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Targeted spatial transcriptomics hold particular promise in analysis of complex tissues. Most such methods, however, measure only a limited panel of transcripts, which need to be selected in advance to inform on the cell types or processes being studied. A limitation of existing gene selection methods is that they rely on scRNA-seq data, ignoring platform effects between technologies. Here we describe gpsFISH, a computational method to perform gene selection through optimizing detection of known cell types. By modeling and adjusting for platform effects, gpsFISH outperforms other methods. Furthermore, gpsFISH can incorporate cell type hierarchies and custom gene preferences to accommodate diverse design requirements.

Cancers develop as a result of driver mutations1,2 that lead to clonal outgrowth and the evolution of disease3,4. The discovery and functional characterization of individual driver mutations are central aims of cancer research, and have elucidated myriad phenotypes5 and therapeutic vulnerabilities6. However, the serial genetic evolution of mutant cancer genes7,8 and the allelic context in which they arise is poorly understood in both common and rare cancer genes and tumour types. Here we find that nearly one in four human tumours contains a composite mutation of a cancer-associated gene, defined as two or more nonsynonymous somatic mutations in the same gene and tumour. Composite mutations are enriched in specific genes, have an elevated rate of use of less-common hotspot mutations acquired in a chronology driven in part by oncogenic fitness, and arise in an allelic configuration that reflects context-specific selective pressures. cis-acting composite mutations are hypermorphic in some genes in which dosage effects predominate (such as TERT), whereas they lead to selection of function in other genes (such as TP53). Collectively, composite mutations are driver alterations that arise from context- and allele-specific selective pressures that are dependent in part on gene and mutation function, and which lead to complex-often neomorphic-functions of biological and therapeutic importance.

SummaryTissue- and organism-level biological processes often involve coordinated action of multiple distinct cell types. Current computational methods for the analysis of single-cell RNA-sequencing (scRNA-seq) data, however, are not designed to capture co-variation of cell states across samples, in part due to the low number of biological samples in most scRNA-seq datasets. Recent advances in sample multiplexing have enabled population-scale scRNA-seq measurements of tens to hundreds of samples. To take advantage of such datasets, here we introduce a computational approach called single-cell Interpretable Tensor Decomposition (scITD). This method extracts “multicellular gene expression patterns” that vary across different biological samples. These patterns capture how changes in one cell type are connected to changes in other cell types. The multicellular patterns can be further associated with known covariates (e.g., disease, treatment, or technical batch effects) and used to stratify heterogeneous samples. We first validated the performance of scITD usingin vitroexperimental data and simulations. We then applied scITD to scRNA-seq data on peripheral blood mononuclear cells (PBMCs) from 115 patients with systemic lupus erythematosus and 56 healthy controls. We recapitulated a well-established pan-cell-type signature of interferon-signaling that was associated with the presence of anti-dsDNA autoantibodies and a disease activity index. We further identified a novel multicellular pattern that appears to potentiate renal involvement for patients with anti-dsDNA autoantibodies. This pattern was characterized by an expansion of activated memory B cells along with helper T cell activation and is predicted to be mediated by an increase in ICOSLG-ICOS interaction between monocytes and helper T cells. Finally, we applied scITD to two PBMC datasets from patients with COVID-19 and identified reproducible multicellular patterns that stratify patients by disease severity. Overall, scITD is a flexible method for exploring co-variation of cell states in multi-sample single-cell datasets, which can yield new insights into complex non-cell-autonomous dependencies that define and stratify disease.

Genome instability and aberrant alterations of transcriptional programs both play important roles in cancer. However, their relationship and relative contribution to tumor evolution and therapy resistance are not well-understood. Single-cell RNA sequencing (scRNA-seq) has the potential to investigate both genetic and non-genetic sources of tumor heterogeneity in a single assay. Here we present a computational method, Numbat, that integrates haplotype information obtained from population-based phasing with allele and expression signals to enhance detection of CNVs from scRNA-seq data. To resolve tumor clonal architecture, Numbat exploits the evolutionary relationships between subclones to iteratively infer the single-cell copy number profiles and tumor clonal phylogeny. Analyzing 21 tumor samples composed of multiple myeloma, breast, and thyroid cancers, we show that Numbat can accurately reconstruct the tumor copy number profile and precisely identify malignant cells in the tumor microenvironment. We uncover additional subclonal complexity contributed by allele-specific alterations, and identify genetic subpopulations with transcriptional signatures relevant to tumor progression and therapy resistance. We hope that the increased power to characterize genomic aberrations and tumor subclonal phylogenies provided by Numbat will help delineate contributions of genetic and non-genetic mechanisms in cancer.

Genome instability and aberrant alterations of transcriptional programs both play important roles in cancer. Single-cell RNA sequencing (scRNA-seq) has the potential to investigate both genetic and nongenetic sources of tumor heterogeneity in a single assay. Here we present a computational method, Numbat, that integrates haplotype information obtained from population-based phasing with allele and expression signals to enhance detection of copy number variations from scRNA-seq. Numbat exploits the evolutionary relationships between subclones to iteratively infer single-cell copy number profiles and tumor clonal phylogeny. Analysis of 22 tumor samples, including multiple myeloma, gastric, breast and thyroid cancers, shows that Numbat can reconstruct the tumor copy number profile and precisely identify malignant cells in the tumor microenvironment. We identify genetic subpopulations with transcriptional signatures relevant to tumor progression and therapy resistance. Numbat requires neither sample-matched DNA data nor a priori genotyping, and is applicable to a wide range of experimental settings and cancer types.

In October 2020, 62 scientists from nine nations worked together remotely in the Second Baylor College of Medicine & DNAnexus hackathon, focusing on different related topics on Structural Variation, Pan-genomes, and SARS-CoV-2 related research. The overarching focus was to assess the current status of the field and identify the remaining challenges. Furthermore, how to combine the strengths of the different interests to drive research and method development forward. Over the four days, eight groups each designed and developed new open-source methods to improve the identification and analysis of variations among species, including humans and SARS-CoV-2. These included improvements in SV calling, genotyping, annotations and filtering. Together with advancements in benchmarking existing methods. Furthermore, groups focused on the diversity of SARS-CoV-2. Daily discussion summary and methods are available publicly at https://github.com/collaborativebioinformatics provides valuable insights for both participants and the research community.

Genetic and epigenetic intra-tumoral heterogeneity cooperate to shape the evolutionary course of cancer1. Chronic lymphocytic leukaemia (CLL) is a highly informative model for cancer evolution as it undergoes substantial genetic diversification and evolution after therapy2,3. The CLL epigenome is also an important disease-defining feature4,5, and growing populations of cells in CLL diversify by stochastic changes in DNA methylation known as epimutations6. However, previous studies using bulk sequencing methods to analyse the patterns of DNA methylation were unable to determine whether epimutations affect CLL populations homogeneously. Here, to measure the epimutation rate at single-cell resolution, we applied multiplexed single-cell reduced-representation bisulfite sequencing to B cells from healthy donors and patients with CLL. We observed that the common clonal origin of CLL results in a consistently increased epimutation rate, with low variability in the cell-to-cell epimutation rate. By contrast, variable epimutation rates across healthy B cells reflect diverse evolutionary ages across the trajectory of B cell differentiation, consistent with epimutations serving as a molecular clock. Heritable epimutation information allowed us to reconstruct lineages at high-resolution with single-cell data, and to apply this directly to patient samples. The CLL lineage tree shape revealed earlier branching and longer branch lengths than in normal B cells, reflecting rapid drift after the initial malignant transformation and a greater proliferative history. Integration of single-cell bisulfite sequencing analysis with single-cell transcriptomes and genotyping confirmed that genetic subclones mapped to distinct clades, as inferred solely on the basis of epimutation information. Finally, to examine potential lineage biases during therapy, we profiled serial samples during ibrutinib-associated lymphocytosis, and identified clades of cells that were preferentially expelled from the lymph node after treatment, marked by distinct transcriptional profiles. The single-cell integration of genetic, epigenetic and transcriptional information thus charts the lineage history of CLL and its evolution with therapy. A single-cell approach is used to follow the heritable stochastic changes to DNA methylation that occur in primary chronic lymphocytic leukaemia and healthy B cells, allowing the tracing of cell lineage histories and evolution during treatment with ibrutinib.

Viruses represent important test cases for data federation due to their genome size and the rapid increase in sequence data in publicly available databases. However, some consequences of previously decentralized (unfederated) data are lack of consensus or comparisons between feature annotations. Unifying or displaying alternative annotations should be a priority both for communities with robust entry representation and for nascent communities with burgeoning data sources. To this end, during this three-day continuation of the Virus Hunting Toolkit codeathon series (VHT-2), a new integrated and federated viral index was elaborated. This Federated Index of Viral Experiments (FIVE) integrates pre-existing and novel functional and taxonomy annotations and virus-host pairings. Variability in the context of viral genomic diversity is often overlooked in virus databases. As a proof-of-concept, FIVE was the first attempt to include viral genome variation for HIV, the most well-studied human pathogen, through viral genome diversity graphs. As per the publication of this manuscript, FIVE is the first implementation of a virus-specific federated index of such scope. FIVE is coded in BigQuery for optimal access of large quantities of data and is publicly accessible. Many projects of database or index federation fail to provide easier alternatives to access or query information. To this end, a Python API query system was developed to enhance the accessibility of FIVE. ispartof: VIRUSES-BASEL vol:12 issue:12 ispartof: location:Switzerland status: published

In March 2019, 45 scientists and software engineers from around the world converged at the University of California, Santa Cruz for the first pangenomics codeathon. The purpose of the meeting was to propose technical specifications and standards for a usable human pangenome as well as to build relevant tools for genome graph infrastructures. During the meeting, the group held several intense and productive discussions covering a diverse set of topics, including advantages of graph genomes over a linear reference representation, design of new methods that can leverage graph-based data structures, and novel visualization and annotation approaches for pangenomes. Additionally, the participants self-organized themselves into teams that worked intensely over a three-day period to build a set of pipelines and tools for specific pangenomic applications. A summary of the questions raised and the tools developed are reported in this manuscript.

Genome graphs are emerging as an important novel approach to the analysis of high-throughput human sequencing data. By explicitly representing genetic variants and alternative haplotypes in a mappable data structure, they can enable the improved analysis of structurally variable and hyperpolymorphic regions of the genome. In most existing approaches, graphs are constructed from variant call sets derived from short-read sequencing. As long-read sequencing becomes more cost-effective and enables de novo assembly for increasing numbers of whole genomes, a method for the direct construction of a genome graph from sets of assembled human genomes would be desirable. Such assembly-based genome graphs would encompass the wide spectrum of genetic variation accessible to long-read-based de novo assembly, including large structural variants and divergent haplotypes. Here we present NovoGraph, a method for the construction of a human genome graph directly from a set of de novo assemblies. NovoGraph constructs a genome-wide multiple sequence alignment of all input contigs and creates a graph by merging the input sequences at positions that are both homologous and sequence-identical. NovoGraph outputs resulting graphs in VCF format that can be loaded into third-party genome graph toolkits. To demonstrate NovoGraph, we construct a genome graph with 23,478,835 variant sites and 30,582,795 variant alleles from de novo assemblies of seven ethnically diverse human genomes (AK1, CHM1, CHM13, HG003, HG004, HX1, NA19240). Initial evaluations show that mapping against the constructed graph reduces the average mismatch rate of reads from sample NA12878 by approximately 0.2%, albeit at a slightly increased rate of reads that remain unmapped.

Introduction Various classes of genomic alterations can now be robustly detected from targeted Next Generation Sequencing (NGS)-based tumor profiling assays. Despite recent advances in genome-directed cancer therapies and lineage-agnostic basket clinical trials, tissue of origin remains a critical determinant of tumor biology and therapeutic sensitivity. Therefore, efficiently harnessing the mutational information to predict tissue of origin from NGS data can further aid diagnosis and treatment. Methods We have developed a novel random forest machine learning classifier that infers tissue of origin from the mutational features of each tumor NGS profile. We trained and validated the model using prospective sequencing data from >30,000 patients profiled by MSK-IMPACT, a custom FDA-authorized clinical sequencing assay. An initial version of this classifier trained on a smaller cohort, presented previously, has been further optimized to incorporate additional tumor types, genomic features, mutational signatures, and accessibility features for clinicians. Altogether, the model evaluates a comprehensive range of mutational features to generate predictions. In addition, we developed a framework for the prospective clinical implementation of our method that allows for extension to the expanding MSK-IMPACT cohort and utilization at the point of care. Results Overall, we predicted the correct cancer type in 74% of cases, with nearly half of cases predicted with high confidence (>95%). In order to make this tool accessible to pathologists for real-time diagnostic and treatment decisions, we also have implemented APIs that transmit our classifier predictions to the cBioPortal for Cancer Genomics and the MPath console from MSK Molecular Diagnostics Service. With the additional samples and genomic features, our model predictions have been improved and are considered during clinical review and sign-out. This practice has brought about critical diagnostic changes and orthogonal validation in several cases. In one case, a patient referred to MSK with a misdiagnosis of metastatic breast cancer to the bladder underwent MSK-IMPACT sequencing for her primary breast tumor and bladder lesion. The model predictions, confirmed by orthogonal testing, classified two lesions as independent primaries of breast and bladder origins, leading to significant changes in treatment regimens. Conclusion Our work delineates the framework of utilizing and optimizing machine learning models on NGS-based sequencing data to aid diagnosis and treatment. The same framework can be applied to cell-free DNA sequencing, which will capture tumor spatial heterogeneity and improve classifier performance. These results indicate that leveraging machine learning to predict tissue of origin complements conventional histologic review to provide integrated diagnoses, often with critical therapeutic implications. Citation Format: Youyun Zheng, Alexander V. Penson, Niedzica Camacho, Evan Biederstedt, Ahmet Zehir, Cyriac Kandoth, Aijazuddin Syed, Anna M. Varghese, Hikmat A. Al-Ahmadie, Nikolaus Schultz, Marc Ladanyi, David B. Solit, David S. Klimstra, David Hyman, Barry Taylor, Michael F. Berger. Clinical validation of a genomics-based classifier to predict tissue of origin from targeted tumor sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1672.

Genetic and epigenetic heterogeneity of cancer cells fundamentally shapes cancer progression and relapse. In chronic lymphocytic leukemia (CLL), we previously reported that intra-leukemic epigenetic diversity in DNA methylation (DNAme) follows a stochastic pattern reminiscent of genetic 'trial and error' in cancer evolution. We measured stochastic DNAme from bulk sequencing by observing the proportion of discordantly methylated sequencing reads (PDR), and found that higher PDR was associated with greater cell-to-cell transcriptional heterogeneity and adverse clinical outcome. However, bulk DNAme sequencing does not allow to phase stochastic DNAme across distant genomic loci for a single cell, as it is limited to length of a short sequencing read. Thus, it can only provide the average PDR for a population of cells, rather than the cell-to-cell variation in this important epigenetic feature. Therefore, to define stochastic DNAme changes at the single cell level, we optimized a multiplexed single cell reduced representation bisulfite sequencing (MscRRBS) protocol to allow high-throughput single cell DNAme sequencing. This protocol significantly improves scalability by multiplexing cells with shorter inline-barcodes at an early stage and utilizing SPRI beads purification to eliminate adapter-dimers. We applied MscRRBS to 393 single CD19pos B cells from two healthy volunteers and 111 single cells from a CLL sample. 88% of cells were evaluable with greater than 100,000 covered CpGs (average of 436,230 CpGs per cell). We achieved bisulfite conversion rates of 99.7%+/-0.0001 (mean+/- SD), without a significant reduction in coverage. A downsampling analysis showed that 2.1 million reads per cell provided 85% of CpG coverage with only marginal increase in coverage with further sequencing. Biallelic coverage was observed in 4.6+/-2% of germline SNPs. With MscRRBS, we measure the PDR of each individual cell. As expected from our prior bulk RRBS analysis, we found that the average PDR across cells was higher in CLL compared with B cells from healthy adult volunteers (0.39+/-0.01 vs. 0.26+/-0.08, P The higher uniformity of PDR in CLL may reflect the relationship between epi-mutation rate (measured through PDR) and the evolutionary age of the cells. As additional stochastic DNAme changes are generated with each generation, we hypothesize that PDR estimates the number of generations in a cells' evolutionary history. Thus, CLL cells have uniformly high PDR reflecting a high but uniform number of generations in their history, consistent with a single common cell of origin. In contrast, normal B cells have diverse histories with newly formed naive cells intermixed with long-lived memory cells. Consistent with this hypothesis, healthy donor CD27neg cells showed lower PDR and less PDR cell-to-cell variation compared with CD27pos cells (mean absolute PDR difference 0.008+/-0.01 vs. 0.03+/-0.03, respectively, P As MscRRBS allows complete phasing of DNAme across distant genomic loci, we further calculated the odds ratio of concordance in methylation state between any two neighboring CpG as a function of their genomic distance, compared with any two randomly paired CpGs. Through this procedure we quantified the properties of DNAme concordance decay and uncovered different genomic scales of stochastically disordered methylation. Finally, the MscRRBS also allowed to reconstruct phylo(epi)genetic relationship between the cells, and provided accurate estimate of the rates of stochastic epi-mutation across the genome. Thus, single cell DNAme is a powerful novel tool to define epigenetic diversification and its impact on CLL evolution. Disclosures No relevant conflicts of interest to declare.