DNA barcoding reveals diverse growth kinetics of human breast tumour subclones in serially passaged xenografts

The Power and Promise of Patient-Derived Xenografts of Human Breast Cancer

In 2016, a group of researchers engaged in the development of patient-derived xenografts (PDXs) of human breast cancer provided a comprehensive review of the state of the field. In that review, they summarized the clinical problem that PDXs might address, the technical approaches to their generation (including a discussion of host animals and transplant conditions tested), and presented transplantation success (take) rates across groups and across transplantation conditions. At the time, there were just over 500 unique PDX models created by these investigators representing all three clinically defined subtypes (ER+, HER2+, and TNBC). Today, many of these PDX resources have at least doubled in size, and several more PDX development groups now exist, such that there may be well upward of 1000 PDX models of human breast cancer in existence worldwide. They also presented a series of open questions for the field. Many of these questions have been addressed. However, several remain open, or only partially addressed. Herein, we revisit these questions, and recount the progress that has been made in a number of areas with respect to generation, characterization, and use of PDXs in translational research, and re-present questions that remain open. These open questions, and others, are now being addressed not only by individual investigators, but also large, well-funded consortia including the PDXNet program of the National Cancer Institute in the United States, and the EuroPDX Consortium, an organization of PDX developers across Europe. Finally, we discuss the new opportunities in PDX-based research.

The molecular map of CLL and Richter's syndrome

Clonal expansion of B-cells, from the early stages of monoclonal B-cell lymphocytosis through to chronic lymphocytic leukemia (CLL), and then in some cases to Richter's syndrome (RS) provides a comprehensive model of cancer evolution, notable for the marked morphological transformation and distinct clinical phenotypes. High-throughput sequencing of large cohorts of patients and single-cell studies have generated a molecular map of CLL and more recently, of RS, yielding fundamental insights into these diseases and of clonal evolution. A selection of CLL driver genes have been functionally interrogated to yield novel insights into the biology of CLL. Such findings have the potential to impact patient care through risk stratification, treatment selection and drug discovery. However, this molecular map remains incomplete, with extant questions concerning the origin of the B-cell clone, the role of the TME, inter- and intra-compartmental heterogeneity and of therapeutic resistance mechanisms. Through the application of multi-modal single-cell technologies across tissues, disease states and clinical contexts, these questions can now be addressed with the answers holding great promise of generating translatable knowledge to improve patient care.

Clonal tracking in cancer and metastasis

The eradication of many cancers has proven challenging due to the presence of functionally and genetically heterogeneous clones maintained by rare cancer stem cells (CSCs), which contribute to disease progression, treatment refractoriness, and late relapse. The characterization of functional CSC activity has necessitated the development of modern clonal tracking strategies. This review describes viral-based and CRISPR-Cas9-based cellular barcoding, lineage tracing, and imaging-based approaches. DNA-based cellular barcoding technology is emerging as a powerful and robust strategy that has been widely applied to in vitro and in vivo model systems, including patient-derived xenograft models. This review also highlights the potential of these methods for use in the clinical and drug discovery contexts and discusses the important insights gained from such approaches.

Experimental and spontaneous metastasis assays can result in divergence in clonal architecture

Intratumoural heterogeneity is associated with poor outcomes in breast cancer. To understand how malignant clones survive and grow in metastatic niches, in vivo models using cell lines and patient-derived xenografts (PDX) have become the gold standard. Injections of cancer cells in orthotopic sites (spontaneous metastasis assays) or into the vasculature (experimental metastasis assays) have been used interchangeably to study the metastatic cascade from early events or post-intravasation, respectively. However, less is known about how these different routes of injection impact heterogeneity. Herein we directly compared the clonality of spontaneous and experimental metastatic assays using the human cell line MDA-MB-231 and a PDX model. Genetic barcoding was used to study the fitness of the subclones in primary and metastatic sites. Using spontaneous assays, we found that intraductal injections resulted in less diverse tumours compared to other routes of injections. Using experimental metastasis assays via tail vein injection of barcoded MDA-MB-231 cells, we also observed an asymmetry in metastatic heterogeneity between lung and liver that was not observed using spontaneous metastasis assays. These results demonstrate that these assays can result in divergent clonal outputs in terms of metastatic heterogeneity and provide a better understanding of the biases inherent to each technique.

Cellular barcoding tracks heterogeneous clones through selective pressures and phenotypic transitions

Genomic DNA barcoding has emerged as a sensitive and flexible tool to measure the fates of clonal subpopulations within a heterogeneous cancer cell population. Coupling cellular barcoding with single-cell transcriptomics permits the longitudinal analysis of molecular mechanisms with detailed clone-level resolution. Numerous recent studies have employed these tools to track clonal cell states in cancer progression and treatment response. With these new technologies comes the opportunity to examine longstanding questions about the origins and contributions of tumor cell heterogeneity and the roles of selection and phenotypic plasticity in disease progression and treatment.

Zebrafish Cancer Avatars: A Translational Platform for Analyzing Tumor Heterogeneity and Predicting Patient Outcomes

The increasing number of available anti-cancer drugs presents a challenge for oncologists, who must choose the most effective treatment for the patient. Precision cancer medicine relies on matching a drug with a tumor's molecular profile to optimize the therapeutic benefit. However, current precision medicine approaches do not fully account for intra-tumoral heterogeneity. Different mutation profiles and cell behaviors within a single heterogeneous tumor can significantly impact therapy response and patient outcomes. Patient-derived avatar models recapitulate a patient's tumor in an animal or dish and provide the means to functionally assess heterogeneity's impact on drug response. Mouse xenograft and organoid avatars are well-established, but the time required to generate these models is not practical for clinical decision-making. Zebrafish are emerging as a time-efficient and cost-effective cancer avatar model. In this review, we highlight recent developments in zebrafish cancer avatar models and discuss the unique features of zebrafish that make them ideal for the interrogation of cancer heterogeneity and as part of precision cancer medicine pipelines.

DNA barcoding and gene expression recording reveal the presence of cancer cells with unique properties during tumor progression

Tumors comprise diverse cancer cell populations with specific capabilities for adaptation to the tumor microenvironment, resistance to anticancer treatments, and metastatic dissemination. However, whether these populations are pre-existing in cancer cells or stochastically appear during tumor growth remains unclear. Here, we show the heterogeneous behaviors of cancer cells regarding response to anticancer drug treatments, formation of lung metastases, and expression of transcription factors related to cancer stem-like cells using a DNA barcoding and gene expression recording system. B16F10 cells maintained clonal diversity after treatment with HVJ-E, a UV-irradiated Sendai virus, and the anticancer drug dacarbazine. PBS treatment of the primary tumor and intravenous injection of B16F10 cells resulted in metastases formed from clones of multiple cell lineages. Conversely, BL6 and 4T1 cells developed spontaneous lung metastases by a small number of clones. Notably, an identical clone of 4T1 cells developed lung metastases in different mice, suggesting the existence of cells with high metastatic potential. Cas9-based transcription recording analysis in a human prostate cancer cell line revealed that specific cells express POU5F1 in response to an anticancer drug and sphere formation. Our findings provide insights into the diversity of cancer cells during tumor progression.

Mastering the use of cellular barcoding to explore cancer heterogeneity

Tumours are often composed of a multitude of malignant clones that are genomically unique, and only a few of them may have the ability to escape cancer therapy and grow as symptomatic lesions. As a result, tumours with a large degree of genomic diversity have a higher chance of leading to patient death. However, clonal fate can be driven by non-genomic features. In this context, new technologies are emerging not only to track the spatiotemporal fate of individual cells and their progeny but also to study their molecular features using various omics analysis. In particular, the recent development of cellular barcoding facilitates the labelling of tens to millions of cancer clones and enables the identification of the complex mechanisms associated with clonal fate in different microenvironments and in response to therapy. In this Review, we highlight the recent discoveries made using lentiviral-based cellular barcoding techniques, namely genetic and optical barcoding. We also emphasize the strengths and limitations of each of these technologies and discuss some of the key concepts that must be taken into consideration when one is designing barcoding experiments. Finally, we suggest new directions to further improve the use of these technologies in cancer research.

Accurate determination of CRISPR-mediated gene fitness in transplantable tumours

Assessing tumour gene fitness in physiologically-relevant model systems is challenging due to biological features of in vivo tumour regeneration, including extreme variations in single cell lineage progeny. Here we develop a reproducible, quantitative approach to pooled genetic perturbation in patient-derived xenografts (PDXs), by encoding single cell output from transplanted CRISPR-transduced cells in combination with a Bayesian hierarchical model. We apply this to 181 PDX transplants from 21 breast cancer patients. We show that uncertainty in fitness estimates depends critically on the number of transplant cell clones and the variability in clone sizes. We use a pathway-directed allelic series to characterize Notch signaling, and quantify TP53 / MDM2 drug-gene conditional fitness in outlier patients. We show that fitness outlier identification can be mirrored by pharmacological perturbation. Overall, we demonstrate that the gene fitness landscape in breast PDXs is dominated by inter-patient differences.

Clonal barcoding with qPCR detection enables live cell functional analyses for cancer research

Single-cell analysis methods are valuable tools; however, current approaches do not easily enable live cell retrieval. That is a particular issue when further study of cells that were eliminated during experimentation could provide critical information. We report a clonal molecular barcoding method, called SunCatcher, that enables longitudinal tracking and live cell functional analysis. From complex cell populations, we generate single cell-derived clonal populations, infect each with a unique molecular barcode, and retain stocks of individual barcoded clones (BCs). We develop quantitative PCR-based and next-generation sequencing methods that we employ to identify and quantify BCs in vitro and in vivo. We apply SunCatcher to various breast cancer cell lines and combine respective BCs to create versions of the original cell lines. While the heterogeneous BC pools reproduce their original parental cell line proliferation and tumor progression rates, individual BCs are phenotypically and functionally diverse. Early spontaneous metastases can also be identified and quantified. SunCatcher thus provides a rapid and sensitive approach for studying live single-cell clones and clonal evolution, and performing functional analyses.

DNA barcoded competitive clone-initiating cell analysis reveals novel features of metastatic growth in a cancer xenograft model

A problematic feature of many human cancers is a lack of understanding of mechanisms controlling organ-specific patterns of metastasis, despite recent progress in identifying many mutations and transcriptional programs shown to confer this potential. To address this gap, we developed a methodology that enables different aspects of the metastatic process to be comprehensively characterized at a clonal resolution. Our approach exploits the application of a computational pipeline to analyze and visualize clonal data obtained from transplant experiments in which a cellular DNA barcoding strategy is used to distinguish the separate clonal contributions of two or more competing cell populations. To illustrate the power of this methodology, we demonstrate its ability to discriminate the metastatic behavior in immunodeficient mice of a well-established human metastatic cancer cell line and its co-transplanted LRRC15 knockdown derivative. We also show how the use of machine learning to quantify clone-initiating cell (CIC) numbers and their subsequent metastatic progeny generated in different sites can reveal previously unknown relationships between different cellular genotypes and their initial sites of implantation with their subsequent respective dissemination patterns. These findings underscore the potential of such combined genomic and computational methodologies to identify new clonally-relevant drivers of site-specific patterns of metastasis.

Comparison of clonal architecture between primary and immunodeficient mouse-engrafted acute myeloid leukemia cells

Patient-derived xenografts (PDX) are widely used as human cancer models. Previous studies demonstrated clonal discordance between PDX and primary cells. However, in acute myeloid leukemia (AML)-PDX models, the significance of the clonal dynamics occurring in PDX remains unclear. By evaluating changes in the variant allele frequencies (VAF) of somatic mutations in serial samples of paired primary AML and their PDX bone marrow cells, we identify the skewing engraftment of relapsed or refractory (R/R) AML clones in 57% of PDX models generated from multiclonal AML cells at diagnosis, even if R/R clones are minor at <5% of VAF in patients. The event-free survival rate of patients whose AML cells successfully engraft in PDX models is consistently lower than that of patients with engraftment failure. We herein demonstrate that primary AML cells including potentially chemotherapy-resistant clones dominantly engraft in AML-PDX models and they enrich pre-existing treatment-resistant subclones.

Validation-Based Insertional Mutagenesis (VBIM), A Powerful Forward Genetic Screening Strategy

Forward genetics begins with a biological phenotype and attempts to identify genetic changes that influence that phenotype. These changes can be induced in a selected group of genes, for instance, by using libraries of cDNAs, shRNAs, CRISPR guide RNAs, or genetic suppressor elements (GSEs), or randomly throughout the genome using chemical or insertional mutagens, with each approach creating distinct genetic changes. The Validation-Based Insertional Mutagenesis (VBIM) strategy utilizes modified lentiviruses as insertional mutagens, placing strong promoters throughout the genome. Generating libraries with millions of cells carrying one or a few VBIM promoter insertions is straightforward, allowing selection of cells in which overexpression of VBIM-driven RNAs or proteins promote the phenotype of interest. VBIM-driven RNAs may encode full-length proteins, truncated proteins (which may have wild-type, constitutive, or dominant-negative activity), or antisense RNAs that can disrupt gene expression. The diversity in VBIM-driven changes allows for the identification of both gain-of-function and loss-of-function mutations in a single screen. Additionally, VBIM can target any genomic locus, regardless of whether it is expressed in the cells under study or known to have a biological function, allowing for true whole-genome screens without the complication and cost of constructing, maintaining, and delivering a comprehensive library. Here, we review the VBIM strategy and discuss examples in which VBIM has been successfully used in diverse screens to identify novel genes or novel functions for known genes. In addition, we discuss considerations for transitioning the VBIM strategy to in vivo screens. We hope that other laboratories will be encouraged to use the VBIM strategy to identify genes that influence their phenotypes of interest. © 2022 Wiley Periodicals LLC.

De novo and cell line models of human mammary cell transformation reveal an essential role for Yb-1 in multiple stages of human breast cancer

Breast cancer heterogeneity has made it challenging to identify mechanisms critical to the initial stages of their genesis in vivo. Here, we sought to interrogate the role of YB-1 in newly arising human breast cancers as well as in established cell lines. In a first series of experiments, we found that short-hairpin RNA-mediated knockdown of YB-1 in MDA-MB-231 cells blocked both their local tumour-forming and lung-colonising activity in immunodeficient mice. Conversely, upregulated expression of YB-1 enhanced the poor in vivo tumorigenicity of T47D cells. We then found that YB-1 knockdown also inhibits the initial generation in mice of invasive ductal carcinomas and ductal carcinomas in situ from freshly isolated human mammary cells transduced, respectively, with KRAS^G12D or myristoylated-AKT1. Interestingly, increased expression of HIF1α and G3BP1, two YB-1 translational targets and elements of a stress-adaptive programme, mirrored the levels of YB-1 in both transformed primary and established MDA-MB-231 breast cancer cells.

Deciphering intratumoral heterogeneity using integrated clonal tracking and single-cell transcriptome analyses

Cellular heterogeneity is a major cause of treatment resistance in cancer. Despite recent advances in single-cell genomic and transcriptomic sequencing, it remains difficult to relate measured molecular profiles to the cellular activities underlying cancer. Here, we present an integrated experimental system that connects single cell gene expression to heterogeneous cancer cell growth, metastasis, and treatment response. Our system integrates single cell transcriptome profiling with DNA barcode based clonal tracking in patient-derived xenograft models. We show that leukemia cells exhibiting unique gene expression respond to different chemotherapies in distinct but consistent manners across multiple mice. In addition, we uncover a form of leukemia expansion that is spatially confined to the bone marrow of single anatomical sites and driven by cells with distinct gene expression. Our integrated experimental system can interrogate the molecular and cellular basis of the intratumoral heterogeneity underlying disease progression and treatment resistance.

DNA barcoding is a promising technology for the simultaneous analysis of genetic and phenotypic heterogeneity. Here, the authors combine DNA barcoding and single-cell RNA-seq to study heterogeneity, progression and response to therapy in B-cell acute lymphoblastic leukaemia patient-derived xenografts.

DNA barcode to trace the development and differentiation of cord blood stem cells (Review)

Umbilical cord blood transplantation was first reported in 1980. Since then, additional research has indicated that umbilical cord blood stem cells (UCBSCs) have various advantages, such as multi-lineage differentiation potential and potent renewal activity, which may be induced to promote their differentiation into a variety of seed cells for tissue engineering and the treatment of clinical and metabolic diseases. Recent studies suggested that UCBSCs are able to differentiate into nerve cells, chondrocytes, hepatocyte-like cells, fat cells and osteoblasts. The culture of UCBSCs has developed from feeder-layer to feeder-free culture systems. The classical techniques of cell labeling and tracing by gene transfection and fluorescent dye and nucleic acid analogs have evolved to DNA barcode technology mediated by transposon/retrovirus, cyclization recombination-recombinase and clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 strategies. DNA barcoding for cell development tracing has advanced to include single cells and single nucleic acid mutations. In the present study, the latest research findings on the development and differentiation, culture techniques and labeling and tracing of UCBSCs are reviewed. The present study may increase the current understanding of UCBSC biology and its clinical applications.

Single-cell DNA and RNA sequencing reveals the dynamics of intra-tumor heterogeneity in a colorectal cancer model

BACKGROUND

Intra-tumor heterogeneity (ITH) encompasses cellular differences in tumors and is related to clinical outcomes such as drug resistance. However, little is known about the dynamics of ITH, owing to the lack of time-series analysis at the single-cell level. Mouse models that recapitulate cancer development are useful for controlled serial time sampling.

RESULTS

We performed single-cell exome and transcriptome sequencing of 200 cells to investigate how ITH is generated in a mouse colorectal cancer model. In the model, a single normal intestinal cell is grown into organoids that mimic the intestinal crypt structure. Upon RNAi-mediated downregulation of a tumor suppressor gene APC, the transduced organoids were serially transplanted into mice to allow exposure to in vivo microenvironments, which play relevant roles in cancer development. The ITH of the transcriptome increased after the transplantation, while that of the exome decreased. Mutations generated during organoid culture did not greatly change at the bulk-cell level upon the transplantation. The RNA ITH increase was due to the emergence of new transcriptional subpopulations. In contrast to the initial cells expressing mesenchymal-marker genes, new subpopulations repressed these genes after the transplantation. Analyses of colorectal cancer data from The Cancer Genome Atlas revealed a high proportion of metastatic cases in human subjects with expression patterns similar to the new cell subpopulations in mouse. These results suggest that the birth of transcriptional subpopulations may be a key for adaptation to drastic micro-environmental changes when cancer cells have sufficient genetic alterations at later tumor stages.

CONCLUSIONS

This study revealed an evolutionary dynamics of single-cell RNA and DNA heterogeneity in tumor progression, giving insights into the mesenchymal-epithelial transformation of tumor cells at metastasis in colorectal cancer.

Spatial omics and multiplexed imaging to explore cancer biology

Understanding intratumoral heterogeneity-the molecular variation among cells within a tumor-promises to address outstanding questions in cancer biology and improve the diagnosis and treatment of specific cancer subtypes. Single-cell analyses, especially RNA sequencing and other genomics modalities, have been transformative in revealing novel biomarkers and molecular regulators associated with tumor growth, metastasis and drug resistance. However, these approaches fail to provide a complete picture of tumor biology, as information on cellular location within the tumor microenvironment is lost. New technologies leveraging multiplexed fluorescence, DNA, RNA and isotope labeling enable the detection of tens to thousands of cancer subclones or molecular biomarkers within their native spatial context. The expeditious growth in these techniques, along with methods for multiomics data integration, promises to yield a more comprehensive understanding of cell-to-cell variation within and between individual tumors. Here we provide the current state and future perspectives on the spatial technologies expected to drive the next generation of research and diagnostic and therapeutic strategies for cancer.

Breast cancer as an example of tumour heterogeneity and tumour cell plasticity during malignant progression

Heterogeneity within a tumour increases its ability to adapt to constantly changing constraints, but adversely affects a patient's prognosis, therapy response and clinical outcome. Intratumoural heterogeneity results from a combination of extrinsic factors from the tumour microenvironment and intrinsic parameters from the cancer cells themselves, including their genetic, epigenetic and transcriptomic traits, their ability to proliferate, migrate and invade, and their stemness and plasticity attributes. Cell plasticity constitutes the ability of cancer cells to rapidly reprogramme their gene expression repertoire, to change their behaviour and identities, and to adapt to microenvironmental cues. These features also directly contribute to tumour heterogeneity and are critical for malignant tumour progression. In this article, we use breast cancer as an example of the origins of tumour heterogeneity (in particular, the mutational spectrum and clonal evolution of progressing tumours) and of tumour cell plasticity (in particular, that shown by tumour cells undergoing epithelial-to-mesenchymal transition), as well as considering interclonal cooperativity and cell plasticity as sources of cancer cell heterogeneity. We review current knowledge on the functional contribution of cell plasticity and tumour heterogeneity to malignant tumour progression, metastasis formation and therapy resistance.

The site of breast cancer metastases dictates their clonal composition and reversible transcriptomic profile

Intratumoral heterogeneity is a driver of breast cancer progression, but the nature of the clonal interactive network involved in this process remains unclear. Here, we optimized the use of optical barcoding to visualize and characterize 31 cancer subclones in vivo. By mapping the clonal composition of thousands of metastases in two clinically relevant sites, the lungs and liver, we found that metastases were highly polyclonal in lungs but not in the liver. Furthermore, the transcriptome of the subclones varied according to their metastatic niche. We also identified a reversible niche-driven signature that was conserved in lung and liver metastases collected during patient autopsies. Among this signature, we found that the tumor necrosis factor-α pathway was up-regulated in lung compared to liver metastases, and inhibition of this pathway affected metastasis diversity. These results highlight that the cellular and molecular heterogeneity observed in metastases is largely dictated by the tumor microenvironment.

Barcoding Technology for Multiplexed Analysis of Metastatic Ability In Vivo

DNA barcoding allows the quantitative, biomarker-free tracking of individual cell populations in mixed/heterogeneous cell pools. Here, we describe a multiplexed in vivo screening platform based on DNA barcoding technology to interrogate compound libraries for their effect on metastatic seeding in vivo. We apply next-generation sequencing (NGS) technology to quantitatively analyze high-throughput compound screening in mice. Up to 96 compounds and controls can be screened for their effect on metastatic ability in a single mouse.

Gene expression profiles of the original tumors influence the generation of PDX models of lung squamous cell carcinoma

Patient-derived xenograft (PDX) murine models are employed for preclinical research on cancers, including non-small cell lung cancers (NSCLCs). Even though lung squamous cell carcinomas (LUSCs) show the highest engraftment rate among NSCLCs, half of them nevertheless show PDX failure in immunodeficient mice. Here, using immunohistochemistry and RNA sequencing, we evaluated the distinct immunohistochemical and gene expression profiles of resected LUSCs that showed successful engraftment. Among various LUSCs, including the basal, classical, secretory, and primitive subtypes, those in the non-engrafting (NEG) group showed gene expression profiles similar to the pure secretory subtype with positivity for CK7, whereas those in the engrafting (EG) group were similar to the mixed secretory subtype with positivity for p63. Pathway analysis of 295 genes that demonstrated significant differences in expression between NEG and EG tumors revealed that the former had enriched expression of genes related to the immune system, whereas the latter had enriched expression of genes related to the cell cycle and DNA replication. Interestingly, NEG tumors showed higher infiltration of B cells (CD19+) and follicular dendritic cells (CD23+) in lymph follicles than EG tumors. Taken together, these findings suggest that the PDX cancer model of LUSC represents only a certain population of LUSCs and that CD19- and CD23-positive tumor-infiltrating immune cells in the original tumors may negatively influence PDX engraftment in immunodeficient mice.

Asymmetric Cell Division of Fibroblasts is An Early Deterministic Step to Generate Elite Cells during Cell Reprogramming

Cell reprogramming is considered a stochastic process, and it is not clear which cells are prone to be reprogrammed and whether a deterministic step exists. Here, asymmetric cell division (ACD) at the early stage of induced neuronal (iN) reprogramming is shown to play a deterministic role in generating elite cells for reprogramming. Within one day, fibroblasts underwent ACD, with one daughter cell being converted into an iN precursor and the other one remaining as a fibroblast. Inhibition of ACD significantly inhibited iN conversion. Moreover, the daughter cells showed asymmetric DNA segregation and histone marks during cytokinesis, and the cells inheriting newly replicated DNA strands during ACD became iN precursors. These results unravel a deterministic step at the early phase of cell reprogramming and demonstrate a novel role of ACD in cell phenotype change. This work also supports a novel hypothesis that daughter cells with newly replicated DNA strands are elite cells for reprogramming, which remains to be tested in various reprogramming processes.

Preclinical models of pancreatic ductal adenocarcinoma: challenges and opportunities in the era of precision medicine

Pancreatic ductal adenocarcinoma (PDAC) is an extremely lethal malignancy, with an average 5-year survival rate of 9% (Siegel RL, Miller KD, Jemal A. Ca Cancer J Clin. 2019;69(1):7-34). The steady increase in mortality rate indicates limited efficacy of the conventional regimen. The heterogeneity of PDAC calls for personalized treatment in clinical practice, which requires the construction of a preclinical system for generating patient-derived models. Currently, the lack of high-quality preclinical models results in ineffective translation of novel targeted therapeutics. This review summarizes applications of commonly used models, discusses major difficulties in PDAC model construction and provides recommendations for integrating workflows for precision medicine.

Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology

We provide an overview on the use of biological assays to calibrate and initialize mechanism-based models of cancer phenomena. Although artificial intelligence methods currently dominate the landscape in computational oncology, mathematical models that seek to explicitly incorporate biological mechanisms into their formalism are of increasing interest. These models can guide experimental design and provide insights into the underlying mechanisms of cancer progression. Historically, these models have included a myriad of parameters that have been difficult to quantify in biologically relevant systems, limiting their practical insights. Recently, however, there has been much interest calibrating biologically based models with the quantitative measurements available from (for example) RNA sequencing, time-resolved microscopy, and in vivo imaging. In this contribution, we summarize how a variety of experimental methods quantify tumor characteristics from the molecular to tissue scales and describe how such data can be directly integrated with mechanism-based models to improve predictions of tumor growth and treatment response.

Methodological Advancements for Investigating Intra-tumoral Heterogeneity in Breast Cancer at the Bench and Bedside

There is a major need to overcome therapeutic resistance and metastasis that eventually arises in many breast cancer patients. Therapy resistant and metastatic tumors are increasingly recognized to possess intra-tumoral heterogeneity (ITH), a diversity of cells within an individual tumor. First hypothesized in the 1970s, the possibility that this complex ITH may endow tumors with adaptability and evolvability to metastasize and evade therapies is now supported by multiple lines of evidence. Our understanding of ITH has been driven by recent methodological advances including next-generation sequencing, computational modeling, lineage tracing, single-cell technologies, and multiplexed in situ approaches. These have been applied across a range of specimens, including patient tumor biopsies, liquid biopsies, cultured cell lines, and mouse models. In this review, we discuss these approaches and how they have deepened our understanding of the mechanistic origins of ITH amongst tumor cells, including stem cell-like differentiation hierarchies and Darwinian evolution, and the functional role for ITH in breast cancer progression. While ITH presents a challenge for combating tumor evolution, in-depth analyses of ITH in clinical biopsies and laboratory models hold promise to elucidate therapeutic strategies that should ultimately improve outcomes for breast cancer patients.

Adaptation and selection shape clonal evolution of tumors during residual disease and recurrence

The survival and recurrence of residual tumor cells following therapy constitutes one of the biggest obstacles to obtaining cures in breast cancer, but it remains unclear how the clonal composition of tumors changes during relapse. We use cellular barcoding to monitor clonal dynamics during tumor recurrence in vivo. We find that clonal diversity decreases during tumor regression, residual disease, and recurrence. The recurrence of dormant residual cells follows several distinct routes. Approximately half of the recurrent tumors exhibit clonal dominance with a small number of subclones comprising the vast majority of the tumor; these clonal recurrences are frequently dependent upon Met gene amplification. A second group of recurrent tumors comprises thousands of subclones, has a clonal architecture similar to primary tumors, and is dependent upon the Jak/Stat pathway. Thus the regrowth of dormant tumors proceeds via multiple routes, producing recurrent tumors with distinct clonal composition, genetic alterations, and drug sensitivities.

The cellular composition of recurrent tumors can provide insight into resistance to therapy and inform on second line therapies. Here, using a genetically modified mouse, the authors perform barcoding experiments of the primary tumors to allow them to study the clonal dynamics of tumor recurrence.

High accuracy gene expression profiling of sorted cell subpopulations from breast cancer PDX model tissue

Intratumor Heterogeneity (ITH) is a functionally important property of tumor tissue and may be involved in drug resistance mechanisms. Although descriptions of ITH can be traced back to very early reports about cancer tissue, mechanistic investigations are still limited by the precision of analysis methods and access to relevant tissue sources. PDX models have provided a reproducible source of tissue with at least a partial representation of naturally occurring ITH. We investigated the properties of phenotypically distinct cell populations by Fluorescence activated cell sorting (FACS) tissue derived cells from multiple tumors from a triple negative breast cancer patient derived xenograft (PDX) model. We subsequently subjected each population to in depth gene expression analysis. Our findings suggest that process related gene expression changes (caused by tissue dissociation and FACS sorting) are restricted to Immediate Early Genes (IEGs). This allowed us to discover highly reproducible gene expression profiles of distinct cellular compartments identifiable by cell surface markers in this particular tumor model. Within the context of data from a previously published model our work suggests that gene expression profiles associated with hypoxia, stemness and drug resistance may reside in tumor subpopulations predictably growing in PDX models. This approach provides a novel opportunity for prospective mechanistic studies of ITH.

Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics

Cancer represents an evolutionary process through which growing malignant populations genetically diversify, leading to tumour progression, relapse and resistance to therapy. In addition to genetic diversity, the cell-to-cell variation that fuels evolutionary selection also manifests in cellular states, epigenetic profiles, spatial distributions and interactions with the microenvironment. Therefore, the study of cancer requires the integration of multiple heritable dimensions at the resolution of the single cell - the atomic unit of somatic evolution. In this Review, we discuss emerging analytic and experimental technologies for single-cell multi-omics that enable the capture and integration of multiple data modalities to inform the study of cancer evolution. These data show that cancer results from a complex interplay between genetic and non-genetic determinants of somatic evolution.

Patient-Derived Urothelial Cancer Xenograft Models: A Systematic Review and Future Perspectives

BACKGROUND: Lack of appropriate models that recapitulate the diversity, heterogeneity, and tumor microenvironment of urothelial cancer (UC) is a limitation to preclinical models. Patient-derived xenograft (PDX) models are a promising tool to overcome some of these issues, and thus we present an up-to-date and comprehensive overview of UC PDX models to aid in their future use. OBJECTIVE: To provide an overview on methodology, applications and limitations as well as future perspectives on bladder cancer PDX models. METHODS: Literature searches using PubMed and Web of Science databases were performed for relevant articles according to the following MeSH terms: “urothelial carcinoma(s)” OR “urothelial cancer” OR “urothelial tumor” OR “bladder cancer(s)” OR “bladder carcinoma(s)” OR “transitional cell carcinoma(s)” AND “xenograft(s)” OR “xenotransplant” at December 6th, 2019. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. RESULTS: Of the 49 studies extracted, 41 studies after the year 2000 were finally analyzed. Published studies show that (1) UC PDX platforms retained the histology and genomic characteristics of the corresponding patient tumors. (2) UC PDX can be applied to ask various questions including to study the mechanisms of disease progression and treatment resistance, to develop novel drugs and biomarkers, as well as to potentially realize personalized drug selection. Recent topics of research using PDX have included the development of humanized mice as well as the use of 3D culture to complement some of the limitations of PDX models. CONCLUSIONS: UC PDX models serve as tools for understanding cancer biology, drug development and empowering precision medicine. The improvement of experimental systems using humanized mice to recapitulate the immune microenvironment of tumors will optimize UC PDX to study future questions in the field of immunotherapy.

Clonal tracking using embedded viral barcoding and high-throughput sequencing

Embedded viral barcoding in combination with high-throughput sequencing is a powerful technology with which to track single-cell clones. It can provide clonal-level insights into cellular proliferation, development, differentiation, migration, and treatment efficacy. Here, we present a detailed protocol for a viral barcoding procedure that includes the creation of barcode libraries, the viral delivery of barcodes, the recovery of barcodes, and the computational analysis of barcode sequencing data. The entire procedure can be completed within a few weeks. This barcoding method requires cells to be susceptible to viral transduction. It provides high sensitivity and throughput, and enables precise quantification of cellular progeny. It is cost efficient and does not require any advanced skills. It can also be easily adapted to many types of applications, including both in vitro and in vivo experiments.

Novel Aptamers Selected on Living Cells for Specific Recognition of Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is a high heterogeneous group of tumors with a distinctly aggressive nature and high rates of relapse. So far, the lack of any known targetable proteins has not allowed a specific anti-tumor treatment. Therefore, the identification of novel agents for specific TNBC targeting and treatment is desperately needed. Here, by integrating cell-SELEX (Systematic Evolution of Ligands by EXponential enrichment) for the specific recognition of TNBC cells with high-throughput sequencing technology, we identified a panel of 2′-fluoropyrimidine-RNA aptamers binding to TNBC cells and their cisplatin- and doxorubicin-resistant derivatives at low nanomolar affinity. These aptamers distinguish TNBC cells from both non-malignant and non-TNBC breast cancer cells and are able to differentiate TNBC histological specimens. Importantly, they inhibit TNBC cell capacity of growing in vitro as mammospheres, indicating they could also act as anti-tumor agents. Therefore, our newly identified aptamers are a valuable tool for selectively dealing with TNBC.

•

Six 2′FPy-RNA aptamers were obtained by TNBC Cell-SELEX/NGS

•

They distinguish TNBC cells from non-malignant and non-TNBC breast cancer cells

•

They differentiate TNBC histological specimens by aptamer-based staining

•

They inhibit TNBC cell lines capacity of growing in vitro as mammospheres

Improved detection of differentially represented DNA barcodes for high-throughput clonal phenomics

Cellular DNA barcoding has become a popular approach to study heterogeneity of cell populations and to identify clones with differential response to cellular stimuli. However, there is a lack of reliable methods for statistical inference of differentially responding clones. Here, we used mixtures of DNA-barcoded cell pools to generate a realistic benchmark read count dataset for modelling a range of outcomes of clone-tracing experiments. By accounting for the statistical properties intrinsic to the DNA barcode read count data, we implemented an improved algorithm that results in a significantly lower false-positive rate, compared to current RNA-seq data analysis algorithms, especially when detecting differentially responding clones in experiments with strong selection pressure. Building on the reliable statistical methodology, we illustrate how multidimensional phenotypic profiling enables one to deconvolute phenotypically distinct clonal subpopulations within a cancer cell line. The mixture control dataset and our analysis results provide a foundation for benchmarking and improving algorithms for clone-tracing experiments.

Genetic heterogeneity and clonal evolution during metastasis in breast cancer patient-derived tumor xenograft models

Genetic heterogeneity within a tumor arises by clonal evolution, and patients with highly heterogeneous tumors are more likely to be resistant to therapy and have reduced survival. Clonal evolution also occurs when a subset of cells leave the primary tumor to form metastases, which leads to reduced genetic heterogeneity at the metastatic site. Although this process has been observed in human cancer, experimental models which recapitulate this process are lacking. Patient-derived tumor xenografts (PDX) have been shown to recapitulate the patient's original tumor's intra-tumor genetic heterogeneity, as well as its genomics and response to treatment, but whether they can be used to model clonal evolution in the metastatic process is currently unknown. Here, we address this question by following genetic changes in two breast cancer PDX models during metastasis. First, we discovered that mouse stroma can be a confounding factor in assessing intra-tumor heterogeneity by whole exome sequencing, thus we developed a new bioinformatic approach to correct for this. Finally, in a spontaneous, but not experimental (tail-vein) metastasis model we observed a loss of heterogeneity in PDX metastases compared to their orthotopic "primary" tumors, confirming that PDX models can faithfully mimic the clonal evolution process undergone in human patients during metastatic spreading.

Cell-Type-Specific Quantification of a Scaffold-Based 3D Liver Co-Culture

In order to increase the metabolic activity of human hepatocytes and liver cancer cell lines, many approaches have been reported in recent years. The metabolic activity could be increased mainly by cultivating the cells in 3D systems or co-cultures (with other cell lines). However, if the system becomes more complex, it gets more difficult to quantify the number of cells (e.g., on a 3D matrix). Until now, it has been impossible to quantify different cell types individually in 3D co-culture systems. Therefore, we developed a PCR-based method that allows the quantification of HepG2 cells and 3T3-J2 cells separately in a 3D scaffold culture. Moreover, our results show that this method allows better comparability between 2D and 3D cultures in comparison to the often-used approaches based on metabolic activity measurements, such as the conversion of resazurin.

The fidelity of cancer cells in PDX models: Characteristics, mechanism and clinical significance

Patient-derived xenograft (PDX) models are widely used as preclinical cancer models and are considered better than cell culture models in recapitulating the histological features, molecular characteristics and intratumoral heterogeneity (ITH) of human tumors. While the PDX model is commonly accepted for use in drug discovery and other translational studies, a growing body of evidence has suggested its limitations. Recently, the fidelity of cancer cells within a PDX has been questioned, which may impede the future application of these models. In this review, we will focus the variable phenotypes of xenograft tumors and the genomic instability and molecular inconsistency of PDX tumors after serial transplantation. Next, we will discuss the underlying mechanism of ITH and its clinical relevance. Stochastic selection bias in the sampling process and/or deterministic clonal dynamics due to murine selective pressure may have detrimental effects on the results of personalized medicine and drug screening studies. In addition, we aim to identify a possible solution for the issue of fidelity in current PDX models and to discuss emerging next-generation preclinical models.

The Complexities of Metastasis

Therapies that prevent metastatic dissemination and tumor growth in secondary organs are severely lacking. A better understanding of the mechanisms that drive metastasis will lead to improved therapies that increase patient survival. Within a tumor, cancer cells are equipped with different phenotypic and functional capacities that can impact their ability to complete the metastatic cascade. That phenotypic heterogeneity can be derived from a combination of factors, in which the genetic make-up, interaction with the environment, and ability of cells to adapt to evolving microenvironments and mechanical forces play a major role. In this review, we discuss the specific properties of those cancer cell subgroups and the mechanisms that confer or restrict their capacity to metastasize.

Basal-like Breast Cancers: From Pathology to Biology and Back Again

Human breast cancers referred to as "basal-like" are of interest because they lack effective therapies and their biology is poorly understood. The term basal-like derives from studies demonstrating tumor gene expression profiles that include some transcripts characteristic of the basal cells of the normal adult human mammary gland and others associated with a subset of normal luminal cells. Elucidating the mechanisms responsible for the profiles of basal-like tumors is an active area of investigation. More refined molecular analysis of patients' samples and genetic strategies to produce breast cancers de novo from defined populations of normal mouse mammary cells have served as complementary approaches to identify relevant pathway alterations. However, both also have limitations. Here, we review some of the underlying reasons, including the unifying concept that some normal luminal cells have both luminal and basal features, as well as some emerging new avenues of investigation.

Current and Future Horizons of Patient-Derived Xenograft Models in Colorectal Cancer Translational Research

Our poor understanding of the intricate biology of cancer and the limited availability of preclinical models that faithfully recapitulate the complexity of tumors are primary contributors to the high failure rate of novel therapeutics in oncology clinical studies. To address this need, patient-derived xenograft (PDX) platforms have been widely deployed and have reached a point of development where we can critically review their utility to model and interrogate relevant clinical scenarios, including tumor heterogeneity and clonal evolution, contributions of the tumor microenvironment, identification of novel drugs and biomarkers, and mechanisms of drug resistance. Colorectal cancer (CRC) constitutes a unique case to illustrate clinical perspectives revealed by PDX studies, as they overcome limitations intrinsic to conventional ex vivo models. Furthermore, the success of molecularly annotated "Avatar" models for co-clinical trials in other diseases suggests that this approach may provide an additional opportunity to improve clinical decisions, including opportunities for precision targeted therapeutics, for patients with CRC in real time. Although critical weaknesses have been identified with regard to the ability of PDX models to predict clinical outcomes, for now, they are certainly the model of choice for preclinical studies in CRC. Ongoing multi-institutional efforts to develop and share large-scale, well-annotated PDX resources aim to maximize their translational potential. This review comprehensively surveys the current status of PDX models in translational CRC research and discusses the opportunities and considerations for future PDX development.

Stem cells in homeostasis and cancer of the gut

The intestinal epithelial lining is one of the most rapidly renewing cell populations in the body. As a result, the gut has been an attractive model to resolve key mechanisms in epithelial homeostasis. In particular the role of intestinal stem cells (ISCs) in the renewal process has been intensely studied. Interestingly, as opposed to the traditional stem cell theory, the ISC is not a static population but displays significant plasticity and in situations of tissue regeneration more differentiated cells can revert back to a stem cell state upon exposure to extracellular signals. Importantly, normal intestinal homeostasis provides important insight into mechanisms that drive colorectal cancer (CRC) development and growth. Specifically, the dynamics of cancer stem cells bear important resemblance to ISC functionality. In this review we present an overview of the current knowledge on ISCs in homeostasis and their role in malignant transformation. Also, we discuss the existence of stem cells in intestinal adenomas and CRC and how these cells contribute to (pre-)malignant growth. Furthermore, we will focus on new paradigms in the field of dynamical cellular hierarchies in CRC and the intimate relationship between tumor cells and their niche.

Lineage Tracing: Computational Reconstruction Goes Beyond the Limit of Imaging

Tracking the fate of individual cells and their progeny through lineage tracing has been widely used to investigate various biological processes including embryonic development, homeostatic tissue turnover, and stem cell function in regeneration and disease. Conventional lineage tracing involves the marking of cells either with dyes or nucleoside analogues or genetic marking with fluorescent and/or colorimetric protein reporters. Both are imaging-based approaches that have played a crucial role in the field of developmental biology as well as adult stem cell biology. However, imaging-based lineage tracing approaches are limited by their scalability and the lack of molecular information underlying fate transitions. Recently, computational biology approaches have been combined with diverse tracing methods to overcome these limitations and so provide high-order scalability and a wealth of molecular information. In this review, we will introduce such novel computational methods, starting from single-cell RNA sequencing-based lineage analysis to DNA barcoding or genetic scar analysis. These novel approaches are complementary to conventional imaging-based approaches and enable us to study the lineage relationships of numerous cell types during vertebrate, and in particular human, development and disease.

Barcoding reveals complex clonal behavior in patient-derived xenografts of metastatic triple negative breast cancer

Primary triple negative breast cancers (TNBC) are prone to dissemination but sub-clonal relationships between tumors and resulting metastases are poorly understood. Here we use cellular barcoding of two treatment-naïve TNBC patient-derived xenografts (PDXs) to track the spatio-temporal fate of thousands of barcoded clones in primary tumors, and their metastases. Tumor resection had a major impact on reducing clonal diversity in secondary sites, indicating that most disseminated tumor cells lacked the capacity to 'seed', hence originated from 'shedders' that did not persist. The few clones that continued to grow after resection i.e. 'seeders', did not correlate in frequency with their parental clones in primary tumors. Cisplatin treatment of one BRCA1-mutated PDX model to non-palpable levels had a surprisingly minor impact on clonal diversity in the relapsed tumor yet purged 50% of distal clones. Therefore, clonal features of shedding, seeding and drug resistance are important factors to consider for the design of therapeutic strategies.

Assays for functionally defined normal and malignant mammary stem cells

The discovery of rare, heterogeneous self-renewing stem cells with shared developmental and molecular features within epithelial components of mammary gland and breast cancers has provided a conceptual framework to understand cellular composition of these tissues and mechanisms that control their number. These normal mammary epithelial stem cells (MaSCs) and breast cancer stem cells (BCSCs) were identified and analyzed using transplant assays (namely mammary repopulating unit (MRU) assay, mammary tumor-initiating cell (TIC) assay), which reveal their latent ability to regenerate respective normal and malignant epithelial tissues with self-renewing units displaying hierarchical cellular differentiation over multiple generations in recipient mice. "Next-generation" methods using "barcoded" normal and malignant mammary cells, with the help of next-generation sequencing (NGS) technology, have revealed hidden complexity and heterogeneous growth potential of MaSCs and BCSCs. Several single markers or combinations of markers have been reported to prospectively enrich MaSCs and BCSCs. Such markers and the extent to which they enrich for MaSCs and BCSCs activity require a critical appraisal. Also, knowledge of the functional assays and their limitations and harmonious reporting of results is a prerequisite to improve our understanding of MaSCs and BCSCs. This chapter describes evolution of the concept of MaSCs and BCSCs, and specific methodologies to investigate them.

Targeting HER2 in patient-derived xenograft ovarian cancer models sensitizes tumors to chemotherapy

Ovarian cancer is the most lethal gynecologic malignancy. About 75% of ovarian cancer patients relapse and/or develop chemo‐resistant disease after initial response to standard‐of‐care treatment with platinum‐based therapies. HER2 amplifications and overexpression in ovarian cancer are reported to vary, and responses to HER2 inhibitors have been poor. Next generation sequencing technologies in conjunction with testing using patient‐derived xenografts (PDX) allow validation of personalized treatments. Using a whole‐genome mate‐pair next generation sequencing (MPseq) protocol, we identified several high grade serous ovarian cancers (HGS‐OC) with DNA alterations in genes encoding members of the ERBB2 pathway. The efficiency of anti‐HER2 therapy was tested in three different PDX lines with the identified alterations and high levels of HER2 protein expression. Treatment responses to pertuzumab or pertuzumab/trastuzumab were compared in each PDX line WITH standard carboplatin and paclitaxel combination treatment. In all three PDX models, HER2‐targeted therapy resulted in significant inhibition of tumor growth compared with untreated controls. However, the responses in each case were inferior to those to chemotherapy, even for chemo‐resistant lines. When chemotherapy and HER2‐targeted therapy were administered together, a significant regression of tumor was observed after 6 weeks of treatment compared with chemotherapy alone. Post‐treatment analysis of these tissues revealed that inhibition of the ERBB2 pathway occurred at the level of phosphorylation and expression of downstream targets. In conclusion, while targeting of presumably activated ERBB2 pathway alone in HGS‐OC results in a modest treatment benefit, a combination therapy including both chemotherapy drugs and HER2 inhibitors provides a far better response. Further studies are needed to address development of recurrence and sensitivity of recurrent disease to HER2‐targeted therapy.

High-resolution deconstruction of evolution induced by chemotherapy treatments in breast cancer xenografts

The processes by which tumors evolve are essential to the efficacy of treatment, but quantitative understanding of intratumoral dynamics has been limited. Although intratumoral heterogeneity is common, quantification of evolution is difficult from clinical samples because treatment replicates cannot be performed and because matched serial samples are infrequently available. To circumvent these problems we derived and assayed large sets of human triple-negative breast cancer xenografts and cell cultures from two patients, including 86 xenografts from cyclophosphamide, doxorubicin, cisplatin, docetaxel, or vehicle treatment cohorts as well as 45 related cell cultures. We assayed these samples via exome-seq and/or high-resolution droplet digital PCR, allowing us to distinguish complex therapy-induced selection and drift processes among endogenous cancer subclones with cellularity uncertainty <3%. For one patient, we discovered two predominant subclones that were granularly intermixed in all 48 co-derived xenograft samples. These two subclones exhibited differential chemotherapy sensitivity–when xenografts were treated with cisplatin for 3 weeks, the post-treatment volume change was proportional to the post-treatment ratio of subclones on a xenograft-to-xenograft basis. A subsequent cohort in which xenografts were treated with cisplatin, allowed a drug holiday, then treated a second time continued to exhibit this proportionality. In contrast, xenografts from other treatment cohorts, spatially dissected xenograft fragments, and cell cultures evolved in diverse ways but with substantial population bottlenecks. These results show that ecosystems susceptible to successive retreatment can arise spontaneously in breast cancer in spite of a background of irregular subclonal bottlenecks, and our work provides to our knowledge the first quantification of the population genetics of such a system. Intriguingly, in such an ecosystem the ratio of common subclones is predictive of the state of treatment susceptibility, showing how measurements of subclonal heterogeneity could guide treatment for some patients.

High-resolution clonal mapping of multi-organ metastasis in triple negative breast cancer

Most triple negative breast cancers (TNBCs) are aggressively metastatic with a high degree of intra-tumoral heterogeneity (ITH), but how ITH contributes to metastasis is unclear. Here, clonal dynamics during metastasis were studied in vivo using two patient-derived xenograft (PDX) models established from the treatment-naive primary breast tumors of TNBC patients diagnosed with synchronous metastasis. Genomic sequencing and high-complexity barcode-mediated clonal tracking reveal robust alterations in clonal architecture between primary tumors and corresponding metastases. Polyclonal seeding and maintenance of heterogeneous populations of low-abundance subclones is observed in each metastasis. However, lung, liver, and brain metastases are enriched for an identical population of high-abundance subclones, demonstrating that primary tumor clones harbor properties enabling them to seed and thrive in multiple organ sites. Further, clones that dominate multi-organ metastases share a genomic lineage. Thus, intrinsic properties of rare primary tumor subclones enable the seeding and colonization of metastases in secondary organs in these models.

It is unclear how intra-tumoral heterogeneity contributes to metastasis. Here the authors study the clonal dynamics of triple negative breast cancer metastasis using patient derived xenografts and demonstrate that primary tumor clones harbor properties that support seeding and colonization of multiple organs.

Single-Cell Transcriptomics Meets Lineage Tracing

Reconstructing lineage relationships between cells within a tissue or organism is a long-standing aim in biology. Traditionally, lineage tracing has been achieved through the (genetic) labeling of a cell followed by the tracking of its offspring. Currently, lineage trajectories can also be predicted using single-cell transcriptomics. Although single-cell transcriptomics provides detailed phenotypic information, the predicted lineage trajectories do not necessarily reflect genetic relationships. Recently, techniques have been developed that unite these strategies. In this Review, we discuss transcriptome-based lineage trajectory prediction algorithms, single-cell genetic lineage tracing, and the promising combination of these techniques for stem cell and cancer research.