AGBT 2015 Workshop Presentation Slides: Dick McCombie from Cold Spring Harbor Laboratory (CSHL) described the use of SMRT Sequencing to analyze a breast cancer cell line with complex genomic events. Still ongoing, the project has already uncovered structural variants missed by other sequencers.
The comprehensive characterization of cancer genomes and epigenomes for understanding drug resistance remains an important challenge in the field of oncology. For example, PC-9, a non-small cell lung cancer (NSCL) cell line, contains a deletion mutation in exon 19 (DelE746A750) of EGRF that renders it sensitive to erlotinib, an EGFR inhibitor. However, sustained treatment of these cells with erlotinib leads to drug-tolerant cell populations that grow in the presence of erlotinib. However, the resistant cells can be resensitized to erlotinib upon treatment with methyltransferase inhibitors, suggesting a role of epigenetic modification in development of drug resistance. We have characterized for the first time cancer genomes of both drug-sensitive and drug-resistant PC- 9 cells using long-read PacBio sequencing. The PacBio data allowed us to generate a high-quality, de novo assembly of this cancer genome, enabling the detection of forms of genomic variations at all size scales, including SNPs, structural variations, copy number alterations, gene fusions, and translocations. The data simultaneously provide a global view of epigenetic DNA modifications such as methylation. We will present findings on large-scale changes in the methylation status across the cancer genome as a function of drug sensitivity.
In addition to the genome and transcriptome, epigenetic information is essential to understand biological processes and their regulation, and their misregulation underlying disease. Traditionally, epigenetic DNA modifications are detected using upfront sample preparation steps such as bisulfite conversion, followed by sequencing. Bisulfite sequencing has provided a wealth of knowledge about human epigenetics, however it does not access the entire genome due to limitations in read length and GC- bias of the sequencing technologies used. In contrast, Single Molecule, Real-Time (SMRT) DNA Sequencing is unique in that it can detect DNA base modifications as part of the sequencing process. It can thereby leverage the long read lengths and lack of GC bias for more comprehensive views of the human epigenome. I will highlight several examples of this capability towards the generation of new biological insights, including the resolution of methylation states in repetitive and GC-rich regions of the genome, and large-scale changes in the methylation status across a cancer genome as a function of drug sensitivity.
Genomic instability is one of the hallmarks of cancer, leading to widespread copy number variations, chromosomal fusions, and other structural variations. The breast cancer cell line SK-BR-3 is an important model for HER2+ breast cancers, which are among the most aggressive forms of the disease and affect one in five cases. Through short read sequencing, copy number arrays, and other technologies, the genome of SK-BR-3 is known to be highly rearranged with many copy number variations, including an approximately twenty-fold amplification of the HER2 oncogene. However, these technologies cannot precisely characterize the nature and context of the identified genomic events and other important mutations may be missed altogether because of repeats, multi-mapping reads, and the failure to reliably anchor alignments to both sides of a variation. To address these challenges, we have sequenced SK-BR-3 using PacBio long read technology. Using the new P6-C4 chemistry, we generated more than 70X coverage of the genome with average read lengths of 9-13kb (max: 71kb). Using Lumpy for split-read alignment analysis, as well as our novel assembly-based algorithms for finding complex variants, we have developed a detailed map of structural variations in this cell line. Taking advantage of the newly identified breakpoints and combining these with copy number assignments, we have developed an algorithm to reconstruct the mutational history of this cancer genome. From this we have discovered a complex series of nested duplications and translocations between chr17 and chr8, two of the most frequent translocation partners in primary breast cancers, resulting in amplification of HER2. We have also carried out full-length transcriptome sequencing using PacBio’s Iso-Seq technology, which has revealed a number of previously unrecognized gene fusions and isoforms. Combining long-read genome and transcriptome sequencing technologies enables an in-depth analysis of how changes in the genome affect the transcriptome, including how gene fusions are created across multiple chromosomes. This analysis has established the most complete cancer reference genome available to date, and is already opening the door to applying long-read sequencing to patient samples with complex genome structures.
Outside of the simplest cases (haploid, bacteria, or inbreds), genomic information is not carried in a single reference per individual, but rather has higher ploidy (n=>2) for almost all organisms. The existence of two or more highly related sequences within an individual makes it extremely difficult to build high quality, highly contiguous genome assemblies from short DNA fragments. Based on the earlier work on a polyploidy aware assembler, FALCON ( https://github.com/PacificBiosciences/FALCON) , we developed new algorithms and software (“FALCON-unzip”) for de novo haplotype reconstructions from SMRT Sequencing data. We generate two datasets for developing the algorithms and the prototype software: (1) whole genome sequencing data from a highly repetitive diploid fungal (Clavicorona pyxidata) and (2) whole genome sequencing data from an F1 hybrid from two inbred Arabidopsis strains: Cvi-0 and Col-0. For the fungal genome, we achieved an N50 of 1.53 Mb (of the 1n assembly contigs) of the ~42 Mb 1n genome and an N50 of the haplotigs (haplotype specific contigs) of 872 kb from a 95X read length N50 ~16 kb dataset. We found that ~ 45% of the genome was highly heterozygous and ~55% of the genome was highly homozygous. We developed methods to assess the base-level accuracy and local haplotype phasing accuracy of the assembly with short-read data from the Illumina® platform. For the ArabidopsisF1 hybrid genome, we found that 80% of the genome could be separated into haplotigs. The long range accuracy of phasing haplotigs was evaluated by comparing them to the assemblies from the two inbred parental lines. We show that a more complete view of all haplotypes could provide useful biological insights through improved annotation, characterization of heterozygous variants of all sizes, and resolution of differential allele expression. The current Falcon-Unzip method will lead to understand how to solve more difficult polyploid genome assembly problems and improve the computational efficiency for large genome assemblies. Based on this work, we can develop a pipeline enabling routinely assemble diploid or polyploid genomes as haplotigs, representing a comprehensive view of the genomes that can be studied with the information at hand.
Past large scale cancer genome sequencing efforts, including The Cancer Genome Atlas and the International Cancer Genome Consortium, have utilized short-read sequencing, which is well-suited for detecting single nucleotide variants (SNVs) but far less reliable for detecting variants larger than 20 base pairs, including insertions, deletions, duplications, inversions and translocations. Recent same-sample comparisons of short- and long-read human reference genome data have revealed that short-read resequencing typically uncovers only ~4,000 structural variants (SVs, =50 bp) per genome and is biased towards deletions, whereas sequencing with PacBio long-reads consistently finds ~20,000 SVs, evenly balanced between insertions and deletions. This discovery has important implications for cancer research, as it is clear that SVs are both common and biologically important in many cancer subtypes, including colorectal, breast and ovarian cancer. Without confident and comprehensive detection of structural variants, it is unlikely we have a sufficiently complete picture of all the genomic changes that impact cancer development, disease progression, treatment response, drug resistance, and relapse. To begin to address this unmet need, we have sequenced the COLO829 tumor and matched normal lymphoblastoid cell lines to 49- and 51-fold coverage, respectively, with PacBio SMRT Sequencing, with the goal of developing a high-confidence structural variant call set that can be used to empirically evaluate cost-effective experimental designs for larger scale studies and develop structural variation calling software suitable for cancer genomics. Structural variant calling revealed over 21,000 deletions and 19,500 insertions larger than 20 bp, nearly four times the number of events detected with short-read sequencing. The vast majority of events are shared between the tumor and normal, with about 100 putative somatic deletions and 400 insertions, primarily in microsatellites. A further 40 rearrangements were detected, nearly exclusively in the tumor. One rearrangement is shared between the tumor and normal, t(5;X) which disrupts the mismatch repeat gene MSH3, and is likely a driver mutation. Generating high-confidence call sets that cover the entire size-spectrum of somatic variants from a range of cancer model systems is the first step in determining what will be the best approach for addressing an ongoing blind spot in our current understanding of cancer genomes. Here the application of PacBio sequencing to a melanoma cancer cell line revealed thousands of previously overlooked variants, including a mutation likely involved in tumorogenesis.
This presentation describes a new genome browser for read alignments around complex variation: genomeribbon.com. Ribbon was built for viewing genomic read alignments around structural variants. It is very useful for…
Tremendous flexibility is maintained in the human proteome via alternative splicing, and cancer genomes often subvert this flexibility to promote survival. Identification and annotation of cancer-specific mRNA isoforms is critical…
In this ASHG workshop presentation, Elizabeth Tseng of PacBio showed how the Iso-Seq method can be used to discover disease-associated alternative splicing. Because this approach to isoform sequencing yields accurate,…
New technologies and analysis methods are enabling genomic structural variants (SVs) to be detected with ever-increasing accuracy, resolution, and comprehensiveness. Translating these methods to routine research and clinical practice requires robust benchmark sets. We developed the first benchmark set for identification of both false negative and false positive germline SVs, which complements recent efforts emphasizing increasingly comprehensive characterization of SVs. To create this benchmark for a broadly consented son in a Personal Genome Project trio with broadly available cells and DNA, the Genome in a Bottle (GIAB) Consortium integrated 19 sequence-resolved variant calling methods, both alignment- and de novo assembly-based, from short-, linked-, and long-read sequencing, as well as optical and electronic mapping. The final benchmark set contains 12745 isolated, sequence-resolved insertion and deletion calls =50 base pairs (bp) discovered by at least 2 technologies or 5 callsets, genotyped as heterozygous or homozygous variants by long reads. The Tier 1 benchmark regions, for which any extra calls are putative false positives, cover 2.66 Gbp and 9641 SVs supported by at least one diploid assembly. Support for SVs was assessed using svviz with short-, linked-, and long-read sequence data. In general, there was strong support from multiple technologies for the benchmark SVs, with 90 % of the Tier 1 SVs having support in reads from more than one technology. The Mendelian genotype error rate was 0.3 %, and genotype concordance with manual curation was >98.7 %. We demonstrate the utility of the benchmark set by showing it reliably identifies both false negatives and false positives in high-quality SV callsets from short-, linked-, and long-read sequencing and optical mapping.
Activating mutations in PIK3CA are frequent in human breast cancer, and phosphoinositide 3-kinase alpha (PI3Ka) inhibitors have been approved for therapy. To characterize determinants of sensitivity to these agents, we analyzed PIK3CA-mutant cancer genomes and observed the presence of multiple PIK3CA mutations in 12 to 15% of breast cancers and other tumor types, most of which (95%) are double mutations. Double PIK3CA mutations are in cis on the same allele and result in increased PI3K activity, enhanced downstream signaling, increased cell proliferation, and tumor growth. The biochemical mechanisms of dual mutations include increased disruption of p110a binding to the inhibitory subunit p85a, which relieves its catalytic inhibition, and increased p110a membrane lipid binding. Double PIK3CA mutations predict increased sensitivity to PI3Ka inhibitors compared with single-hotspot mutations.Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.
The human reference genome serves as the foundation for genomics by providing a scaffold for alignment of sequencing reads, but currently only reflects a single consensus haplotype, thus impairing analysis accuracy. Here we present a graph reference genome implementation that enables read alignment across 2,800 diploid genomes encompassing 12.6 million SNPs and 4.0 million insertions and deletions (indels). The pipeline processes one whole-genome sequencing sample in 6.5?h using a system with 36?CPU cores. We show that using a graph genome reference improves read mapping sensitivity and produces a 0.5% increase in variant calling recall, with unaffected specificity. Structural variations incorporated into a graph genome can be genotyped accurately under a unified framework. Finally, we show that iterative augmentation of graph genomes yields incremental gains in variant calling accuracy. Our implementation is an important advance toward fulfilling the promise of graph genomes to radically enhance the scalability and accuracy of genomic analyses.
Comprehensive molecular characterization of myriad somatic alterations and aberrant gene expressions at personal level is key to precision cancer therapy, yet limited by current short-read sequencing technology, individualized catalog of complete genomic and transcriptomic features is thus far elusive. Here, we integrated second- and third-generation sequencing platforms to generate a multidimensional dataset on a patient affected by metastatic epithelial ovarian cancer. Whole-genome and hybrid transcriptome dissection captured global genetic and transcriptional variants at previously unparalleled resolution. Particularly, single-molecule mRNA sequencing identified a vast array of unannotated transcripts, novel long noncoding RNAs and gene chimeras, permitting accurate determination of transcription start, splice, polyadenylation and fusion sites. Phylogenetic and enrichment inference of isoform-level measurements implicated early functional divergence and cytosolic proteostatic stress in shaping ovarian tumorigenesis. A complementary imaging-based high-throughput drug screen was performed and subsequently validated, which consistently pinpointed proteasome inhibitors as an effective therapeutic regime by inducing protein aggregates in ovarian cancer cells. Therefore, our study suggests that clinical application of the emerging long-read full-length analysis for improving molecular diagnostics is feasible and informative. An in-depth understanding of the tumor transcriptome complexity allowed by leveraging the hybrid sequencing approach lays the basis to reveal novel and valid therapeutic vulnerabilities in advanced ovarian malignancies.
IGH@ proto-oncogene translocation is a common oncogenic event in lymphoid lineage cancers such as B-ALL, lymphoma and multiple myeloma. Here, to investigate the interplay between IGH@ proto-oncogene translocation and IGH allelic exclusion, we perform long-read whole-genome and transcriptome sequencing along with epigenetic and 3D genome profiling of Nalm6, an IGH-DUX4 positive B-ALL cell line. We detect significant allelic imbalance on the wild-type over the IGH-DUX4 haplotype in expression and epigenetic data, showing IGH-DUX4 translocation occurs on the silenced IGH allele. In vitro, this reduces the oncogenic stress of DUX4 high-level expression. Moreover, patient samples of IGH-DUX4 B-ALL have similar expression profile and IGH breakpoints as Nalm6, suggesting a common mechanism to allow optimal dosage of non-toxic DUX4 expression.
Accurate detection of somatic mutations is still a challenge in cancer analysis. Here we present NeuSomatic, the first convolutional neural network approach for somatic mutation detection, which significantly outperforms previous methods on different sequencing platforms, sequencing strategies, and tumor purities. NeuSomatic summarizes sequence alignments into small matrices and incorporates more than a hundred features to capture mutation signals effectively. It can be used universally as a stand-alone somatic mutation detection method or with an ensemble of existing methods to achieve the highest accuracy.
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