scarHRD.md

In farkkilab/scarHRD: Estimating Genomic scars signatures (LST, TAI,LOH)

title: "scarHRD R package manual" author: - name: Zsofia Sztupinszki, Miklos Diossy, Marcin Krzystanek, Nicolai J. Birkbak, Francesco Favero, Lilla Reiniger, István Csabai, Aron C. Eklund, Ali Syed, Zoltan Szallasi affiliation: Technical University of Denmark, Semmelweis University, Eötvös Loránd University, University of Copenhagen, The Francis Crick Institute, Danish National Life Science Supercomputing Center, Harvard Medical School email: [e-mail] package: scarHRD bibliography: scarHRD.bib output: rmarkdown::html_vignette: toc: true keep_md: true readme: true

vignette: > %\VignetteEngine{knitr::rmarkdown} %\VignetteIndexEntry{scarHRD R package manual} %\VignetteEncoding{UTF-8} %\VignetteDepends{scarHRD} %\VignetteKeywords{LOH} %\VignetteKeywords{TAI} %\VignetteKeywords{nTAI} %\VignetteKeywords{LST} %\VignetteKeywords{HRD} %\VignetteKeywords{HRD-score} %\VignetteKeywords{Copy number} %\VignetteKeywords{Cancer sequencing} %\VignettePackage{scarHRD} %\VignettePackage{Homologous Recombination}

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Introduction

scarHRD is an R package which determines the levels of homologous recombination deficiency (telomeric allelic imbalance, loss off heterozygosity, number of large-scale transitions) based on NGS (WES, WGS) data.

The first genomic scar based homologous recombination deficiency measures were produced using SNP arrays. Since this technology has been largely replaced by next generation sequencing it has become important to develop algorithms that derive the same type of genomic scar-scores from next generation sequencing (WXS, WGS) data. In order to perform this analysis, here we introduce the scarHRD R package and show that using this method the SNP-array based and next generation sequencing based derivation of HRD scores show good correlation.

Getting started

Minimum requirements

• Software: R
• Operating system: Linux, OS X, Windows
• R version: 3.4.0
• latest version of Sequenza R package must be installed from Bitbucket https://bitbucket.org/sequenza_tools/sequenza

library(devtools)
install_bitbucket('sequenza_tools/sequenza')

Installation

scarHRD can be installed via devtools from github:

library(devtools)
install_github('sztup/scarHRD')

Citation

Please cite the following paper: manuscript submitted.

Workflow overview

A typical workflow of determining the genomic scar scores for a tumor sample has the following steps:

1. Call allele specific copy number profile on paired normal-tumor BAM files. This step has to be executed before running scarHRD. We recommend using Sequenza [@pmid25319062] http://www.cbs.dtu.dk/biotools/sequenza/ for copy number segmentation, Other tools (e.g. ASCAT [@pmid20837533]) may also be used in this step.

2. Determine the scar scores with scarHRD R package

Input file examples

The scarHRD example

a<-read.table("/examples/test1.small.seqz.gz", header=T)
head(a)

##   chromosome position base.ref depth.normal depth.tumor depth.ratio    Af
## 1       chr1    12975        N            7          20       2.841 1.000
## 2       chr1    13020        A            8          28       3.500 0.964
## 3       chr1    13026        N           15          43       2.964 1.000
## 4       chr1    13038        T           11          35       3.182 0.971
## 5       chr1    13041        A           11          37       3.364 0.946
## 6       chr1    13077        N           26          65       2.465 1.000
##   Bf zygosity.normal GC.percent good.reads AB.normal AB.tumor tumor.strand
## 1  0             hom         60         51         N        .            0
## 2  0             hom         60         28         A   G0.036         G1.0
## 3  0             hom         59         51         N        .            0
## 4  0             hom         59         35         T   C0.029         C1.0
## 5  0             hom         59         37         A   G0.054         G0.5
## 6  0             hom         62         51         N        .            0

a<-read.table("/examples/test2.txt", header=T)
head(a)

##         SampleID Chromosome Start_position End_position total_cn A_cn B_cn
## 1 SamplePatient1       chr1          14574       952448        5    0    5
## 2 SamplePatient1       chr1         953394      1259701        3    0    3
## 3 SamplePatient1       chr1        1278085      4551743        2    0    2
## 4 SamplePatient1       chr1        4551885     14124232        2    0    2
## 5 SamplePatient1       chr1       14161231     31062374        3    1    2
## 6 SamplePatient1       chr1       31074785     47428120        4    2    2
##   ploidy
## 1    3.7
## 2    3.7
## 3    3.7
## 4    3.7
## 5    3.7
## 6    3.7

Usage example

scar_score("/test1.small.seqz.gz",reference = "grch38", seqz=TRUE)
scar_score("/test2.txt",reference = "grch38", seqz=FALSE)

Optional parameters

reference -- the reference genome used, grch38 or grch37

Genomic scar scores

Loss of Heterozygosity (HRD-LOH)

The HRD-LOH score was described based on investigation in SNP-array-based copy number profiles of ovarian cancer [@pmid22933060]. In this paper the authors showed that the samples with deficient BRCA1, BRCA2 have higher HRD-LOH scores compared to BRCA-intact samples, thus this measurement may be a reliable tool to estimate the sample's homologous recombination capacity.

The definition of a sample's HRD-LOH score is the number of 15 Mb exceeding LOH regions which do not cover the whole chromosome..

In the first paper publishing HRD-LOH-score (Abkevich et al., 2012) the authors examine the correlation between HRD-LOH-score and HR deficiency calculated for different LOH region length cut-offs. In that paper the cut-off of 15 Mb approximately in the middle of the interval was arbitrarily selected for further analysis. The authors argue that the rational for this selection rather than selecting the cut-off with the lowest p-value is that the latter cut-off is more sensitive to statistical noise present in the data. In our manuscript we also investigated if this 15 Mb cutoff is appropriate for WXS-based HRD-LOH score.We followed the same principles as Abkievits et al, thus while there was small difference between the p-values for the different minimum length cutoff values, we chose to use the same, 15 Mb limit as Abkevich et al. We also performed Spearman rank correlation between the SNP-array-based and WXS-based HRD-LOH scores for the different cutoff minimum LOH length cutoff (manuscript, Supplementary Figure S3C). Here the 14 Mb and 15 Mb cutoff-based WXS-HRD-LOH score had the highest correlation with the SNP-based HRD score. (0.700 and 0.695 respectively). This result reassured our choice of using the 15 Mb cutoff like in the SNP-array-based HRD-LOH score.

 ##Large Scale Transitions (LST) The presence of Large Scale Transitions in connection with homologous recombination deficiency was first in studied in basal-like breast cancer [@pmid23047548]. Based on SNP-array derived copy number profiles ... the number of telomeric allelic imbalances

A large scale transition is defined as a chromosomal break between adjacent regions of at least 10 Mb, with a distance between them not larger than 3Mb...


##Number of Telomeric Allelic Imbalances Allelic imbalance (AI) is the unequal contribution of parental allele sequences with or without changes in the overall copy number of the region. Our group have previously found, that the number telomeric AIs is indicative of defective DNA repair in ovarian cancer and triple-negative breast cancer, and that higher number of telomeric AI is associated with better response to cisplatin treatment [@pmid22576213].

The number of telomeric allelic imbalances is the number AIs that extend to the telomeric end of a chromosome..


#References

farkkilab/scarHRD documentation built on May 6, 2020, 6:32 p.m.