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 14 clinical features across 290 patients, no significant finding detected with Q value < 0.25.
-
3 subtypes identified in current cancer cohort by 'APOBEC MUTLOAD MINESTIMATE'. These subtypes do not correlate to any clinical features.
-
3 subtypes identified in current cancer cohort by 'APOBEC ENRICH'. These subtypes do not correlate to any clinical features.
Clinical Features |
Statistical Tests |
APOBEC MUTLOAD MINESTIMATE |
APOBEC ENRICH |
Time from Specimen Diagnosis to Death | logrank test |
0.182 (0.448) |
0.257 (0.554) |
Time to Death | logrank test |
0.192 (0.448) |
0.279 (0.558) |
YEARS TO BIRTH | Kruskal-Wallis (anova) |
0.0554 (0.264) |
0.529 (0.767) |
PATHOLOGIC STAGE | Fisher's exact test |
0.593 (0.79) |
0.548 (0.767) |
PATHOLOGY T STAGE | Fisher's exact test |
0.495 (0.767) |
0.177 (0.448) |
PATHOLOGY N STAGE | Fisher's exact test |
0.399 (0.746) |
0.479 (0.767) |
PATHOLOGY M STAGE | Fisher's exact test |
0.732 (0.891) |
1 (1.00) |
MELANOMA ULCERATION | Fisher's exact test |
0.0498 (0.264) |
0.0421 (0.264) |
MELANOMA PRIMARY KNOWN | Fisher's exact test |
0.544 (0.767) |
0.8 (0.934) |
BRESLOW THICKNESS | Kruskal-Wallis (anova) |
0.712 (0.891) |
0.151 (0.448) |
GENDER | Fisher's exact test |
0.0149 (0.264) |
0.14 (0.448) |
RADIATION THERAPY | Fisher's exact test |
1 (1.00) |
1 (1.00) |
RACE | Fisher's exact test |
0.0565 (0.264) |
0.03 (0.264) |
ETHNICITY | Fisher's exact test |
0.165 (0.448) |
1 (1.00) |
Cluster Labels | 0 | HIGH | LOW |
---|---|---|---|
Number of samples | 86 | 73 | 131 |
Cluster Labels | FC.HIGH.SIG | FC.LOW.NONSIG | FC.NEUTRAL |
---|---|---|---|
Number of samples | 3 | 82 | 205 |
-
APOBEC groups file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/APOBEC_Pipelines/SKCM-TM/20278290/APOBEC_clinical_corr_input_20359542/APOBEC_for_clinical.correlaion.input.categorical.txt
-
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/SKCM-TM/19775519/SKCM-TM.merged_data.txt
-
Number of patients = 290
-
Number of selected clinical features = 14
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.