Correlation between APOBEC groups and selected clinical features
Overview
Introduction

This pipeline computes the correlation between APOBRC groups and selected clinical features.

Summary

Testing the association between APOBEC groups identified by 2 different apobec score and 4 clinical features across 248 patients, 4 significant findings detected with Q value < 0.25.

  • 3 subtypes identified in current cancer cohort by 'APOBEC MUTLOAD MINESTIMATE'. These subtypes correlate to 'HISTOLOGICAL_TYPE' and 'RESIDUAL_TUMOR'.

  • 2 subtypes identified in current cancer cohort by 'APOBEC ENRICH'. These subtypes correlate to 'HISTOLOGICAL_TYPE' and 'RESIDUAL_TUMOR'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between APOBEC groups by 2 different APOBEC scores and 4 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 4 significant findings detected.

Clinical
Features 		Statistical
Tests 		APOBEC
MUTLOAD
MINESTIMATE 		APOBEC
ENRICH
Time to Death logrank test 0.983
(1) 		0.992
(1)
RADIATION THERAPY Fisher's exact test 0.891
(1) 		1
(1)
HISTOLOGICAL TYPE Fisher's exact test 0.00021
(0.00168) 		0.0017
(0.0068)
RESIDUAL TUMOR Fisher's exact test 0.0258
(0.0517) 		0.00823
(0.0219)
APOBEC group #1: 'APOBEC MUTLOAD MINESTIMATE'

Table S1.  Description of APOBEC group #1: 'APOBEC MUTLOAD MINESTIMATE'

Cluster Labels 0 HIGH LOW
Number of samples 236 6 6
'APOBEC MUTLOAD MINESTIMATE' versus 'HISTOLOGICAL_TYPE'

P value = 0.00021 (Fisher's exact test), Q value = 0.0017

Table S2.  Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 200 4 44
0 196 4 36
HIGH 4 0 2
LOW 0 0 6

Figure S1.  Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

'APOBEC MUTLOAD MINESTIMATE' versus 'RESIDUAL_TUMOR'

P value = 0.0258 (Fisher's exact test), Q value = 0.052

Table S3.  Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #4: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 172 10 7 18
0 165 10 4 18
HIGH 3 0 2 0
LOW 4 0 1 0

Figure S2.  Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #4: 'RESIDUAL_TUMOR'

APOBEC group #2: 'APOBEC ENRICH'

Table S4.  Description of APOBEC group #2: 'APOBEC ENRICH'

Cluster Labels FC.HIGH.ENRICH FC.LOW.ENRICH FC.NO.ENRICH
Number of samples 11 1 236
'APOBEC ENRICH' versus 'HISTOLOGICAL_TYPE'

P value = 0.0017 (Fisher's exact test), Q value = 0.0068

Table S5.  Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 200 4 43
FC.HIGH.ENRICH 4 0 7
FC.NO.ENRICH 196 4 36

Figure S3.  Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

'APOBEC ENRICH' versus 'RESIDUAL_TUMOR'

P value = 0.00823 (Fisher's exact test), Q value = 0.022

Table S6.  Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #4: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 171 10 7 18
FC.HIGH.ENRICH 6 0 3 0
FC.NO.ENRICH 165 10 4 18

Figure S4.  Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #4: 'RESIDUAL_TUMOR'

Methods & Data
Input

  • APOBEC groups file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/APOBEC_Pipelines/UCEC-TP/22555825/__DELETED__1436046:APOBEC_clinical_corr_input_22572090/APOBEC_for_clinical.correlaion.input.categorical.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/UCEC-TP/22507145/UCEC-TP.merged_data.txt

  • Number of patients = 248

  • Number of selected clinical features = 4

APOBEC classification

APOBEC classification based on APOBEC_MutLoad_MinEstimate : a. APOBEC non group -- samples with zero value, b. APOBEC high group -- samples above median value in non zero samples, c. APOBEC low 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. Low enrichment group -- samples with BH_Fisher_p-value_tCw = < 0.05 and APOBEC_enrich=<2, c. High enrichment group -- samples with BH_Fisher_p-value_tCw =< 0.05 and APOBEC_enrich>2.

Survival analysis

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

Fisher's exact test

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

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] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[2] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[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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