scarHRD/scarHRD.html at master · sztup/scarHRD

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<body>

<h1 class="title toc-ignore">scarHRD R package manual</h1>

<h4 class="author">Zsofia Sztupinszki, Miklos Diossy, Marcin Krzystanek, Nicolai J. Birkbak, Francesco Favero, Lilla Reiniger, István Csabai, Aron C. Eklund, Ali Syed, Zoltan Szallasi</h4>

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<br><a class="author_email" href="mailto:#"><a href="mailto:zsofia.sztupinszki@childrens.harvard.edu" class="email">zsofia.sztupinszki@childrens.harvard.edu</a></a>

</address>

<ul>

<li><a href="#introduction">Introduction</a></li>

<li><a href="#getting-started">Getting started</a><ul>

<li><a href="#minimum-requirements">Minimum requirements</a></li>

<li><a href="#installation">Installation</a></li>

</ul></li>

<li><a href="#citation">Citation</a></li>

<li><a href="#workflow-overview">Workflow overview</a><ul>

<li><a href="#input-file-examples">Input file examples</a></li>

<li><a href="#usage-example">Usage example</a></li>

<li><a href="#optional-parameters">Optional parameters</a></li>

</ul></li>

<li><a href="#genomic-scar-scores">Genomic scar scores</a><ul>

<li><a href="#loss-of-heterozygosity-hrd-loh">Loss of Heterozygosity (HRD-LOH)</a></li>

<li><a href="#large-scale-transitions-lst">Large Scale Transitions (LST)</a></li>

<li><a href="#number-of-telomeric-allelic-imbalances">Number of Telomeric Allelic Imbalances</a></li>

</ul></li>

<li><a href="#running-on-mouse-genomes">Running on mouse genomes</a></li>

<li><a href="#references">References</a></li>

</ul>

</div>

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<h1>Introduction</h1>

<p><code>scarHRD</code> 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.</p>

<p>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 <strong>we introduce the <code>scarHRD</code> R package</strong> and show that using this method the <strong>SNP-array based and next generation sequencing based derivation of HRD scores show good correlation.</strong></p>

</div>

<h1>Getting started</h1>

<h2>Minimum requirements</h2>

<ul>

<li>Software: R<br />

</li>

<li>Operating system: Linux, OS X, Windows<br />

</li>

<li>R version: 3.4.0</li>

</ul>

</div>

<h2>Installation</h2>

<p><code>scarHRD</code> can be installed via devtools from github:</p>

<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1"><span class="kw">library</span>(devtools)</a>

<a class="sourceLine" id="cb1-2" title="2"><span class="kw">install_github</span>(<span class="st">'sztup/scarHRD'</span>,<span class="dt">build_vignettes =</span> <span class="ot">TRUE</span>)</a></code></pre></div>

</div>

<h1>Citation</h1>

<p>Please cite the following paper: Sztupinszki et al, Migrating the SNP array-based homologous recombination deficiency measures to next generation sequencing data of breast cancer, npj Breast Cancer, <a href="https://www.nature.com/articles/s41523-018-0066-6" class="uri">https://www.nature.com/articles/s41523-018-0066-6</a>.</p>

</div>

<h1>Workflow overview</h1>

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

<li><p>Call allele specific copy number profile on paired normal-tumor BAM files. This step has to be executed before running scarHRD. We recommend using <strong>Sequenza</strong> <span class="citation">(Favero et al. 2015)</span> <a href="http://www.cbs.dtu.dk/biotools/sequenza/" class="uri">http://www.cbs.dtu.dk/biotools/sequenza/</a> for copy number segmentation, Other tools (e.g. ASCAT <span class="citation">(Van Loo et al. 2010)</span>) may also be used in this step.</p></li>

<li><p>Determine the scar scores with scarHRD R package</p></li>

</ol>

<h2>Input file examples</h2>

<p>The scarHRD input may be a detailed segmentation file from Sequenza:</p>

<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" title="1">example1<-<span class="kw">system.file</span>(<span class="st">"extdata"</span>, <span class="st">"test1.small.seqz.gz"</span>, <span class="dt">package =</span> <span class="st">"scarHRD"</span>)</a>

<a class="sourceLine" id="cb2-2" title="2">a<-<span class="kw">read.table</span>(example1, <span class="dt">header=</span>T)</a>

<pre><code>## chromosome position base.ref depth.normal depth.tumor depth.ratio Af Bf

## 1 chr1 12975 N 7 20 2.841 1.000 0

## 2 chr1 13020 A 8 28 3.500 0.964 0

## 3 chr1 13026 N 15 43 2.964 1.000 0

## 4 chr1 13038 T 11 35 3.182 0.971 0

## 5 chr1 13041 A 11 37 3.364 0.946 0

## 6 chr1 13077 N 26 65 2.465 1.000 0

## zygosity.normal GC.percent good.reads AB.normal AB.tumor tumor.strand

## 1 hom 60 51 N . 0

## 2 hom 60 28 A G0.036 G1.0

## 3 hom 59 51 N . 0

## 4 hom 59 35 T C0.029 C1.0

## 5 hom 59 37 A G0.054 G0.5

## 6 hom 62 51 N . 0</code></pre>

<p>or a simplified file, including the total, and allele-specific copy-number:</p>

<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" title="1">example2<-<span class="kw">system.file</span>(<span class="st">"extdata"</span>, <span class="st">"test1.small.seqz.gz"</span>, <span class="dt">package =</span> <span class="st">"scarHRD"</span>)</a>

<a class="sourceLine" id="cb4-2" title="2">a<-<span class="kw">read.table</span>(example2, <span class="dt">header=</span>T)</a>

<pre><code>## chromosome position base.ref depth.normal depth.tumor depth.ratio Af Bf

## 1 chr1 12975 N 7 20 2.841 1.000 0

## 2 chr1 13020 A 8 28 3.500 0.964 0

## 3 chr1 13026 N 15 43 2.964 1.000 0

## 4 chr1 13038 T 11 35 3.182 0.971 0

## 5 chr1 13041 A 11 37 3.364 0.946 0

## 6 chr1 13077 N 26 65 2.465 1.000 0

## zygosity.normal GC.percent good.reads AB.normal AB.tumor tumor.strand

## 1 hom 60 51 N . 0

## 2 hom 60 28 A G0.036 G1.0

## 3 hom 59 51 N . 0

## 4 hom 59 35 T C0.029 C1.0

## 5 hom 59 37 A G0.054 G0.5

## 6 hom 62 51 N . 0</code></pre>

</div>

<h2>Usage example</h2>

<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" title="1"><span class="kw">scar_score</span>(<span class="st">"/examples/test1.small.seqz.gz"</span>,<span class="dt">reference =</span> <span class="st">"grch38"</span>, <span class="dt">seqz=</span><span class="ot">TRUE</span>)</a>

<a class="sourceLine" id="cb6-2" title="2"><span class="kw">scar_score</span>(<span class="st">"/examples/test2.txt"</span>,<span class="dt">reference =</span> <span class="st">"grch38"</span>, <span class="dt">seqz=</span><span class="ot">FALSE</span>)</a></code></pre></div>

<p>If your file does not contain ‘chr’ in chromosome names, use <code>chr.in.names=FALSE</code>.</p>

</div>

<h2>Optional parameters</h2>

<p><code>reference</code> – the reference genome used, <code>grch38</code> or <code>grch37</code> or <code>mouse</code> <code>chr.in.names</code> – default: TRUE, set to FALSE if input file does not contain ‘chr’ in chromosome names</p>

</div>

<h1>Genomic scar scores</h1>

<h2>Loss of Heterozygosity (HRD-LOH)</h2>

The HRD-LOH score was described based on investigation in SNP-array-based copy number profiles of ovarian cancer <span class="citation">(Popova et al. 2012)</span>. 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.<br />

The definition of a sample’s HRD-LOH score is the </span> <span style="font-weight:bold">number of 15 Mb exceeding LOH regions which do not cover the whole chromosome.</span>.

</p>

<p>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.<br />

In our manuscript we also investigated <strong>if this 15 Mb cutoff is appropriate for WXS-based HRD-LOH score</strong>.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.</p>

</div>

<h2>Large Scale Transitions (LST)</h2>

The presence of Large Scale Transitions in connection with homologous recombination deficiency was first in studied in basal-like breast cancer <span class="citation">(Abkevich et al. 2012)</span>. Based on SNP-array derived copy number profiles … the number of telomeric allelic imbalances

A large scale transition is defined as a </span> <span style="font-weight:bold">chromosomal break between adjacent regions of at least 10 Mb, with a distance between them not larger than 3Mb..</span>.

</p>

<p class="caption">Figure 2.A: Visual representation of the LST score on short theoretical chromosomes. Figure 2.B: Calculating LST scores from a biallelic copy-number profile; events that are marked with green “marked” signs would increase the score, while events marked with red crosses would not. The grey areas represent the centromeric regions. (From left to right; Chromosome 1: the first event passes the definition of an LST, the second bounded by a shorter than 10 Mb segment from the right, the third is bounded by a segment from the left, which extends to the centromere, the fourth’s gap is greater than 3 Mb. Chromosome 2: The first event is a valid LST, the second and third are not because they are bounded by centromeric segments, and the fourth is a valid LST)</p>

</div>

</div>

<h2>Number of Telomeric Allelic Imbalances</h2>

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 <span class="citation">(Birkbak et al. 2012)</span>.<br />

The number of telomeric allelic imbalances is the </span> <span style="font-weight:bold">number AIs that extend to the telomeric end of a chromosome.</span>.

</p>

<p class="caption">Figure 3.A: Visual representation of the ntAI on short theoretical chromosomes. Figure 3.B: Illustration of possible telomeric allelic imbalances in an allele specific copy number profile.</p>

</div>

</div>

<h1>Running on mouse genomes</h1>

<p><code>scarHRD()</code> can be run on mouse genome using <code>referrence="mouse"</code>. However it is problematic to evaluate the HRD-score in mouse. The mouse chromosome are telocentric, meaning that the centrome is next to the telomeric region. This makes it hard to determine and evaluate Telomeric Allelic Imbalance score. Moreover the mouse cancer models are usually isogenic, which yield low number of heterogeneous positions, which are essential for determining HRD-LOH scores. So calculating genomic scar scores (telomeric AI, HRD-LOH, LST) is possible for mice, but it has a very limited utility.</p>

</div>

<h1>References</h1>

<p>Abkevich, V., K. M. Timms, B. T. Hennessy, J. Potter, M. S. Carey, L. A. Meyer, K. Smith-McCune, et al. 2012. “Patterns of genomic loss of heterozygosity predict homologous recombination repair defects in epithelial ovarian cancer.” <em>Br. J. Cancer</em> 107 (10): 1776–82.</p>

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<p>Birkbak, N. J., Z. C. Wang, J. Y. Kim, A. C. Eklund, Q. Li, R. Tian, C. Bowman-Colin, et al. 2012. “Telomeric allelic imbalance indicates defective DNA repair and sensitivity to DNA-damaging agents.” <em>Cancer Discov</em> 2 (4): 366–75.</p>

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<p>Favero, F., T. Joshi, A. M. Marquard, N. J. Birkbak, M. Krzystanek, Q. Li, Z. Szallasi, and A. C. Eklund. 2015. “Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data.” <em>Ann. Oncol.</em> 26 (1): 64–70.</p>

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<p>Popova, T., E. Manie, G. Rieunier, V. Caux-Moncoutier, C. Tirapo, T. Dubois, O. Delattre, et al. 2012. “Ploidy and large-scale genomic instability consistently identify basal-like breast carcinomas with BRCA1/2 inactivation.” <em>Cancer Res.</em> 72 (21): 5454–62.</p>

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<p>Van Loo, P., S. H. Nordgard, O. C. Lingj?rde, H. G. Russnes, I. H. Rye, W. Sun, V. J. Weigman, et al. 2010. “Allele-specific copy number analysis of tumors.” <em>Proc. Natl. Acad. Sci. U.S.A.</em> 107 (39): 16910–5.</p>

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