Correlation between APOBEC groups and selected clinical features
Uveal Melanoma (Primary solid tumor)
13 July 2018 | None
Maintainer Information
Maintained by Hailei Zhang (Broad Institute)
Overview
Introduction

This pipeline computes the correlation between APOBRC groups and selected clinical features.

Summary

Testing the association between 'APOBEC ENRICH' and 7 clinical features across 80 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.

- 2 subtypes identified in current cancer cohort by 'APOBEC ENRICH'. These subtypes do not correlate to any clinical features.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between APOBEC groups by 1 different APOBEC scores and 7 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, no significant finding detected.

Clinical Features | Statistical Tests | APOBEC ENRICH
DAYS TO DEATH OR LAST FUP | logrank test | 0.411 (0.576)
YEARS TO BIRTH | Wilcoxon-test | 0.224 (0.435)
PATHOLOGIC STAGE | Fisher's exact test | 0.248 (0.435)
PATHOLOGY T STAGE | Fisher's exact test | 0.205 (0.435)
PATHOLOGY M STAGE | Fisher's exact test | 1 (1.00)
GENDER | Fisher's exact test | 0.174 (0.435)
RADIATION THERAPY | Fisher's exact test | 1 (1.00)

APOBEC group #1: 'APOBEC ENRICH'

Table S1. Description of APOBEC group #1: 'APOBEC ENRICH'

Cluster Labels | FC.LOW.NONSIG | FC.NEUTRAL
--- | --- | ---
Number of samples | 70 | 10

'APOBEC ENRICH' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.411 (logrank test), Q value = 0.58

Table S2. Clustering Approach #1: 'APOBEC ENRICH' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 79 | 22 | 0.1 - 85.5 (26.1)
FC.LOW.NONSIG | 69 | 18 | 0.1 - 85.5 (26.1)
FC.NEUTRAL | 10 | 4 | 1.3 - 49.7 (25.3)

Figure S1. Get High-res Image Clustering Approach #1: 'APOBEC ENRICH' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

'APOBEC ENRICH' versus 'YEARS_TO_BIRTH'

P value = 0.224 (Wilcoxon-test), Q value = 0.43

Table S3. Clustering Approach #1: 'APOBEC ENRICH' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 79 | 61.5 (14.0)
FC.LOW.NONSIG | 69 | 62.2 (14.7)
FC.NEUTRAL | 10 | 57.2 (5.8)

Figure S2. Get High-res Image Clustering Approach #1: 'APOBEC ENRICH' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'APOBEC ENRICH' versus 'PATHOLOGIC_STAGE'

P value = 0.248 (Fisher's exact test), Q value = 0.43

Table S4. Clustering Approach #1: 'APOBEC ENRICH' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV
--- | --- | --- | --- | --- | --- | ---
ALL | 11 | 27 | 25 | 10 | 1 | 4
FC.LOW.NONSIG | 11 | 25 | 20 | 7 | 1 | 4
FC.NEUTRAL | 0 | 2 | 5 | 3 | 0 | 0

Figure S3. Get High-res Image Clustering Approach #1: 'APOBEC ENRICH' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'APOBEC ENRICH' versus 'PATHOLOGY_T_STAGE'

P value = 0.205 (Wilcoxon-test), Q value = 0.43

Table S5. Clustering Approach #1: 'APOBEC ENRICH' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

| T2 | T3 | T4
--- | --- | --- | ---
ALL | 13 | 32 | 34
FC.LOW.NONSIG | 12 | 30 | 27
FC.NEUTRAL | 1 | 2 | 7

Figure S4. Get High-res Image Clustering Approach #1: 'APOBEC ENRICH' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'APOBEC ENRICH' versus 'PATHOLOGY_M_STAGE'

P value = 1 (Fisher's exact test), Q value = 1

Table S6. Clustering Approach #1: 'APOBEC ENRICH' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients | CLASS0 | CLASS1
--- | --- | ---
ALL | 51 | 4
FC.LOW.NONSIG | 45 | 4
FC.NEUTRAL | 6 | 0

Figure S5. Get High-res Image Clustering Approach #1: 'APOBEC ENRICH' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'APOBEC ENRICH' versus 'GENDER'

P value = 0.174 (Fisher's exact test), Q value = 0.43

Table S7. Clustering Approach #1: 'APOBEC ENRICH' versus Clinical Feature #6: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 34 | 45
FC.LOW.NONSIG | 32 | 37
FC.NEUTRAL | 2 | 8

Figure S6. Get High-res Image Clustering Approach #1: 'APOBEC ENRICH' versus Clinical Feature #6: 'GENDER'

'APOBEC ENRICH' versus 'RADIATION_THERAPY'

P value = 1 (Fisher's exact test), Q value = 1

Table S8. Clustering Approach #1: 'APOBEC ENRICH' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 75 | 3
FC.LOW.NONSIG | 65 | 3
FC.NEUTRAL | 10 | 0

Figure S7. Get High-res Image Clustering Approach #1: 'APOBEC ENRICH' versus Clinical Feature #7: 'RADIATION_THERAPY'

Methods & Data
Input

- APOBEC groups file = /cromwell_root/fc-f5144117-2d5a-42c2-8998-5b38e52db5d9/2628519b-0bbe-4d43-870c-8341f404f12d/mutation_apobec/62df159e-e508-4990-832c-eaf91f6332ed/call-preprocess_clinical_apobec/APOBEC_for_clinical.correlaion.input.categorical.txt

- Clinical data file = /cromwell_root/fc-2289d790-de74-4808-9b0a-cefafc34d859/0d7c7dcf-18e0-4b2d-afc0-a0b2ee1e45ff/preprocess_clinical_workflow/70152ac6-f707-4277-8d60-8770b1b366c6/call-preprocess_clinical/TCGA-UVM-TP.clin.merged.picked.txt

- Number of patients = 80

- Number of selected clinical features = 7

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.

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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