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 13 clinical features across 395 patients, 8 significant findings detected with Q value < 0.25.

  • 3 subtypes identified in current cancer cohort by 'APOBEC MUTLOAD MINESTIMATE'. These subtypes correlate to 'Time to Death',  'GENDER',  'RADIATION_THERAPY',  'NUMBER_OF_LYMPH_NODES', and 'RACE'.

  • 3 subtypes identified in current cancer cohort by 'APOBEC ENRICH'. These subtypes correlate to 'Time to Death',  'RADIATION_THERAPY', and 'RACE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between APOBEC groups by 2 different APOBEC scores and 13 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 8 significant findings detected.

Clinical
Features 		Statistical
Tests 		APOBEC
MUTLOAD
MINESTIMATE 		APOBEC
ENRICH
Time to Death logrank test 0.000892
(0.00773) 		0.0188
(0.092)
YEARS TO BIRTH Kruskal-Wallis (anova) 0.637
(0.772) 		0.611
(0.772)
PATHOLOGIC STAGE Fisher's exact test 0.121
(0.337) 		0.365
(0.635)
PATHOLOGY T STAGE Fisher's exact test 0.381
(0.635) 		0.135
(0.337)
PATHOLOGY N STAGE Fisher's exact test 0.143
(0.337) 		0.552
(0.755)
PATHOLOGY M STAGE Fisher's exact test 0.907
(0.982) 		0.216
(0.468)
GENDER Fisher's exact test 0.0248
(0.092) 		0.653
(0.772)
RADIATION THERAPY Fisher's exact test 0.0227
(0.092) 		0.00376
(0.0244)
KARNOFSKY PERFORMANCE SCORE Kruskal-Wallis (anova) 0.808
(0.914) 		0.488
(0.704)
NUMBER PACK YEARS SMOKED Kruskal-Wallis (anova) 0.39
(0.635) 		0.391
(0.635)
NUMBER OF LYMPH NODES Kruskal-Wallis (anova) 0.0501
(0.163) 		0.454
(0.695)
RACE Fisher's exact test 0.00022
(0.00286) 		4e-05
(0.00104)
ETHNICITY Fisher's exact test 1
(1.00) 		1
(1.00)
APOBEC group #1: 'APOBEC MUTLOAD MINESTIMATE'

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

Cluster Labels 0 HIGH LOW
Number of samples 46 172 177
'APOBEC MUTLOAD MINESTIMATE' versus 'Time to Death'

P value = 0.000892 (logrank test), Q value = 0.0077

Table S2.  Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 392 175 0.6 - 166.0 (17.6)
0 45 27 2.0 - 93.0 (13.6)
HIGH 171 62 0.6 - 166.0 (19.8)
LOW 176 86 0.6 - 165.7 (16.1)

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

'APOBEC MUTLOAD MINESTIMATE' versus 'GENDER'

P value = 0.0248 (Fisher's exact test), Q value = 0.092

Table S3.  Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 102 293
0 13 33
HIGH 33 139
LOW 56 121

Figure S2.  Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #7: 'GENDER'

'APOBEC MUTLOAD MINESTIMATE' versus 'RADIATION_THERAPY'

P value = 0.0227 (Fisher's exact test), Q value = 0.092

Table S4.  Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 350 20
0 35 6
HIGH 156 5
LOW 159 9

Figure S3.  Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #8: 'RADIATION_THERAPY'

'APOBEC MUTLOAD MINESTIMATE' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0501 (Kruskal-Wallis (anova)), Q value = 0.16

Table S5.  Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 288 1.8 (4.4)
0 29 0.9 (1.7)
HIGH 128 1.7 (4.8)
LOW 131 2.1 (4.3)

Figure S4.  Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'APOBEC MUTLOAD MINESTIMATE' versus 'RACE'

P value = 0.00022 (Fisher's exact test), Q value = 0.0029

Table S6.  Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #12: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 39 22 317
0 13 4 27
HIGH 9 7 150
LOW 17 11 140

Figure S5.  Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #12: 'RACE'

APOBEC group #2: 'APOBEC ENRICH'

Table S7.  Description of APOBEC group #2: 'APOBEC ENRICH'

Cluster Labels FC.HIGH.ENRICH FC.LOW.ENRICH FC.NO.ENRICH
Number of samples 327 22 46
'APOBEC ENRICH' versus 'Time to Death'

P value = 0.0188 (logrank test), Q value = 0.092

Table S8.  Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 392 175 0.6 - 166.0 (17.6)
FC.HIGH.ENRICH 325 137 0.6 - 166.0 (17.9)
FC.LOW.ENRICH 22 11 1.9 - 52.0 (13.7)
FC.NO.ENRICH 45 27 2.0 - 93.0 (13.6)

Figure S6.  Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #1: 'Time to Death'

'APOBEC ENRICH' versus 'RADIATION_THERAPY'

P value = 0.00376 (Fisher's exact test), Q value = 0.024

Table S9.  Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 350 20
FC.HIGH.ENRICH 297 11
FC.LOW.ENRICH 18 3
FC.NO.ENRICH 35 6

Figure S7.  Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #8: 'RADIATION_THERAPY'

'APOBEC ENRICH' versus 'RACE'

P value = 4e-05 (Fisher's exact test), Q value = 0.001

Table S10.  Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #12: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 39 22 317
FC.HIGH.ENRICH 22 16 276
FC.LOW.ENRICH 4 2 14
FC.NO.ENRICH 13 4 27

Figure S8.  Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #12: 'RACE'

Methods & Data
Input

  • APOBEC groups file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/APOBEC_Pipelines/BLCA-TP/22506785/APOBEC_clinical_corr_input_22536864/APOBEC_for_clinical.correlaion.input.categorical.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/BLCA-TP/22506467/BLCA-TP.merged_data.txt

  • Number of patients = 395

  • Number of selected clinical features = 13

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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