Executable cancer models: successes and challenges

Making decisions on how best to treat cancer patients requires the integration of different data sets, including genomic profiles, tumour histopathology, radiological images, proteomic analysis and more. This wealth of biological information calls for novel strategies to integrate such information in a meaningful, predictive and experimentally verifiable way. In this Perspective we explain how executable computational models meet this need. Such models provide a means for comprehensive data integration, can be experimentally validated, are readily interpreted both biologically and clinically, and have the potential to predict effective therapies for different cancer types and subtypes. We explain what executable models are and how they can be used to represent the dynamic biological behaviours inherent in cancer, and demonstrate how such models, when coupled with automated reasoning, facilitate our understanding of the mechanisms by which oncogenic signalling pathways regulate tumours. We explore how executable models have impacted the field of cancer research and argue that extending them to represent a tumour in a specific patient (that is, an avatar) will pave the way for improved personalized treatments and precision medicine. Finally, we highlight some of the ongoing challenges in developing executable models and stress that effective cross-disciplinary efforts are key to forward progress in the field

Science Forum: 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

Modelling timing in blood cancers

Dysregulation of biological processes in normal cells can lead to the abnormal growth of tumours. Oncogenesis requires the acquisition of advantageous mutations to expand in a fluctuating environment. Cancer cells gain these genetic and epigenetic alterations at different timing in their development, resulting in the formation of heterogeneous cell populations which interact and compete with each others inside tumours. At later stages, by escaping the immune system and acquiring malignant properties, some cancer cells manage to evade the primary tumour and spread in different organs to form metastases. Hence, tumour development in healthy tissues endure several biological changes whilst progressing and the order between these molecular and cellular events may modify prognosis. This thesis addresses the influence of biological event timing on blood cancer progression and clinical outcomes. It first investigates the therapeutic efficacy of p53 restoration in a lymphoma mouse model. While several therapy schedules are tested, all fail due to resistance emergence. Computational modelling establishes the cell dynamics in these tumours and how to use it to propose alternative treatment strategies. Data availability leads this work to explore the impact of molecular evolution in myeloid malignancies. Notably, one study has found that Myeloproliferative Neoplasms patients with both JAK2 and TET2 mutations have different disease characteristics with distinct mutation order. My analyses identify HOXA9 as a potential prognosis marker and biological switch responsible for patient stratification in these patients and in Acute Myeloid Leukemia. Additionally, a molecular network identifies the hematopoietic regulators involved in the branching evolution of Myeloproliferative Neoplasms. Further investigations of the Acute Myeloid Leukemia data show the possible involvement of APP, a gene associated to Alzheimer disease, in early cell fate commitment in hematopoiesis and in poor survival prognosis in undifferentiated leukemia when lowly expressed. Finally, this thesis examines the regulatory dynamics behind three clusters of Acute Myeloid Leukemia patients with distinct levels of HOXA9 and APP expression. By building a program inferring molecular motifs from biological observations, genes which may interact with HOXA9 and APP are identified.Microsoft Research and the MRC Cancer Unit

Evolutionary and Topological Properties of Genes and Community Structures in Human Gene Regulatory Networks

abstract: The diverse, specialized genes present in today’s lifeforms evolved from a common core of ancient, elementary genes. However, these genes did not evolve individually: gene expression is controlled by a complex network of interactions, and alterations in one gene may drive reciprocal changes in its proteins’ binding partners. Like many complex networks, these gene regulatory networks (GRNs) are composed of communities, or clusters of genes with relatively high connectivity. A deep understanding of the relationship between the evolutionary history of single genes and the topological properties of the underlying GRN is integral to evolutionary genetics. Here, we show that the topological properties of an acute myeloid leukemia GRN and a general human GRN are strongly coupled with its genes’ evolutionary properties. Slowly evolving (“cold”), old genes tend to interact with each other, as do rapidly evolving (“hot”), young genes. This naturally causes genes to segregate into community structures with relatively homogeneous evolutionary histories. We argue that gene duplication placed old, cold genes and communities at the center of the networks, and young, hot genes and communities at the periphery. We demonstrate this with single-node centrality measures and two new measures of efficiency, the set efficiency and the interset efficiency. We conclude that these methods for studying the relationships between a GRN’s community structures and its genes’ evolutionary properties provide new perspectives for understanding evolutionary genetics.The article is published at http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.100500

Functional interpretation of single cell similarity maps.

We present Vision, a tool for annotating the sources of variation in single cell RNA-seq data in an automated and scalable manner. Vision operates directly on the manifold of cell-cell similarity and employs a flexible annotation approach that can operate either with or without preconceived stratification of the cells into groups or along a continuum. We demonstrate the utility of Vision in several case studies and show that it can derive important sources of cellular variation and link them to experimental meta-data even with relatively homogeneous sets of cells. Vision produces an interactive, low latency and feature rich web-based report that can be easily shared among researchers, thus facilitating data dissemination and collaboration

Mapping the transcriptional landscape of haematopoietic stem and progenitor cells

Maintenance of the blood system requires balanced cell-fate decisions of haematopoietic stem and progenitor cells (HSPCs). Individual haematopoietic stem cells (HSCs) decide between self-renewal and differentiation and can generate all mature cell types. Cell-fate decisions are made at the single-cell level and are governed by regulatory networks. Dysregulation in this balanced process could lead to serious blood disorders such as leukaemia; therefore, it is important to understand how individual cells make these cell-fate decisions. To investigate HSPC populations, 1,654 cells were profiled by single-cell RNA-sequencing. Index sorting made it possible to sort HSPCs using broad sorting gates and retrospectively assign them to common HSPC populations, retaining all information about specific functionally pure populations while also capturing any intermediate cells normally excluded by conventional gating. Reconstruction of differentiation trajectories revealed dynamic expression changes associated with early lineage differentiation from HSCs. This transcriptional atlas of HSPC differentiation was further used to identify candidate genes for a CRISPR screen investigating genes implicated in HSC biology. These candidate gene perturbations were interrogated for changes in the expression of the HSC marker EPCR, as well as changes in apoptosis and lineage output. Transcription factors play a key role in regulating cell-fate decisions and operate within organized regulatory programs. To study relationships between transcription factors in HSPC populations, qRT-PCR was used to profile the expression of 41 genes, including 31 transcription factors, in HSPCs at the single-cell level. This approach confirmed known aspects of haematopoiesis and made deeper investigation of HSPC heterogeneity possible. Regulatory networks were reconstructed using Boolean network inference models and recapitulated differentiation of HSCs towards megakaryocyte–erythrocyte progenitors and lymphoid-primed multipotent progenitors. By comparing these two models, a rule specific to the megakaryocyte-erythrocyte progenitor network was identified, in which GATA2 positively regulated Nfe2 and Cbfa2t3h. This was subsequently validated using transcription factor binding profiles and in vitro luciferase assays using a model cell line. Overall, the work presented in this thesis confirmed known aspects of HSPC biology using single-cell gene expression analysis and demonstrated how in silico approaches can be used to guide in vitro and in vivo investigations. In addition, the single-cell RNA-sequencing data was developed into an intuitive web interface that can be used to visualise the gene expression for any gene of choice at single-cell resolution across the HSPC atlas, providing a powerful resource for the haematopoietic community.My funding for the CIMR 4 year programme was provided by the Medical Research Council (MRC)

Control of lineage commitment in acute leukaemia

PhD ThesisAcute leukaemia with the t(4;11) translocation is strongly associated with pro B-acute lymphoblastic phenotype. Here is described a lineage switch from acute lymphoblastic leukaemia (ALL) to acute myeloid leukaemia (AML) which carries identical t(4;11) breakpoints that provides insight into regulation of lineage commitment and the haematopoietic origin of leukaemia. Stable DNA microsatellite sequences argue against a therapy-related AML. Genome sequencing and RNAseq identified 12 novel and deleterious mutations unique to the AML. Immunoglobulin rearrangement analysis suggested the common cell of origin lied within a population prior to B cell differentiation. Sorting of haematopoietic stem/progenitor cell populations followed by multiplex PCR and next generation sequencing for the fusion and secondary mutations demonstrated the occurrence of the leukaemogenic MLL-AF4 fusion gene in cell populations as early as the multipotent progenitor, MPP, population in both ALL and AML. In this most primitive population, the AML carries mutations in chromatin modulating genes CHD4 and PHF3, suggesting their importance in lineage commitment. Knockdown CHD4 and PHF3 individually and in combination in the pro-B ALL t(4;11) SEM cell line resulted in ~3 fold higher expression of the myeloid cell surface marker CD33. Further analysis was performed using a recently described model of MLL-AF4 leukaemogenesis consisting of CD34+ cord blood cells transduced with a chimeric MLL-Af4 fusion gene. Knockdown of CHD4 and PHF3 resulted in loss of lymphoid differentiation potential in vitro. Analysis of different PHF3 splice variants revealed that only mutation-carrying PHF3 variants increased CD33 on SEM cells and that a balance between PHF3 variants was required for the lineage fidelity. This study suggests that the ALL and AML share a common primitive cell of origin and that mutations in CHD4 and PHF3 shift the lymphoid phenotype towards a myeloid lineage leukaemia

Deciphering Leukaemogenic Mechanisms through System-Scale Analysis of Single-Cell RNA Sequencing Data

Haematopoietic stem cells are responsible for producing and sustaining the diverse array of cell types present in the adult blood system. This complex process requires the strict regulation of haematopoietic fate decisions and differentiation trajectories in order to maintain a healthy state. Haematological malignancies such as leukaemia are associated with various perturbations that disrupt this regulation and drive aberrant cell fate decisions, leading to disease. Much of this dysregulation is proposed to occur at the transcriptional level, and recent technological advancements in single-cell sequencing have made it possible to study the transcriptional effects of leukaemic perturbations at the scale of individual haematopoietic stem and progenitor cells. However, the mechanisms through which specific perturbations lead to dysregulation of the blood system remain poorly understood. The primary aim of this work was to build an integrative computational framework for the analysis and comparison of leukaemic perturbations of the murine blood system as measured by single-cell RNA sequencing. Presented in Chapter 3, this framework aims to dissect the perturbation response across different scales – from individual genes to specific progenitor cell types to the entire blood system – and allow informative comparisons to be made about the similarities and differences between several perturbations. In total, eight genetic perturbations known to associate with leukaemia were analysed, resulting in novel biological insights concerning the behaviour of coordinated gene modules and the cellular abundance shifts driven by them. As many leukaemic drivers act directly upon the most immature long-term haematopoietic stem cells, a highly targeted analysis of these cells was performed across the leukaemic perturbations. In Chapter 4 a novel computational pipeline was built to link FACS-sorted cell populations and single-cell transcriptional landscapes. Using this, the cellular and molecular responses of the perturbations were investigated, resulting in several novel hypotheses. For example, the data suggests that many leukaemic perturbations gain a competitive advantage against wild-type cells by pushing their MPP1 cells into more active states. Additionally the data suggests that increases in the transcriptional variability of blood stem cells is associated with pro-erythroid fate decision shifts and vice-versa. Many different types of haematopoietic perturbations exist and can drive disease progression in the blood system. Chapter 5 focuses on single-cell RNA sequencing data from three further perturbations in various settings, including an infection model of Malaria and a model susceptible to endogenous DNA damage by aldehydes. These analyses have driven and validated bodies of experimental work, and comparing them to the previously described perturbation models highlighted both conserved changes and differences in the response of the haematopoietic system across different perturbation settings. The final project aimed to improve upon current computational methods for cellular trajectory inference from single-cell data. Whilst high-throughput experiments allow for the sequencing of large cell numbers, this is balanced by the sparse and noisy nature of the returned data. Current methods perform poorly on such datasets and either cannot deal with large cell numbers or cannot extract enough relevant signal from sparse count matrices. A new computational tool was designed to work best on these large, sparse datasets, and infer the most likely cellular trajectories through snapshot sequencing data using an iterative process. In Chapter 6 this algorithm was applied to different systems including adult haematopoiesis, and was compared to state-of-the-art methods. Overall, this thesis has investigated the transcriptional consequences of numerous preleukaemic perturbations on the haematopoietic stem and progenitor cell compartment at the single-cell level. New methods have been built for integration of single-cell perturbation experiments and their analysis across different biological scales. This has revealed novel biological insights regarding the mechanisms underpinning leukaemic transformation of the blood system

Mammalian Transcription Factor Networks: Recent Advances in Interrogating Biological Complexity

Transcription factor (TF) networks are a key determinant of cell fate decisions in mammalian development and adult tissue homeostasis and are frequently corrupted in disease. However, our inability to experimentally resolve and interrogate the complexity of mammalian TF networks has hampered the progress in this field. Recent technological advances, in particular large-scale genome-wide approaches, single-cell methodologies, live-cell imaging, and genome editing, are emerging as important technologies in TF network biology. Several recent studies even suggest a need to re-evaluate established models of mammalian TF networks. Here, we provide a brief overview of current and emerging methods to define mammalian TF networks. We also discuss how these emerging technologies facilitate new ways to interrogate complex TF networks, consider the current open questions in the field, and comment on potential future directions and biomedical applications.ACW is funded by a Bloodwise Visiting Fellowship. HN is funded by the Japan Science and Technology Agency, the California Institute of Regenerative Medicine and Ludwig Foundation. BG is funded by Bloodwise, Cancer Research UK, the Wellcome Trust, the MRC, NIH-NIDDK and core funding from the Wellcome Trust to the Cambridge Stem Cell Institute

Computational Methods for the Analysis of Genomic Data and Biological Processes

In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality