Correlation between APOBEC groups and selected clinical features

Head and Neck Squamous Cell 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/C1F18XXR

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 510 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 'YEARS_TO_BIRTH', 'PATHOLOGY_T_STAGE', 'GENDER', and 'NUMBER_PACK_YEARS_SMOKED'.
3 subtypes identified in current cancer cohort by 'APOBEC ENRICH'. These subtypes correlate to 'YEARS_TO_BIRTH', 'GENDER', 'YEAR_OF_TOBACCO_SMOKING_ONSET', and 'NUMBER_OF_LYMPH_NODES'.

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	0.16 (0.398)	0.295 (0.511)
YEARS TO BIRTH	Kruskal-Wallis (anova)	0.000667 (0.0173)	0.00254 (0.0272)
PATHOLOGIC STAGE	Fisher's exact test	0.152 (0.398)	0.329 (0.512)
PATHOLOGY T STAGE	Fisher's exact test	0.00771 (0.0501)	0.391 (0.536)
PATHOLOGY N STAGE	Fisher's exact test	0.245 (0.49)	0.383 (0.536)
GENDER	Fisher's exact test	0.00314 (0.0272)	0.0125 (0.0651)
RADIATION THERAPY	Fisher's exact test	0.937 (0.975)	0.454 (0.59)
HISTOLOGICAL TYPE	Fisher's exact test	0.847 (0.957)	0.725 (0.856)
NUMBER PACK YEARS SMOKED	Kruskal-Wallis (anova)	0.0361 (0.156)	0.168 (0.398)
YEAR OF TOBACCO SMOKING ONSET	Kruskal-Wallis (anova)	0.21 (0.455)	0.0451 (0.167)
NUMBER OF LYMPH NODES	Kruskal-Wallis (anova)	0.517 (0.64)	0.0611 (0.198)
RACE	Fisher's exact test	0.268 (0.498)	0.335 (0.512)
ETHNICITY	Fisher's exact test	0.933 (0.975)	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	251	129	130

'APOBEC MUTLOAD MINESTIMATE' versus 'YEARS_TO_BIRTH'

P value = 0.000667 (Kruskal-Wallis (anova)), Q value = 0.017

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

	nPatients	Mean (Std.Dev)
ALL	510	61.0 (12.0)
0	251	58.7 (12.3)
HIGH	129	64.4 (11.3)
LOW	130	62.0 (11.2)

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

'APOBEC MUTLOAD MINESTIMATE' versus 'PATHOLOGY_T_STAGE'

P value = 0.00771 (Fisher's exact test), Q value = 0.05

Table S3. Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	48	134	99	168
0	29	72	46	77
HIGH	7	25	19	57
LOW	12	37	34	34

Figure S2. Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'APOBEC MUTLOAD MINESTIMATE' versus 'GENDER'

P value = 0.00314 (Fisher's exact test), Q value = 0.027

Table S4. Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	140	370
0	55	196
HIGH	50	79
LOW	35	95

Figure S3. Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #6: 'GENDER'

'APOBEC MUTLOAD MINESTIMATE' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0361 (Kruskal-Wallis (anova)), Q value = 0.16

Table S5. Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	286	46.3 (35.5)
0	151	46.1 (35.5)
HIGH	60	40.2 (30.7)
LOW	75	51.4 (38.7)

Figure S4. Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

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	219	250	41

'APOBEC ENRICH' versus 'YEARS_TO_BIRTH'

P value = 0.00254 (Kruskal-Wallis (anova)), Q value = 0.027

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

	nPatients	Mean (Std.Dev)
ALL	510	61.0 (12.0)
FC.HIGH.SIG	219	63.4 (11.3)
FC.LOW.NONSIG	250	58.8 (12.2)
FC.NEUTRAL	41	61.2 (12.1)

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

'APOBEC ENRICH' versus 'GENDER'

P value = 0.0125 (Fisher's exact test), Q value = 0.065

Table S8. Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	140	370
FC.HIGH.SIG	72	147
FC.LOW.NONSIG	54	196
FC.NEUTRAL	14	27

Figure S6. Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #6: 'GENDER'

'APOBEC ENRICH' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.0451 (Kruskal-Wallis (anova)), Q value = 0.17

Table S9. Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	274	1967.0 (12.6)
FC.HIGH.SIG	115	1966.6 (13.0)
FC.LOW.NONSIG	138	1968.2 (12.0)
FC.NEUTRAL	21	1961.1 (13.5)

Figure S7. Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'APOBEC ENRICH' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0611 (Kruskal-Wallis (anova)), Q value = 0.2

Table S10. Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	400	2.2 (4.3)
FC.HIGH.SIG	168	2.8 (5.9)
FC.LOW.NONSIG	201	1.8 (2.6)
FC.NEUTRAL	31	1.2 (1.9)

Figure S8. Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

Methods & Data

Input

APOBEC groups file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/APOBEC_Pipelines/HNSC-TP/20203263/APOBEC_clinical_corr_input_20214860/APOBEC_for_clinical.correlaion.input.categorical.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/HNSC-TP/19775211/HNSC-TP.merged_data.txt
Number of patients = 510
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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