Correlation between APOBEC groups and selected clinical features

Skin Cutaneous Melanoma (Metastatic)

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/C16W99J6

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 14 clinical features across 290 patients, 6 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', 'MELANOMA_ULCERATION', 'GENDER', and 'RACE'.
3 subtypes identified in current cancer cohort by 'APOBEC ENRICH'. These subtypes correlate to 'MELANOMA_ULCERATION' 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 14 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 6 significant findings detected.


Clinical Features	Statistical Tests	APOBEC MUTLOAD MINESTIMATE	APOBEC ENRICH
Time from Specimen Diagnosis to Death	logrank test	0.171 (0.439)	0.157 (0.439)
Time to Death	logrank test	0.183 (0.439)	0.189 (0.439)
YEARS TO BIRTH	Kruskal-Wallis (anova)	0.0304 (0.213)	0.51 (0.679)
PATHOLOGIC STAGE	Fisher's exact test	0.386 (0.568)	0.48 (0.672)
PATHOLOGY T STAGE	Fisher's exact test	0.63 (0.763)	0.267 (0.499)
PATHOLOGY N STAGE	Fisher's exact test	0.374 (0.568)	0.356 (0.568)
PATHOLOGY M STAGE	Fisher's exact test	0.819 (0.85)	0.633 (0.763)
MELANOMA ULCERATION	Fisher's exact test	0.0494 (0.23)	0.0142 (0.193)
MELANOMA PRIMARY KNOWN	Fisher's exact test	0.295 (0.516)	0.713 (0.782)
BRESLOW THICKNESS	Kruskal-Wallis (anova)	0.654 (0.763)	0.193 (0.439)
GENDER	Fisher's exact test	0.0207 (0.193)	0.116 (0.439)
RADIATION THERAPY	Fisher's exact test	0.726 (0.782)	1 (1.00)
RACE	Fisher's exact test	0.02 (0.193)	0.0387 (0.217)
ETHNICITY	Fisher's exact test	0.204 (0.439)	0.235 (0.47)

APOBEC group #1: 'APOBEC MUTLOAD MINESTIMATE'

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

Cluster Labels	0	HIGH	LOW
Number of samples	86	99	105

'APOBEC MUTLOAD MINESTIMATE' versus 'YEARS_TO_BIRTH'

P value = 0.0304 (Kruskal-Wallis (anova)), Q value = 0.21

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

	nPatients	Mean (Std.Dev)
ALL	285	55.9 (15.5)
0	85	56.6 (14.3)
HIGH	96	59.1 (14.6)
LOW	104	52.5 (16.7)

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

'APOBEC MUTLOAD MINESTIMATE' versus 'MELANOMA_ULCERATION'

P value = 0.0494 (Fisher's exact test), Q value = 0.23

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

nPatients	NO	YES
ALL	107	73
0	23	28
HIGH	36	20
LOW	48	25

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

'APOBEC MUTLOAD MINESTIMATE' versus 'GENDER'

P value = 0.0207 (Fisher's exact test), Q value = 0.19

Table S4. Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #11: 'GENDER'

nPatients	FEMALE	MALE
ALL	109	181
0	40	46
HIGH	27	72
LOW	42	63

Figure S3. Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #11: 'GENDER'

'APOBEC MUTLOAD MINESTIMATE' versus 'RACE'

P value = 0.02 (Fisher's exact test), Q value = 0.19

Table S5. Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #13: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	5	1	284
0	4	1	81
HIGH	0	0	99
LOW	1	0	104

Figure S4. Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #13: 'RACE'

APOBEC group #2: 'APOBEC ENRICH'

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

Cluster Labels	FC.HIGH.ENRICH	FC.LOW.ENRICH	FC.NO.ENRICH
Number of samples	3	201	86

'APOBEC ENRICH' versus 'MELANOMA_ULCERATION'

P value = 0.0142 (Fisher's exact test), Q value = 0.19

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

nPatients	NO	YES
ALL	107	73
FC.HIGH.ENRICH	1	2
FC.LOW.ENRICH	83	43
FC.NO.ENRICH	23	28

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

'APOBEC ENRICH' versus 'RACE'

P value = 0.0387 (Fisher's exact test), Q value = 0.22

Table S8. Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #13: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	5	1	284
FC.HIGH.ENRICH	0	0	3
FC.LOW.ENRICH	1	0	200
FC.NO.ENRICH	4	1	81

Figure S6. Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #13: 'RACE'

Methods & Data

Input

APOBEC groups file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/APOBEC_Pipelines/SKCM-TM/22541894/__DELETED__1436046:APOBEC_clinical_corr_input_22563710/APOBEC_for_clinical.correlaion.input.categorical.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/SKCM-TM/22507034/SKCM-TM.merged_data.txt
Number of patients = 290
Number of selected clinical features = 14

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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