Correlation between APOBEC groups and selected clinical features

Breast Invasive Carcinoma (Primary solid tumor)

28 January 2016 | analyses__2016_01_28

Maintainer Information

Citation Information

Maintained by Hailei Zhang (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between APOBEC groups and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1RF5TC4

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 977 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 'RACE'.
3 subtypes identified in current cancer cohort by 'APOBEC ENRICH'. These subtypes correlate to 'HISTOLOGICAL_TYPE' 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, 4 significant findings detected.


Clinical Features	Statistical Tests	APOBEC MUTLOAD MINESTIMATE	APOBEC ENRICH
Time to Death	logrank test	0.244 (0.586)	0.769 (0.951)
YEARS TO BIRTH	Kruskal-Wallis (anova)	0.505 (0.818)	0.232 (0.586)
PATHOLOGIC STAGE	Fisher's exact test	0.539 (0.818)	0.846 (0.951)
PATHOLOGY T STAGE	Fisher's exact test	0.184 (0.586)	0.545 (0.818)
PATHOLOGY N STAGE	Fisher's exact test	0.0911 (0.437)	0.872 (0.951)
PATHOLOGY M STAGE	Fisher's exact test	0.825 (0.951)	1 (1.00)
GENDER	Fisher's exact test	0.42 (0.818)	1 (1.00)
RADIATION THERAPY	Fisher's exact test	0.457 (0.818)	0.351 (0.765)
HISTOLOGICAL TYPE	Fisher's exact test	0.00202 (0.0179)	0.00179 (0.0179)
NUMBER OF LYMPH NODES	Kruskal-Wallis (anova)	0.213 (0.586)	0.171 (0.586)
RACE	Fisher's exact test	0.00355 (0.0213)	0.00224 (0.0179)
ETHNICITY	Fisher's exact test	0.59 (0.833)	0.728 (0.951)

APOBEC group #1: 'APOBEC MUTLOAD MINESTIMATE'

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

Cluster Labels	0	HIGH	LOW
Number of samples	750	111	116

'APOBEC MUTLOAD MINESTIMATE' versus 'HISTOLOGICAL_TYPE'

P value = 0.00202 (Fisher's exact test), Q value = 0.018

Table S2. Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #9: '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	711	172	5	6	27	14	40
0	1	554	117	1	5	22	14	36
HIGH	0	75	33	1	0	1	0	1
LOW	0	82	22	3	1	4	0	3

Figure S1. Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'APOBEC MUTLOAD MINESTIMATE' versus 'RACE'

P value = 0.00355 (Fisher's exact test), Q value = 0.021

Table S3. 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	121	705
0	1	33	86	561
HIGH	0	11	15	72
LOW	0	13	20	72

Figure S2. Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #11: 'RACE'

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	220	7	750

'APOBEC ENRICH' versus 'HISTOLOGICAL_TYPE'

P value = 0.00179 (Fisher's exact test), Q value = 0.018

Table S5. Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #9: '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	711	172	5	6	27	14	40
FC.HIGH.ENRICH	0	152	54	4	1	5	0	3
FC.LOW.ENRICH	0	5	1	0	0	0	0	1
FC.NO.ENRICH	1	554	117	1	5	22	14	36

Figure S3. Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'APOBEC ENRICH' versus 'RACE'

P value = 0.00224 (Fisher's exact test), Q value = 0.018

Table S6. 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	121	705
FC.HIGH.ENRICH	0	24	35	138
FC.LOW.ENRICH	0	0	0	6
FC.NO.ENRICH	1	33	86	561

Figure S4. 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/22570937/APOBEC_clinical_corr_input_22572582/APOBEC_for_clinical.correlaion.input.categorical.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/BRCA-TP/22506573/BRCA-TP.merged_data.txt
Number of patients = 977
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 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.

Download Results

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.

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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