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 12 clinical features across 967 patients, 7 significant findings detected with Q value < 0.25.

3 subtypes identified in current cancer cohort by 'APOBEC MUTLOAD MINESTIMATE'. These subtypes correlate to 'YEARS_TO_BIRTH', 'HISTOLOGICAL_TYPE', 'RADIATIONS_RADIATION_REGIMENINDICATION', and 'RACE'.
3 subtypes identified in current cancer cohort by 'APOBEC ENRICH'. These subtypes correlate to 'HISTOLOGICAL_TYPE', 'RADIATIONS_RADIATION_REGIMENINDICATION', 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 12 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 7 significant findings detected.


Clinical Features	Statistical Tests	APOBEC MUTLOAD MINESTIMATE	APOBEC ENRICH
Time to Death	logrank test	0.184 (0.481)	0.618 (0.872)
YEARS TO BIRTH	Kruskal-Wallis (anova)	0.0568 (0.195)	0.875 (0.913)
NEOPLASM DISEASESTAGE	Fisher's exact test	0.727 (0.913)	0.802 (0.913)
PATHOLOGY T STAGE	Fisher's exact test	0.358 (0.573)	0.341 (0.573)
PATHOLOGY N STAGE	Fisher's exact test	0.284 (0.525)	0.81 (0.913)
PATHOLOGY M STAGE	Fisher's exact test	0.579 (0.869)	0.662 (0.882)
GENDER	Fisher's exact test	0.237 (0.481)	1 (1.00)
HISTOLOGICAL TYPE	Fisher's exact test	6e-05 (0.00144)	0.00019 (0.00228)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.0013 (0.00749)	0.00156 (0.00749)
NUMBER OF LYMPH NODES	Kruskal-Wallis (anova)	0.174 (0.481)	0.241 (0.481)
RACE	Fisher's exact test	0.00407 (0.0163)	0.00034 (0.00272)
ETHNICITY	Fisher's exact test	0.862 (0.913)	0.208 (0.481)

APOBEC group #1: 'APOBEC MUTLOAD MINESTIMATE'

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

Cluster Labels	0	HIGH	LOW
Number of samples	745	129	93

'APOBEC MUTLOAD MINESTIMATE' versus 'YEARS_TO_BIRTH'

P value = 0.0568 (Kruskal-Wallis (anova)), Q value = 0.19

Table S2. Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	954	58.7 (13.1)
0	736	58.7 (13.1)
HIGH	126	60.6 (13.8)
LOW	92	56.0 (12.0)

Figure S1. Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'APOBEC MUTLOAD MINESTIMATE' versus 'HISTOLOGICAL_TYPE'

P value = 6e-05 (Fisher's exact test), Q value = 0.0014

Table S3. Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	INFILTRATING CARCINOMA NOS	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	METAPLASTIC CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER SPECIFY
ALL	1	710	164	5	1	27	14	44
0	1	555	111	1	0	23	14	40
HIGH	0	86	39	2	0	1	0	1
LOW	0	69	14	2	1	3	0	3

Figure S2. Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'APOBEC MUTLOAD MINESTIMATE' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.0013 (Fisher's exact test), Q value = 0.0075

Table S4. Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	282	685
0	236	509
HIGH	21	108
LOW	25	68

Figure S3. Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'APOBEC MUTLOAD MINESTIMATE' versus 'RACE'

P value = 0.00407 (Fisher's exact test), Q value = 0.016

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	57	116	698
0	1	34	82	556
HIGH	0	16	18	82
LOW	0	7	16	60

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

APOBEC group #2: 'APOBEC ENRICH'

Table S6. Description of APOBEC group #2: 'APOBEC ENRICH'

Cluster Labels	FC.HIGH.SIG	FC.LOW.NONSIG	FC.NEUTRAL
Number of samples	213	645	109

'APOBEC ENRICH' versus 'HISTOLOGICAL_TYPE'

P value = 0.00019 (Fisher's exact test), Q value = 0.0023

Table S7. Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	INFILTRATING CARCINOMA NOS	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	METAPLASTIC CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER SPECIFY
ALL	1	710	164	5	1	27	14	44
FC.HIGH.SIG	0	148	52	4	1	4	0	3
FC.LOW.NONSIG	1	487	90	1	0	20	13	33
FC.NEUTRAL	0	75	22	0	0	3	1	8

Figure S5. Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'APOBEC ENRICH' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.00156 (Fisher's exact test), Q value = 0.0075

Table S8. Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	282	685
FC.HIGH.SIG	42	171
FC.LOW.NONSIG	208	437
FC.NEUTRAL	32	77

Figure S6. Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'APOBEC ENRICH' versus 'RACE'

P value = 0.00034 (Fisher's exact test), Q value = 0.0027

Table S9. Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	57	116	698
FC.HIGH.SIG	0	23	34	133
FC.LOW.NONSIG	0	31	73	477
FC.NEUTRAL	1	3	9	88

Figure S7. Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #11: 'RACE'

Methods & Data

Input

APOBEC groups file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/APOBEC_Pipelines/BRCA-TP/15165570/APOBEC_clinical_corr_input_15169895/APOBEC_for_clinical.correlaion.input.categorical.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/BRCA-TP/15076769/BRCA-TP.merged_data.txt
Number of patients = 967
Number of selected clinical features = 12

APOBEC classification

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.

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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