Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution

Cancer drug-tolerant persister cells: from biological questions to clinical opportunities

The emergence of drug resistance is the most substantial challenge to the effectiveness of anticancer therapies. Orthogonal approaches have revealed that a subset of cells, known as drug-tolerant 'persister' (DTP) cells, have a prominent role in drug resistance. Although long recognized in bacterial populations which have acquired resistance to antibiotics, the presence of DTPs in various cancer types has come to light only in the past two decades, yet several aspects of their biology remain enigmatic. Here, we delve into the biological characteristics of DTPs and explore potential strategies for tracking and targeting them. Recent findings suggest that DTPs exhibit remarkable plasticity, being capable of transitioning between different cellular states, resulting in distinct DTP phenotypes within a single tumour. However, defining the biological features of DTPs has been challenging, partly due to the complex interplay between clonal dynamics and tissue-specific factors influencing their phenotype. Moreover, the interactions between DTPs and the tumour microenvironment, including their potential to evade immune surveillance, remain to be discovered. Finally, the mechanisms underlying DTP-derived drug resistance and their correlation with clinical outcomes remain poorly understood. This Roadmap aims to provide a comprehensive overview of the field of DTPs, encompassing past achievements and current endeavours in elucidating their biology. We also discuss the prospect of future advancements in technologies in helping to unveil the features of DTPs and propose novel therapeutic strategies that could lead to their eradication.

Defining heritability, plasticity, and transition dynamics of cellular phenotypes in somatic evolution

Single-cell sequencing has characterized cell state heterogeneity across diverse healthy and malignant tissues. However, the plasticity or heritability of these cell states remains largely unknown. To address this, we introduce PATH (phylogenetic analysis of trait heritability), a framework to quantify cell state heritability versus plasticity and infer cell state transition and proliferation dynamics from single-cell lineage tracing data. Applying PATH to a mouse model of pancreatic cancer, we observed heritability at the ends of the epithelial-to-mesenchymal transition spectrum, with higher plasticity at more intermediate states. In primary glioblastoma, we identified bidirectional transitions between stem- and mesenchymal-like cells, which use the astrocyte-like state as an intermediary. Finally, we reconstructed a phylogeny from single-cell whole-genome sequencing in B cell acute lymphoblastic leukemia and delineated the heritability of B cell differentiation states linked with genetic drivers. Altogether, PATH replaces qualitative conceptions of plasticity with quantitative measures, offering a framework to study somatic evolution.

Inferring cell differentiation maps from lineage tracing data

During development, mulitpotent cells differentiate through a hierarchy of increasingly restricted progenitor cell types until they realize specialized cell types. A cell differentiation map describes this hierarchy, and inferring these maps is an active area of research spanning traditional single marker lineage studies to data-driven trajectory inference methods on single-cell RNA-seq data. Recent high-throughput lineage tracing technologies profile lineages and cell types at scale, but current methods to infer cell differentiation maps from these data rely on simple models with restrictive assumptions about the developmental process. We introduce a mathematical framework for cell differentiation maps based on the concept of potency, and develop an algorithm, Carta , that infers an optimal cell differentiation map from single-cell lineage tracing data. The key insight in Carta is to balance the trade-off between the complexity of the cell differentiation map and the number of unobserved cell type transitions on the lineage tree. We show that Carta more accurately infers cell differentiation maps on both simulated and real data compared to existing methods. In models of mammalian trunk development and mouse hematopoiesis, Carta identifies important features of development that are not revealed by other methods including convergent differentiation of specialized cell types, progenitor differentiation dynamics, and the refinement of routes of differentiation via new intermediate progenitors.

Code availability

Carta software is available at https://github.com/raphael-group/CARTA.

New frameworks for hematopoiesis derived from single-cell genomics

ABSTRACT

Recent advancements in single-cell genomics have enriched our understanding of hematopoiesis, providing intricate details about hematopoietic stem cell biology, differentiation, and lineage commitment. Technological advancements have highlighted extensive heterogeneity of cell populations and continuity of differentiation routes. Nevertheless, intermediate "attractor" states signify structure in stem and progenitor populations that link state transition dynamics to fate potential. We discuss how innovative model systems quantify lineage bias and how stress accelerates differentiation, thereby reducing fate plasticity compared with native hematopoiesis. We conclude by offering our perspective on the current model of hematopoiesis and discuss how a more precise understanding can translate to strategies that extend healthy hematopoiesis and prevent disease.

Cancer tissue of origin constrains the growth and metabolism of metastases

Metastases arise from subsets of cancer cells that disseminate from the primary tumour1,2. The ability of cancer cells to thrive in a new tissue site is influenced by genetic and epigenetic changes that are important for disease initiation and progression, but these factors alone do not predict if and where cancers metastasize3,4. Specific cancer types metastasize to consistent subsets of tissues, suggesting that primary tumour-associated factors influence where cancers can grow. We find primary and metastatic pancreatic tumours have metabolic similarities and that the tumour-initiating capacity and proliferation of both primary-derived and metastasis-derived cells is favoured in the primary site relative to the metastatic site. Moreover, propagating cells as tumours in the lung or the liver does not enhance their relative ability to form large tumours in those sites, change their preference to grow in the primary site, nor stably alter aspects of their metabolism relative to primary tumours. Primary liver and lung cancer cells also exhibit a preference to grow in their primary site relative to metastatic sites. These data suggest cancer tissue of origin influences both primary and metastatic tumour metabolism and may impact where cancer cells can metastasize.

Multi-omic lineage tracing predicts the transcriptional, epigenetic and genetic determinants of cancer evolution

Cancer is a highly heterogeneous disease, where phenotypically distinct subpopulations coexist and can be primed to different fates. Both genetic and epigenetic factors may drive cancer evolution, however little is known about whether and how such a process is pre-encoded in cancer clones. Using single-cell multi-omic lineage tracing and phenotypic assays, we investigate the predictive features of either tumour initiation or drug tolerance within the same cancer population. Clones primed to tumour initiation in vivo display two distinct transcriptional states at baseline. Remarkably, these states share a distinctive DNA accessibility profile, highlighting an epigenetic basis for tumour initiation. The drug tolerant niche is also largely pre-encoded, but only partially overlaps the tumour-initiating one and evolves following two genetically and transcriptionally distinct trajectories. Our study highlights coexisting genetic, epigenetic and transcriptional determinants of cancer evolution, unravelling the molecular complexity of pre-encoded tumour phenotypes.

Toward DNA-Based Recording of Biological Processes

Exploiting the inherent compatibility of DNA-based data storage with living cells, various cellular recording approaches have been developed for recording and retrieving biologically relevant signals in otherwise inaccessible locations, such as inside the body. This review provides an overview of the current state of engineered cellular memory systems, highlighting their design principles, advantages, and limitations. We examine various technologies, including CRISPR-Cas systems, recombinases, retrons, and DNA methylation, that enable these recording systems. Additionally, we discuss potential strategies for improving recording accuracy, scalability, and durability to address current limitations in the field. This emerging modality of biological measurement will be key to gaining novel insights into diverse biological processes and fostering the development of various biotechnological applications, from environmental sensing to disease monitoring and beyond.

Tracking clonal evolution of drug resistance in ovarian cancer patients by exploiting structural variants in cfDNA

Drug resistance is the major cause of therapeutic failure in high-grade serous ovarian cancer (HGSOC). Yet, the mechanisms by which tumors evolve to drug resistant states remains largely unknown. To address this, we aimed to exploit clone-specific genomic structural variations by combining scaled single-cell whole genome sequencing with longitudinally collected cell-free DNA (cfDNA), enabling clonal tracking before, during and after treatment. We developed a cfDNA hybrid capture, deep sequencing approach based on leveraging clone-specific structural variants as endogenous barcodes, with orders of magnitude lower error rates than single nucleotide variants in ctDNA (circulating tumor DNA) detection, demonstrated on 19 patients at baseline. We then applied this to monitor and model clonal evolution over several years in ten HGSOC patients treated with systemic therapy from diagnosis through recurrence. We found drug resistance to be polyclonal in most cases, but frequently dominated by a single high-fitness and expanding clone, reducing clonal diversity in the relapsed disease state in most patients. Drug-resistant clones frequently displayed notable genomic features, including high-level amplifications of oncogenes such as CCNE1, RAB25, NOTCH3, and ERBB2. Using a population genetics Wright-Fisher model, we found evolutionary trajectories of these features were consistent with drug-induced positive selection. In select cases, these alterations impacted selection of secondary lines of therapy with positive patient outcomes. For cases with matched single-cell RNA sequencing data, pre-existing and genomically encoded phenotypic states such as upregulation of EMT and VEGF were linked to drug resistance. Together, our findings indicate that drug resistant states in HGSOC pre-exist at diagnosis and lead to dramatic clonal expansions that alter clonal composition at the time of relapse. We suggest that combining tumor single cell sequencing with cfDNA enables clonal tracking in patients and harbors potential for evolution-informed adaptive treatment decisions.

Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics

Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.

Mathematical modeling and quantitative analysis of phenotypic plasticity during tumor evolution based on single-cell data

Tumor is a complex and aggressive type of disease that poses significant health challenges. Understanding the cellular mechanisms underlying its progression is crucial for developing effective treatments. In this study, we develop a novel mathematical framework to investigate the role of cellular plasticity and heterogeneity in tumor progression. By leveraging temporal single-cell data, we propose a reaction-convection-diffusion model that effectively captures the spatiotemporal dynamics of tumor cells and macrophages within the tumor microenvironment. Through theoretical analysis, we obtain the estimate of the pulse wave speed and analyze the stability of the homogeneous steady state solutions. Notably, we employe the AddModuleScore function to quantify cellular plasticity. One of the highlights of our approach is the introduction of pulse wave speed as a quantitative measure to precisely gauge the rate of cell phenotype transitions, as well as the novel implementation of the high-plasticity cell state/low-plasticity cell state ratio as an indicator of tumor malignancy. Furthermore, the bifurcation analysis reveals the complex dynamics of tumor cell populations. Our extensive analysis demonstrates that an increased rate of phenotype transition is associated with heightened malignancy, attributable to the tumor's ability to explore a wider phenotypic space. The study also investigates how the proliferation rate and the death rate of tumor cells, phenotypic convection velocity, and the midpoint of the phenotype transition stage affect the speed of tumor cell phenotype transitions and the progression to adenocarcinoma. These insights and quantitative measures can help guide the development of targeted therapeutic strategies to regulate cellular plasticity and control tumor progression effectively.

Simulation of CRISPR-Cas9 editing on evolving barcode and accuracy of lineage tracing

We designed a simulation program that mimics the CRISPR-Cas9 editing on evolving barcode and double strand break repair procedure along with cell divisions. Emerging barcode mutations tend to build upon previously existing mutations, occurring sequentially with each generation. This process results in a unique mutation profile in each cell. We sample the barcodes in leaf cells and reconstruct the lineage, comparing it to the original lineage tree to test algorithm accuracy under different parameter settings. Our computational simulations validate the reasonable assumptions deduced from experimental observations, emphasizing that factors such as sampling size, barcode length, multiple barcodes, indel probabilities, and Cas9 activity are critical for accurate and successful lineage tracing. Among the many factors we found that sampling size and indel probabilities are two major ones that affect lineage tracing accuracy. Large segment deletions in early generations could greatly impact lineage accuracy. These simulation results offer insightful recommendations for enhancing the design and analysis of Cas9-mediated molecular barcodes in actual experiments.

Identifying a gene signature of metastatic potential by linking pre-metastatic state to ultimate metastatic fate

Identifying the key molecular pathways that enable metastasis by analyzing the eventual metastatic tumor is challenging because the state of the founder subclone likely changes following metastatic colonization. To address this challenge, we labeled primary mouse pancreatic ductal adenocarcinoma (PDAC) subclones with DNA barcodes to characterize their pre-metastatic state using ATAC-seq and RNA-seq and determine their relative in vivo metastatic potential prospectively. We identified a gene signature separating metastasis-high and metastasis-low subclones orthogonal to the normal-to-PDAC and classical-to-basal axes. The metastasis-high subclones feature activation of IL-1 pathway genes and high NF-κB and Zeb/Snail family activity and the metastasis-low subclones feature activation of neuroendocrine, motility, and Wnt pathway genes and high CDX2 and HOXA13 activity. In a functional screen, we validated novel mediators of PDAC metastasis in the IL-1 pathway, including the NF-κB targets Fos and Il23a, and beyond the IL-1 pathway including Myo1b and Tmem40. We scored human PDAC tumors for our signature of metastatic potential from mouse and found that metastases have higher scores than primary tumors. Moreover, primary tumors with higher scores are associated with worse prognosis. We also found that our metastatic potential signature is enriched in other human carcinomas, suggesting that it is conserved across epithelial malignancies. This work establishes a strategy for linking cancer cell state to future behavior, reveals novel functional regulators of PDAC metastasis, and establishes a method for scoring human carcinomas based on metastatic potential.

Exact and efficient phylodynamic simulation from arbitrarily large populations

Many biological studies involve inferring the evolutionary history of a sample of individuals from a large population and interpreting the reconstructed tree. Such an ascertained tree typically represents only a small part of a comprehensive population tree and is distorted by survivorship and sampling biases. Inferring evolutionary parameters from ascertained trees requires modeling both the underlying population dynamics and the ascertainment process. A crucial component of this phylodynamic modeling involves tree simulation, which is used to benchmark probabilistic inference methods. To simulate an ascertained tree, one must first simulate the full population tree and then prune unobserved lineages. Consequently, the computational cost is determined not by the size of the final simulated tree, but by the size of the population tree in which it is embedded. In most biological scenarios, simulations of the entire population are prohibitively expensive due to computational demands placed on lineages without sampled descendants. Here, we address this challenge by proving that, for any partially ascertained process from a general multi-type birth-death-mutation-sampling model, there exists an equivalent process with complete sampling and no death, a property which we leverage to develop a highly efficient algorithm for simulating trees. Our algorithm scales linearly with the size of the final simulated tree and is independent of the population size, enabling simulations from extremely large populations beyond the reach of current methods but essential for various biological applications. We anticipate that this unprecedented speedup will significantly advance the development of novel inference methods that require extensive training data.

Detecting branching rate heterogeneity in multifurcating trees with applications in lineage tracing data

Understanding cellular birth rate differences is crucial for predicting cancer progression and interpreting tumor-derived genetic data. Lineage tracing experiments enable detailed reconstruction of cellular genealogies, offering new opportunities to measure branching rate heterogeneity. However, the lineage tracing process can introduce complex tree features that complicate this effort. Here, we examine tree characteristics in lineage tracing-derived genealogies and find that editing window placement leads to multifurcations at a tree's root or tips. We propose several ways in which existing tree topology-based metrics can be extended to test for rate heterogeneity on trees even in the presence of lineage-tracing associated distortions. Although these methods vary in power and robustness, a test based on theJ 1 statistic effectively detects branching rate heterogeneity in simulated lineage tracing data. Tests based on other common statistics ( s ^ and the Sackin index) show interior performance toJ 1 . We apply our validated methods to xenograft experimental data and find widespread rate heterogeneity across multiple study systems. Our results demonstrate the potential of tree topology statistics in analyzing lineage tracing data, and highlight the challenges associated with adapting phylogenetic methods to these systems.

Mouse Models of Metastasis and Dormancy

Metastasis is the ultimate and often lethal stage of cancer. Metastasis occurs in three phases that may vary across individuals: First, dissemination from the primary tumor. Second, tumor dormancy at the metastatic site where micrometastatic cancer cells remain quiescent or, in dynamic cycles of proliferation and elimination, remaining clinically undetectable. Finally, cancer cells are able to overcome microenvironmental constraints for outgrowth, or the formation of clinically detectable macrometastases that colonize distant organs and are largely incurable. A variety of approaches have been used to model metastasis to elucidate molecular mechanisms and identify putative therapeutic targets. In particular, metastatic dormancy has been challenging to model in vivo due to the sparse numbers of cancer cells in micrometastasis nodules and the long latency times required for tumor outgrowth. Here, we review state-of-the art genetically engineered mouse, syngeneic, and patient-derived xenograft approaches for modeling metastasis and dormancy. We describe the advantages and limitations of various metastasis models, novel findings enabled by such approaches, and highlight opportunities for future improvement.

Programmable promoter editing for precise control of transgene expression

Subtle changes in gene expression direct cells to distinct cellular states. Identifying and controlling dose-dependent transgenes require tools for precisely titrating expression. To this end, we developed a highly modular, extensible framework called DIAL for building editable promoters that allow for fine-scale, heritable changes in transgene expression. Using DIAL, we increase expression by recombinase-mediated excision of spacers between the binding sites of a synthetic zinc finger transcription factor and the core promoter. By nesting varying numbers and lengths of spacers, DIAL generates a tunable range of unimodal setpoints from a single promoter. Through small-molecule control of transcription factors and recombinases, DIAL supports temporally defined, user-guided control of transgene expression that is extensible to additional transcription factors. Lentiviral delivery of DIAL generates multiple setpoints in primary cells and iPSCs. As promoter editing generates stable states, DIAL setpoints are heritable, facilitating mapping of transgene levels to phenotypes. The DIAL framework opens new opportunities for tailoring transgene expression and improving the predictability and performance of gene circuits across diverse applications.

Interaction between MED12 and ΔNp63 activates basal identity in pancreatic ductal adenocarcinoma

The presence of basal lineage characteristics signifies hyperaggressive human adenocarcinomas of the breast, bladder and pancreas. However, the biochemical mechanisms that maintain this aberrant cell state are poorly understood. Here we performed marker-based genetic screens in search of factors needed to maintain basal identity in pancreatic ductal adenocarcinoma (PDAC). This approach revealed MED12 as a powerful regulator of the basal cell state in this disease. Using biochemical reconstitution and epigenomics, we show that MED12 carries out this function by bridging the transcription factor ΔNp63, a known master regulator of the basal lineage, with the Mediator complex to activate lineage-specific enhancer elements. Consistent with this finding, the growth of basal-like PDAC is hypersensitive to MED12 loss when compared to PDAC cells lacking basal characteristics. Taken together, our genetic screens have revealed a biochemical interaction that sustains basal identity in human cancer, which could serve as a target for tumor lineage-directed therapeutics.

Quantifying cell-state densities in single-cell phenotypic landscapes using Mellon

Cell-state density characterizes the distribution of cells along phenotypic landscapes and is crucial for unraveling the mechanisms that drive diverse biological processes. Here, we present Mellon, an algorithm for estimation of cell-state densities from high-dimensional representations of single-cell data. We demonstrate Mellon's efficacy by dissecting the density landscape of differentiating systems, revealing a consistent pattern of high-density regions corresponding to major cell types intertwined with low-density, rare transitory states. We present evidence implicating enhancer priming and the activation of master regulators in emergence of these transitory states. Mellon offers the flexibility to perform temporal interpolation of time-series data, providing a detailed view of cell-state dynamics during developmental processes. Mellon facilitates density estimation across various single-cell data modalities, scaling linearly with the number of cells. Our work underscores the importance of cell-state density in understanding the differentiation processes, and the potential of Mellon to provide insights into mechanisms guiding biological trajectories.

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.

Evolutionary Trend Analysis of Research on Immunotherapy for Brain Metastasis Based on Machine-Learning Scientometrics

Brain metastases challenge cancer treatments with poor prognoses, despite ongoing advancements. Immunotherapy effectively alleviates advanced cancer, exhibiting immense potential to revolutionize brain metastasis management. To identify research priorities that optimize immunotherapies for brain metastases, 2164 related publications were analyzed. Scientometric visualization via R software, VOSviewer, and CiteSpace showed the interrelationships among literature, institutions, authors, and topic areas of focus. The publication rate and citations have grown exponentially over the past decade, with the US, China, and Germany as the major contributors. The University of Texas MD Anderson Cancer Center ranked highest in publications, while Memorial Sloan Kettering Cancer Center was most cited. Clusters of keywords revealed six hotspots: 'Immunology', 'Check Point Inhibitors', 'Lung Cancer', 'Immunotherapy', 'Melanoma', 'Breast Cancer', and 'Microenvironment'. Melanoma, the most studied primary tumor with brain metastases offers promising immunotherapy advancements with generalizability and adaptability to other cancers. Our results outline the holistic overview of immunotherapy research for brain metastases, which pinpoints the forefront in the field, and directs researchers toward critical inquiries for enhanced mechanistic insight and improved clinical outcomes. Moreover, governmental and funding agencies will benefit from assigning financial resources to entities and regions with the greatest potential for combating brain metastases through immunotherapy.

Reconstructing axial progenitor field dynamics in mouse stem cell-derived embryoids

Embryogenesis requires substantial coordination to translate genetic programs to the collective behavior of differentiating cells, but understanding how cellular decisions control tissue morphology remains conceptually and technically challenging. Here, we combine continuous Cas9-based molecular recording with a mouse embryonic stem cell-based model of the embryonic trunk to build single-cell phylogenies that describe the behavior of transient, multipotent neuro-mesodermal progenitors (NMPs) as they commit into neural and somitic cell types. We find that NMPs show subtle transcriptional signatures related to their recent differentiation and contribute to downstream lineages through a surprisingly broad distribution of individual fate outcomes. Although decision-making can be heavily influenced by environmental cues to induce morphological phenotypes, axial progenitors intrinsically mature over developmental time to favor the neural lineage. Using these data, we present an experimental and analytical framework for exploring the non-homeostatic dynamics of transient progenitor populations as they shape complex tissues during critical developmental windows.

A developmental constraint model of cancer cell states and tumor heterogeneity

Cancer is a disease that stems from a fundamental liability inherent to multicellular life forms in which an individual cell is capable of reneging on the interests of the collective organism. Although cancer is commonly described as an evolutionary process, a less appreciated aspect of tumorigenesis may be the constraints imposed by the organism's developmental programs. Recent work from single-cell transcriptomic analyses across a range of cancer types has revealed the recurrence, plasticity, and co-option of distinct cellular states among cancer cell populations. Here, we note that across diverse cancer types, the observed cell states are proximate within the developmental hierarchy of the cell of origin. We thus posit a model by which cancer cell states are directly constrained by the organism's "developmental map." According to this model, a population of cancer cells traverses the developmental map, thereby generating a heterogeneous set of states whose interactions underpin emergent tumor behavior.

Oncogenic Cell Tagging and Single-Cell Transcriptomics Reveal Cell Type-Specific and Time-Resolved Responses to Vhl Inactivation in the Kidney

Defining the initial events in oncogenesis and the cellular responses they entrain, even in advance of morphologic abnormality, is a fundamental challenge in understanding cancer initiation. As a paradigm to address this, we longitudinally studied the changes induced by loss of the tumor suppressor gene von Hippel Lindau (VHL), which ultimately drives clear cell renal cell carcinoma. Vhl inactivation was directly coupled to expression of a tdTomato reporter within a single allele, allowing accurate visualization of affected cells in their native context and retrieval from the kidney for single-cell RNA sequencing. This strategy uncovered cell type-specific responses to Vhl inactivation, defined a proximal tubular cell class with oncogenic potential, and revealed longer term adaptive changes in the renal epithelium and the interstitium. Oncogenic cell tagging also revealed markedly heterogeneous cellular effects including time-limited proliferation and elimination of specific cell types. Overall, this study reports an experimental strategy for understanding oncogenic processes in which cells bearing genetic alterations can be generated in their native context, marked, and analyzed over time. The observed effects of loss of Vhl in kidney cells provide insights into VHL tumor suppressor action and development of renal cell carcinoma.

SIGNIFICANCE

Single-cell analysis of heterogeneous and dynamic responses to Vhl inactivation in the kidney suggests that early events shape the cell type specificity of oncogenesis, providing a focus for mechanistic understanding and therapeutic targeting.

Phenotypic noise and plasticity in cancer evolution

Non-genetic alterations can produce changes in a cell's phenotype. In cancer, these phenomena can influence a cell's fitness by conferring access to heritable, beneficial phenotypes. Herein, we argue that current discussions of 'phenotypic plasticity' in cancer evolution ignore a salient feature of the original definition: namely, that it occurs in response to an environmental change. We suggest 'phenotypic noise' be used to distinguish non-genetic changes in phenotype that occur independently from the environment. We discuss the conceptual and methodological techniques used to identify these phenomena during cancer evolution. We propose that the distinction will guide efforts to define mechanisms of phenotype change, accelerate translational work to manipulate phenotypes through treatment, and, ultimately, improve patient outcomes.

Pharmacological targeting of the cancer epigenome

Epigenetic dysregulation is increasingly appreciated as a hallmark of cancer, including disease initiation, maintenance and therapy resistance. As a result, there have been advances in the development and evaluation of epigenetic therapies for cancer, revealing substantial promise but also challenges. Three epigenetic inhibitor classes are approved in the USA, and many more are currently undergoing clinical investigation. In this Review, we discuss recent developments for each epigenetic drug class and their implications for therapy, as well as highlight new insights into the role of epigenetics in cancer.

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.

Single-cell lineage tracing approaches to track kidney cell development and maintenance

The kidney is a complex organ consisting of various cell types. Previous studies have aimed to elucidate the cellular relationships among these cell types in developing and mature kidneys using Cre-loxP-based lineage tracing. However, this methodology falls short of fully capturing the heterogeneous nature of the kidney, making it less than ideal for comprehensively tracing cellular progression during kidney development and maintenance. Recent technological advancements in single-cell genomics have revolutionized lineage tracing methods. Single-cell lineage tracing enables the simultaneous tracing of multiple cell types within complex tissues and their transcriptomic profiles, thereby allowing the reconstruction of their lineage tree with cell state information. Although single-cell lineage tracing has been successfully applied to investigate cellular hierarchies in various organs and tissues, its application in kidney research is currently lacking. This review comprehensively consolidates the single-cell lineage tracing methods, divided into 4 categories (clustered regularly interspaced short palindromic repeat [CRISPR]/CRISPR-associated protein 9 [Cas9]-based, transposon-based, Polylox-based, and native barcoding methods), and outlines their technical advantages and disadvantages. Furthermore, we propose potential future research topics in kidney research that could benefit from single-cell lineage tracing and suggest suitable technical strategies to apply to these topics.

Stem-cell states converge in multistage cutaneous squamous cell carcinoma development

Stem cells play a critical role in cancer development by contributing to cell heterogeneity, lineage plasticity, and drug resistance. We created gene expression networks from hundreds of mouse tissue samples (both normal and tumor) and integrated these with lineage tracing and single-cell RNA-seq, to identify convergence of cell states in premalignant tumor cells expressing markers of lineage plasticity and drug resistance. Two of these cell states representing multilineage plasticity or proliferation were inversely correlated, suggesting a mutually exclusive relationship. Treatment of carcinomas in vivo with chemotherapy repressed the proliferative state and activated multilineage plasticity whereas inhibition of differentiation repressed plasticity and potentiated responses to cell cycle inhibitors. Manipulation of this cell state transition point may provide a source of potential combinatorial targets for cancer therapy.

Efficient and multiplexed somatic genome editing with Cas12a mice

Somatic genome editing in mouse models has increased our understanding of the in vivo effects of genetic alterations in areas ranging from neuroscience to cancer biology and beyond. However, existing models are limited in their ability to create multiple targeted edits. Thus, our understanding of the complex genetic interactions that underlie development, homeostasis, and disease remains incomplete. Cas12a is an RNA-guided endonuclease with unique attributes that enable simple targeting of multiple genes with crRNA arrays containing tandem guides. To accelerate and expand the generation of complex genotypes in somatic cells, we generated transgenic mice with Cre-regulated and constitutive expression of enhanced Acidaminococcus sp. Cas12a (enAsCas12a). In these mice, enAsCas12a-mediated somatic genome editing robustly generated compound genotypes, as exemplified by the initiation of diverse cancer types driven by homozygous inactivation of trios of tumor suppressor genes. We further integrated these modular crRNA arrays with clonal barcoding to quantify the size and number of tumors with each array, as well as the efficiency of each crRNA. These Cas12a alleles will enable the rapid generation of disease models and broadly facilitate the high-throughput investigation of coincident genomic alterations in somatic cells in vivo .

Hallmarks of cancer stemness

Cancer stemness is recognized as a key component of tumor development. Previously coined "cancer stem cells" (CSCs) and believed to be a rare population with rigid hierarchical organization, there is good evidence to suggest that these cells exhibit a plastic cellular state influenced by dynamic CSC-niche interplay. This revelation underscores the need to reevaluate the hallmarks of cancer stemness. Herein, we summarize the techniques used to identify and characterize the state of these cells and discuss their defining and emerging hallmarks, along with their enabling and associated features. We also highlight potential future directions in this field of research.

How CRISPR Is Revolutionizing the Generation of New Models for Cancer Research

Cancers arise through acquisition of mutations in genes that regulate core biological processes like cell proliferation and cell death. Decades of cancer research have led to the identification of genes and mutations causally involved in disease development and evolution, yet defining their precise function across different cancer types and how they influence therapy responses has been challenging. Mouse models have helped define the in vivo function of cancer-associated alterations, and genome-editing approaches using CRISPR have dramatically accelerated the pace at which these models are developed and studied. Here, we highlight how CRISPR technologies have impacted the development and use of mouse models for cancer research and discuss the many ways in which these rapidly evolving platforms will continue to transform our understanding of this disease.

Lung endothelium exploits susceptible tumor cell states to instruct metastatic latency

In metastasis, cancer cells travel around the circulation to colonize distant sites. Due to the rarity of these events, the immediate fates of metastasizing tumor cells (mTCs) are poorly understood while the role of the endothelium as a dissemination interface remains elusive. Using a newly developed combinatorial mTC enrichment approach, we provide a transcriptional blueprint of the early colonization process. Following their arrest at the metastatic site, mTCs were found to either proliferate intravascularly or extravasate, thereby establishing metastatic latency. Endothelial-derived angiocrine Wnt factors drive this bifurcation, instructing mTCs to follow the extravasation-latency route. Surprisingly, mTC responsiveness towards niche-derived Wnt was established at the epigenetic level, which predetermined tumor cell behavior. Whereas hypomethylation enabled high Wnt activity leading to metastatic latency, methylated mTCs exhibited low activity and proliferated intravascularly. Collectively the data identify the predetermined methylation status of disseminated tumor cells as a key regulator of mTC behavior in the metastatic niche.

Decoding the interplay between genetic and non-genetic drivers of metastasis

Metastasis is a multistep process by which cancer cells break away from their original location and spread to distant organs, and is responsible for the vast majority of cancer-related deaths. Preventing early metastatic dissemination would revolutionize the ability to fight cancer. Unfortunately, the relatively poor understanding of the molecular underpinnings of metastasis has hampered the development of effective anti-metastatic drugs. Although it is now accepted that disseminating tumour cells need to acquire multiple competencies to face the many obstacles they encounter before reaching their metastatic site(s), whether these competencies are acquired through an accumulation of metastasis-specific genetic alterations and/or non-genetic events is often debated. Here we review a growing body of literature highlighting the importance of both genetic and non-genetic reprogramming events during the metastatic cascade, and discuss how genetic and non-genetic processes act in concert to confer metastatic competencies. We also describe how recent technological advances, and in particular the advent of single-cell multi-omics and barcoding approaches, will help to better elucidate the cross-talk between genetic and non-genetic mechanisms of metastasis and ultimately inform innovative paths for the early detection and interception of this lethal process.

Genetic Tools for Cell Lineage Tracing and Profiling Developmental Trajectories in the Skin

The epidermis is the body's first line of protection against dehydration and pathogens, continually regenerating the outermost protective skin layers throughout life. During both embryonic development and wound healing, epidermal stem and progenitor cells must respond to external stimuli and insults to build, maintain, and repair the cutaneous barrier. Recent advances in CRISPR-based methods for cell lineage tracing have remarkably expanded the potential for experiments that track stem and progenitor cell proliferation and differentiation over the course of tissue and even organismal development. Additional tools for DNA-based recording of cellular signaling cues promise to deepen our understanding of the mechanisms driving normal skin morphogenesis and response to stressors as well as the dysregulation of cell proliferation and differentiation in skin diseases and cancer. In this review, we highlight cutting-edge methods for cell lineage tracing, including in organoids and model organisms, and explore how cutaneous biology researchers might leverage these techniques to elucidate the developmental programs that support the regenerative capacity and plasticity of the skin.

A novel bioinformatic approach reveals cooperation between Cancer/Testis genes in basal-like breast tumors

Breast cancer is the most prevalent type of cancer in women worldwide. Within breast tumors, the basal-like subtype has the worst prognosis, prompting the need for new tools to understand, detect, and treat these tumors. Certain germline-restricted genes show aberrant expression in tumors and are known as Cancer/Testis genes; their misexpression has diagnostic and therapeutic applications. Here we designed a new bioinformatic approach to examine Cancer/Testis gene misexpression in breast tumors. We identify several new markers in Luminal and HER-2 positive tumors, some of which predict response to chemotherapy. We then use machine learning to identify the two Cancer/Testis genes most associated with basal-like breast tumors: HORMAD1 and CT83. We show that these genes are expressed by tumor cells and not by the microenvironment, and that they are not expressed by normal breast progenitors; in other words, their activation occurs de novo. We find these genes are epigenetically repressed by DNA methylation, and that their activation upon DNA demethylation is irreversible, providing a memory of past epigenetic disturbances. Simultaneous expression of both genes in breast cells in vitro has a synergistic effect that increases stemness and activates a transcriptional profile also observed in double-positive tumors. Therefore, we reveal a functional cooperation between Cancer/Testis genes in basal breast tumors; these findings have consequences for the understanding, diagnosis, and therapy of the breast tumors with the worst outcomes.

PhyloVelo enhances transcriptomic velocity field mapping using monotonically expressed genes

Single-cell RNA sequencing (scRNA-seq) is a powerful approach for studying cellular differentiation, but accurately tracking cell fate transitions can be challenging, especially in disease conditions. Here we introduce PhyloVelo, a computational framework that estimates the velocity of transcriptomic dynamics by using monotonically expressed genes (MEGs) or genes with expression patterns that either increase or decrease, but do not cycle, through phylogenetic time. Through integration of scRNA-seq data with lineage information, PhyloVelo identifies MEGs and reconstructs a transcriptomic velocity field. We validate PhyloVelo using simulated data and Caenorhabditis elegans ground truth data, successfully recovering linear, bifurcated and convergent differentiations. Applying PhyloVelo to seven lineage-traced scRNA-seq datasets, generated using CRISPR-Cas9 editing, lentiviral barcoding or immune repertoire profiling, demonstrates its high accuracy and robustness in inferring complex lineage trajectories while outperforming RNA velocity. Additionally, we discovered that MEGs across tissues and organisms share similar functions in translation and ribosome biogenesis.

Deconstructing cancer with precision genome editing

Recent advances in genome editing technologies are allowing investigators to engineer and study cancer-associated mutations in their endogenous genetic contexts with high precision and efficiency. Of these, base editing and prime editing are quickly becoming gold-standards in the field due to their versatility and scalability. Here, we review the merits and limitations of these precision genome editing technologies, their application to modern cancer research, and speculate how these could be integrated to address future directions in the field.

A logic-incorporated gene regulatory network deciphers principles in cell fate decisions

Organisms utilize gene regulatory networks (GRN) to make fate decisions, but the regulatory mechanisms of transcription factors (TF) in GRNs are exceedingly intricate. A longstanding question in this field is how these tangled interactions synergistically contribute to decision-making procedures. To comprehensively understand the role of regulatory logic in cell fate decisions, we constructed a logic-incorporated GRN model and examined its behavior under two distinct driving forces (noise-driven and signal-driven). Under the noise-driven mode, we distilled the relationship among fate bias, regulatory logic, and noise profile. Under the signal-driven mode, we bridged regulatory logic and progression-accuracy trade-off, and uncovered distinctive trajectories of reprogramming influenced by logic motifs. In differentiation, we characterized a special logic-dependent priming stage by the solution landscape. Finally, we applied our findings to decipher three biological instances: hematopoiesis, embryogenesis, and trans-differentiation. Orthogonal to the classical analysis of expression profile, we harnessed noise patterns to construct the GRN corresponding to fate transition. Our work presents a generalizable framework for top-down fate-decision studies and a practical approach to the taxonomy of cell fate decisions.

Stochastic modelling of single-cell gene expression adaptation reveals non-genomic contribution to evolution of tumor subclones

Cancer progression is an evolutionary process driven by the selection of cells adapted to gain growth advantage. We present the first formal study on the adaptation of gene expression in subclonal evolution. We model evolutionary changes in gene expression as stochastic Ornstein-Uhlenbeck processes, jointly leveraging the evolutionary history of subclones and single-cell expression data. Applying our model to sublines derived from single cells of a mouse melanoma revealed that sublines with distinct phenotypes are underlined by different patterns of gene expression adaptation, indicating non-genetic mechanisms of cancer evolution. Interestingly, sublines previously observed to be resistant to anti-CTLA-4 treatment showed adaptive expression of genes related to invasion and non-canonical Wnt signaling, whereas sublines that responded to treatment showed adaptive expression of genes related to proliferation and canonical Wnt signaling. Our results suggest that clonal phenotypes emerge as the result of specific adaptivity patterns of gene expression.

Cell States in Cancer: Drivers, Passengers, and Trailers

SUMMARY

Cancer is traditionally perceived through a genetic lens, with therapeutic strategies targeting oncogenic driver mutations. We advocate an overarching framework recognizing tumors as comprising driver, passenger, and trailer cell states: Tailoring therapies to simultaneously target driver genetics and cell states may enhance effectiveness and durability.

SIGNIFICANCE

We redefine cancer progression by introducing a model that categorizes tumor cells into "driver," "passenger," and "trailer" phenotypes, expanding the focus on genetic aberrations to cellular behavior. This approach offers a roadmap to guide refining therapeutic strategies for more precise and durable cancer treatments that address tumor heterogeneity and plasticity.

Changes in chromatin accessibility and transcriptional landscape induced by HDAC inhibitors in TP53 mutated patient-derived colon cancer organoids

Here we present the generation and characterization of patient-derived organoids (PDOs) from colorectal cancer patients. PDOs derived from two patients with TP53 mutations were tested with two different HDAC inhibitors (SAHA and NKL54). Cell death induction, transcriptome, and chromatin accessibility changes were analyzed. HDACIs promote the upregulation of low expressed genes and the downregulation of highly expressed genes. A similar differential effect is observed at the level of chromatin accessibility. Only SAHA is a potent inducer of cell death, which is characterized by the upregulation of BH3-only genes BIK and BMF. Up-regulation of BIK is associated with increased accessibility in an intronic region that has enhancer properties. SAHA, but not NKL54, also causes downregulation of BCL2L1 and decreases chromatin accessibility in three distinct regions of the BCL2L1 locus. Both inhibitors upregulate the expression of innate immunity genes and members of the MHC family. In summary, our exploratory study indicates a mechanism of action for SAHA and demonstrate the low efficacy of NKL54 as a single agent for apoptosis induction, using two PDOs. These observations need to be validated in a larger cohort of PDOs.

Lineage motifs as developmental modules for control of cell type proportions

In multicellular organisms, cell types must be produced and maintained in appropriate proportions. One way this is achieved is through committed progenitor cells or extrinsic interactions that produce specific patterns of descendant cell types on lineage trees. However, cell fate commitment is probabilistic in most contexts, making it difficult to infer these dynamics and understand how they establish overall cell type proportions. Here, we introduce Lineage Motif Analysis (LMA), a method that recursively identifies statistically overrepresented patterns of cell fates on lineage trees as potential signatures of committed progenitor states or extrinsic interactions. Applying LMA to published datasets reveals spatial and temporal organization of cell fate commitment in zebrafish and rat retina and early mouse embryonic development. Comparative analysis of vertebrate species suggests that lineage motifs facilitate adaptive evolutionary variation of retinal cell type proportions. LMA thus provides insight into complex developmental processes by decomposing them into simpler underlying modules.

Developmental progression of DNA double-strand break repair deciphered by a single-allele resolution mutation classifier

DNA double-strand breaks (DSBs) are repaired by a hierarchically regulated network of pathways. Factors influencing the choice of particular repair pathways, however remain poorly characterized. Here we develop an Integrated Classification Pipeline (ICP) to decompose and categorize CRISPR/Cas9 generated mutations on genomic target sites in complex multicellular insects. The ICP outputs graphic rank ordered classifications of mutant alleles to visualize discriminating DSB repair fingerprints generated from different target sites and alternative inheritance patterns of CRISPR components. We uncover highly reproducible lineage-specific mutation fingerprints in individual organisms and a developmental progression wherein Microhomology-Mediated End-Joining (MMEJ) or Insertion events predominate during early rapid mitotic cell cycles, switching to distinct subsets of Non-Homologous End-Joining (NHEJ) alleles, and then to Homology-Directed Repair (HDR)-based gene conversion. These repair signatures enable marker-free tracking of specific mutations in dynamic populations, including NHEJ and HDR events within the same samples, for in-depth analysis of diverse gene editing events.

Maximum Likelihood Inference of Time-scaled Cell Lineage Trees with Mixed-type Missing Data

Recent dynamic lineage tracing technologies combine CRISPR-based genome editing with single-cell sequencing to track cell divisions during development. A key computational problem in dynamic lineage tracing is to infer a cell lineage tree from the measured CRISPR-induced mutations. Three features of dynamic lineage tracing data distinguish this problem from standard phylogenetic tree inference. First, the CRISPR-editing process modifies a genomic location exactly once. This non-modifiable property is not well described by the time-reversible models commonly used in phylogenetics. Second, as a consequence of non-modifiability, the number of mutations per time unit decreases over time. Third, CRISPR-based genome-editing and single-cell sequencing results in high rates of both heritable and non-heritable (dropout) missing data. To model these features, we introduce the Probabilistic Mixed-type Missing (PMM) model. We describe an algorithm, LAML (Lineage Analysis via Maximum Likelihood), to search for the maximum likelihood (ML) tree under the PMM model. LAML combines an Expectation Maximization (EM) algorithm with a heuristic tree search to jointly estimate tree topology, branch lengths and missing data parameters. We derive a closed-form solution for the M-step in the case of no heritable missing data, and a block coordinate ascent approach in the general case which is more efficient than the standard General Time Reversible (GTR) phylogenetic model. On simulated data, LAML infers more accurate tree topologies and branch lengths than existing methods, with greater advantages on datasets with higher ratios of heritable to non-heritable missing data. We show that LAML provides unbiased time-scaled estimates of branch lengths. In contrast, we demonstrate that maximum parsimony methods for lineage tracing data not only underestimate branch lengths, but also yield branch lengths which are not proportional to time, due to the nonlinear decay in the number of mutations on branches further from the root. On lineage tracing data from a mouse model of lung adenocarcinoma, we show that LAML infers phylogenetic distances that are more concordant with gene expression data compared to distances derived from maximum parsimony. The LAML tree topology is more plausible than existing published trees, with fewer total cell migrations between distant metastases and fewer reseeding events where cells migrate back to the primary tumor. Crucially, we identify three distinct time epochs of metastasis progression, which includes a burst of metastasis events to various anatomical sites during a single month.

Mapping single-cell developmental potential in health and disease with interpretable deep learning

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cell fate in developmental systems. However, identifying the molecular hallmarks of potency - the capacity of a cell to differentiate into other cell types - has remained challenging. Here, we introduce CytoTRACE 2, an interpretable deep learning framework for characterizing potency and differentiation states on an absolute scale from scRNA-seq data. Across 31 human and mouse scRNA-seq datasets encompassing 28 tissue types, CytoTRACE 2 outperformed existing methods for recovering experimentally determined potency levels and differentiation states covering the entire range of cellular ontogeny. Moreover, it reconstructed the temporal hierarchy of mouse embryogenesis across 62 timepoints; identified pan-tissue expression programs that discriminate major potency levels; and facilitated discovery of cellular phenotypes in cancer linked to survival and immunotherapy resistance. Our results illuminate a fundamental feature of cell biology and provide a broadly applicable platform for delineating single-cell differentiation landscapes in health and disease.

Bayesian inference of relative fitness on high-throughput pooled competition assays

The tracking of lineage frequencies via DNA barcode sequencing enables the quantification of microbial fitness. However, experimental noise coming from biotic and abiotic sources complicates the computation of a reliable inference. We present a Bayesian pipeline to infer relative microbial fitness from high-throughput lineage tracking assays. Our model accounts for multiple sources of noise and propagates uncertainties throughout all parameters in a systematic way. Furthermore, using modern variational inference methods based on automatic differentiation, we are able to scale the inference to a large number of unique barcodes. We extend this core model to analyze multi-environment assays, replicate experiments, and barcodes linked to genotypes. On simulations, our method recovers known parameters within posterior credible intervals. This work provides a generalizable Bayesian framework to analyze lineage tracking experiments. The accompanying open-source software library enables the adoption of principled statistical methods in experimental evolution.

Deciphering cell states and genealogies of human haematopoiesis

The human blood system is maintained through the differentiation and massive amplification of a limited number of long-lived haematopoietic stem cells (HSCs)1. Perturbations to this process underlie diverse diseases, but the clonal contributions to human haematopoiesis and how this changes with age remain incompletely understood. Although recent insights have emerged from barcoding studies in model systems2-5, simultaneous detection of cell states and phylogenies from natural barcodes in humans remains challenging. Here we introduce an improved, single-cell lineage-tracing system based on deep detection of naturally occurring mitochondrial DNA mutations with simultaneous readout of transcriptional states and chromatin accessibility. We use this system to define the clonal architecture of HSCs and map the physiological state and output of clones. We uncover functional heterogeneity in HSC clones, which is stable over months and manifests as both differences in total HSC output and biases towards the production of different mature cell types. We also find that the diversity of HSC clones decreases markedly with age, leading to an oligoclonal structure with multiple distinct clonal expansions. Our study thus provides a clonally resolved and cell-state-aware atlas of human haematopoiesis at single-cell resolution, showing an unappreciated functional diversity of human HSC clones and, more broadly, paving the way for refined studies of clonal dynamics across a range of tissues in human health and disease.

Mutations, Bottlenecks, and Clonal Sweeps: How Environmental Carcinogens and Genomic Changes Shape Clonal Evolution during Tumor Progression

The transition from a single, initiated cell to a full-blown malignant tumor involves significant genomic evolution. Exposure to carcinogens-whether directly mutagenic or not-can drive progression toward malignancy, as can stochastic acquisition of cancer-promoting genetic events. Mouse models using both carcinogens and germline genetic manipulations have enabled precise inquiry into the evolutionary dynamics that take place as a tumor progresses from benign to malignant to metastatic stages. Tumor progression is characterized by changes in somatic point mutations and copy-number alterations, even though any single tumor can itself have a high or low burden of genomic alterations. Further, lineage-tracing, single-cell analyses and CRISPR barcoding have revealed the distinct clonal dynamics within benign and malignant tumors. Application of these tools in a range of mouse models can shed unique light on the patterns of clonal evolution that take place in both mouse and human tumors.

The contribution of asymmetric cell division to phenotypic heterogeneity in cancer

Cells have evolved intricate mechanisms for dividing their contents in the most symmetric way during mitosis. However, a small proportion of cell divisions results in asymmetric segregation of cellular components, which leads to differences in the characteristics of daughter cells. Although the classical function of asymmetric cell division (ACD) in the regulation of pluripotency is the generation of one differentiated daughter cell and one self-renewing stem cell, recent evidence suggests that ACD plays a role in other physiological processes. In cancer, tumor heterogeneity can result from the asymmetric segregation of genetic material and other cellular components, resulting in cell-to-cell differences in fitness and response to therapy. Defining the contribution of ACD in generating differences in key features relevant to cancer biology is crucial to advancing our understanding of the causes of tumor heterogeneity and developing strategies to mitigate or counteract it. In this Review, we delve into the occurrence of asymmetric mitosis in cancer cells and consider how ACD contributes to the variability of several phenotypes. By synthesizing the current literature, we explore the molecular mechanisms underlying ACD, the implications of phenotypic heterogeneity in cancer, and the complex interplay between these two phenomena.

Past, present, and future of CRISPR genome editing technologies

Genome editing has been a transformative force in the life sciences and human medicine, offering unprecedented opportunities to dissect complex biological processes and treat the underlying causes of many genetic diseases. CRISPR-based technologies, with their remarkable efficiency and easy programmability, stand at the forefront of this revolution. In this Review, we discuss the current state of CRISPR gene editing technologies in both research and therapy, highlighting limitations that constrain them and the technological innovations that have been developed in recent years to address them. Additionally, we examine and summarize the current landscape of gene editing applications in the context of human health and therapeutics. Finally, we outline potential future developments that could shape gene editing technologies and their applications in the coming years.