Cell lineage analysis of acute leukemia relapse uncovers the role of replication-rate heterogeneity and microsatellite instability

GMP-like and MLP-like Subpopulations of Hematopoietic Stem and Progenitor Cells Harboring Mutated EZH2 and TP53 at Diagnosis Promote Acute Myeloid Leukemia Relapse: Data of Combined Molecular, Functional, and Genomic Single-Stem-Cell Analyses

Acute myeloid leukemia (AML) is associated with unfavorable patient outcomes primarily related to disease relapse. Since specific types of leukemic hematopoietic stem and progenitor cells (HSPCs) are suggested to contribute to AML propagation, this study aimed to identify and explore relapse-initiating HSPC subpopulations present at diagnosis, using single-cell analysis (SCA). We developed unique high-resolution techniques capable of tracking single-HSPC-derived subclones during AML evolution. Each subclone was evaluated for chemo-resistance, in vivo leukemogenic potential, mutational profile, and the cell of origin. In BM samples of 15 AML patients, GMP-like and MLP-like HSPC subpopulations were identified as prevalent at relapse, exhibiting chemo-resistance to commonly used chemotherapy agents cytosine arabinoside (Ara-C) and daunorubicin. Reconstruction of phylogenetic lineage trees combined with genetic analysis of single HSPCs and single-HSPC-derived subclones demonstrated two distinct clusters, originating from MLP-like or GMP-like subpopulations, observed both at diagnosis and relapse. These subpopulations induced leukemia development ex vivo and in vivo. Genetic SCA showed that these relapse-related subpopulations harbored mutated EZH2 and TP53, detected already at diagnosis. This study, using combined molecular, functional, and genomic analyses at the level of single cells, identified patient-specific chemo-resistant HSPC subpopulations at the time of diagnosis, promoting AML relapse.

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.

Characterizing chromosomal instability-driven cancer evolution and cell fitness at a glance

Chromosomal instability (CIN), an increased rate of chromosome segregation errors during mitosis, is a hallmark of cancer cells. CIN leads to karyotype differences between cells and thus large-scale heterogeneity among individual cancer cells; therefore, it plays an important role in cancer evolution. Studying CIN and its consequences is technically challenging, but various technologies have been developed to track karyotype dynamics during tumorigenesis, trace clonal lineages and link genomic changes to cancer phenotypes at single-cell resolution. These methods provide valuable insight not only into the role of CIN in cancer progression, but also into cancer cell fitness. In this Cell Science at a Glance article and the accompanying poster, we discuss the relationship between CIN, cancer cell fitness and evolution, and highlight techniques that can be used to study the relationship between these factors. To that end, we explore methods of assessing cancer cell fitness, particularly for chromosomally unstable cancer.

Subclinical minute FLT3-ITD clone can be detected in clinically FLT3-ITD-negative acute myeloid leukaemia at diagnosis

Recent advances in next-generation sequencing (NGS) have enabled the detection of subclinical minute FLT3-ITD. We selected 74 newly diagnosed, cytogenetically normal acute myeloid leukaemia (AML) samples in which FLT3-ITD was not detected by gel electrophoresis. We sequenced them using NGS and found minute FLT3-ITDs in 19 cases. We compared cases with clinically relevant FLT3-ITD (n = 37), cases with minute FLT3-ITD (n = 19) and cases without detectable FLT3-ITD (n = 55). Molecular characteristics (location and length) of minute FLT3-ITD were similar to those of clinically relevant FLT3-ITD. Survival of cases with minute FLT3-ITD was similar to that of cases without detectable FLT3-ITD, whereas the relapse rate within 1 year after onset was significantly higher in cases with minute FLT3-ITD. We followed 18 relapsed samples of cases with clinically FLT3-ITD-negative at diagnosis. Two of 3 cases with minute FLT3-ITD relapsed with progression to clinically relevant FLT3-ITD. Two of 15 cases in which FLT3-ITD was not detected by NGS relapsed with the emergence of minute FLT3-ITD, and one of them showed progression to clinically relevant FLT3-ITD at the second relapse. We revealed the clonal dynamics of subclinical minute FLT3-ITD in clinically FLT3-ITD-negative AML. Minute FLT3-ITD at the initial AML can expand to become a dominant clone at relapse.

Cancer Stem Cell Relationship with Pro-Tumoral Inflammatory Microenvironment

Inflammatory processes and cancer stem cells (CSCs) are increasingly recognized as factors in the development of tumors. Emerging evidence indicates that CSCs are associated with cancer properties such as metastasis, treatment resistance, and disease recurrence. However, the precise interaction between CSCs and the immune microenvironment remains unexplored. Although evasion of the immune system by CSCs has been extensively studied, new research demonstrates that CSCs can also control and even profit from the immune response. This review provides an overview of the reciprocal interplay between CSCs and tumor-infiltrating immune cells, collecting pertinent data about how CSCs stimulate leukocyte reprogramming, resulting in pro-tumor immune cells that promote metastasis, chemoresistance, tumorigenicity, and even a rise in the number of CSCs. Tumor-associated macrophages, neutrophils, Th17 and regulatory T cells, mesenchymal stem cells, and cancer-associated fibroblasts, as well as the signaling pathways involved in these pro-tumor activities, are among the immune cells studied. Although cytotoxic leukocytes have the potential to eliminate CSCs, immune evasion mechanisms in CSCs and their clinical implications are also known. We intended to compile experimental findings that provide direct evidence of interactions between CSCs and the immune system and CSCs and the inflammatory milieu. In addition, we aimed to summarize key concepts in order to comprehend the cross-talk between CSCs and the tumor microenvironment as a crucial process for the effective design of anti-CSC therapies.

Replicative history marks transcriptional and functional disparity in the CD8+ T cell memory pool

Clonal expansion is a core aspect of T cell immunity. However, little is known with respect to the relationship between replicative history and the formation of distinct CD8⁺ memory T cell subgroups. To address this issue, we developed a genetic-tracing approach, termed the DivisionRecorder, that reports the extent of past proliferation of cell pools in vivo. Using this system to genetically ‘record’ the replicative history of different CD8⁺ T cell populations throughout a pathogen-specific immune response, we demonstrate that the central memory T cell (T_CM) pool is marked by a higher number of prior divisions than the effector memory T cell pool, due to the combination of strong proliferative activity during the acute immune response and selective proliferative activity after pathogen clearance. Furthermore, by combining DivisionRecorder analysis with single cell transcriptomics and functional experiments, we show that replicative history identifies distinct cell pools within the T_CM compartment. Specifically, we demonstrate that lowly divided T_CM display enriched expression of stem-cell-associated genes, exist in a relatively quiescent state, and are superior in eliciting a proliferative recall response upon activation. These data provide the first evidence that a stem cell like memory T cell pool that reconstitutes the CD8⁺ T cell effector pool upon reinfection is marked by prior quiescence.

Comparing Phylogenetic Approaches to Reconstructing Cell Lineage From Microsatellites With Missing Data

Due to the imperfect fidelity of DNA replication, somatic cells acquire DNA mutations at each division which record their lineage history. Microsatellites, tandem repeats of DNA nucleotide motifs, mutate more frequently than other genomic regions and by observing microsatellite lengths in single cells and implementing suitable inference procedures, the cell lineage tree of an organism can be reconstructed. Due to recent advances in single cell Next Generation Sequencing (NGS) and the phylogenetic methods used to infer lineage trees, this work investigates which computational approaches best exploit the lineage information found in single cell NGS data. We simulated trees representing cell division with mutating microsatellites, and tested a range of available phylogenetic algorithms to reconstruct cell lineage. We found that distance-based approaches are fast and accurate with fully observed data. However, Maximum Parsimony and the computationally intensive probabilistic methods are more robust to missing data and therefore better suited to reconstructing cell lineage from NGS datasets. We also investigated how robust reconstruction algorithms are to different tree topologies and mutation generation models. Our results show that the flexibility of Maximum Parsimony and the probabilistic approaches mean they can be adapted to allow good reconstruction across a range of biologically relevant scenarios.

The evolution of leukaemia from pre-leukaemic and leukaemic stem cells

Haematopoietic stem and progenitor cells (HSPCs) are defined as unspecialized cells that give rise to more differentiated cells. In a similar way, leukaemic stem and progenitor cells (LSPCs) are defined as unspecialized leukaemic cells, which can give rise to more differentiated cells. Leukaemic cells carry leukaemic mutations/variants and have clear differentiation abnormalities. Pre-leukaemic HSPCs (PreL-HSPCs) carry pre-leukaemic mutations/variants (pLMs) and are capable of producing mature functional cells, which will carry the same variants. Under the roof of LSPCs, one can find a broad range of cell types genetic and disease phenotypes. Present-day knowledge suggests that this phenotypic heterogeneity is the result of interactions between the cell of origin, the genetic background and the microenvironment background. The combination of these attributes will define the LSPC phenotype, frequency, differentiation capacity and evolutionary trajectory. Importantly, as LSPCs are leukaemia-initiating cells that sustain clinical remission and are the source of relapse, an improved understanding of LSPCs phenotype would offer better clinical opportunities for the treatment and hopefully prevention of human leukaemia. The current review will focus on LSPCs attributes in the context of human haematologic malignancies.

Dasatinib response in acute myeloid leukemia is correlated with FLT3/ITD, PTPN11 mutations and a unique gene expression signature

Novel targeted therapies demonstrate improved survival in specific subgroups (defined by genetic variants) of acute myeloid leukemia (AML) patients, validating the paradigm of molecularly targeted therapy. However, identifying correlations between AML molecular attributes and effective therapies is challenging. Recent advances in high-throughput in vitro drug sensitivity screening applied to primary AML blasts were used to uncover such correlations; however, these methods cannot predict the response of leukemic stem cells (LSCs). Our study aimed to predict in vitro response to targeted therapies, based on molecular markers, with subsequent validation in LSCs. We performed ex vivo sensitivity screening to 46 drugs on 29 primary AML samples at diagnosis or relapse. Using unsupervised hierarchical clustering analysis we identified group with sensitivity to several tyrosine kinase inhibitors (TKIs), including the multi-TKI, dasatinib, and searched for correlations between dasatinib response, exome sequencing and gene expression from our dataset and from the Beat AML dataset. Unsupervised hierarchical clustering analysis of gene expression resulted in clustering of dasatinib responders and non-responders. In vitro response to dasatinib could be predicted based on gene expression (AUC=0.78). Furthermore, mutations in FLT3/ITD and PTPN11 were enriched in the dasatinib sensitive samples as opposed to mutations in TP53 which were enriched in resistant samples. Based on these results, we selected FLT3/ITD AML samples and injected them to NSG-SGM3 mice. Our results demonstrate that in a subgroup of FLT3/ITD AML (4 out of 9) dasatinib significantly inhibits LSC engraftment. In summary we show that dasatinib has an anti-leukemic effect both on bulk blasts and, more importantly, LSCs from a subset of AML patients that can be identified based on mutational and expression profiles. Our data provide a rational basis for clinical trials of dasatinib in a molecularly selected subset of AML patients.

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.

Alignment of Cell Lineage Trees Elucidates Genetic Programs for the Development and Evolution of Cell Types

A full understanding of the developmental process requires fine-scale characterization of cell divisions and cell types, which are naturally organized as the developmental cell lineage tree (CLT). Technological breakthroughs facilitated determination of more CLTs, but complete comprehension of the data remains difficult without quantitative comparison among CLTs. We hereby quantified phenotypic similarity between CLTs using a novel computational method that exhaustively searches for optimal correspondence between individual cells meanwhile retaining their topological relationships. The revealed CLT similarities allowed us to infer functional similarity at the transcriptome level, identify cell fate transformations, predict functional relationships between mutants, and find evolutionary correspondence between cell types of different species. By allowing quantitative comparison between CLTs, our work is expected to greatly enhance the interpretability of relevant data and help answer the myriad of questions surrounding the developmental process.

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Align cell lineage trees (CLTs) to search/quantify their phenotypic similarities

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Aligning worm CLTs captured known genetic/developmental programs

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Similarities between knockdown CLTs revealed functional relationships between genes

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CLT alignments between species gave insight on the evolution of cell types

Influence of Innate Immunity on Cancer Cell Stemness

Even if cancer stem cells (CSCs) represent only a small proportion of the tumor mass, they significantly account for tumor maintenance, resistance to therapies, relapse and metastatic spread, due to their increased capacity of self-renewal, multipotency, tumorigenicity and quiescence. Emerging evidence suggests that the immune contexture within the tumor microenvironment (TME) determines both the response to therapy and the clinical outcome. In this context, CSCs acquire immune evasion skills by editing immune cell functions and sculpting the immunosuppressive landscape of TME. Reciprocally, infiltrating immune cells influence CSCs self-renewal, tumorigenicity and metastasis. In this review, we summarize the immunomodulatory properties of CSCs, as well as the impact of innate immune cells on cancer cells stemness in the different phases of cancer immunoediting process and neoplastic progression.

Normal myeloid progenitor cell subset-associated gene signatures for acute myeloid leukaemia subtyping with prognostic impact

Acute myeloid leukaemia (AML) is characterised by phenotypic heterogeneity, which we hypothesise is a consequence of deregulated differentiation with transcriptional reminiscence of the normal compartment or cell-of-origin. Here, we propose a classification system based on normal myeloid progenitor cell subset-associated gene signatures (MAGS) for individual assignments of AML subtypes. We generated a MAGS classifier including the progenitor compartments CD34⁺/CD38^- for haematopoietic stem cells (HSCs), CD34⁺/CD38⁺/CD45RA^- for megakaryocyte-erythroid progenitors (MEPs), and CD34⁺/CD38⁺/CD45RA⁺ for granulocytic-monocytic progenitors (GMPs) using regularised multinomial regression with three discrete outcomes and an elastic net penalty. The regularisation parameters were chosen by cross-validation, and MAGS assignment accuracy was validated in an independent data set (N = 38; accuracy = 0.79) of sorted normal myeloid subpopulations. The prognostic value of MAGS assignment was studied in two clinical cohorts (TCGA: N = 171; GSE6891: N = 520) and had a significant prognostic impact. Furthermore, multivariate Cox regression analysis using the MAGS subtype, FAB subtype, cytogenetics, molecular genetics, and age as explanatory variables showed independent prognostic value. Molecular characterisation of subtypes by differential gene expression analysis, gene set enrichment analysis, and mutation patterns indicated reduced proliferation and overrepresentation of RUNX1 and IDH2 mutations in the HSC subtype; increased proliferation and overrepresentation of CEBPA mutations in the MEP subtype; and innate immune activation and overrepresentation of WT1 mutations in the GMP subtype. We present a differentiation-dependent classification system for AML subtypes with distinct pathogenetic and prognostic importance that can help identify candidates poorly responding to combination chemotherapy and potentially guide alternative treatments.

High-throughput dense reconstruction of cell lineages

The first meeting exclusively dedicated to the 'High-throughput dense reconstruction of cell lineages' took place at Janelia Research Campus (Howard Hughes Medical Institute) from 14 to 18 April 2019. Organized by Tzumin Lee, Connie Cepko, Jorge Garcia-Marques and Isabel Espinosa-Medina, this meeting echoed the recent eruption of new tools that allow the reconstruction of lineages based on the phylogenetic analysis of DNA mutations induced during development. Combined with single-cell RNA sequencing, these tools promise to solve the lineage of complex model organisms at single-cell resolution. Here, we compile the conference consensus on the technological and computational challenges emerging from the use of the new strategies, as well as potential solutions.

Epigenetic evolution and lineage histories of chronic lymphocytic leukaemia

Genetic and epigenetic intra-tumoral heterogeneity cooperate to shape the evolutionary course of cancer¹. Chronic lymphocytic leukaemia (CLL) is a highly informative model for cancer evolution as it undergoes substantial genetic diversification and evolution after therapy^2,3. The CLL epigenome is also an important disease-defining feature^4,5, and growing populations of cells in CLL diversify by stochastic changes in DNA methylation known as epimutations⁶. However, previous studies using bulk sequencing methods to analyse the patterns of DNA methylation were unable to determine whether epimutations affect CLL populations homogeneously. Here, to measure the epimutation rate at single-cell resolution, we applied multiplexed single-cell reduced-representation bisulfite sequencing to B cells from healthy donors and patients with CLL. We observed that the common clonal origin of CLL results in a consistently increased epimutation rate, with low variability in the cell-to-cell epimutation rate. By contrast, variable epimutation rates across healthy B cells reflect diverse evolutionary ages across the trajectory of B cell differentiation, consistent with epimutations serving as a molecular clock. Heritable epimutation information allowed us to reconstruct lineages at high-resolution with single-cell data, and to apply this directly to patient samples. The CLL lineage tree shape revealed earlier branching and longer branch lengths than in normal B cells, reflecting rapid drift after the initial malignant transformation and a greater proliferative history. Integration of single-cell bisulfite sequencing analysis with single-cell transcriptomes and genotyping confirmed that genetic subclones mapped to distinct clades, as inferred solely on the basis of epimutation information. Finally, to examine potential lineage biases during therapy, we profiled serial samples during ibrutinib-associated lymphocytosis, and identified clades of cells that were preferentially expelled from the lymph node after treatment, marked by distinct transcriptional profiles. The single-cell integration of genetic, epigenetic and transcriptional information thus charts the lineage history of CLL and its evolution with therapy.

Microsatellite Instability Occurs in a Subset of Follicular Thyroid Cancers

BACKGROUND

Inactivation of DNA mismatch repair (MMR) and the resulting microsatellite instability (MSI) are frequently observed in endometrial, stomach, and colorectal cancers, as well as more rarely in other solid tumor types. The prevalence of MSI in thyroid cancer has not been explored in depth, although recent studies utilizing data from large cancer sequencing efforts such as The Cancer Genome Atlas indicate that MSI is absent or at least very rare in the most common and most well studied histologic subtype, papillary thyroid carcinoma. This study aimed to determine the prevalence of MSI in thyroid cancer by using a large series comprising all major histological subtypes.

METHODS

A total of 485 thyroid cancer patients were screened for MSI/MMR deficiency, including all major histologic subtypes (195 papillary thyroid carcinoma, 156 follicular thyroid carcinoma [FTC], 50 anaplastic thyroid carcinoma, 65 medullary thyroid carcinoma, and 17 poorly differentiated thyroid carcinomas) by using a combination of polymerase chain reaction-based detection, immunohistochemistry, and next-generation sequencing.

RESULTS

A total of four tumors were MSI-high and had loss of MMR protein expression, all of which were from FTC patients. Whole-exome sequencing was performed on two MSI-high FTCs and revealed a hemizygous loss of function mutation in MSH2 in one tumor.

CONCLUSIONS

Based on these data, it is estimated that the overall prevalence of MSI in FTC is 2.5%, and MSI is either entirely absent or rare in other histology subtypes of thyroid carcinoma. These findings highlight the importance of testing for MSI in FTC.

hsa-miR-9 controls the mobility behavior of glioblastoma cells via regulation of MAPK14 signaling elements

Background

Glioblastoma Multiforme (GBM) is the most common and lethal primary tumor of the brain. GBM is associated with one of the worst 5-year survival rates among all human cancers, despite much effort in different modes of treatment.

Results

Here, we demonstrate specific GBM cancer phenotypes that are governed by modifications to the MAPAKAP network. We then demonstrate a novel regulation mode by which a set of five key factors of the MAPKAP pathway are regulated by the same microRNA, hsa-miR-9.

We demonstrate that hsa-miR-9 overexpression leads to MAPKAP signaling inhibition, partially by interfering with the MAPK14/MAPKAP3 complex. Further, hsa-miR-9 overexpression initiates re-arrangement of actin filaments, which leads us to hypothesize a mechanism for the observed phenotypic shift.

Conclusion

The work presented here exposes novel microRNA features and situates hsa-miR-9 as a therapeutic target, which governs metastasis and thus determines prognosis in GBM through MAPKAP signaling.

W. A. H, Lewis RJ, Vetter I. Transcriptomics in pain research: insights from new and old technologies

Physiological and pathological pain involves a complex interplay of multiple cell types and signaling pathways.

The Human Cell Atlas

The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.

The Human Cell Atlas

The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.

Methods for lineage tracing on the organism-wide level

Determining the lineage origin of cell types is a major goal in developmental biology. Furthermore, lineage tracing is a powerful approach for understanding the origin of developmental defects as well as the origin of diseases such as cancer. There is now a variety of complementary approaches for identifying lineage relationships, ranging from direct observation of cell divisions by light microscopy to genetic labeling of cells using inducible recombinases and fluorescent reporters. A recent development, and the main topic of this review article, is the use of high-throughput sequencing data for lineage analysis. This emerging approach holds the promise of increased multiplexing capacity, allowing lineage analysis of large cell numbers up to the organism-wide level combined with simultaneous transcription profiling by single cell RNA sequencing.

Age-related clonal hematopoiesis

Age-related alterations in the human blood system occur in B cells, T cells, cells of the innate system, as well as hematopoietic stem and progenitor cells (HSPCs). Interestingly, age-related, reduced genetic diversity can be identified at the stem cell level and also independently in B cells and T cells. This reduced diversity is most probably related to somatic mutations or to changes in the microenvironmental niche. Either process can select for specific clones or cause repeated evolutionary bottlenecks. This review discusses the age-related clonal expansions in the human HSPC pool, which was termed in the past age-related clonal hematopoiesis (ARCH). ARCH is defined as the gradual, clonal expansion of HSPCs carrying specific, disruptive, and recurrent genetic variants, in individuals without clear diagnosis of hematological malignancies. ARCH is associated not just with chronological aging but also with several other, age-related pathological conditions, including inflammation, vascular diseases, cancer mortality, and high risk for hematological malignancies. Although it remains unclear whether ARCH is a marker of aging or plays an active role in these various pathophysiologies, it is suggested here that treating or even preventing ARCH may prove to be beneficial for human health. This review also describes a decision tree for the diagnosis and follow-up for ARCH in a research setting.

Quantitative Analysis of Synthetic Cell Lineage Tracing Using Nuclease Barcoding

Lineage tracing by the determination and mapping of progeny arising from single cells is an important approach enabling the elucidation of mechanisms underlying diverse biological processes ranging from development to disease. We developed a dynamic sequence-based barcode system for synthetic lineage tracing and have demonstrated its performance in C. elegans, a model organism whose lineage tree is well established. The strategy we use creates lineage trees based upon the introduction of synthetically controlled mutations into cells and the propagation of these mutations to daughter cells at each cell division. We analyzed this experimental proof of concept along with a corresponding simulation and analytical model to gain a deeper understanding of the coding capacity of the system. Our results provide specific bounds on the fidelity of lineage tracing using such approaches.

Scaling single-cell genomics from phenomenology to mechanism

Three of the most fundamental questions in biology are how individual cells differentiate to form tissues, how tissues function in a coordinated and flexible fashion and which gene regulatory mechanisms support these processes. Single-cell genomics is opening up new ways to tackle these questions by combining the comprehensive nature of genomics with the microscopic resolution that is required to describe complex multicellular systems. Initial single-cell genomic studies provided a remarkably rich phenomenology of heterogeneous cellular states, but transforming observational studies into models of dynamics and causal mechanisms in tissues poses fresh challenges and requires stronger integration of theoretical, computational and experimental frameworks.

The evolution of tumour phylogenetics: principles and practice

Rapid advances in high-throughput sequencing and a growing realization of the importance of evolutionary theory to cancer genomics have led to a proliferation of phylogenetic studies of tumour progression. These studies have yielded not only new insights but also a plethora of experimental approaches, sometimes reaching conflicting or poorly supported conclusions. Here, we consider this body of work in light of the key computational principles underpinning phylogenetic inference, with the goal of providing practical guidance on the design and analysis of scientifically rigorous tumour phylogeny studies. We survey the range of methods and tools available to the researcher, their key applications, and the various unsolved problems, closing with a perspective on the prospects and broader implications of this field.

Building a lineage from single cells: genetic techniques for cell lineage tracking

Resolving lineage relationships between cells in an organism is a fundamental interest of developmental biology. Furthermore, investigating lineage can drive understanding of pathological states, including cancer, as well as understanding of developmental pathways that are amenable to manipulation by directed differentiation. Although lineage tracking through the injection of retroviral libraries has long been the state of the art, a recent explosion of methodological advances in exogenous labelling and single-cell sequencing have enabled lineage tracking at larger scales, in more detail, and in a wider range of species than was previously considered possible. In this Review, we discuss these techniques for cell lineage tracking, with attention both to those that trace lineage forwards from experimental labelling, and those that trace backwards across the life history of an organism.

Molecular and genetic alterations associated with therapy resistance and relapse of acute myeloid leukemia

BACKGROUND

The majority of individuals with acute myeloid leukemia (AML) respond to initial chemotherapy and achieve a complete remission, yet only a minority experience long-term survival because a large proportion of patients eventually relapse with therapy-resistant disease. Relapse therefore represents a central problem in the treatment of AML. Despite this, and in contrast to the extensive knowledge about the molecular events underlying the process of leukemogenesis, information about the mechanisms leading to therapy resistance and relapse is still limited.

PURPOSE AND CONTENT OF REVIEW

Recently, a number of studies have aimed to fill this gap and provided valuable information about the clonal composition and evolution of leukemic cell populations during the course of disease, and about genetic, epigenetic, and gene expression changes associated with relapse. In this review, these studies are summarized and discussed, and the data reported in them are compiled in order to provide a resource for the identification of molecular aberrations recurrently acquired at, and thus potentially contributing to, disease recurrence and the associated therapy resistance. This survey indeed uncovered genetic aberrations with known associations with therapy resistance that were newly gained at relapse in a subset of patients. Furthermore, the expression of a number of protein coding and microRNA genes was reported to change between diagnosis and relapse in a statistically significant manner.

CONCLUSIONS

Together, these findings foster the expectation that future studies on larger and more homogeneous patient cohorts will uncover pathways that are robustly associated with relapse, thus representing potential targets for rationally designed therapies that may improve the treatment of patients with relapsed AML, or even facilitate the prevention of relapse in the first place.

Emergence of heterogeneity in acute leukemias

BACKGROUND

Leukemias are malignant proliferative disorders of the blood forming system. Sequencing studies demonstrate that the leukemic cell population consists of multiple clones. The genetic relationship between the different clones, referred to as the clonal hierarchy, shows high interindividual variability. So far, the source of this heterogeneity and its clinical relevance remain unknown. We propose a mathematical model to study the emergence and evolution of clonal heterogeneity in acute leukemias. The model allows linking properties of leukemic clones in terms of self-renewal and proliferation rates to the structure of the clonal hierarchy.

RESULTS

Computer simulations imply that the self-renewal potential of the first emerging leukemic clone has a major impact on the total number of leukemic clones and on the structure of their hierarchy. With increasing depth of the clonal hierarchy the self-renewal of leukemic clones increases, whereas the proliferation rates do not change significantly. The emergence of deep clonal hierarchies is a complex process that is facilitated by a cooperativity of different mutations.

CONCLUSION

Comparison of patient data and simulation results suggests that the self-renewal of leukemic clones increases with the emergence of clonal heterogeneity. The structure of the clonal hierarchy may serve as a marker for patient prognosis.

REVIEWERS

This article was reviewed by Marek Kimmel, Tommaso Lorenzi and Tomasz Lipniacki.

A generic, cost-effective, and scalable cell lineage analysis platform

Advances in single-cell genomics enable commensurate improvements in methods for uncovering lineage relations among individual cells. Current sequencing-based methods for cell lineage analysis depend on low-resolution bulk analysis or rely on extensive single-cell sequencing, which is not scalable and could be biased by functional dependencies. Here we show an integrated biochemical-computational platform for generic single-cell lineage analysis that is retrospective, cost-effective, and scalable. It consists of a biochemical-computational pipeline that inputs individual cells, produces targeted single-cell sequencing data, and uses it to generate a lineage tree of the input cells. We validated the platform by applying it to cells sampled from an ex vivo grown tree and analyzed its feasibility landscape by computer simulations. We conclude that the platform may serve as a generic tool for lineage analysis and thus pave the way toward large-scale human cell lineage discovery.

Classifying the Progression of Ductal Carcinoma from Single-Cell Sampled Data via Integer Linear Programming: A Case Study

Ductal Carcinoma In Situ (DCIS) is a precursor lesion of Invasive Ductal Carcinoma (IDC) of the breast. Investigating its temporal progression could provide fundamental new insights for the development of better diagnostic tools to predict which cases of DCIS will progress to IDC. We investigate the problem of reconstructing a plausible progression from single-cell sampled data of an individual with synchronous DCIS and IDC. Specifically, by using a number of assumptions derived from the observation of cellular atypia occurring in IDC, we design a possible predictive model using integer linear programming (ILP). Computational experiments carried out on a preexisting data set of 13 patients with simultaneous DCIS and IDC show that the corresponding predicted progression models are classifiable into categories having specific evolutionary characteristics. The approach provides new insights into mechanisms of clonal progression in breast cancers and helps illustrate the power of the ILP approach for similar problems in reconstructing tumor evolution scenarios under complex sets of constraints.

Accuracy of Answers to Cell Lineage Questions Depends on Single-Cell Genomics Data Quality and Quantity

Advances in single-cell (SC) genomics enable commensurate improvements in methods for uncovering lineage relations among individual cells, as determined by phylogenetic analysis of the somatic mutations harbored by each cell. Theoretically, complete and accurate knowledge of the genome of each cell of an individual can produce an extremely accurate cell lineage tree of that individual. However, the reality of SC genomics is that such complete and accurate knowledge would be wanting, in quality and in quantity, for the foreseeable future. In this paper we offer a framework for systematically exploring the feasibility of answering cell lineage questions based on SC somatic mutational analysis, as a function of SC genomics data quality and quantity. We take into consideration the current limitations of SC genomics in terms of mutation data quality, most notably amplification bias and allele dropouts (ADO), as well as cost, which puts practical limits on mutation data quantity obtained from each cell as well as on cell sample density. We do so by generating in silico cell lineage trees using a dedicated formal language, eSTG, and show how the ability to answer correctly a cell lineage question depends on the quality and quantity of the SC mutation data. The presented framework can serve as a baseline for the potential of current SC genomics to unravel cell lineage dynamics, as well as the potential contributions of future advancement, both biochemical and computational, for the task.

A human cell lineage tree describes the entire developmental dynamics of a person starting from the zygote and ending with each and every extant cell. Fundamental open problems in biology and medicine are in fact questions about the human cell lineage tree: its structure and its dynamics in development, growth, renewal, aging, and disease. Consequently, a method to know the human cell lineage tree would allow resolving these problems and enable a leapfrog advance in human knowledge and health. Recent advancements in single-cell genomics have the potential to uncover various properties of the human cell lineage tree and thus promote our understanding of various biological phenomena. In this paper we present a computational framework along with specific results, which enable to understand what can be achieved using the limitations of current technologies and predict future capabilities based on future improvements. This approach can serve as a valuable tool for researchers who plan to perform lineage experiments both in designing and optimizing the actual experimental needs and predicting the costs and limitations of the plan. This work can also help researchers focus on developing what is needed for future advancements.

Acute myeloid leukemia in the era of precision medicine: recent advances in diagnostic classification and risk stratification

Acute myeloid leukemia (AML) is a genetically heterogeneous myeloid malignancy that occurs more commonly in adults, and has an increasing incidence, most likely due to increasing age. Precise diagnostic classification of AML requires clinical and pathologic information, the latter including morphologic, immunophenotypic, cytogenetic and molecular genetic analysis. Risk stratification in AML requires cytogenetics evaluation as the most important predictor, with genetic mutations providing additional necessary information. AML with normal cytogenetics comprises about 40%-50% of all AML, and has been intensively investigated. The currently used 2008 World Health Organization classification of hematopoietic neoplasms has been proposed to be updated in 2016, also to include an update on the classification of AML, due to the continuously increasing application of genomic techniques that have led to major advances in our knowledge of the pathogenesis of AML. The purpose of this review is to describe some of these recent major advances in the diagnostic classification and risk stratification of AML.

Clonal Analysis of Cells with Cellular Barcoding: When Numbers and Sizes Matter

Cellular barcoding is a recently rediscovered tool to trace the clonal output of individual cells with genetically distinct and heritable DNA sequences. Each year a few dozens of papers are published using the cellular barcoding technique. Those publications largely focus on mutually related issues, namely: counting cells capable of clonal proliferation and expansion, monitoring clonal dynamics in time, tracing the origin of differentiated cells, characterizing the differentiation potential of stem cells and similar topics. Apart from their biological content, claims and conclusions, these studies show remarkable diversity in technical aspects of the barcoding method and sometimes in major conclusions. Although a diversity of approaches is quite usual in data analysis, deviant handling of barcode data might directly affect experimental results and their biological interpretation. Here, we will describe typical challenges and caveats in cellular barcoding publications available so far.

AML evolution from preleukemia to leukemia and relapse

Dismal outcomes of acute myeloid leukemia (AML), especially in the elderly, are mainly associated with leukemia relapse and primary no response to initial therapy. This review will focus on AML relapse, and how a better understanding of the evolutionary stages that lead to relapse might help us improve disease outcome. The fact that the relapse rate for some AMLs is so high indicates that we do not truly understand the biology of relapse or possibly that we are not implementing our current understanding into, clinical practice. Therefore, this review will also aim to explore some of the current understanding of AML relapse biology in order to identify the gaps in our knowledge and translation. Accumulating evidence suggests that the root of relapse evolves even before the time of diagnosis, meaning that the complex clonal structure of AML is created before patients present to the clinic. Some of the clones that exist at diagnosis can survive chemotherapy and give rise to relapse. Accordingly, in order to better understand the mechanisms of relapse, we must consider both early and late steps in AML evolution.

Aging, clonal hematopoiesis and preleukemia: not just bad luck?

Chronological human aging is associated with a number of changes in the hematopoietic system, occurring at many levels from stem to mature cells, and the marrow microenvironment as well. This review will focus mainly on the aging of hematopoietic stem and progenitor cells (HSPCs), and on the associated increases in the incidence of hematological malignancies. HSPCs manifest reduced function and acquire molecular changes with chronological aging. Furthermore, while for many years it has been known that the human hematopoietic system becomes increasingly clonal with chronological aging (clonal hematopoiesis), only in the last few years has it become clear that clonal hematopoiesis may result from the accumulation of preleukemic mutations in HSPCs. Such mutations confer a selective advantage that leads to clonal hematopoiesis, and that may occasionally result in the development of leukemia, and define the existence of both preleukemic stem cells, and of 'preleukemia' as a clinical entity. While it is well appreciated that clonal hematopoiesis is very common in the elderly, several questions remain unanswered: why and how does clonal hematopoiesis develop? How is clonal hematopoiesis related to the age-related changes observed in the hematopoietic system? And why do only some individuals with clonal hematopoiesis develop leukemia?

Theory and Practice of Lineage Tracing

Lineage tracing is a method that delineates all progeny produced by a single cell or a group of cells. The possibility of performing lineage tracing initiated the field of Developmental Biology and continues to revolutionize Stem Cell Biology. Here, I introduce the principles behind a successful lineage-tracing experiment. In addition, I summarize and compare different methods for conducting lineage tracing and provide examples of how these strategies can be implemented to answer fundamental questions in development and regeneration. The advantages and limitations of each method are also discussed.

Evolution of the cancer stem cell model

Genetic analyses have shaped much of our understanding of cancer. However, it is becoming increasingly clear that cancer cells display features of normal tissue organization, where cancer stem cells (CSCs) can drive tumor growth. Although often considered as mutually exclusive models to describe tumor heterogeneity, we propose that the genetic and CSC models of cancer can be harmonized by considering the role of genetic diversity and nongenetic influences in contributing to tumor heterogeneity. We offer an approach to integrating CSCs and cancer genetic data that will guide the field in interpreting past observations and designing future studies.

Algorithms to model single gene, single chromosome, and whole genome copy number changes jointly in tumor phylogenetics

We present methods to construct phylogenetic models of tumor progression at the cellular level that include copy number changes at the scale of single genes, entire chromosomes, and the whole genome. The methods are designed for data collected by fluorescence in situ hybridization (FISH), an experimental technique especially well suited to characterizing intratumor heterogeneity using counts of probes to genetic regions frequently gained or lost in tumor development. Here, we develop new provably optimal methods for computing an edit distance between the copy number states of two cells given evolution by copy number changes of single probes, all probes on a chromosome, or all probes in the genome. We then apply this theory to develop a practical heuristic algorithm, implemented in publicly available software, for inferring tumor phylogenies on data from potentially hundreds of single cells by this evolutionary model. We demonstrate and validate the methods on simulated data and published FISH data from cervical cancers and breast cancers. Our computational experiments show that the new model and algorithm lead to more parsimonious trees than prior methods for single-tumor phylogenetics and to improved performance on various classification tasks, such as distinguishing primary tumors from metastases obtained from the same patient population.

Cancer is an evolutionary system whose growth and development is attributed to aberrations in well-known genes and to cancer-type specific genomic imbalances. Here, we present methods for reconstructing the evolution of individual tumors based on cell-to-cell variations between copy numbers of targeted regions of the genome. The methods are designed to work with fluorescence in situ hybridization (FISH), a technique that allows one to profile copy number changes in potentially thousands of single cells per study. Our work advances the prior art by developing theory and practical algorithms for building evolutionary trees of single tumors that can model gain or loss of genetic regions at the scale of single genes, whole chromosomes, or the entire genome, all common events in tumor evolution. We apply these methods on simulated and real tumor data to demonstrate substantial improvements in tree-building accuracy and in our ability to accurately classify tumors from their inferred evolutionary models. The newly developed algorithms have been released through our publicly available software, FISHtrees.

Cancer stem cell immunology: key to understanding tumorigenesis and tumor immune escape?

Cancer stem cell (CSC) biology and tumor immunology have shaped our understanding of tumorigenesis. However, we still do not fully understand why tumors can be contained but not eliminated by the immune system and whether rare CSCs are required for tumor propagation.

Long latency or recurrence periods have been described for most tumors. Conceptually, this requires a subset of malignant cells which is capable of initiating tumors, but is neither eliminated by immune cells nor able to grow straight into overt tumors. These criteria would be fulfilled by CSCs. Stem cells are pluripotent, immune-privileged, and long-living, but depend on specialized niches. Thus, latent tumors may be maintained by a niche-constrained reservoir of long-living CSCs that are exempt from immunosurveillance while niche-independent and more immunogenic daughter cells are constantly eliminated.

The small subpopulation of CSCs is often held responsible for tumor initiation, metastasis, and recurrence. Experimentally, this hypothesis was supported by the observation that only this subset can propagate tumors in non-obese diabetic/scid mice, which lack T and B cells. Yet, the concept was challenged when an unexpectedly large proportion of melanoma cells were found to be capable of seeding complex tumors in mice which further lack NK cells. Moreover, the link between stem cell-like properties and tumorigenicity was not sustained in these highly immunodeficient animals. In humans, however, tumor-propagating cells must also escape from immune-mediated destruction. The ability to persist and to initiate neoplastic growth in the presence of immunosurveillance – which would be lost in a maximally immunodeficient animal model – could hence be a decisive criterion for CSCs.

Consequently, integrating scientific insight from stem cell biology and tumor immunology to build a new concept of “CSC immunology” may help to reconcile the outlined contradictions and to improve our understanding of tumorigenesis.

Lineage grammars: describing, simulating and analyzing population dynamics

Background

Precise description of the dynamics of biological processes would enable the mathematical analysis and computational simulation of complex biological phenomena. Languages such as Chemical Reaction Networks and Process Algebras cater for the detailed description of interactions among individuals and for the simulation and analysis of ensuing behaviors of populations. However, often knowledge of such interactions is lacking or not available. Yet complete oblivion to the environment would make the description of any biological process vacuous. Here we present a language for describing population dynamics that abstracts away detailed interaction among individuals, yet captures in broad terms the effect of the changing environment, based on environment-dependent Stochastic Tree Grammars (eSTG). It is comprised of a set of stochastic tree grammar transition rules, which are context-free and as such abstract away specific interactions among individuals. Transition rule probabilities and rates, however, can depend on global parameters such as population size, generation count, and elapsed time.

Results

We show that eSTGs conveniently describe population dynamics at multiple levels including cellular dynamics, tissue development and niches of organisms. Notably, we show the utilization of eSTG for cases in which the dynamics is regulated by environmental factors, which affect the fate and rate of decisions of the different species. eSTGs are lineage grammars, in the sense that execution of an eSTG program generates the corresponding lineage trees, which can be used to analyze the evolutionary and developmental history of the biological system under investigation. These lineage trees contain a representation of the entire events history of the system, including the dynamics that led to the existing as well as to the extinct individuals.

Conclusions

We conclude that our suggested formalism can be used to easily specify, simulate and analyze complex biological systems, and supports modular description of local biological dynamics that can be later used as “black boxes” in a larger scope, thus enabling a gradual and hierarchical definition and simulation of complex biological systems. The simple, yet robust formalism enables to target a broad class of stochastic dynamic behaviors, especially those that can be modeled using global environmental feedback regulation rather than direct interaction between individuals.

Single cell behavior in T cell differentiation

Upon primary infection, naïve T cells that recognize their cognate antigen become activated, proliferate, and simultaneously differentiate into various subsets. A long-standing question in the field has been how this cellular diversification is achieved. Conceptually, diverse cellular output may either arise from every single cell or only from populations of naïve cells. Furthermore, such diversity may either be driven by cell-intrinsic heterogeneity or by external, niche-derived signals. In this review, we discuss how recently developed technologies have allowed the analysis of the mechanisms underlying T cell diversification at the single cell level. In addition, we outline the implications of this work on our understanding of the formation of immunological memory, and describe a number of unresolved key questions in this field.

Functional heterogeneity of genetically defined subclones in acute myeloid leukemia

The relationships between clonal architecture and functional heterogeneity in acute myeloid leukemia (AML) samples are not yet clear. We used targeted sequencing to track AML subclones identified by whole-genome sequencing using a variety of experimental approaches. We found that virtually all AML subclones trafficked from the marrow to the peripheral blood, but some were enriched in specific cell populations. Subclones showed variable engraftment potential in immunodeficient mice. Xenografts were predominantly comprised of a single genetically defined subclone, but there was no predictable relationship between the engrafting subclone and the evolutionary hierarchy of the leukemia. These data demonstrate the importance of integrating genetic and functional data in studies of primary cancer samples, both in xenograft models and in patients.