Structural Variation In Cancer Genomes Research Articles

Abstract LB080: SAVANA: a computational method to characterize structural variation in human cancer genomes using nanopore sequencing

Abstract Whole-genome sequencing (WGS) of human cancers has revealed that structural variation, which refers to the rearrangement of the genome leading to the deletion, amplification of reshuffling of DNA segments ranging from a few hundred bp to entire chromosomes, is a key mutational process in cancer evolution. Notably, pan-cancer analyses have revealed that both simple and complex forms of structural variation are pervasive across diverse human cancers, and often underpin drug resistance and metastasis. To date, the study of cancer genomes has relied on the analysis of short-read WGS on the dominant Illumina platform, which generates short, highly-accurate reads of 100-300bp that allow the study of point mutations at high resolution. However, detection of structural variants (SVs) using short reads is limited, as breakpoints falling in repetitive regions cannot be reliably mapped to the human genome. As a result, our understanding of the patterns and mechanisms underpinning structural variation in cancer genomes remains incomplete. In contrast to short-read sequencing, long-read sequencing technologies, such as Oxford Nanopore and PacBio, permit continuous reading of individual DNA molecules over 10 kilobases, thus providing unparalleled information to resolve SVs in repetitive regions and complex genome rearrangements. However, novel bioinformatics methods that account for the higher error rate of long-read methods are needed to take advantage of their capabilities for cancer genome analysis. Here, we present SAVANA, a novel structural variant caller for long-read sequencing data specifically designed for the analysis of cancer genomes. To identify both somatic and germline SVs, SAVANA takes as input long-read WGS data from a tumor and normal sample pair. SAVANA scans sequencing reads to detect split reads and gapped alignments, which are then clustered to define putative SVs. Next, SAVANA applies a machine learning-informed set of heuristics to remove false positives arising from mapping errors and sequencing artifacts. Extensively validated against a multi-platform truthset, we show that SAVANA identifies a range of somatic rearrangements with high recall and precision, outperforming existing tools while maintaining a lower execution time than competing methods. In patient samples, SAVANA identifies clinically relevant alterations, such as oncogenic gene fusions, with high accuracy. Additionally, SAVANA permits the reconstruction of double minutes, multi-chromosomal chromothripsis events, and SVs mapping to highly repetitive regions, including centromeres. In sum, SAVANA permits the characterization of complex structural variants and can uncover clinically relevant mutations across diverse cancer types with high accuracy. Citation Format: Hillary Elrick, Jose Espejo Valle-Inclan, Katherine E. Trevers, Francesc Muyas, Rita Cascão, Angela Afonso, Cláudia C. Faria, Adrienne M. Flanagan, Isidro Cortés-Ciriano. SAVANA: a computational method to characterize structural variation in human cancer genomes using nanopore sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB080.

Clustering and graph mining techniques for classification of complex structural variations in cancer genomes

For many years, a major question in cancer genomics has been the identification of those variations that can have a functional role in cancer, and distinguish from the majority of genomic changes that have no functional consequences. This is particularly challenging when considering complex chromosomal rearrangements, often composed of multiple DNA breaks, resulting in difficulties in classifying and interpreting them functionally. Despite recent efforts towards classifying structural variants (SVs), more robust statistical frames are needed to better classify these variants and isolate those that derive from specific molecular mechanisms. We present a new statistical approach to analyze SVs patterns from 2392 tumor samples from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium and identify significant recurrence, which can inform relevant mechanisms involved in the biology of tumors. The method is based on recursive KDE clustering of 152,926 SVs, randomization methods, graph mining techniques and statistical measures. The proposed methodology was able not only to identify complex patterns across different cancer types but also to prove them as not random occurrences. Furthermore, a new class of pattern that was not previously described has been identified.

Correction to: Computational Analysis of Structural Variation in Cancer Genomes.

The author originally had the wrong programming command and the chapter was inadvertently published with error. The same has been updated later as below.

Computational Analysis of Structural Variation in Cancer Genomes.

Cancer onset and progression is often triggered by the accumulation of structural abnormalities in the genome. Somatically acquired large structural variants (SV) are one class of abnormalities that can lead to cancer onset by, for example, deactivating tumor suppressor genes and by upregulating oncogenes. Detecting and classifying these variantscan lead to improved therapies and diagnostics for cancer patients.This chapter provides an overview of the problem of computational genomic SV detection using next-generation sequencing (NGS) platforms, along with a brief overview of typical approaches for addressing this problem. It also discusses the general protocol that should be followed to analyze a cancer genome for SV detection in NGS data.

Integrative detection and analysis of structural variation in cancer genomes.

Structural variants (SVs) can contribute to oncogenesis through a variety of mechanisms. Despite their importance, the identification of SVs in cancer genomes remains challenging. Here, we present a framework that integrates optical mapping, high-throughput chromosome conformation capture (Hi-C), and whole-genome sequencing to systematically detect SVs in a variety of normal or cancer samples and cell lines. We identify the unique strengths of each method and demonstrate that only integrative approaches can comprehensively identify SVs in the genome. By combining Hi-C and optical mapping, we resolve complex SVs and phase multiple SV events to a single haplotype. Furthermore, we observe widespread structural variation events affecting the functions of noncoding sequences, including the deletion of distal regulatory sequences, alteration of DNA replication timing, and the creation of novel three-dimensional chromatin structural domains. Our results indicate that noncoding SVs may be underappreciated mutational drivers in cancer genomes.

Allele-Specific Quantification of Structural Variations in Cancer Genomes

SummaryAneuploidy and structural variations (SVs) generate cancer genomes containing a mixture of rearranged genomic segments with extensive somatic copy number alterations. However, existing methods can identify either SVs or allele-specific copy number alterations, but not both simultaneously, which provides a limited view of cancer genome structure. Here we introduce Weaver, an algorithm for the quantification and analysis of allele-specific copy numbers of SVs. Weaver uses a Markov Random Field to estimate joint probabilities of allele-specific copy number of SVs and their inter-connectivity based on paired-end whole-genome sequencing data. Weaver also predicts the timing of SVs relative to chromosome amplifications. We demonstrate the accuracy of Weaver using simulations and findings from whole-genome Optical Mapping. We apply Weaver to generate allele-specific copy numbers of SVs for MCF-7 and HeLa cell lines, and identify recurrent SV patterns in 44 TCGA ovarian cancer whole-genome sequencing datasets. Our approach provides a more complete assessment of the complex genomic architectures inherent to many cancer genomes.

Identification of large-scale genomic variation in cancer genomes using in silico reference models.

Identifying large-scale structural variation in cancer genomes continues to be a challenge to researchers. Current methods rely on genome alignments based on a reference that can be a poor fit to highly variant and complex tumor genomes. To address this challenge we developed a method that uses available breakpoint information to generate models of structural variations. We use these models as references to align previously unmapped and discordant reads from a genome. By using these models to align unmapped reads, we show that our method can help to identify large-scale variations that have been previously missed.

Somatic structural variation and cancer.

With the advent of next-generation sequencing technologies, we have witnessed a rapid pace of discovery of new patterns of somatic structural variation in cancer genomes, and an attempt to figure out their underlying mechanisms. Some of these mechanisms are associated with particular cancer types, and in some cases are the main cause of the structural mutations that drive the oncogenic process. This review provides an overview of the patterns of somatic structural variation and chromosomal structures that characterize cancer genomes, their causal mechanisms and their impact in oncogenesis.

Cancer genomics: new software tools making sequencing more accessible.

Next-generation sequencing transforms today’s cancer research Next-generation sequencing (NGS) technologies can parallelize sequencing processes and produce millions of short-read sequences concurrently. The advent of NGS technologies has transformed today’s cancer genome research by providing an unbiased and comprehensive method of detecting somatic genome alterations [1]. The application of NGS technologies, through whole-genome, -exome and -transcriptome approaches has led to the discovery of mutated genes that drive oncogenic phenotypes in tumors. In a recent The Cancer Genome Atlas (TCGA) Pan-Cancer effort [2], 127 mutated driver genes were identified from 3281 tumors across 12 tumor types. These cancer drivers converged on several well-known pathways such as cell cycle and MAPK pathways. Interestingly, these drivers are also involved in cellular processes in cancers that have previously been less well characterized such as histone modification, RNA splicing, cellular metabolism and proteolysis. Mutations in transcription factors are more tissue specific, whereas mutations in histone modifiers are shared among multiple cancer types. The average number of driver mutations varies across tumor types and most individual tumors have two to six mutations [2]. Generally, larger numbers of driver mutations are involved in tumors with high levels of background mutations [3]. For example, most squamous cell lung carcinomas are attributed to life-long tobacco exposure. Carcinogens from tobacco exposure can cause a broad spectrum of DNA lesions on the genome, and this increases the chance that mutations conferring a small growth advantage to cells are selected in the lung microenvironment. Many such mutations with small effects can accumulate in a specific group of cells over time and collectively lead to carcinogenesis. This is in contrast to hematologic cancers, where fewer driver mutations (with potentially large effects) are observed in tumors with low background mutations. Driver genes are promising drug targets and their discovery has the potential to revolutionize personalized gene-targeted treatments that guide patient therapy according to the genomic profile of the tumor. The battle of Dr Lukas Wartman at Washington University in St Louis (WUSTL; MO, USA) against leukemia is a prime example of the promise of personalized medicine [4]. When Wartman had relapsed a second time, his colleagues at WUSTL sequenced the entire genome of DNA and RNA from his cancerous cells. His RNA sequencing showed that a gene called FLT3 was unexpectedly overproduced by his cancer cells. It so happens that the drug Sutent, previously approved for treating advanced kidney cancer, effectively inhibits FLT3. Sutent was prescribed to Wartman, and his leukemia was in remission 2 weeks later. In next decade, we will witness many such cases come to the forefront of cancer treatment and soon realize the promise of personalized medicines purred by advances in cancer genomics and associated drug development. part of Editorial

COPS: A Sensitive and Accurate Tool for Detecting Somatic Copy Number Alterations Using Short-Read Sequence Data from Paired Samples

Copy Number Alterations (CNAs) such as deletions and duplications; compose a larger percentage of genetic variations than single nucleotide polymorphisms or other structural variations in cancer genomes that undergo major chromosomal re-arrangements. It is, therefore, imperative to identify cancer-specific somatic copy number alterations (SCNAs), with respect to matched normal tissue, in order to understand their association with the disease. We have devised an accurate, sensitive, and easy-to-use tool, COPS, COpy number using Paired Samples, for detecting SCNAs. We rigorously tested the performance of COPS using short sequence simulated reads at various sizes and coverage of SCNAs, read depths, read lengths and also with real tumor:normal paired samples. We found COPS to perform better in comparison to other known SCNA detection tools for all evaluated parameters, namely, sensitivity (detection of true positives), specificity (detection of false positives) and size accuracy. COPS performed well for sequencing reads of all lengths when used with most upstream read alignment tools. Additionally, by incorporating a downstream boundary segmentation detection tool, the accuracy of SCNA boundaries was further improved. Here, we report an accurate, sensitive and easy to use tool in detecting cancer-specific SCNAs using short-read sequence data. In addition to cancer, COPS can be used for any disease as long as sequence reads from both disease and normal samples from the same individual are available. An added boundary segmentation detection module makes COPS detected SCNA boundaries more specific for the samples studied. COPS is available at ftp://115.119.160.213 with username “cops” and password “cops”.

Fastbreak: a tool for analysis and visualization of structural variations in genomic data

Genomic studies are now being undertaken on thousands of samples requiring new computational tools that can rapidly analyze data to identify clinically important features. Inferring structural variations in cancer genomes from mate-paired reads is a combinatorially difficult problem. We introduce Fastbreak, a fast and scalable toolkit that enables the analysis and visualization of large amounts of data from projects such as The Cancer Genome Atlas.

High order chromatin architecture shapes the landscape of chromosomal alterations in cancer

The accumulation of data on structural variation in cancer genomes provides an opportunity to better understand the mechanisms of genomic alterations and the forces of selection that act upon these alterations in cancer. Here we test evidence supporting the influence of two major forces, spatial chromosome structure and purifying (or negative) selection, on the landscape of somatic copy-number alterations (SCNAs) in cancer. Using a maximum likelihood approach, we compare SCNA maps and three-dimensional genome architecture as determined by genome-wide chromosome conformation capture (HiC) and described by the proposed fractal-globule model. This analysis suggests that the distribution of chromosomal alterations in cancer is spatially related to three-dimensional genomic architecture and that purifying selection, as well as positive selection, influences SCNAs during somatic evolution of cancer cells.

CREST maps somatic structural variation in cancer genomes with base-pair resolution

We developed 'clipping reveals structure' (CREST), an algorithm that uses next-generation sequencing reads with partial alignments to a reference genome to directly map structural variations at the nucleotide level of resolution. Application of CREST to whole-genome sequencing data from five pediatric T-lineage acute lymphoblastic leukemias (T-ALLs) and a human melanoma cell line, COLO-829, identified 160 somatic structural variations. Experimental validation exceeded 80%, demonstrating that CREST had a high predictive accuracy.