Tree inference for single-cell data

Prolonged persistence of mutagenic DNA lesions in somatic cells

DNA is subject to continual damage, leaving each cell with thousands of individual DNA lesions at any given moment1-3. The efficiency of DNA repair means that most known classes of lesion have a half-life of minutes to hours3,4, but the extent to which DNA damage can persist for longer durations remains unknown. Here, using high-resolution phylogenetic trees from 89 donors, we identified mutations arising from 818 DNA lesions that persisted across multiple cell cycles in normal human stem cells from blood, liver and bronchial epithelium5-12. Persistent DNA lesions occurred at increased rates, with distinctive mutational signatures, in donors exposed to tobacco or chemotherapy, suggesting that they can arise from exogenous mutagens. In haematopoietic stem cells, persistent DNA lesions, probably from endogenous sources, generated the characteristic mutational signature SBS1913; occurred steadily throughout life, including in utero; and endured for 2.2 years on average, with 15-25% of lesions lasting at least 3 years. We estimate that on average, a haematopoietic stem cell has approximately eight such lesions at any moment in time, half of which will generate a mutation with each cell cycle. Overall, 16% of mutations in blood cells are attributable to SBS19, and similar proportions of driver mutations in blood cancers exhibit this signature. These data indicate the existence of a family of DNA lesions that arise from endogenous and exogenous mutagens, are present in low numbers per genome, persist for months to years, and can generate a substantial fraction of the mutation burden of somatic cells.

The Bayesian Phylogenetic Bootstrap and its Application to Short Trees and Branches

Felsenstein's bootstrap is the most commonly used method to measure branch support in phylogenetics. Current sequencing technologies can result in massive sampling of taxa (e.g. SARS-CoV-2). In this case, the sequences are very similar, the trees are short, and the branches correspond to a small number of mutations (possibly 0). Nevertheless, these trees contain a strong signal, with unresolved parts but a low rate of false branches. With such data, Felsenstein's bootstrap is not satisfactory. Due to the frequentist nature of bootstrap sampling, the expected support of a branch corresponding to a single mutation is ∼63%, even though it is highly likely to be correct. Here, we propose a Bayesian version of the phylogenetic bootstrap in which sites are assigned uninformative prior probabilities. The branch support can then be interpreted as a posterior probability. We do not view the alignment as a small subsample of a large sample of sites, but rather as containing all available information (e.g. as with complete viral genomes, which are becoming routine). We give formulas for expected supports under the assumption of perfect phylogeny, in both the frequentist and Bayesian frameworks, where a branch corresponding to a single mutation now has an expected support of ∼90%. Simulations show that these theoretical results are robust to realistic data. Analyses on low-homoplasy viral and nonviral datasets show that Bayesian bootstrap support is easier to interpret, with high supports for branches very likely to be correct. As homoplasy increases, the two supports become closer and strongly correlated.

Multiomic single cell sequencing identifies stemlike nature of mixed phenotype acute leukemia

Despite recent work linking mixed phenotype acute leukemia (MPAL) to certain genetic lesions, specific driver mutations remain undefined for a significant proportion of patients and no genetic subtype is predictive of clinical outcomes. Moreover, therapeutic strategy for MPAL remains unclear, and prognosis is overall poor. We performed multiomic single cell profiling of 14 newly diagnosed adult MPAL patients to characterize the inter- and intra-tumoral transcriptional, immunophenotypic, and genetic landscapes of MPAL. We show that neither genetic profile nor transcriptome reliably correlate with specific MPAL immunophenotypes. Despite this, we find that MPAL blasts express a shared stem cell-like transcriptional profile indicative of high differentiation potential. Patients with the highest differentiation potential demonstrate inferior survival in our dataset. A gene set score, MPAL95, derived from genes highly enriched in the most stem-like MPAL cells, is applicable to bulk RNA sequencing data and is predictive of survival in an independent patient cohort, suggesting a potential strategy for clinical risk stratification.

Isotype-aware inference of B cell clonal lineage trees from single-cell sequencing data

Single-cell RNA sequencing (scRNA-seq) enables comprehensive characterization of the micro-evolutionary processes of B cells during an adaptive immune response, capturing features of somatic hypermutation (SHM) and class switch recombination (CSR). Existing phylogenetic approaches for reconstructing B cell evolution have primarily focused on the SHM process alone. Here, we present tree inference of B cell clonal lineages (TRIBAL), an algorithm designed to optimally reconstruct the evolutionary history of B cell clonal lineages undergoing both SHM and CSR from scRNA-seq data. Through simulations, we demonstrate that TRIBAL produces more comprehensive and accurate B cell lineage trees compared to existing methods. Using real-world datasets, TRIBAL successfully recapitulates expected biological trends in a model affinity maturation system while reconstructing evolutionary histories with more parsimonious class switching than state-of-the-art methods. Thus, TRIBAL significantly improves B cell lineage tracing, useful for modeling vaccine responses, disease progression, and the identification of therapeutic antibodies.

Mitoclone2: an R package for elucidating clonal structure in single-cell RNA-sequencing data using mitochondrial variants

Clonal cell population dynamics play a critical role in both disease and development. Due to high mitochondrial mutation rates under both healthy and diseased conditions, mitochondrial genomic variability is a particularly useful resource in facilitating the identification of clonal population structure. Here we present mitoClone2, an all-inclusive R package allowing for the identification of clonal populations through integration of mitochondrial heteroplasmic variants discovered from single-cell sequencing experiments. Our package streamlines the investigation of this phenomenon by providing: built-in compatibility with commonly used tools for the delineation of clonal structure, the ability to directly use multiplexed BAM files as input, annotations for both human and mouse mitochondrial genomes, and helper functions for calling, filtering, clustering, and visualizing variants.

Applications of single-cell technologies in drug discovery for tumor treatment

Single-cell technologies have been known as advanced and powerful tools to study tumor biological systems at the single-cell resolution and are playing increasingly critical roles in multiple stages of drug discovery and development. Specifically, single-cell technologies can promote the discovery of drug targets, help high-throughput screening at single-cell level, and contribute to pharmacokinetic studies of anti-tumor drugs. Emerging single-cell analysis technologies have been developed to further integrating multidimensional single-cell molecular features, expanding the scale of single-cell data, profiling phenotypic impact of genes in single cell, and providing full-length coverage single-cell sequencing. In this review, we systematically summarized the applications of single-cell technologies in various sections of drug discovery for tumor treatment, including target identification, high-throughput drug screening, and pharmacokinetic evaluation and highlighted emerging single-cell technologies in providing in-depth understanding of tumor biology. Single-cell-technology-based drug discovery is expected to further optimize therapeutic strategies and improve clinical outcomes of tumor patients.

LNK/SH2B3 as a novel driver in juvenile myelomonocytic leukemia

Mutations in five canonical Ras pathway genes (NF1, NRAS, KRAS, PTPN11 and CBL) are detected in nearly 90% of patients with juvenile myelomonocytic leukemia (JMML), a frequently fatal malignant neoplasm of early childhood. In this report, we describe seven patients diagnosed with SH2B3-mutated JMML, including five patients who were found to have initiating, loss-of-function mutations in the gene. SH2B3 encodes the adaptor protein LNK, a negative regulator of normal hematopoiesis upstream of the Ras pathway. These mutations were identified to be germline, somatic or a combination of both. Loss of function of LNK, which has been observed in other myeloid malignancies, results in abnormal proliferation of hematopoietic cells due to cytokine hypersensitivity and activation of the JAK/STAT signaling pathway. In vitro studies of induced pluripotent stem cell-derived JMML-like hematopoietic progenitor cells also demonstrated sensitivity of SH2B3-mutated hematopoietic progenitor cells to JAK inhibition. Lastly, we describe two patients with JMML and SH2B3 mutations who were treated with the JAK1/2 inhibitor ruxolitinib. This report expands the spectrum of initiating mutations in JMML and raises the possibility of targeting the JAK/STAT pathway in patients with SH2B3 mutations.

Selective advantage of mutant stem cells in human clonal hematopoiesis is associated with attenuated response to inflammation and aging

Clonal hematopoiesis (CH) arises when hematopoietic stem cells (HSCs) acquire mutations, most frequently in the DNMT3A and TET2 genes, conferring a competitive advantage through mechanisms that remain unclear. To gain insight into how CH mutations enable gradual clonal expansion, we used single-cell multi-omics with high-fidelity genotyping on human CH bone marrow (BM) samples. Most of the selective advantage of mutant cells occurs within HSCs. DNMT3A- and TET2-mutant clones expand further in early progenitors, while TET2 mutations accelerate myeloid maturation in a dose-dependent manner. Unexpectedly, both mutant and non-mutant HSCs from CH samples are enriched for inflammatory and aging transcriptomic signatures, compared with HSCs from non-CH samples, revealing a non-cell-autonomous effect. However, DNMT3A- and TET2-mutant HSCs have an attenuated inflammatory response relative to wild-type HSCs within the same sample. Our data support a model whereby CH clones are gradually selected because they are resistant to the deleterious impact of inflammation and aging.

A Probabilistic Approach to Estimate the Temporal Order of Pathway Mutations Accounting for Intra-Tumor Heterogeneity

The development of cancer involves the accumulation of somatic mutations in several essential biological pathways. Delineating the temporal order of pathway mutations during tumorigenesis is crucial for comprehending the biological mechanisms underlying cancer development and identifying potential targets for therapeutic intervention. Several computational and statistical methods have been introduced for estimating the order of somatic mutations based on mutation profile data from a cohort of patients. However, one major issue of current methods is that they do not take into account intra-tumor heterogeneity (ITH), which limits their ability to accurately discern the order of pathway mutations. To address this problem, we propose PATOPAI, a probabilistic approach to estimate the temporal order of mutations at the pathway level by incorporating ITH information as well as pathway and functional annotation information of mutations. PATOPAI uses a maximum likelihood approach to estimate the probability of pathway mutational events occurring in a specific sequence, wherein it focuses on the orders that are consistent with the phylogenetic structure of the tumors. Applications to whole exome sequencing data from The Cancer Genome Atlas (TCGA) illustrate our method's ability to recover the temporal order of pathway mutations in several cancer types.

Weighted centroid trees: a general approach to summarize phylogenies in single-labeled tumor mutation tree inference

MOTIVATION

Tumor trees, which depict the evolutionary process of cancer, provide a backbone for discovering recurring evolutionary processes in cancer. While they are not the primary information extracted from genomic data, they are valuable for this purpose. One such extraction method involves summarizing multiple trees into a single representative tree, such as consensus trees or supertrees.

RESULTS

We define the "weighted centroid tree problem" to find the centroid tree of a set of single-labeled rooted trees through the following steps: (i) mapping the given trees into the Euclidean space, (ii) computing the weighted centroid matrix of the mapped trees, and (iii) finding the nearest mapped tree (NMTP) to the centroid matrix. We show that this setup encompasses previously studied parent-child and ancestor-descendent metrics as well as the GraPhyC and TuELiP consensus tree algorithms. Moreover, we show that, while the NMTP problem is polynomial-time solvable for the adjacency embedding, it is NP-hard for ancestry and distance mappings. We introduce integer linear programs for NMTP in different setups where we also provide a new algorithm for the case of ancestry embedding called 2-AncL2, that uses a novel weighting scheme for ancestry signals. Our experimental results show that 2-AncL2 has a superior performance compared to available consensus tree algorithms. We also illustrate our setup's application on providing representative trees for a large real breast cancer dataset, deducing that the cluster centroid trees summarize reliable evolutionary information about the original dataset.

AVAILABILITY AND IMPLEMENTATION

https://github.com/vasei/WAncILP.

Mutation order in acute myeloid leukemia identifies uncommon patterns of evolution and illuminates phenotypic heterogeneity

Acute myeloid leukemia (AML) has a poor prognosis and a heterogeneous mutation landscape. Although common mutations are well-studied, little research has characterized how the sequence of mutations relates to clinical features. Using published, single-cell DNA sequencing data from three institutions, we compared clonal evolution patterns in AML to patient characteristics, disease phenotype, and outcomes. Mutation trees, which represent the order of select mutations, were created for 207 patients from targeted panel sequencing data using 1 639 162 cells, 823 mutations, and 275 samples. In 224 distinct orderings of mutated genes, mutations related to DNA methylation typically preceded those related to cell signaling, but signaling-first cases did occur, and had higher peripheral cell counts, increased signaling mutation homozygosity, and younger patient age. Serial sample analysis suggested that NPM1 and DNA methylation mutations provide an advantage to signaling mutations in AML. Interestingly, WT1 mutation evolution shared features with signaling mutations, such as WT1-early being proliferative and occurring in younger individuals, trends that remained in multivariable regression. Some mutation orderings had a worse prognosis, but this was mediated by unfavorable mutations, not mutation order. These findings add a dimension to the mutation landscape of AML, identifying uncommon patterns of leukemogenesis and shedding light on heterogeneous phenotypes.

Oncotree2vec - a method for embedding and clustering of tumor mutation trees

MOTIVATION

Understanding the genomic heterogeneity of tumors is an important task in computational oncology, especially in the context of finding personalized treatments based on the genetic profile of each patient's tumor. Tumor clustering that takes into account the temporal order of genetic events, as represented by tumor mutation trees, is a powerful approach for grouping together patients with genetically and evolutionarily similar tumors and can provide insights into discovering tumor subtypes, for more accurate clinical diagnosis and prognosis.

RESULTS

Here, we propose oncotree2vec, a method for clustering tumor mutation trees by learning vector representations of mutation trees that capture the different relationships between subclones in an unsupervised manner. Learning low-dimensional tree embeddings facilitates the visualization of relations between trees in large cohorts and can be used for downstream analyses, such as deep learning approaches for single-cell multi-omics data integration. We assessed the performance and the usefulness of our method in three simulation studies and on two real datasets: a cohort of 43 trees from six cancer types with different branching patterns corresponding to different modes of spatial tumor evolution and a cohort of 123 AML mutation trees.

AVAILABILITY AND IMPLEMENTATION

https://github.com/cbg-ethz/oncotree2vec.

Joint inference of cell lineage and mitochondrial evolution from single-cell sequencing data

MOTIVATION

Eukaryotic cells contain organelles called mitochondria that have their own genome. Most cells contain thousands of mitochondria which replicate, even in nondividing cells, by means of a relatively error-prone process resulting in somatic mutations in their genome. Because of the higher mutation rate compared to the nuclear genome, mitochondrial mutations have been used to track cellular lineage, particularly using single-cell sequencing that measures mitochondrial mutations in individual cells. However, existing methods to infer the cell lineage tree from mitochondrial mutations do not model "heteroplasmy," which is the presence of multiple mitochondrial clones with distinct sets of mutations in an individual cell. Single-cell sequencing data thus provide a mixture of the mitochondrial clones in individual cells, with the ancestral relationships between these clones described by a mitochondrial clone tree. While deconvolution of somatic mutations from a mixture of evolutionarily related genomes has been extensively studied in the context of bulk sequencing of cancer tumor samples, the problem of mitochondrial deconvolution has the additional constraint that the mitochondrial clone tree must be concordant with the cell lineage tree.

RESULTS

We formalize the problem of inferring a concordant pair of a mitochondrial clone tree and a cell lineage tree from single-cell sequencing data as the Nested Perfect Phylogeny Mixture (NPPM) problem. We derive a combinatorial characterization of the solutions to the NPPM problem, and formulate an algorithm, MERLIN, to solve this problem exactly using a mixed integer linear program. We show on simulated data that MERLIN outperforms existing methods that do not model mitochondrial heteroplasmy nor the concordance between the mitochondrial clone tree and the cell lineage tree. We use MERLIN to analyze single-cell whole-genome sequencing data of 5220 cells of a gastric cancer cell line and show that MERLIN infers a more biologically plausible cell lineage tree and mitochondrial clone tree compared to existing methods.

AVAILABILITY AND IMPLEMENTATION

https://github.com/raphael-group/MERLIN.

Assessing the merits: an opinion on the effectiveness of simulation techniques in tumor subclonal reconstruction

Summary

Neoplastic tumors originate from a single cell, and their evolution can be traced through lineages characterized by mutations, copy number alterations, and structural variants. These lineages are reconstructed and mapped onto evolutionary trees with algorithmic approaches. However, without ground truth benchmark sets, the validity of an algorithm remains uncertain, limiting potential clinical applicability. With a growing number of algorithms available, there is urgent need for standardized benchmark sets to evaluate their merits. Benchmark sets rely on in silico simulations of tumor sequence, but there are no accepted standards for simulation tools, presenting a major obstacle to progress in this field.

Availability and implementation

All analysis done in the paper was based on publicly available data from the publication of each accessed tool.

Resolving tumor evolution: a phylogenetic approach

The evolutionary dynamics of cancer, characterized by its profound heterogeneity, demand sophisticated tools for a holistic understanding. This review delves into tumor phylogenetics, an essential approach bridging evolutionary biology with oncology, offering unparalleled insights into cancer's evolutionary trajectory. We provide an overview of the workflow, encompassing study design, data acquisition, and phylogeny reconstruction. Notably, the integration of diverse data sets emerges as a transformative step, enhancing the depth and breadth of evolutionary insights. With this integrated perspective, tumor phylogenetics stands poised to redefine our understanding of cancer evolution and influence therapeutic strategies.

Reconstructing phylogenetic trees from genome-wide somatic mutations in clonal samples

Phylogenetic trees are a powerful means to display the evolutionary history of species, pathogens and, more recently, individual cells of the human body. Whole-genome sequencing of laser capture microdissections or expanded stem cells has allowed the discovery of somatic mutations in clones, which can be used as natural barcodes to reconstruct the developmental history of individual cells. Here we describe Sequoia, our pipeline to reconstruct lineage trees from clones of normal cells. Candidate somatic mutations are called against the human reference genome and filtered to exclude germline mutations and artifactual variants. These filtered somatic mutations form the basis for phylogeny reconstruction using a maximum parsimony framework. Lastly, we use a maximum likelihood framework to explicitly map mutations to branches in the phylogenetic tree. The resulting phylogenies can then serve as a basis for many subsequent analyses, including investigating embryonic development, tissue dynamics in health and disease, and mutational signatures. Sequoia can be readily applied to any clonal somatic mutation dataset, including single-cell DNA sequencing datasets, using the commands and scripts provided. Moreover, Sequoia is highly flexible and can be easily customized. Typically, the runtime of the core script ranges from minutes to an hour for datasets with a moderate number (50,000-150,000) of variants. Competent bioinformatic skills, including in-depth knowledge of the R programming language, are required. A high-performance computing cluster (one that is capable of running mutation-calling algorithms and other aspects of the analysis at scale) is also required, especially if handling large datasets.

Scuphr: A probabilistic framework for cell lineage tree reconstruction

Cell lineage tree reconstruction methods are developed for various tasks, such as investigating the development, differentiation, and cancer progression. Single-cell sequencing technologies enable more thorough analysis with higher resolution. We present Scuphr, a distance-based cell lineage tree reconstruction method using bulk and single-cell DNA sequencing data from healthy tissues. Common challenges of single-cell DNA sequencing, such as allelic dropouts and amplification errors, are included in Scuphr. Scuphr computes the distance between cell pairs and reconstructs the lineage tree using the neighbor-joining algorithm. With its embarrassingly parallel design, Scuphr can do faster analysis than the state-of-the-art methods while obtaining better accuracy. The method's robustness is investigated using various synthetic datasets and a biological dataset of 18 cells.

Canopy2: tumor phylogeny inference by bulk DNA and single-cell RNA sequencing

Tumors are comprised of a mixture of distinct cell populations that differ in terms of genetic makeup and function. Such heterogeneity plays a role in the development of drug resistance and the ineffectiveness of targeted cancer therapies. Insight into this complexity can be obtained through the construction of a phylogenetic tree, which illustrates the evolutionary lineage of tumor cells as they acquire mutations over time. We propose Canopy2, a Bayesian framework that uses single nucleotide variants derived from bulk DNA and single-cell RNA sequencing to infer tumor phylogeny and conduct mutational profiling of tumor subpopulations. Canopy2 uses Markov chain Monte Carlo methods to sample from a joint probability distribution involving a mixture of binomial and beta-binomial distributions, specifically chosen to account for the sparsity and stochasticity of the single-cell data. Canopy2 demystifies the sources of zeros in the single-cell data and separates zeros categorized as non-cancerous (cells without mutations), stochastic (mutations not expressed due to bursting), and technical (expressed mutations not picked up by sequencing). Simulations demonstrate that Canopy2 consistently outperforms competing methods and reconstructs the clonal tree with high fidelity, even in situations involving low sequencing depth, poor single-cell yield, and highly-advanced and polyclonal tumors. We further assess the performance of Canopy2 through application to breast cancer and glioblastoma data, benchmarking against existing methods. Canopy2 is an open-source R package available at https://github.com/annweideman/canopy2.

Evaluation of simulation methods for tumor subclonal reconstruction

Most neoplastic tumors originate from a single cell, and their evolution can be genetically traced through lineages characterized by common alterations such as small somatic mutations (SSMs), copy number alterations (CNAs), structural variants (SVs), and aneuploidies. Due to the complexity of these alterations in most tumors and the errors introduced by sequencing protocols and calling algorithms, tumor subclonal reconstruction algorithms are necessary to recapitulate the DNA sequence composition and tumor evolution in silico. With a growing number of these algorithms available, there is a pressing need for consistent and comprehensive benchmarking, which relies on realistic tumor sequencing generated by simulation tools. Here, we examine the current simulation methods, identifying their strengths and weaknesses, and provide recommendations for their improvement. Our review also explores potential new directions for research in this area. This work aims to serve as a resource for understanding and enhancing tumor genomic simulations, contributing to the advancement of the field.

Single Nucleotide Polymorphism (SNP) and Antibody-based Cell Sorting (SNACS): A tool for demultiplexing single-cell DNA sequencing data

Motivation

Recently, single-cell DNA sequencing (scDNA-seq) and multi-modal profiling with the addition of cell-surface antibodies (scDAb-seq) have provided key insights into cancer heterogeneity. Scaling these technologies across large patient cohorts, however, is cost and time prohibitive. Multiplexing, in which cells from unique patients are pooled into a single experiment, offers a possible solution. While multiplexing methods exist for scRNAseq, accurate demultiplexing in scDNAseq remains an unmet need.

Results

Here, we introduce SNACS: Single-Nucleotide Polymorphism (SNP) and Antibody-based Cell Sorting. SNACS relies on a combination of patient-level cell-surface identifiers and natural variation in genetic polymorphisms to demultiplex scDNAseq data. We demonstrated the performance of SNACS on a dataset consisting of multi-sample experiments from patients with leukemia where we knew truth from single-sample experiments from the same patients. Using SNACS, accuracy ranged from 0.948 - 0.991 vs 0.552 - 0.934 using demultiplexing methods from the single-cell literature.

Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review

The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields-Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy-are discussed.

Inferring Markov Chains to Describe Convergent Tumor Evolution With CIMICE

The field of tumor phylogenetics focuses on studying the differences within cancer cell populations. Many efforts are done within the scientific community to build cancer progression models trying to understand the heterogeneity of such diseases. These models are highly dependent on the kind of data used for their construction, therefore, as the experimental technologies evolve, it is of major importance to exploit their peculiarities. In this work we describe a cancer progression model based on Single Cell DNA Sequencing data. When constructing the model, we focus on tailoring the formalism on the specificity of the data. We operate by defining a minimal set of assumptions needed to reconstruct a flexible DAG structured model, capable of identifying progression beyond the limitation of the infinite site assumption. Our proposal is conservative in the sense that we aim to neither discard nor infer knowledge which is not represented in the data. We provide simulations and analytical results to show the features of our model, test it on real data, show how it can be integrated with other approaches to cope with input noise. Moreover, our framework can be exploited to produce simulated data that follows our theoretical assumptions. Finally, we provide an open source R implementation of our approach, called CIMICE, that is publicly available on BioConductor.

A protocol for simultaneous high-sensitivity genotyping and chromatin accessibility profiling in single cells

Single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq) resolves the heterogeneity of epigenetic states across cells but does not typically capture exonic mutations, which limits our knowledge of how somatic mutations alter chromatin landscapes. Here, we present a plate-based approach coupling high-sensitivity genotyping of genomic loci with high-content scATAC-seq libraries from the same single cells. We first describe steps for optimization of genotyping primers, followed by detailed guidance on the preparation of both scATAC-seq and single-cell genotyping libraries, fully automated on high-throughput liquid handling platforms. For complete details on the use and execution of this protocol, please refer to Turkalj, Jakobsen et al.1.

Quartets enable statistically consistent estimation of cell lineage trees under an unbiased error and missingness model

Cancer progression and treatment can be informed by reconstructing its evolutionary history from tumor cells. Although many methods exist to estimate evolutionary trees (called phylogenies) from molecular sequences, traditional approaches assume the input data are error-free and the output tree is fully resolved. These assumptions are challenged in tumor phylogenetics because single-cell sequencing produces sparse, error-ridden data and because tumors evolve clonally. Here, we study the theoretical utility of methods based on quartets (four-leaf, unrooted phylogenetic trees) in light of these barriers. We consider a popular tumor phylogenetics model, in which mutations arise on a (highly unresolved) tree and then (unbiased) errors and missing values are introduced. Quartets are then implied by mutations present in two cells and absent from two cells. Our main result is that the most probable quartet identifies the unrooted model tree on four cells. This motivates seeking a tree such that the number of quartets shared between it and the input mutations is maximized. We prove an optimal solution to this problem is a consistent estimator of the unrooted cell lineage tree; this guarantee includes the case where the model tree is highly unresolved, with error defined as the number of false negative branches. Lastly, we outline how quartet-based methods might be employed when there are copy number aberrations and other challenges specific to tumor phylogenetics.

ConDoR: tumor phylogeny inference with a copy-number constrained mutation loss model

A tumor contains a diverse collection of somatic mutations that reflect its past evolutionary history and that range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). However, no current single-cell DNA sequencing (scDNA-seq) technology produces accurate measurements of both SNVs and CNAs, complicating the inference of tumor phylogenies. We introduce a new evolutionary model, the constrained k-Dollo model, that uses SNVs as phylogenetic markers but constrains losses of SNVs according to clusters of cells. We derive an algorithm, ConDoR, that infers phylogenies from targeted scDNA-seq data using this model. We demonstrate the advantages of ConDoR on simulated and real scDNA-seq data.

Convergent evolution and B-cell recirculation in germinal centers in a human lymph node

Germinal centers (GCs) play a central role in generating an effective immune response against infectious pathogens, and failures in their regulating mechanisms can lead to the development of autoimmune diseases and cancer. Although previous works study experimental systems of the immune response with mouse models that are immunized with specific antigens, our study focused on a real-life situation, with an ongoing GC response in a human lymph node (LN) involving multiple asynchronized GCs reacting simultaneously to unknown antigens. We combined laser capture microdissection of individual GCs from human LN with next-generation repertoire sequencing to characterize individual GCs as distinct evolutionary spaces. In line with well-characterized GC responses in mice, elicited by immunization with model antigens, we observe a heterogeneous clonal diversity across individual GCs from the same human LN. Still, we identify shared clones in several individual GCs, and phylogenetic tree analysis combined with paratope modeling suggest the re-engagement and rediversification of B-cell clones across GCs and expanded clones exhibiting shared antigen responses across distinct GCs, indicating convergent evolution of the GCs.

Phertilizer: Growing a clonal tree from ultra-low coverage single-cell DNA sequencing of tumors

Emerging ultra-low coverage single-cell DNA sequencing (scDNA-seq) technologies have enabled high resolution evolutionary studies of copy number aberrations (CNAs) within tumors. While these sequencing technologies are well suited for identifying CNAs due to the uniformity of sequencing coverage, the sparsity of coverage poses challenges for the study of single-nucleotide variants (SNVs). In order to maximize the utility of increasingly available ultra-low coverage scDNA-seq data and obtain a comprehensive understanding of tumor evolution, it is important to also analyze the evolution of SNVs from the same set of tumor cells. We present Phertilizer, a method to infer a clonal tree from ultra-low coverage scDNA-seq data of a tumor. Based on a probabilistic model, our method recursively partitions the data by identifying key evolutionary events in the history of the tumor. We demonstrate the performance of Phertilizer on simulated data as well as on two real datasets, finding that Phertilizer effectively utilizes the copy-number signal inherent in the data to more accurately uncover clonal structure and genotypes compared to previous methods.

Assessing the performance of methods for cell clustering from single-cell DNA sequencing data

BACKGROUND

Many cancer genomes have been known to contain more than one subclone inside one tumor, the phenomenon of which is called intra-tumor heterogeneity (ITH). Characterizing ITH is essential in designing treatment plans, prognosis as well as the study of cancer progression. Single-cell DNA sequencing (scDNAseq) has been proven effective in deciphering ITH. Cells corresponding to each subclone are supposed to carry a unique set of mutations such as single nucleotide variations (SNV). While there have been many studies on the cancer evolutionary tree reconstruction, not many have been proposed that simply characterize the subclonality without tree reconstruction. While tree reconstruction is important in the study of cancer evolutionary history, typically they are computationally expensive in terms of running time and memory consumption due to the huge search space of the tree structure. On the other hand, subclonality characterization of single cells can be converted into a cell clustering problem, the dimension of which is much smaller, and the turnaround time is much shorter. Despite the existence of a few state-of-the-art cell clustering computational tools for scDNAseq, there lacks a comprehensive and objective comparison under different settings.

RESULTS

In this paper, we evaluated six state-of-the-art cell clustering tools-SCG, BnpC, SCClone, RobustClone, SCITE and SBMClone-on simulated data sets given a variety of parameter settings and a real data set. We designed a simulator specifically for cell clustering, and compared these methods' performances in terms of their clustering accuracy, specificity and sensitivity and running time. For SBMClone, we specifically designed an ultra-low coverage large data set to evaluate its performance in the face of an extremely high missing rate.

CONCLUSION

From the benchmark study, we conclude that BnpC and SCG's clustering accuracy are the highest and comparable to each other. However, BnpC is more advantageous in terms of running time when cell number is high (> 1500). It also has a higher clustering accuracy than SCG when cluster number is high (> 16). SCClone's accuracy in estimating the number of clusters is the highest. RobustClone and SCITE's clustering accuracy are the lowest for all experiments. SCITE tends to over-estimate the cluster number and has a low specificity, whereas RobustClone tends to under-estimate the cluster number and has a much lower sensitivity than other methods. SBMClone produced reasonably good clustering (V-measure > 0.9) when coverage is > = 0.03 and thus is highly recommended for ultra-low coverage large scDNAseq data sets.

Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patients

Recurring sequences of genomic alterations occurring across patients can highlight repeated evolutionary processes with significant implications for predicting cancer progression. Leveraging the ever-increasing availability of cancer omics data, here we unveil cancer's evolutionary signatures tied to distinct disease outcomes, representing "favored trajectories" of acquisition of driver mutations detected in patients with similar prognosis. We present a framework named ASCETIC (Agony-baSed Cancer EvoluTion InferenCe) to extract such signatures from sequencing experiments generated by different technologies such as bulk and single-cell sequencing data. We apply ASCETIC to (i) single-cell data from 146 myeloid malignancy patients and bulk sequencing from 366 acute myeloid leukemia patients, (ii) multi-region sequencing from 100 early-stage lung cancer patients, (iii) exome/genome data from 10,000+ Pan-Cancer Atlas samples, and (iv) targeted sequencing from 25,000+ MSK-MET metastatic patients, revealing subtype-specific single-nucleotide variant signatures associated with distinct prognostic clusters. Validations on several datasets underscore the robustness and generalizability of the extracted signatures.

Single-cell phylogenies reveal changes in the evolutionary rate within cancer and healthy tissues

Cell lineages accumulate somatic mutations during organismal development, potentially leading to pathological states. The rate of somatic evolution within a cell population can vary due to multiple factors, including selection, a change in the mutation rate, or differences in the microenvironment. Here, we developed a statistical test called the Poisson Tree (PT) test to detect varying evolutionary rates among cell lineages, leveraging the phylogenetic signal of single-cell DNA sequencing (scDNA-seq) data. We applied the PT test to 24 healthy and cancer samples, rejecting a constant evolutionary rate in 11 out of 15 cancer and five out of nine healthy scDNA-seq datasets. In six cancer datasets, we identified subclonal mutations in known driver genes that could explain the rate accelerations of particular cancer lineages. Our findings demonstrate the efficacy of scDNA-seq for studying somatic evolution and suggest that cell lineages often evolve at different rates within cancer and healthy tissues.

Single-cell multi-omics identifies chronic inflammation as a driver of TP53-mutant leukemic evolution

Understanding the genetic and nongenetic determinants of tumor protein 53 (TP53)-mutation-driven clonal evolution and subsequent transformation is a crucial step toward the design of rational therapeutic strategies. Here we carry out allelic resolution single-cell multi-omic analysis of hematopoietic stem/progenitor cells (HSPCs) from patients with a myeloproliferative neoplasm who transform to TP53-mutant secondary acute myeloid leukemia (sAML). All patients showed dominant TP53 'multihit' HSPC clones at transformation, with a leukemia stem cell transcriptional signature strongly predictive of adverse outcomes in independent cohorts, across both TP53-mutant and wild-type (WT) AML. Through analysis of serial samples, antecedent TP53-heterozygous clones and in vivo perturbations, we demonstrate a hitherto unrecognized effect of chronic inflammation, which suppressed TP53 WT HSPCs while enhancing the fitness advantage of TP53-mutant cells and promoted genetic evolution. Our findings will facilitate the development of risk-stratification, early detection and treatment strategies for TP53-mutant leukemia, and are of broad relevance to other cancer types.

COMPASS: joint copy number and mutation phylogeny reconstruction from amplicon single-cell sequencing data

Reconstructing the history of somatic DNA alterations can help understand the evolution of a tumor and predict its resistance to treatment. Single-cell DNA sequencing (scDNAseq) can be used to investigate clonal heterogeneity and to inform phylogeny reconstruction. However, most existing phylogenetic methods for scDNAseq data are designed either for single nucleotide variants (SNVs) or for large copy number alterations (CNAs), or are not applicable to targeted sequencing. Here, we develop COMPASS, a computational method for inferring the joint phylogeny of SNVs and CNAs from targeted scDNAseq data. We evaluate COMPASS on simulated data and apply it to several datasets including a cohort of 123 patients with acute myeloid leukemia. COMPASS detected clonal CNAs that could be orthogonally validated with bulk data, in addition to subclonal ones that require single-cell resolution, some of which point toward convergent evolution.

Phylogenetic inference from single-cell RNA-seq data

Tumors are comprised of subpopulations of cancer cells that harbor distinct genetic profiles and phenotypes that evolve over time and during treatment. By reconstructing the course of cancer evolution, we can understand the acquisition of the malignant properties that drive tumor progression. Unfortunately, recovering the evolutionary relationships of individual cancer cells linked to their phenotypes remains a difficult challenge. To address this need, we have developed PhylinSic, a method that reconstructs the phylogenetic relationships among cells linked to their gene expression profiles from single cell RNA-sequencing (scRNA-Seq) data. This method calls nucleotide bases using a probabilistic smoothing approach and then estimates a phylogenetic tree using a Bayesian modeling algorithm. We showed that PhylinSic identified evolutionary relationships underpinning drug selection and metastasis and was sensitive enough to identify subclones from genetic drift. We found that breast cancer tumors resistant to chemotherapies harbored multiple genetic lineages that independently acquired high K-Ras and β-catenin, suggesting that therapeutic strategies may need to control multiple lineages to be durable. These results demonstrated that PhylinSic can reconstruct evolution and link the genotypes and phenotypes of cells across monophyletic tumors using scRNA-Seq.

Improving cellular phylogenies through the integrated use of mutation order and optimality principles

The study of tumor evolution is being revolutionalized by single-cell sequencing technologies that survey the somatic variation of cancer cells. In these endeavors, reliable inference of the evolutionary relationship of single cells is a key step. However, single-cell sequences contain many errors and missing bases, which necessitate advancing standard molecular phylogenetics approaches for applications in analyzing these datasets. We have developed a computational approach that integratively applies standard phylogenetic optimality principles and patterns of co-occurrence of sequence variations to produce more expansive and accurate cellular phylogenies from single-cell sequence datasets. We found the new approach to also perform well for CRISPR/Cas9 genome editing datasets, suggesting that it can be useful for various applications. We apply the new approach to some empirical datasets to showcase its use for reconstructing recurrent mutations and mutational reversals as well as for phylodynamics analysis to infer metastatic cell migrations between tumors.

Modeling and predicting cancer clonal evolution with reinforcement learning

Cancer results from an evolutionary process that typically yields multiple clones with varying sets of mutations within the same tumor. Accurately modeling this process is key to understanding and predicting cancer evolution. Here, we introduce clone to mutation (CloMu), a flexible and low-parameter tree generative model of cancer evolution. CloMu uses a two-layer neural network trained via reinforcement learning to determine the probability of new mutations based on the existing mutations on a clone. CloMu supports several prediction tasks, including the determination of evolutionary trajectories, tree selection, causality and interchangeability between mutations, and mutation fitness. Importantly, previous methods support only some of these tasks, and many suffer from overfitting on data sets with a large number of mutations. Using simulations, we show that CloMu either matches or outperforms current methods on a wide variety of prediction tasks. In particular, for simulated data with interchangeable mutations, current methods are unable to uncover causal relationships as effectively as CloMu. On breast cancer and leukemia cohorts, we show that CloMu determines similarities and causal relationships between mutations as well as the fitness of mutations. We validate CloMu's inferred mutation fitness values for the leukemia cohort by comparing them to clonal proportion data not used during training, showing high concordance. In summary, CloMu's low-parameter model facilitates a wide range of prediction tasks regarding cancer evolution on increasingly available cohort-level data sets.

A weighted distance-based approach for deriving consensus tumor evolutionary trees

MOTIVATION

The acquisition of somatic mutations by a tumor can be modeled by a type of evolutionary tree. However, it is impossible to observe this tree directly. Instead, numerous algorithms have been developed to infer such a tree from different types of sequencing data. But such methods can produce conflicting trees for the same patient, making it desirable to have approaches that can combine several such tumor trees into a consensus or summary tree. We introduce The Weighted m-Tumor Tree Consensus Problem (W-m-TTCP) to find a consensus tree among multiple plausible tumor evolutionary histories, each assigned a confidence weight, given a specific distance measure between tumor trees. We present an algorithm called TuELiP that is based on integer linear programming which solves the W-m-TTCP, and unlike other existing consensus methods, allows the input trees to be weighted differently.

RESULTS

On simulated data we show that TuELiP outperforms two existing methods at correctly identifying the true underlying tree used to create the simulations. We also show that the incorporation of weights can lead to more accurate tree inference. On a Triple-Negative Breast Cancer dataset, we show that including confidence weights can have important impacts on the consensus tree identified.

AVAILABILITY

An implementation of TuELiP and simulated datasets are available at https://bitbucket.org/oesperlab/consensus-ilp/src/main/.

Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees

Cancer progression is an evolutionary process shaped by both deterministic and stochastic forces. Multi-region and single-cell sequencing of tumors enable high-resolution reconstruction of the mutational history of each tumor and highlight the extensive diversity across tumors and patients. Resolving the interactions among mutations and recovering recurrent evolutionary processes may offer greater opportunities for successful therapeutic strategies. To this end, we present a novel probabilistic framework, called TreeMHN, for the joint inference of exclusivity patterns and recurrent trajectories from a cohort of intra-tumor phylogenetic trees. Through simulations, we show that TreeMHN outperforms existing alternatives that can only focus on one aspect of the task. By analyzing datasets of blood, lung, and breast cancers, we find the most likely evolutionary trajectories and mutational patterns, consistent with and enriching our current understanding of tumorigenesis. Moreover, TreeMHN facilitates the prediction of tumor evolution and provides probabilistic measures on the next mutational events given a tumor tree, a prerequisite for evolution-guided treatment strategies.

SUP: a probabilistic framework to propagate genome sequence uncertainty, with applications

Genetic sequencing is subject to many different types of errors, but most analyses treat the resultant sequences as if they are known without error. Next generation sequencing methods rely on significantly larger numbers of reads than previous sequencing methods in exchange for a loss of accuracy in each individual read. Still, the coverage of such machines is imperfect and leaves uncertainty in many of the base calls. In this work, we demonstrate that the uncertainty in sequencing techniques will affect downstream analysis and propose a straightforward method to propagate the uncertainty. Our method (which we have dubbed Sequence Uncertainty Propagation, or SUP) uses a probabilistic matrix representation of individual sequences which incorporates base quality scores as a measure of uncertainty that naturally lead to resampling and replication as a framework for uncertainty propagation. With the matrix representation, resampling possible base calls according to quality scores provides a bootstrap- or prior distribution-like first step towards genetic analysis. Analyses based on these re-sampled sequences will include a more complete evaluation of the error involved in such analyses. We demonstrate our resampling method on SARS-CoV-2 data. The resampling procedures add a linear computational cost to the analyses, but the large impact on the variance in downstream estimates makes it clear that ignoring this uncertainty may lead to overly confident conclusions. We show that SARS-CoV-2 lineage designations via Pangolin are much less certain than the bootstrap support reported by Pangolin would imply and the clock rate estimates for SARS-CoV-2 are much more variable than reported.

Multiomic Single Cell Sequencing Identifies Stemlike Nature of Mixed Phenotype Acute Leukemia and Provides Novel Risk Stratification

Mixed phenotype acute leukemia (MPAL) is a leukemia whose biologic drivers are poorly understood, therapeutic strategy remains unclear, and prognosis is poor. We performed multiomic single cell (SC) profiling of 14 newly diagnosed adult MPAL patients to characterize the immunophenotypic, genetic, and transcriptional landscapes of MPAL. We show that neither genetic profile nor transcriptome reliably correlate with specific MPAL immunophenotypes. However, progressive acquisition of mutations is associated with increased expression of immunophenotypic markers of immaturity. Using SC transcriptional profiling, we find that MPAL blasts express a stem cell-like transcriptional profile distinct from other acute leukemias and indicative of high differentiation potential. Further, patients with the highest differentiation potential demonstrated inferior survival in our dataset. A gene set score, MPAL95, derived from genes highly enriched in this cohort, is applicable to bulk RNA sequencing data and was predictive of survival in an independent patient cohort, suggesting utility for clinical risk stratification.

GTAC enables parallel genotyping of multiple genomic loci with chromatin accessibility profiling in single cells

Understanding clonal evolution and cancer development requires experimental approaches for characterizing the consequences of somatic mutations on gene regulation. However, no methods currently exist that efficiently link high-content chromatin accessibility with high-confidence genotyping in single cells. To address this, we developed Genotyping with the Assay for Transposase-Accessible Chromatin (GTAC), enabling accurate mutation detection at multiple amplified loci, coupled with robust chromatin accessibility readout. We applied GTAC to primary acute myeloid leukemia, obtaining high-quality chromatin accessibility profiles and clonal identities for multiple mutations in 88% of cells. We traced chromatin variation throughout clonal evolution, showing the restriction of different clones to distinct differentiation stages. Furthermore, we identified switches in transcription factor motif accessibility associated with a specific combination of driver mutations, which biased transformed progenitors toward a leukemia stem cell-like chromatin state. GTAC is a powerful tool to study clonal heterogeneity across a wide spectrum of pre-malignant and neoplastic conditions.

Tumor heterogeneity: preclinical models, emerging technologies, and future applications

Heterogeneity describes the differences among cancer cells within and between tumors. It refers to cancer cells describing variations in morphology, transcriptional profiles, metabolism, and metastatic potential. More recently, the field has included the characterization of the tumor immune microenvironment and the depiction of the dynamics underlying the cellular interactions promoting the tumor ecosystem evolution. Heterogeneity has been found in most tumors representing one of the most challenging behaviors in cancer ecosystems. As one of the critical factors impairing the long-term efficacy of solid tumor therapy, heterogeneity leads to tumor resistance, more aggressive metastasizing, and recurrence. We review the role of the main models and the emerging single-cell and spatial genomic technologies in our understanding of tumor heterogeneity, its contribution to lethal cancer outcomes, and the physiological challenges to consider in designing cancer therapies. We highlight how tumor cells dynamically evolve because of the interactions within the tumor immune microenvironment and how to leverage this to unleash immune recognition through immunotherapy. A multidisciplinary approach grounded in novel bioinformatic and computational tools will allow reaching the integrated, multilayered knowledge of tumor heterogeneity required to implement personalized, more efficient therapies urgently required for cancer patients.

Clonally resolved single-cell multi-omics identifies routes of cellular differentiation in acute myeloid leukemia

Inter-patient variability and the similarity of healthy and leukemic stem cells (LSCs) have impeded the characterization of LSCs in acute myeloid leukemia (AML) and their differentiation landscape. Here, we introduce CloneTracer, a novel method that adds clonal resolution to single-cell RNA-seq datasets. Applied to samples from 19 AML patients, CloneTracer revealed routes of leukemic differentiation. Although residual healthy and preleukemic cells dominated the dormant stem cell compartment, active LSCs resembled their healthy counterpart and retained erythroid capacity. By contrast, downstream myeloid progenitors constituted a highly aberrant, disease-defining compartment: their gene expression and differentiation state affected both the chemotherapy response and leukemia's ability to differentiate into transcriptomically normal monocytes. Finally, we demonstrated the potential of CloneTracer to identify surface markers misregulated specifically in leukemic cells. Taken together, CloneTracer reveals a differentiation landscape that mimics its healthy counterpart and may determine biology and therapy response in AML.

Sequence-Based Platforms for Discovering Biomarkers in Liquid Biopsy of Non-Small-Cell Lung Cancer

Lung cancer detection and monitoring are hampered by a lack of sensitive biomarkers, which results in diagnosis at late stages and difficulty in tracking response to treatment. Recent developments have established liquid biopsies as promising non-invasive methods for detecting biomarkers in lung cancer patients. With concurrent advances in high-throughput sequencing technologies and bioinformatics tools, new approaches for biomarker discovery have emerged. In this article, we survey established and emerging biomarker discovery methods using nucleic acid materials derived from bodily fluids in the context of lung cancer. We introduce nucleic acid biomarkers extracted from liquid biopsies and outline biological sources and methods of isolation. We discuss next-generation sequencing (NGS) platforms commonly used to identify novel biomarkers and describe how these have been applied to liquid biopsy. We highlight emerging biomarker discovery methods, including applications of long-read sequencing, fragmentomics, whole-genome amplification methods for single-cell analysis, and whole-genome methylation assays. Finally, we discuss advanced bioinformatics tools, describing methods for processing NGS data, as well as recently developed software tailored for liquid biopsy biomarker detection, which holds promise for early diagnosis of lung cancer.

Testing for Phylogenetic Signal in Single-Cell RNA-Seq Data

Phylogenetic methods are emerging as a useful tool to understand cancer evolutionary dynamics, including tumor structure, heterogeneity, and progression. Most currently used approaches utilize either bulk whole genome sequencing or single-cell DNA sequencing and are based on calling copy number alterations and single nucleotide variants (SNVs). Single-cell RNA sequencing (scRNA-seq) is commonly applied to explore differential gene expression of cancer cells throughout tumor progression. The method exacerbates the single-cell sequencing problem of low yield per cell with uneven expression levels. This accounts for low and uneven sequencing coverage and makes SNV detection and phylogenetic analysis challenging. In this article, we demonstrate for the first time that scRNA-seq data contain sufficient evolutionary signal and can also be utilized in phylogenetic analyses. We explore and compare results of such analyses based on both expression levels and SNVs called from scRNA-seq data. Both techniques are shown to be useful for reconstructing phylogenetic relationships between cells, reflecting the clonal composition of a tumor. Both standardized expression values and SNVs appear to be equally capable of reconstructing a similar pattern of phylogenetic relationship. This pattern is stable even when phylogenetic uncertainty is taken in account. Our results open up a new direction of somatic phylogenetics based on scRNA-seq data. Further research is required to refine and improve these approaches to capture the full picture of somatic evolutionary dynamics in cancer.

LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution

BACKGROUND

Longitudinal single-cell sequencing experiments of patient-derived models are increasingly employed to investigate cancer evolution. In this context, robust computational methods are needed to properly exploit the mutational profiles of single cells generated via variant calling, in order to reconstruct the evolutionary history of a tumor and characterize the impact of therapeutic strategies, such as the administration of drugs. To this end, we have recently developed the LACE framework for the Longitudinal Analysis of Cancer Evolution.

RESULTS

The LACE 2.0 release aimed at inferring longitudinal clonal trees enhances the original framework with new key functionalities: an improved data management for preprocessing of standard variant calling data, a reworked inference engine, and direct connection to public databases.

CONCLUSIONS

All of this is accessible through a new and interactive Shiny R graphical interface offering the possibility to apply filters helpful in discriminating relevant or potential driver mutations, set up inferential parameters, and visualize the results. The software is available at: github.com/BIMIB-DISCo/LACE.

Reconstructing clonal tree for phylo-phenotypic characterization of cancer using single-cell transcriptomics

Functional characterization of the cancer clones can shed light on the evolutionary mechanisms driving cancer's proliferation and relapse mechanisms. Single-cell RNA sequencing data provide grounds for understanding the functional state of cancer as a whole; however, much research remains to identify and reconstruct clonal relationships toward characterizing the changes in functions of individual clones. We present PhylEx that integrates bulk genomics data with co-occurrences of mutations from single-cell RNA sequencing data to reconstruct high-fidelity clonal trees. We evaluate PhylEx on synthetic and well-characterized high-grade serous ovarian cancer cell line datasets. PhylEx outperforms the state-of-the-art methods both when comparing capacity for clonal tree reconstruction and for identifying clones. We analyze high-grade serous ovarian cancer and breast cancer data to show that PhylEx exploits clonal expression profiles beyond what is possible with expression-based clustering methods and clear the way for accurate inference of clonal trees and robust phylo-phenotypic analysis of cancer.

Analysis of mutational history of multidrug-resistant genotypes with a mutagenetic tree model

Human immunodeficiency virus (HIV) can develop resistance to all antiretroviral drugs. Multidrug resistance, however, is a rare event in modern HIV treatment, but can be life-threatening, particular in patients with very long therapy histories and in areas with limited access to novel drugs. To understand the evolution of multidrug resistance, we analyzed the EuResist database to uncover the accumulation of mutations over time. We hypothesize that the accumulation of resistance mutations is not acquired simultaneously and randomly across viral genotypes but rather tends to follow a predetermined order. The knowledge of this order might help to elucidate potential mechanisms of multidrug resistance. Our evolutionary model shows an almost monotonic increase of resistance with each acquired mutation, including less well-known nucleoside reverse transcriptase (RT) inhibitor-related mutations like K223Q, L228H, and Q242H. Mutations within the integrase (IN) (T97A, E138A/K G140S, Q148H, N155H) indicate high probability of multidrug resistance. Hence, these IN mutations also tend to be observed together with mutations in the protease (PR) and RT. We followed up with an analysis of the mutation-specific error rates of our model given the data. We identified several mutations with unusual rates (PR: M41L, L33F, IN: G140S). This could imply the existence of previously unknown virus variants in the viral quasispecies. In conclusion, our bioinformatics model supports the analysis and understanding of multidrug resistance.

bmVAE: a variational autoencoder method for clustering single-cell mutation data

MOTIVATION

Genetic intra-tumor heterogeneity (ITH) characterizes the differences in genomic variations between tumor clones, and accurately unmasking ITH is important for personalized cancer therapy. Single-cell DNA sequencing now emerges as a powerful means for deciphering underlying ITH based on point mutations of single cells. However, detecting tumor clones from single-cell mutation data remains challenging due to the error-prone and discrete nature of the data.

RESULTS

We introduce bmVAE, a bioinformatics tool for learning low-dimensional latent representation of single cell based on a variational autoencoder and then clustering cells into subpopulations in the latent space. bmVAE takes single-cell binary mutation data as inputs, and outputs inferred cell subpopulations as well as their genotypes. To achieve this, the bmVAE framework is designed to consist of three modules including dimensionality reduction, cell clustering and genotype estimation. We assess the method on various synthetic datasets where different factors including false negative rate, data size and data heterogeneity are considered in simulation, and further demonstrate its effectiveness on two real datasets. The results suggest bmVAE is highly effective in reasoning ITH, and performs competitive to existing methods.

AVAILABILITY AND IMPLEMENTATION

bmVAE is freely available at https://github.com/zhyu-lab/bmvae.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A phylogenetic approach to inferring the order in which mutations arise during cancer progression

Although the role of evolutionary process in cancer progression is widely accepted, increasing attention is being given to the evolutionary mechanisms that can lead to differences in clinical outcome. Recent studies suggest that the temporal order in which somatic mutations accumulate during cancer progression is important. Single-cell sequencing (SCS) provides a unique opportunity to examine the effect that the mutation order has on cancer progression and treatment effect. However, the error rates associated with single-cell sequencing are known to be high, which greatly complicates the task. We propose a novel method for inferring the order in which somatic mutations arise within an individual tumor using noisy data from single-cell sequencing. Our method incorporates models at two levels in that the evolutionary process of somatic mutation within the tumor is modeled along with the technical errors that arise from the single-cell sequencing data collection process. Through analyses of simulations across a wide range of realistic scenarios, we show that our method substantially outperforms existing approaches for identifying mutation order. Most importantly, our method provides a unique means to capture and quantify the uncertainty in the inferred mutation order along a given phylogeny. We illustrate our method by analyzing data from colorectal and prostate cancer patients, in which our method strengthens previously reported mutation orders. Our work is an important step towards producing meaningful prediction of mutation order with high accuracy and measuring the uncertainty of predicted mutation order in cancer patients, with the potential to lead to new insights about the evolutionary trajectories of cancer.

SIEVE: joint inference of single-nucleotide variants and cell phylogeny from single-cell DNA sequencing data

We present SIEVE, a statistical method for the joint inference of somatic variants and cell phylogeny under the finite-sites assumption from single-cell DNA sequencing. SIEVE leverages raw read counts for all nucleotides and corrects the acquisition bias of branch lengths. In our simulations, SIEVE outperforms other methods in phylogenetic reconstruction and variant calling accuracy, especially in the inference of homozygous variants. Applying SIEVE to three datasets, one for triple-negative breast (TNBC), and two for colorectal cancer (CRC), we find that double mutant genotypes are rare in CRC but unexpectedly frequent in the TNBC samples.