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How cell-to-cell copy number alterations that underpin genomic instability 1 in human cancers drive genomic and phenotypic variation, and consequently the evolution of cancer 2 , remains understudied. Here, by applying scaled single-cell whole-genome sequencing 3 to wild-type, TP53-deficient and TP53-deficient;BRCA1-deficient or TP53-deficient;BRCA2-deficient mammary epithelial cells (13,818 genomes), and to primary triple-negative breast cancer (TNBC) and high-grade serous ovarian cancer (HGSC) cells (22,057 genomes), we identify three distinct 'foreground' mutational patterns that are defined by cell-to-cell structural variation. Cell- and clone-specific high-level amplifications, parallel haplotype-specific copy number alterations and copy number segment length variation (serrate structural variations) had measurable phenotypic and evolutionary consequences. In TNBC and HGSC, clone-specific high-level amplifications in known oncogenes were highly prevalent in tumours bearing fold-back inversions, relative to tumours with homologous recombination deficiency, and were associated with increased clone-to-clone phenotypic variation. Parallel haplotype-specific alterations were also commonly observed, leading to phylogenetic evolutionary diversity and clone-specific mono-allelic expression. Serrate variants were increased in tumours with fold-back inversions and were highly correlated with increased genomic diversity of cellular populations. Together, our findings show that cell-to-cell structural variation contributes to the origins of phenotypic and evolutionary diversity in TNBC and HGSC, and provide insight into the genomic and mutational states of individual cancer cells., (© 2022. The Author(s).)

Metastatic progression is the main cause of death in cancer patients, whereas the underlying genomic mechanisms driving metastasis remain largely unknown. Here, we assembled MSK-MET, a pan-cancer cohort of over 25,000 patients with metastatic diseases. By analyzing genomic and clinical data from this cohort, we identified associations between genomic alterations and patterns of metastatic dissemination across 50 tumor types. We found that chromosomal instability is strongly correlated with metastatic burden in some tumor types, including prostate adenocarcinoma, lung adenocarcinoma, and HR+/HER2+ breast ductal carcinoma, but not in others, including colorectal cancer and high-grade serous ovarian cancer, where copy-number alteration patterns may be established early in tumor development. We also identified somatic alterations associated with metastatic burden and specific target organs. Our data offer a valuable resource for the investigation of the biological basis for metastatic spread and highlight the complex role of chromosomal instability in cancer progression., Competing Interests: Declaration of interests S.C. receives consulting fees from Novartis, Lilly, and Sanofi and research funding from Daiichi-Sankyo and Paige.ai. J.J.H. receives consulting fees from Bristol Myers Squibb, Merck, Exelexis, Eisai, QED, Cytomx, Zymeworks, Adaptiimmune, and ImVax and research funding from Bristol Myers Squibb. C.A.I.-D. receives research funding from Bristol Myers Squibb. P. Razavi received consultation/Ad board/Honoraria from Novartis, Foundation Medicine, AstraZeneca, Epic Sciences, Inivata, Natera, and Tempus and institutional grant/funding from Grail, Illumina, Novartis, Epic Sciences, and ArcherDx. C.M.R. has consulted regarding oncology drug development with AbbVie, Amgen, Astra Zeneca, Epizyme, Genentech/Roche, Ipsen, Jazz, Lilly, and Syros and serves on the scientific advisory boards of Bridge Medicines, Earli, and Harpoon Therapeutics. D.Z. receives research funding from Astra Zeneca, Plexxikon, and Genentech and consulting fees from Merck, Synlogic Therapeutics, GSK, Bristol Myers Squibb, Genentech, Xencor, Memgen, Immunos, Agenus, Hookipa, Calidi, and Synthekine. B.W. has an ad hoc membership advisory board Repare Therapeutics. W.A. receives speaking honoraria from Roche, Medscape, Aptitude Health, and Clinical Education Alliance; consulting fees from Clovis Oncology, Janssen, ORIC pharmaceuticals, Daiichi Sankyo; and research funding from AstraZeneca, Zenith Epigenetics, Clovis Oncology, ORIC pharmaceuticals, and Epizyme. G.K.A.-A. receives research funding from Arcus, Agios, Astra Zeneca, Bayer, BioNtech, BMS, Celgene, Flatiron, Genentech/Roche, Genoscience, Incyte, Polaris, Puma, QED, Sillajen, and Yiviva and consulting fees from Adicet, Agios, Astra Zeneca, Alnylam, Autem, Bayer, Beigene, Berry Genomics, Cend, Celgene, CytomX, Eisai, Eli Lilly, Exelixis, Flatiron, Genentech/Roche, Genoscience, Helio, Incyte, Ipsen, Legend Biotech, Loxo, Merck, MINA, Nerviano, QED, Redhill, Rafael, Silenseed, Sillajen, Sobi, Surface Oncology, Therabionics, Twoxar, Vector, and Yiviva. E.M.O. receives research funding from Genentech/Roche, Celgene/BMS, BioNTech, BioAtla, AstraZeneca, Arcus, Elicio, Parker Institute, and AstraZeneca and consulting fees from Cytomx Therapeutics (DSMB), Rafael Therapeutics (DSMB), Sobi, Silenseed, Tyme, Seagen, Molecular Templates, Boehringer Ingelheim, BioNTech, Ipsen, Polaris, Merck, IDEAYA, Cend, AstraZeneca, Noxxon, BioSapien, Bayer (spouse), Genentech-Roche (spouse), Celgene-BMS (spouse), and Eisai (spouse). M.A.P. receive consulting fees from BMS, Merck, Array BioPharma, Novartis, Incyte, NewLink Genetics, Aduro, Eisai, and Pfizer; honoraria from BMS and Merck; research support from RGenix, Infinity, BMS, Merck, Array BioPharma, Novartis, and AstraZeneca. G.J.R. has institutional research funding from Mirait, Takeda, Merck, Roche, Novartis, and Pfizer. S.F.B. holds a patent related to some of the work described targeting CIN in advanced cancer. He owns equity in, receives compensation from, and serves as a consultant and the scientific advisory board and board of directors of Volastra Therapeutics Inc. A.N.S. has advisory board/personal fees from Bristol-Myers Squibb, Immunocore, and Castle Biosciences; research support from Bristol-Myers Squibb, Immunocore, Xcovery, Polaris, Novartis, Pfizer, and Checkmate Pharmaceuticals; and research funding from OBI-Pharma, GSK, Silenseed, BMS, and Lilly. R.Y. receives consulting fees from Array BioPharma/Pfizer, Mirati Therapeutics, and Natera and research funding from Pfizer and Boehringer Ingelheim. D.R.J. is member of the advisory council for Astra Zeneca and member of the Clinical Trial Steering Committee for Merck. D.M. reports disclosures from AstraZeneca, Johnson & Johnson, Boston Scientific, Bristol-Myers Squibb, and Merck. S.P.S. is shareholder and consultant for Canexia Health Inc. M.F.B receives consulting fees from Roche, Eli Lilly, and PetDx and research funding from Grail. D.B.S. has received consulted for and received honoraria from Pfizer, Lilly/Loxo Oncology, Vividion Therapeutics, Scorpion Therapetuics, and BridgeBio. B.N. is an employee of Loxo Oncology at Lilly., (Copyright © 2022 Elsevier Inc. All rights reserved.)

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

Background: Single-cell RNA sequencing (scRNA-seq) is a powerful tool for studying complex biological systems, such as tumor heterogeneity and tissue microenvironments. However, the sources of technical and biological variation in primary solid tumor tissues and patient-derived mouse xenografts for scRNA-seq are not well understood., Results: We use low temperature (6 °C) protease and collagenase (37 °C) to identify the transcriptional signatures associated with tissue dissociation across a diverse scRNA-seq dataset comprising 155,165 cells from patient cancer tissues, patient-derived breast cancer xenografts, and cancer cell lines. We observe substantial variation in standard quality control metrics of cell viability across conditions and tissues. From the contrast between tissue protease dissociation at 37 °C or 6 °C, we observe that collagenase digestion results in a stress response. We derive a core gene set of 512 heat shock and stress response genes, including FOS and JUN, induced by collagenase (37 °C), which are minimized by dissociation with a cold active protease (6 °C). While induction of these genes was highly conserved across all cell types, cell type-specific responses to collagenase digestion were observed in patient tissues., Conclusions: The method and conditions of tumor dissociation influence cell yield and transcriptome state and are both tissue- and cell-type dependent. Interpretation of stress pathway expression differences in cancer single-cell studies, including components of surface immune recognition such as MHC class I, may be especially confounded. We define a core set of 512 genes that can assist with the identification of such effects in dissociated scRNA-seq experiments.

The field of cancer genomics has demonstrated the power of massively parallel sequencing techniques to inform on the genes and specific alterations that drive tumor onset and progression. Although large comprehensive sequence data sets continue to be made increasingly available, data analysis remains an ongoing challenge, particularly for laboratories lacking dedicated resources and bioinformatics expertise. To address this, we have produced a collection of Galaxy tools that represent many popular algorithms for detecting somatic genetic alterations from cancer genome and exome data. We developed new methods for parallelization of these tools within Galaxy to accelerate runtime and have demonstrated their usability and summarized their runtimes on multiple cloud service providers. Some tools represent extensions or refinement of existing toolkits to yield visualizations suited to cohort-wide cancer genomic analysis. For example, we present Oncocircos and Oncoprintplus, which generate data-rich summaries of exome-derived somatic mutation. Workflows that integrate these to achieve data integration and visualizations are demonstrated on a cohort of 96 diffuse large B-cell lymphomas and enabled the discovery of multiple candidate lymphoma-related genes. Our toolkit is available from our GitHub repository as Galaxy tool and dependency definitions and has been deployed using virtualization on multiple platforms including Docker., (© The Author 2017. Published by Oxford University Press.)

Single-cell genomics is critical for understanding cellular heterogeneity in cancer, but existing library preparation methods are expensive, require sample preamplification and introduce coverage bias. Here we describe direct library preparation (DLP), a robust, scalable, and high-fidelity method that uses nanoliter-volume transposition reactions for single-cell whole-genome library preparation without preamplification. We examined 782 cells from cell lines and triple-negative breast xenograft tumors. Low-depth sequencing, compared with existing methods, revealed greater coverage uniformity and more reliable detection of copy-number alterations. Using phylogenetic analysis, we found minor xenograft subpopulations that were undetectable by bulk sequencing, as well as dynamic clonal expansion and diversification between passages. Merging single-cell genomes in silico, we generated 'bulk-equivalent' genomes with high depth and uniform coverage. Thus, low-depth sequencing of DLP libraries may provide an attractive replacement for conventional bulk sequencing methods, permitting analysis of copy number at the cell level and of other genomic variants at the population level.

The genomes of large numbers of single cells must be sequenced to further understanding of the biological significance of genomic heterogeneity in complex systems. Whole genome amplification (WGA) of single cells is generally the first step in such studies, but is prone to nonuniformity that can compromise genomic measurement accuracy. Despite recent advances, robust performance in high-throughput single-cell WGA remains elusive. Here, we introduce droplet multiple displacement amplification (MDA), a method that uses commercially available liquid dispensing to perform high-throughput single-cell MDA in nanoliter volumes. The performance of droplet MDA is characterized using a large dataset of 129 normal diploid cells, and is shown to exceed previously reported single-cell WGA methods in amplification uniformity, genome coverage, and/or robustness. We achieve up to 80% coverage of a single-cell genome at 5× sequencing depth, and demonstrate excellent single-nucleotide variant (SNV) detection using targeted sequencing of droplet MDA product to achieve a median allelic dropout of 15%, and using whole genome sequencing to achieve false and true positive rates of 9.66 × 10(-6) and 68.8%, respectively, in a G1-phase cell. We further show that droplet MDA allows for the detection of copy number variants (CNVs) as small as 30 kb in single cells of an ovarian cancer cell line and as small as 9 Mb in two high-grade serous ovarian cancer samples using only 0.02× depth. Droplet MDA provides an accessible and scalable method for performing robust and accurate CNV and SNV measurements on large numbers of single cells.

BackgroundThe field of next-generation sequencing informatics has matured to a point where algorithmic advances in sequence alignment and individual feature detection methods have stabilized. Practical and robust implementation of complex analytical workflows (where such tools are structured into "best practices" for automated analysis of next-generation sequencing datasets) still requires significant programming investment and expertise.ResultsWe present Kronos, a software platform for facilitating the development and execution of modular, auditable, and distributable bioinformatics workflows. Kronos obviates the need for explicit coding of workflows by compiling a text configuration file into executable Python applications. Making analysis modules would still require programming. The framework of each workflow includes a run manager to execute the encoded workflows locally (or on a cluster or cloud), parallelize tasks, and log all runtime events. The resulting workflows are highly modular and configurable by construction, facilitating flexible and extensible meta-applications that can be modified easily through configuration file editing. The workflows are fully encoded for ease of distribution and can be instantiated on external systems, a step toward reproducible research and comparative analyses. We introduce a framework for building Kronos components that function as shareable, modular nodes in Kronos workflows.ConclusionsThe Kronos platform provides a standard framework for developers to implement custom tools, reuse existing tools, and contribute to the community at large. Kronos is shipped with both Docker and Amazon Web Services Machine Images. It is free, open source, and available through the Python Package Index and at https://github.com/jtaghiyar/kronos.

Mutation signatures in cancer genomes reflect endogenous and exogenous mutational processes, offering insights into tumour etiology, features for prognostic and biologic stratification and vulnerabilities to be exploited therapeutically. We present a novel machine learning formalism for improved signature inference, based on multi-modal correlated topic models (MMCTM) which can at once infer signatures from both single nucleotide and structural variation counts derived from cancer genome sequencing data. We exemplify the utility of our approach on two hormone driven, DNA repair deficient cancers: breast and ovary (n = 755 samples total). We show how introducing correlated structure both within and between modes of mutation can increase accuracy of signature discovery, particularly in the context of sparse data. Our study emphasizes the importance of integrating multiple mutation modes for signature discovery and patient stratification, and provides a statistical modeling framework to incorporate additional features of interest for future studies. [ABSTRACT FROM AUTHOR]

Complex focal chromosomal rearrangements in cancer genomes, also called “firestorms”, can be scored from DNA copy number data. The complex arm-wise aberration index (CAAI) is a score that captures DNA copy number alterations that appear as focal complex events in tumors, and has potential prognostic value in breast cancer. This study aimed to validate this DNA-based prognostic index in breast cancer and test for the first time its potential prognostic value in ovarian cancer. Copy number alteration (CNA) data from 1950 breast carcinomas (METABRIC cohort) and 508 high-grade serous ovarian carcinomas (TCGA dataset) were analyzed. Cases were classified as CAAI positive if at least one complex focal event was scored. Complex alterations were frequently localized on chromosome 8p ( n = 159), 17q ( n = 176) and 11q ( n = 251). CAAI events on 11q were most frequent in estrogen receptor positive (ER+) cases and on 17q in estrogen receptor negative (ER−) cases. We found only a modest correlation between CAAI and the overall rate of genomic instability (GII) and number of breakpoints ( r = 0.27 and r = 0.42, p < 0.001). Breast cancer specific survival (BCSS), overall survival (OS) and ovarian cancer progression free survival (PFS) were used as clinical end points in Cox proportional hazard model survival analyses. CAAI positive breast cancers (43%) had higher mortality: hazard ratio (HR) of 1.94 (95%CI, 1.62–2.32) for BCSS, and of 1.49 (95%CI, 1.30–1.71) for OS. Representations of the 70-gene and the 21-gene predictors were compared with CAAI in multivariable models and CAAI was independently significant with a Cox adjusted HR of 1.56 (95%CI, 1.23–1.99) for ER+ and 1.55 (95%CI, 1.11–2.18) for ER− disease. None of the expression-based predictors were prognostic in the ER− subset. We found that a model including CAAI and the two expression-based prognostic signatures outperformed a model including the 21-gene and 70-gene signatures but excluding CAAI. Inclusion of CAAI in the clinical prognostication tool PREDICT significantly improved its performance. CAAI positive ovarian cancers (52%) also had worse prognosis: HRs of 1.3 (95%CI, 1.1–1.7) for PFS and 1.3 (95%CI, 1.1–1.6) for OS. This study validates CAAI as an independent predictor of survival in both ER+ and ER− breast cancer and reveals a significant prognostic value for CAAI in high-grade serous ovarian cancer. [ABSTRACT FROM AUTHOR]

Background: We present Pegasys -- a flexible, modular and customizable software system that facilitates the execution and data integration from heterogeneous biological sequence analysis tools. Results: The Pegasys system includes numerous tools for pair-wise and multiple sequence alignment, ab initio gene prediction, RNA gene detection, masking repetitive sequences in genomic DNA as well as filters for database formatting and processing raw output from various analysis tools. We introduce a novel data structure for creating workflows of sequence analyses and a unified data model to store its results. The software allows users to dynamically create analysis workflows at run-time by manipulating a graphical user interface. All non-serial dependent analyses are executed in parallel on a compute cluster for efficiency of data generation. The uniform data model and backend relational database management system of Pegasys allow for results of heterogeneous programs included in the workflow to be integrated and exported into General Feature Format for further analyses in GFF-dependent tools, or GAME XML for import into the Apollo genome editor. The modularity of the design allows for new tools to be added to the system with little programmer overhead. The database application programming interface allows programmatic access to the data stored in the backend through SQL queries. Conclusions: The Pegasys system enables biologists and bioinformaticians to create and manage sequence analysis workflows. The software is released under the Open Source GNU General Public License. All source code and documentation is available for download at http://bioinformatics.ubc.ca/pegasys/. [ABSTRACT FROM AUTHOR]

Loss of heterozygosity (LOH) and copy number alteration (CNA) feature prominently in the somatic genomic landscape of tumors. As such, karyotypic aberrations in cancer genomes have been studied extensively to discover novel oncogenes and tumor-suppressor genes. Advances in sequencing technology have enabled the cost-effective detection of tumor genome and transcriptome mutation events at single-base-pair resolution; however, computational methods for predicting segmental regions of LOH in this context are not yet fully explored. Consequently, whole transcriptome, nucleotide- level resolution analysis of monoallelic expression patterns associated with LOH has not yet been undertaken in cancer. We developed a novel approach for inference of LOH from paired tumor/normal sequence data and applied it to a cohort of 23 triple-negative breast cancer (TNBC) genomes. Following extensive benchmarking experiments, we describe the nucleotide-resolution landscape of LOH in TNBC and assess the consequent effect of LOH on the transcriptomes of these tumors using RNA-seq-derived measurements of allele-specific expression. We show that the majority of monoallelic expression in the transcriptomes of triple-negative breast cancer can be explained by genomic regions of LOH and establish an upper bound for monoallelic expression that may be explained by other tumor-specific modifications such as epigenetics or mutations. Monoallelically expressed genes associated with LOH reveal that cell cycle, homologous recombination and actin-cytoskeletal functions are putatively disrupted by LOH in TNBC. Finally, we show how inference of LOH can be used to interpret allele frequencies of somatic mutations and postulate on temporal ordering of mutations in the evolutionary history of these tumors. [ABSTRACT FROM AUTHOR]