Massive genomic rearrangement acquired in a single catastrophic event during cancer development.

Cancer is driven by somatically acquired point mutations and chromosomal rearrangements, conventionally thought to accumulate gradually over time. Using next-generation sequencing, we characterize a phenomenon, which we term chromothripsis, whereby tens to hundreds of genomic rearrangements occur in a one-off cellular crisis. Rearrangements involving one or a few chromosomes crisscross back and forth across involved regions, generating frequent oscillations between two copy number states. These genomic hallmarks are highly improbable if rearrangements accumulate over time and instead imply that nearly all occur during a single cellular catastrophe. The stamp of chromothripsis can be seen in at least 2%-3% of all cancers, across many subtypes, and is present in ∼25% of bone cancers. We find that one, or indeed more than one, cancer-causing lesion can emerge out of the genomic crisis. This phenomenon has important implications for the origins of genomic remodeling and temporal emergence of cancer.

The patterns and dynamics of genomic instability in metastatic pancreatic cancer.

Pancreatic cancer is an aggressive malignancy with a five-year mortality of 97-98%, usually due to widespread metastatic disease. Previous studies indicate that this disease has a complex genomic landscape, with frequent copy number changes and point mutations, but genomic rearrangements have not been characterized in detail. Despite the clinical importance of metastasis, there remain fundamental questions about the clonal structures of metastatic tumours, including phylogenetic relationships among metastases, the scale of ongoing parallel evolution in metastatic and primary sites, and how the tumour disseminates. Here we harness advances in DNA sequencing to annotate genomic rearrangements in 13 patients with pancreatic cancer and explore clonal relationships among metastases. We find that pancreatic cancer acquires rearrangements indicative of telomere dysfunction and abnormal cell-cycle control, namely dysregulated G1-to-S-phase transition with intact G2-M checkpoint. These initiate amplification of cancer genes and occur predominantly in early cancer development rather than the later stages of the disease. Genomic instability frequently persists after cancer dissemination, resulting in ongoing, parallel and even convergent evolution among different metastases. We find evidence that there is genetic heterogeneity among metastasis-initiating cells, that seeding metastasis may require driver mutations beyond those required for primary tumours, and that phylogenetic trees across metastases show organ-specific branches. These data attest to the richness of genetic variation in cancer, brought about by the tandem forces of genomic instability and evolutionary selection.

A small-cell lung cancer genome with complex signatures of tobacco exposure.

Cancer is driven by mutation. Worldwide, tobacco smoking is the principal lifestyle exposure that causes cancer, exerting carcinogenicity through >60 chemicals that bind and mutate DNA. Using massively parallel sequencing technology, we sequenced a small-cell lung cancer cell line, NCI-H209, to explore the mutational burden associated with tobacco smoking. A total of 22,910 somatic substitutions were identified, including 134 in coding exons. Multiple mutation signatures testify to the cocktail of carcinogens in tobacco smoke and their proclivities for particular bases and surrounding sequence context. Effects of transcription-coupled repair and a second, more general, expression-linked repair pathway were evident. We identified a tandem duplication that duplicates exons 3-8 of CHD7 in frame, and another two lines carrying PVT1-CHD7 fusion genes, indicating that CHD7 may be recurrently rearranged in this disease. These findings illustrate the potential for next-generation sequencing to provide unprecedented insights into mutational processes, cellular repair pathways and gene networks associated with cancer.

A comprehensive catalogue of somatic mutations from a human cancer genome.

All cancers carry somatic mutations. A subset of these somatic alterations, termed driver mutations, confer selective growth advantage and are implicated in cancer development, whereas the remainder are passengers. Here we have sequenced the genomes of a malignant melanoma and a lymphoblastoid cell line from the same person, providing the first comprehensive catalogue of somatic mutations from an individual cancer. The catalogue provides remarkable insights into the forces that have shaped this cancer genome. The dominant mutational signature reflects DNA damage due to ultraviolet light exposure, a known risk factor for malignant melanoma, whereas the uneven distribution of mutations across the genome, with a lower prevalence in gene footprints, indicates that DNA repair has been preferentially deployed towards transcribed regions. The results illustrate the power of a cancer genome sequence to reveal traces of the DNA damage, repair, mutation and selection processes that were operative years before the cancer became symptomatic.

Estimation of rearrangement phylogeny for cancer genomes.

Cancer genomes are complex, carrying thousands of somatic mutations including base substitutions, insertions and deletions, rearrangements, and copy number changes that have been acquired over decades. Recently, technologies have been introduced that allow generation of high-resolution, comprehensive catalogs of somatic alterations in cancer genomes. However, analyses of these data sets generally do not indicate the order in which mutations have occurred, or the resulting karyotype. Here, we introduce a mathematical framework that begins to address this problem. By using samples with accurate data sets, we can reconstruct relatively complex temporal sequences of rearrangements and provide an assembly of genomic segments into digital karyotypes. For cancer genes mutated in rearranged regions, this information can provide a chronological examination of the selective events that have taken place.

Complex landscapes of somatic rearrangement in human breast cancer genomes.

Multiple somatic rearrangements are often found in cancer genomes; however, the underlying processes of rearrangement and their contribution to cancer development are poorly characterized. Here we use a paired-end sequencing strategy to identify somatic rearrangements in breast cancer genomes. There are more rearrangements in some breast cancers than previously appreciated. Rearrangements are more frequent over gene footprints and most are intrachromosomal. Multiple rearrangement architectures are present, but tandem duplications are particularly common in some cancers, perhaps reflecting a specific defect in DNA maintenance. Short overlapping sequences at most rearrangement junctions indicate that these have been mediated by non-homologous end-joining DNA repair, although varying sequence patterns indicate that multiple processes of this type are operative. Several expressed in-frame fusion genes were identified but none was recurrent. The study provides a new perspective on cancer genomes, highlighting the diversity of somatic rearrangements and their potential contribution to cancer development.

Exploration of Germline Correlates and Risk of Immune-Related Adverse Events in Advanced Cancer Patients Treated with Immune Checkpoint Inhibitors.

Immune checkpoint inhibitors (ICIs) are increasingly used in the treatment of many tumor types, and durable responses can be observed in select populations. However, patients may exhibit significant immune-related adverse events (irAEs) that may lead to morbidity. There is limited information on whether the presence of specific germline mutations may highlight those at elevated risk of irAEs. We evaluated 117 patients with metastatic solid tumors or hematologic malignancies who underwent genomic analysis through the ongoing Personalized OncoGenomics (POG) program at BC Cancer and received an ICI during their treatment history. Charts were reviewed for irAEs. Whole genome sequencing of a fresh biopsy and matched normal specimens (blood) was performed at the time of POG enrollment. Notably, we found that MHC class I alleles in the HLA-B27 family, which have been previously associated with autoimmune conditions, were associated with grade 3 hepatitis and pneumonitis (q = 0.007) in patients treated with combination PD-1/PD-L1 and CTLA-4 inhibitors, and PD-1 inhibitors in combination with IDO-1 inhibitors. These data highlight that some patients may have a genetic predisposition to developing irAEs.

TMBur: a distributable tumor mutation burden approach for whole genome sequencing.

BACKGROUND: Tumor mutation burden (TMB) is a key characteristic used in a tumor-type agnostic context to inform the use of immune checkpoint inhibitors (ICI). Accurate and consistent measurement of TMB is crucial as it can significantly impact patient selection for therapy and clinical trials, with a threshold of 10 mutations/Mb commonly used as an inclusion criterion. Studies have shown that the most significant contributor to variability in mutation counts in whole genome sequence (WGS) data is differences in analysis methods, even more than differences in extraction or library construction methods. Therefore, tools for improving consistency in whole genome TMB estimation are of clinical importance. METHODS: We developed a distributable TMB analysis suite, TMBur, to address the need for genomic TMB estimate consistency in projects that span jurisdictions. TMBur is implemented in Nextflow and performs all analysis steps to generate TMB estimates directly from fastq files, incorporating somatic variant calling with Manta, Strelka2, and Mutect2, and microsatellite instability profiling with MSISensor. These tools are provided in a Singularity container downloaded by the workflow at runtime, allowing the entire workflow to be run identically on most computing platforms. To test the reproducibility of TMBur TMB estimates, we performed replicate runs on WGS data derived from the COLO829 and COLO829BL cell lines at multiple research centres. The clinical value of derived TMB estimates was then evaluated using a cohort of 90 patients with advanced, metastatic cancer that received ICIs following WGS analysis. Patients were split into groups based on a threshold of 10/Mb, and time to progression from initiation of ICIs was examined using Kaplan-Meier and cox-proportional hazards analyses. RESULTS: TMBur produced identical TMB estimates across replicates and at multiple analysis centres. The clinical utility of TMBur-derived TMB estimates were validated, with a genomic TMB ≥ 10/Mb demonstrating improved time to progression, even after correcting for differences in tumor type (HR = 0.39, p = 0.012). CONCLUSIONS: TMBur, a shareable workflow, generates consistent whole genome derived TMB estimates predictive of response to ICIs across multiple analysis centres. Reproducible TMB estimates from this approach can improve collaboration and ensure equitable treatment and clinical trial access spanning jurisdictions.

The Neoantigen Landscape of the Coding and Noncoding Cancer Genome Space.

Tumor mutation burden (TMB) is a measure to predict patient responsiveness to immune checkpoint immunotherapy because with increased mutation frequency, the likelihood of a greater neoantigen burden is increased. Although neoantigen prediction tools exist, tumor neoantigen burden has not been adopted as a measure to predict immunotherapy response. With both measures, current guidelines are limited to the coding regions, but ectopic expression of sequences in the noncoding space may potentially be a source of neoantigens. A pan-cancer cohort of 574 advanced disease stage patients with whole genome and transcriptome sequencing was analyzed to report mutation burden and neoantigen counts within the coding and noncoding regions. The efficacy of tumor neoantigen burden, reported as tumor neoantigen count (TNC), including neoantigens derived from the expression of noncoding regions, compared with TMB as a predictor of response to immunotherapy for 80 patients who had received treatment, was evaluated. TMB was found to be the best predictor of response to immunotherapy, whereas expression-derived TNC from the noncoding regions did not improve prediction of response. Therefore, there is minimal benefit in extending the calculation of TNC to the noncoding space for the purposes of predicting response. However, it is likely that there is a wealth of neoantigens derived from the noncoding space that may impact patient outcomes and treatments.

Subclonal phylogenetic structures in cancer revealed by ultra-deep sequencing.

During the clonal expansion of cancer from an ancestral cell with an initiating oncogenic mutation to symptomatic neoplasm, the occurrence of somatic mutations (both driver and passenger) can be used to track the on-going evolution of the neoplasm. All subclones within a cancer are phylogenetically related, with the prevalence of each subclone determined by its evolutionary fitness and the timing of its origin relative to other subclones. Recently developed massively parallel sequencing platforms promise the ability to detect rare subclones of genetic variants without a priori knowledge of the mutations involved. We used ultra-deep pyrosequencing to investigate intraclonal diversification at the Ig heavy chain locus in 22 patients with B-cell chronic lymphocytic leukemia. Analysis of a non-polymorphic control locus revealed artifactual insertions and deletions resulting from sequencing errors and base substitutions caused by polymerase misincorporation during PCR amplification. We developed an algorithm to differentiate genuine haplotypes of somatic hypermutations from such artifacts. This proved capable of detecting multiple rare subclones with frequencies as low as 1 in 5000 copies and allowed the characterization of phylogenetic interrelationships among subclones within each patient. This study demonstrates the potential for ultra-deep resequencing to recapitulate the dynamics of clonal evolution in cancer cell populations.

Genome and Transcriptome Biomarkers of Response to Immune Checkpoint Inhibitors in Advanced Solid Tumors.

PURPOSE: Immune checkpoint inhibitors (ICI) have revolutionized the treatment of solid tumors with dramatic and durable responses seen across multiple tumor types. However, identifying patients who will respond to these drugs remains challenging, particularly in the context of advanced and previously treated cancers. EXPERIMENTAL DESIGN: We characterized fresh tumor biopsies from a heterogeneous pan-cancer cohort of 98 patients with metastatic predominantly pretreated disease through the Personalized OncoGenomics program at BC Cancer (Vancouver, Canada) using whole genome and transcriptome analysis (WGTA). Baseline characteristics and follow-up data were collected retrospectively. RESULTS: We found that tumor mutation burden, independent of mismatch repair status, was the most predictive marker of time to progression (P = 0.007), but immune-related CD8+ T-cell and M1-M2 macrophage ratio scores were more predictive for overall survival (OS; P = 0.0014 and 0.0012, respectively). While CD274 [programmed death-ligand 1 (PD-L1)] gene expression is comparable with protein levels detected by IHC, we did not observe a clinical benefit for patients with this marker. We demonstrate that a combination of markers based on WGTA provides the best stratification of patients (P = 0.00071, OS), and also present a case study of possible acquired resistance to pembrolizumab in a patient with non-small cell lung cancer. CONCLUSIONS: Interpreting the tumor-immune interface to predict ICI efficacy remains challenging. WGTA allows for identification of multiple biomarkers simultaneously that in combination may help to identify responders, particularly in the context of a heterogeneous population of advanced and previously treated cancers, thus precluding tumor type-specific testing.

Endogenous Retrovirus Transcript Levels Are Associated with Immunogenic Signatures in Multiple Metastatic Cancer Types.

Next-generation sequencing of solid tumors has revealed variable signatures of immunogenicity across tumors, but underlying molecular characteristics driving such variation are not fully understood. Although expression of endogenous retrovirus (ERV)-containing transcripts can provide a source of tumor-specific neoantigen in some cancer models, associations between ERV levels and immunogenicity across different types of metastatic cancer are not well established. We performed bioinformatics analysis of genomic, transcriptomic, and clinical data across an integrated cohort of 199 patients with metastatic breast, colorectal, and pancreatic ductal adenocarcinoma tumors. Within each cancer type, we identified a subgroup of viral mimicry tumors in which increased ERV levels were coupled with transcriptional signatures of autonomous antiviral response and immunogenicity. In addition, viral mimicry colorectal and pancreatic tumors showed increased expression of DNA demethylation gene TET2 Taken together, these data demonstrate the existence of an ERV-associated viral mimicry phenotype across three distinct metastatic cancer types, while indicating links between ERV abundance, epigenetic dysregulation, and immunogenicity.

A SAGE approach to discovery of genes involved in autophagic cell death.

Programmed cell death (PCD), important in normal animal physiology and disease, can be divided into at least two morphological subtypes, including type I, or apoptosis, and type II, or autophagic cell death. While many molecules involved in apoptosis have been discovered and studied intensively during the past decade, autophagic cell death is not well characterized molecularly. Here we report the first comprehensive identification of molecules associated with autophagic cell death during normal metazoan development in vivo. During Drosophila metamorphosis, the larval salivary glands undergo autophagic cell death regulated by a hormonally induced transcriptional cascade. To identify and analyze the genes expressed, we examined wild-type patterns of gene expression in three predeath stages of Drosophila salivary glands using serial analysis of gene expression (SAGE) [7]. 1244 transcripts, including genes involved in autophagy, defense response, cytoskeleton remodeling, noncaspase proteolysis, and apoptosis, were expressed differentially prior to salivary gland death. Mutant expression analysis indicated that several of these genes were regulated by E93, a gene required for salivary gland cell death. Our analyses strongly support both the emerging notion that there is overlap with respect to the molecules involved in autophagic cell death and apoptosis, and that there are important differences.

Identification of somatically acquired rearrangements in cancer using genome-wide massively parallel paired-end sequencing.

Human cancers often carry many somatically acquired genomic rearrangements, some of which may be implicated in cancer development. However, conventional strategies for characterizing rearrangements are laborious and low-throughput and have low sensitivity or poor resolution. We used massively parallel sequencing to generate sequence reads from both ends of short DNA fragments derived from the genomes of two individuals with lung cancer. By investigating read pairs that did not align correctly with respect to each other on the reference human genome, we characterized 306 germline structural variants and 103 somatic rearrangements to the base-pair level of resolution. The patterns of germline and somatic rearrangement were markedly different. Many somatic rearrangements were from amplicons, although rearrangements outside these regions, notably including tandem duplications, were also observed. Some somatic rearrangements led to abnormal transcripts, including two from internal tandem duplications and two fusion transcripts created by interchromosomal rearrangements. Germline variants were predominantly mediated by retrotransposition, often involving AluY and LINE elements. The results demonstrate the feasibility of systematic, genome-wide characterization of rearrangements in complex human cancer genomes, raising the prospect of a new harvest of genes associated with cancer using this strategy.

Identification and analysis of internal promoters in Caenorhabditis elegans operons.

The current Caenorhabditis elegans genomic annotation has many genes organized in operons. Using directionally stitched promoterGFP methodology, we have conducted the largest survey to date on the regulatory regions of annotated C. elegans operons and identified 65, over 25% of those studied, with internal promoters. We have termed these operons "hybrid operons." GFP expression patterns driven from internal promoters differ in tissue specificity from expression of operon promoters, and serial analysis of gene expression data reveals that there is a lack of expression correlation between genes in many hybrid operons. The average length of intergenic regions with putative promoter activity in hybrid operons is larger than previous estimates for operons as a whole. Genes with internal promoters are more commonly involved in gene duplications and have a significantly lower incidence of alternative splicing than genes without internal promoters, although we have observed almost all trans-splicing patterns in these two distinct groups. Finally, internal promoter constructs are able to rescue lethal knockout phenotypes, demonstrating their necessity in gene regulation and survival. Our work suggests that hybrid operons are common in the C. elegans genome and that internal promoters influence not only gene organization and expression but also operon evolution.

Assessment and integration of publicly available SAGE, cDNA microarray, and oligonucleotide microarray expression data for global coexpression analyses.

Large amounts of gene expression data from several different technologies are becoming available to the scientific community. A common practice is to use these data to calculate global gene coexpression for validation or integration of other "omic" data. To assess the utility of publicly available datasets for this purpose we have analyzed Homo sapiens data from 1202 cDNA microarray experiments, 242 SAGE libraries, and 667 Affymetrix oligonucleotide microarray experiments. The three datasets compared demonstrate significant but low levels of global concordance (rc<0.11). Assessment against Gene Ontology (GO) revealed that all three platforms identify more coexpressed gene pairs with common biological processes than expected by chance. As the Pearson correlation for a gene pair increased it was more likely to be confirmed by GO. The Affymetrix dataset performed best individually with gene pairs of correlation 0.9-1.0 confirmed by GO in 74% of cases. However, in all cases, gene pairs confirmed by multiple platforms were more likely to be confirmed by GO. We show that combining results from different expression platforms increases reliability of coexpression. A comparison with other recently published coexpression studies found similar results in terms of performance against GO but with each method producing distinctly different gene pair lists.

Assessment of SAGE in transcript identification.

An essential step in Serial Analysis of Gene Expression (SAGE) is tag mapping, which refers to the unambiguous determination of the gene represented by a SAGE tag. Current resources for tag mapping are incomplete, and thus do not allow assessment of the efficacy of SAGE in transcript identification. A method of tag mapping is described here and applied to the Drosophila melanogaster and Caenorhabditis elegans genomes, which permits detailed SAGE assessment and provides tag-mapping resources that were unavailable previously for these organisms. In our method, a conceptual transcriptome is constructed using genomic sequence and annotation by extending predicted coding regions to include UTRs on the basis of EST and cDNA alignments, UTR length distributions, and polyadenylation signals. Analysis of extracted tags suggests that, using the standard SAGE procedure, expression of 8% of D. melanogaster and 15% of C. elegans genes cannot be detected unambiguously by SAGE due to shared sequence or lack of NlaIII-anchoring enzyme sites. Both increasing tag length by 2-3 bp and using Sau3A instead of NlaIII as the anchoring enzyme increases potential for transcript detection. This work identifies and quantifies genes not amenable to SAGE analysis, in addition to providing tag-to-gene mappings for two model organisms.