Correlation between APOBEC groups and selected clinical features

Bladder Urothelial Carcinoma (Primary solid tumor)

21 August 2015 | analyses__2015_08_21

Maintainer Information

Citation Information

Maintained by Hailei Zhang (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between APOBEC groups and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1KW5F7Q

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, 7 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', 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, 7 significant findings detected.


Clinical Features	Statistical Tests	APOBEC MUTLOAD MINESTIMATE	APOBEC ENRICH
Time to Death	logrank test	0.0103 (0.0667)	0.0218 (0.105)
YEARS TO BIRTH	Kruskal-Wallis (anova)	0.567 (0.702)	0.611 (0.722)
PATHOLOGIC STAGE	Fisher's exact test	0.457 (0.661)	0.361 (0.635)
PATHOLOGY T STAGE	Fisher's exact test	0.378 (0.635)	0.137 (0.444)
PATHOLOGY N STAGE	Fisher's exact test	0.361 (0.635)	0.549 (0.702)
PATHOLOGY M STAGE	Fisher's exact test	0.824 (0.892)	0.216 (0.624)
GENDER	Fisher's exact test	0.0446 (0.166)	0.655 (0.741)
RADIATION THERAPY	Fisher's exact test	0.0242 (0.105)	0.00835 (0.0667)
KARNOFSKY PERFORMANCE SCORE	Kruskal-Wallis (anova)	0.354 (0.635)	0.488 (0.667)
NUMBER PACK YEARS SMOKED	Kruskal-Wallis (anova)	0.25 (0.635)	0.391 (0.635)
NUMBER OF LYMPH NODES	Kruskal-Wallis (anova)	0.317 (0.635)	0.454 (0.661)
RACE	Fisher's exact test	0.00041 (0.00533)	0.0001 (0.0026)
ETHNICITY	Fisher's exact test	0.888 (0.923)	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	136	213

'APOBEC MUTLOAD MINESTIMATE' versus 'Time to Death'

P value = 0.0103 (logrank test), Q value = 0.067

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	393	174	0.1 - 166.0 (17.2)
0	45	27	2.0 - 93.0 (13.6)
HIGH	136	52	0.6 - 166.0 (19.2)
LOW	212	95	0.1 - 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 = 0.0446 (Fisher's exact test), Q value = 0.17

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

nPatients	FEMALE	MALE
ALL	102	293
0	13	33
HIGH	25	111
LOW	64	149

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 = 0.0242 (Fisher's exact test), Q value = 0.11

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

nPatients	NO	YES
ALL	350	19
0	35	6
HIGH	124	4
LOW	191	9

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

'APOBEC MUTLOAD MINESTIMATE' versus 'RACE'

P value = 0.00041 (Fisher's exact test), Q value = 0.0053

Table S5. 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	8	5	119
LOW	18	13	171

Figure S4. Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #12: '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	327	46	22

'APOBEC ENRICH' versus 'Time to Death'

P value = 0.0218 (logrank test), Q value = 0.11

Table S7. Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	393	174	0.1 - 166.0 (17.2)
FC.HIGH.SIG	326	136	0.6 - 166.0 (17.7)
FC.LOW.NONSIG	45	27	2.0 - 93.0 (13.6)
FC.NEUTRAL	22	11	0.1 - 52.0 (13.7)

Figure S5. Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #1: 'Time to Death'

'APOBEC ENRICH' versus 'RADIATION_THERAPY'

P value = 0.00835 (Fisher's exact test), Q value = 0.067

Table S8. Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	350	19
FC.HIGH.SIG	297	11
FC.LOW.NONSIG	35	6
FC.NEUTRAL	18	2

Figure S6. Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #8: 'RADIATION_THERAPY'

'APOBEC ENRICH' versus 'RACE'

P value = 1e-04 (Fisher's exact test), Q value = 0.0026

Table S9. Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	39	22	317
FC.HIGH.SIG	22	16	276
FC.LOW.NONSIG	13	4	27
FC.NEUTRAL	4	2	14

Figure S7. 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/19921873/APOBEC_clinical_corr_input_19929340/APOBEC_for_clinical.correlaion.input.categorical.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/BLCA-TP/19775034/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 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.

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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