Identification of copy number variants from exome sequence data

Background With advances in next generation sequencing technologies and genomic capture techniques, exome sequencing has become a cost-effective approach for mutation detection in genetic diseases. However, computational prediction of copy number variants (CNVs) from exome sequence data is a challenging task. Whilst numerous programs are available, they have different sensitivities, and have low sensitivity to detect smaller CNVs (1–4 exons). Additionally, exonic CNV discovery using standard aCGH has limitations due to the low probe density over exonic regions. The goal of our study was to develop a protocol to detect exonic CNVs (including shorter CNVs that cover 1–4 exons), combining computational prediction algorithms and a high-resolution custom CGH array. Results We used six published CNV prediction programs (ExomeCNV, CONTRA, ExomeCopy, ExomeDepth, CoNIFER, XHMM) and an in-house modification to ExomeCopy and ExomeDepth (ExCopyDepth) for computational CNV prediction on 30 exomes from the 1000 genomes project and 9 exomes from primary immunodeficiency patients. CNV predictions were tested using a custom CGH array designed to capture all exons (exaCGH). After this validation, we next evaluated the computational prediction of shorter CNVs. ExomeCopy and the in-house modified algorithm, ExCopyDepth, showed the highest capability in detecting shorter CNVs. Finally, the performance of each computational program was assessed by calculating the sensitivity and false positive rate. Conclusions In this paper, we assessed the ability of 6 computational programs to predict CNVs, focussing on short (1–4 exon) CNVs. We also tested these predictions using a custom array targeting exons. Based on these results, we propose a protocol to identify and confirm shorter exonic CNVs combining computational prediction algorithms and custom aCGH experiments

The interplay between neoantigens and immune cells in sarcomas treated with checkpoint inhibition

IntroductionSarcomas are comprised of diverse bone and connective tissue tumors with few effective therapeutic options for locally advanced unresectable and/or metastatic disease. Recent advances in immunotherapy, in particular immune checkpoint inhibition (ICI), have shown promising outcomes in several cancer indications. Unfortunately, ICI therapy has provided only modest clinical responses and seems moderately effective in a subset of the diverse subtypes.MethodsTo explore the immune parameters governing ICI therapy resistance or immune escape, we performed whole exome sequencing (WES) on tumors and their matched normal blood, in addition to RNA-seq from tumors of 31 sarcoma patients treated with pembrolizumab. We used advanced computational methods to investigate key immune properties, such as neoantigens and immune cell composition in the tumor microenvironment (TME).ResultsA multifactorial analysis suggested that expression of high quality neoantigens in the context of specific immune cells in the TME are key prognostic markers of progression-free survival (PFS). The presence of several types of immune cells, including T cells, B cells and macrophages, in the TME were associated with improved PFS. Importantly, we also found the presence of both CD8+ T cells and neoantigens together was associated with improved survival compared to the presence of CD8+ T cells or neoantigens alone. Interestingly, this trend was not identified with the combined presence of CD8+ T cells and TMB; suggesting that a combined CD8+ T cell and neoantigen effect on PFS was important.DiscussionThe outcome of this study may inform future trials that may lead to improved outcomes for sarcoma patients treated with ICI

The ELIXIR Human Copy Number Variations Community:building bioinformatics infrastructure for research

Copy number variations (CNVs) are major causative contributors both in the genesis of genetic diseases and human neoplasias. While 'High-Throughput' sequencing technologies are increasingly becoming the primary choice for genomic screening analysis, their ability to efficiently detect CNVs is still heterogeneous and remains to be developed. The aim of this white paper is to provide a guiding framework for the future contributions of ELIXIR's recently established h uman CNV Community, with implications beyond human disease diagnostics and population genomics. This white paper is the direct result of a strategy meeting that took place in September 2018 in Hinxton (UK) and involved representatives of 11 ELIXIR Nodes. The meeting led to the definition of priority objectives and tasks, to address a wide range of CNV-related challenges ranging from detection and interpretation to sharing and training. Here, we provide suggestions on how to align these tasks within the ELIXIR Platforms strategy, and on how to frame the activities of this new ELIXIR Community in the international context

Computational prediction of diseasecausing CNVs from exome sequence data

Copy number variants (CNVs) are a class of structural variants containing deletions and duplications, and contribute to a broad range of human diseases. Therefore, disease-causing CNV detection has become an important aspect of genetic disease diagnosis. With the widespread utility of exome sequencing as a genetic diagnostic test, a range of prediction programs was developed to detect clinically relevant CNVs. The objective of this study is to evaluate strengths and weaknesses of exome-based CNV prediction programs and introduce methods to overcome the challenges of disease-causing CNV detection . This thesis presents a systematic approach to identify clinically relevant CNVs. Here, a detailed study on commonly used exome-based CNV prediction programs is provided while introducing a custom prediction algorithm (ExCopyDepth), custom aCGH (exaCGH) and a new software package (cnvScan). Clinical importance of these tools are demonstrated by identifying disease-causing CNVs in a large patient cohort. In conclusion , software products and array platform developed in this study provide necessary resources to improve the diagnosis of patients with genetic diseases

Current Practice Epigenomics and Genome Wide Methylation Profiling

Epigenetics is the study of the changes in gene expression that are heritable and do not involve a change in the DNA sequence. DNA methylation is one of the key epigenetic mechanisms that is clearly understood. DNA methylation plays a major role in transcriptional silencing in X inactivation, genomic imprinting and tumor or cancer formation. Today the field of epigenetics has evolved to epigenomics and the focus of DNA methylation analysis has shifted to genome wide methylation analysis. With the increasing interest in the field of epigenomics, initiatives such as the NIH Roadmap Epigenomics Program were established aiming to transform biomedical research by developing new technologies and resources for comprehensive epigenomic studies. Because of high productivity and high accuracy, “high throughput DNA methylation profiling ” techniques are at the heart of these initiatives. Methylation profiling is performed on chemically treated DNA fragments using bead array platforms and DNA sequences resulting from high throughput sequencing (HTS). Bead array platforms use sets of probes for the identification of the methylation status and computer algorithms identify the methylation status by mapping DNA sequences to the reference genome

cnvScan: a CNV screening and annotation tool to improve the clinical utility of computational CNV prediction from exome sequencing data

Background With advances in next generation sequencing technology and analysis methods, single nucleotide variants (SNVs) and indels can be detected with high sensitivity and specificity in exome sequencing data. Recent studies have demonstrated the ability to detect disease-causing copy number variants (CNVs) in exome sequencing data. However, exonic CNV prediction programs have shown high false positive CNV counts, which is the major limiting factor for the applicability of these programs in clinical studies. Results We have developed a tool (cnvScan) to improve the clinical utility of computational CNV prediction in exome data. cnvScan can accept input from any CNV prediction program. cnvScan consists of two steps: CNV screening and CNV annotation. CNV screening evaluates CNV prediction using quality scores and refines this using an in-house CNV database, which greatly reduces the false positive rate. The annotation step provides functionally and clinically relevant information using multiple source datasets. We assessed the performance of cnvScan on CNV predictions from five different prediction programs using 64 exomes from Primary Immunodeficiency (PIDD) patients, and identified PIDD-causing CNVs in three individuals from two different families. Conclusions In summary, cnvScan reduces the time and effort required to detect disease-causing CNVs by reducing the false positive count and providing annotation. This improves the clinical utility of CNV detection in exome data

Additional file 1: Table S1. of cnvScan: a CNV screening and annotation tool to improve the clinical utility of computational CNV prediction from exome sequencing data

Statistical methods used to calculate CNV quality scores. Table S2. CNVQ ratio for common TP CNVs. Table S3. Format of the cnvScan input file. Figure S1. Overview of cnvScan algorithm. Figure S2. CNV length vs Quality score for five CNV prediction programs. Figure S3. GC % vs Quality score for five CNV prediction programs. Figure S4. Length of simple repeats internal to CNVs vs Quality score for five CNV prediction programs. Figure S5a. Coverage of duplications vs Quality score for five CNV prediction programs. Figure S5b. Coverage of deletions vs Quality score for five CNV prediction programs. Figure S6. TP and FP counts in the in-house CNV database. Figure S7. Comparison of filtration efficiency using default quality score, CNVQ, database CNV count. Figure S8. Filtration efficiency of XHMM. Text S1. In-house database creation. Text S2. Thresholds used in CoNIFER and XHMM predictions. (PDF 2191 kb

DataSheet_2_The interplay between neoantigens and immune cells in sarcomas treated with checkpoint inhibition.pdf

IntroductionSarcomas are comprised of diverse bone and connective tissue tumors with few effective therapeutic options for locally advanced unresectable and/or metastatic disease. Recent advances in immunotherapy, in particular immune checkpoint inhibition (ICI), have shown promising outcomes in several cancer indications. Unfortunately, ICI therapy has provided only modest clinical responses and seems moderately effective in a subset of the diverse subtypes.MethodsTo explore the immune parameters governing ICI therapy resistance or immune escape, we performed whole exome sequencing (WES) on tumors and their matched normal blood, in addition to RNA-seq from tumors of 31 sarcoma patients treated with pembrolizumab. We used advanced computational methods to investigate key immune properties, such as neoantigens and immune cell composition in the tumor microenvironment (TME).ResultsA multifactorial analysis suggested that expression of high quality neoantigens in the context of specific immune cells in the TME are key prognostic markers of progression-free survival (PFS). The presence of several types of immune cells, including T cells, B cells and macrophages, in the TME were associated with improved PFS. Importantly, we also found the presence of both CD8+ T cells and neoantigens together was associated with improved survival compared to the presence of CD8+ T cells or neoantigens alone. Interestingly, this trend was not identified with the combined presence of CD8+ T cells and TMB; suggesting that a combined CD8+ T cell and neoantigen effect on PFS was important.DiscussionThe outcome of this study may inform future trials that may lead to improved outcomes for sarcoma patients treated with ICI.</p

DataSheet_5_The interplay between neoantigens and immune cells in sarcomas treated with checkpoint inhibition.pdf

IntroductionSarcomas are comprised of diverse bone and connective tissue tumors with few effective therapeutic options for locally advanced unresectable and/or metastatic disease. Recent advances in immunotherapy, in particular immune checkpoint inhibition (ICI), have shown promising outcomes in several cancer indications. Unfortunately, ICI therapy has provided only modest clinical responses and seems moderately effective in a subset of the diverse subtypes.MethodsTo explore the immune parameters governing ICI therapy resistance or immune escape, we performed whole exome sequencing (WES) on tumors and their matched normal blood, in addition to RNA-seq from tumors of 31 sarcoma patients treated with pembrolizumab. We used advanced computational methods to investigate key immune properties, such as neoantigens and immune cell composition in the tumor microenvironment (TME).ResultsA multifactorial analysis suggested that expression of high quality neoantigens in the context of specific immune cells in the TME are key prognostic markers of progression-free survival (PFS). The presence of several types of immune cells, including T cells, B cells and macrophages, in the TME were associated with improved PFS. Importantly, we also found the presence of both CD8+ T cells and neoantigens together was associated with improved survival compared to the presence of CD8+ T cells or neoantigens alone. Interestingly, this trend was not identified with the combined presence of CD8+ T cells and TMB; suggesting that a combined CD8+ T cell and neoantigen effect on PFS was important.DiscussionThe outcome of this study may inform future trials that may lead to improved outcomes for sarcoma patients treated with ICI.</p

Table_4_The interplay between neoantigens and immune cells in sarcomas treated with checkpoint inhibition.xlsx

IntroductionSarcomas are comprised of diverse bone and connective tissue tumors with few effective therapeutic options for locally advanced unresectable and/or metastatic disease. Recent advances in immunotherapy, in particular immune checkpoint inhibition (ICI), have shown promising outcomes in several cancer indications. Unfortunately, ICI therapy has provided only modest clinical responses and seems moderately effective in a subset of the diverse subtypes.MethodsTo explore the immune parameters governing ICI therapy resistance or immune escape, we performed whole exome sequencing (WES) on tumors and their matched normal blood, in addition to RNA-seq from tumors of 31 sarcoma patients treated with pembrolizumab. We used advanced computational methods to investigate key immune properties, such as neoantigens and immune cell composition in the tumor microenvironment (TME).ResultsA multifactorial analysis suggested that expression of high quality neoantigens in the context of specific immune cells in the TME are key prognostic markers of progression-free survival (PFS). The presence of several types of immune cells, including T cells, B cells and macrophages, in the TME were associated with improved PFS. Importantly, we also found the presence of both CD8+ T cells and neoantigens together was associated with improved survival compared to the presence of CD8+ T cells or neoantigens alone. Interestingly, this trend was not identified with the combined presence of CD8+ T cells and TMB; suggesting that a combined CD8+ T cell and neoantigen effect on PFS was important.DiscussionThe outcome of this study may inform future trials that may lead to improved outcomes for sarcoma patients treated with ICI.</p