0.2
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 36 | 15.3 (31.8) |
0 | 16 | 33.1 (41.4) |
HIGH | 9 | 0.0 (0.0) |
LOW | 11 | 1.8 (6.0) |
P value = 0.0272 (Fisher's exact test), Q value = 0.2
nPatients | NO | YES |
---|---|---|
ALL | 5 | 172 |
0 | 0 | 67 |
HIGH | 0 | 43 |
LOW | 5 | 62 |
Cluster Labels | FC.HIGH.SIG | FC.LOW.NONSIG | FC.NEUTRAL |
---|---|---|---|
Number of samples | 58 | 66 | 53 |
P value = 0.0147 (Kruskal-Wallis (anova)), Q value = 0.2
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 36 | 15.3 (31.8) |
FC.HIGH.SIG | 11 | 0.0 (0.0) |
FC.LOW.NONSIG | 16 | 33.1 (41.4) |
FC.NEUTRAL | 9 | 2.2 (6.7) |
P value = 0.0246 (Fisher's exact test), Q value = 0.2
nPatients | NO | YES |
---|---|---|
ALL | 5 | 172 |
FC.HIGH.SIG | 1 | 57 |
FC.LOW.NONSIG | 0 | 66 |
FC.NEUTRAL | 4 | 49 |
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APOBEC groups file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/APOBEC_Pipelines/LUSC-TP/15165081/APOBEC_clinical_corr_input_15169729/APOBEC_for_clinical.correlaion.input.categorical.txt
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Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/LUSC-TP/15084586/LUSC-TP.merged_data.txt
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Number of patients = 177
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Number of selected clinical features = 15
APOBEC classification based on APOBEC_MutLoad_MinEstimate : a. APOBEC non group -- samples with zero value, b. APOBEC hig group -- samples above median value in non zero samples, c. APOBEC hig group -- samples below median value in non zero samples.
APOBEC classification based on APOBEC_enrich : a. No Enrichmment group -- all samples with BH_Fisher_p-value_tCw >=0.05, b. Small enrichment group -- samples with BH_Fisher_p-value_tCw = < 0.05 and APOBEC_enrich=<2, c. High enrichment gruop -- samples with BH_Fisher_p-value_tCw =< 0.05 and APOBEC_enrich>2.
For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R
For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R
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.
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.