A multiscale model of the bone marrow and hematopoiesis

The structure of the hematopoietic system can explain chronic myeloid leukemia progression

Almost all cancer types share the hallmarks of cancer and a similar tumor formation: fueled by stochastic mutations in somatic cells. In case of chronic myeloid leukemia (CML), this evolutionary process can be tracked from an asymptomatic long-lasting chronic phase to a final rapidly evolving blast phase. Somatic evolution in CML occurs in the context of healthy blood production, a hierarchical process of cell division; initiated by stem cells that self-renew and differentiate to produce mature blood cells. Here we introduce a general model of hierarchical cell division explaining the particular progression of CML as resulting from the structure of the hematopoietic system. Driver mutations confer a growth advantage to the cells carrying them, for instance, the BCR::ABL1 gene, which also acts as a marker for CML. We investigated the relation of the BCR::ABL1 mutation strength to the hematopoietic stem cell division rate by employing computer simulations and fitting the model parameters to the reported median duration for the chronic and accelerated phases. Our results demonstrate that driver mutations (additional to the BCR::ABL1 mutation) are necessary to explain CML progression if stem cells divide sufficiently slowly. We observed that the number of mutations accumulated by cells at the more differentiated levels of the hierarchy is not affected by driver mutations present in the stem cells. Our results shed light on somatic evolution in a hierarchical tissue and show that the clinical hallmarks of CML progression result from the structural characteristics of blood production.

Preleukemic Fusion Genes Induced via Ionizing Radiation

Although the prevalence of leukemia is increasing, the agents responsible for this increase are not definitely known. While ionizing radiation (IR) was classified as a group one carcinogen by the IARC, the IR-induced cancers, including leukemia, are indistinguishable from those that are caused by other factors, so the risk estimation relies on epidemiological data. Several epidemiological studies on atomic bomb survivors and persons undergoing IR exposure during medical investigations or radiotherapy showed an association between radiation and leukemia. IR is also known to induce chromosomal translocations. Specific chromosomal translocations resulting in preleukemic fusion genes (PFGs) are generally accepted to be the first hit in the onset of many leukemias. Several studies indicated that incidence of PFGs in healthy newborns is up to 100-times higher than childhood leukemia with the same chromosomal aberrations. Because of this fact, it has been suggested that PFGs are not able to induce leukemia alone, but secondary mutations are necessary. PFGs also have to occur in specific cell populations of hematopoetic stem cells with higher leukemogenic potential. In this review, we describe the connection between IR, PFGs, and cancer, focusing on recurrent PFGs where an association with IR has been established.

J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments

Background

The combined effects of biological variability and measurement-related errors on cancer sequencing data remain largely unexplored. However, the spatio-temporal simulation of multi-cellular systems provides a powerful instrument to address this issue. In particular, efficient algorithmic frameworks are needed to overcome the harsh trade-off between scalability and expressivity, so to allow one to simulate both realistic cancer evolution scenarios and the related sequencing experiments, which can then be used to benchmark downstream bioinformatics methods.

Result

We introduce a Julia package for SPAtial Cancer Evolution (J-SPACE), which allows one to model and simulate a broad set of experimental scenarios, phenomenological rules and sequencing settings.Specifically, J-SPACE simulates the spatial dynamics of cells as a continuous-time multi-type birth-death stochastic process on a arbitrary graph, employing different rules of interaction and an optimised Gillespie algorithm. The evolutionary dynamics of genomic alterations (single-nucleotide variants and indels) is simulated either under the Infinite Sites Assumption or several different substitution models, including one based on mutational signatures. After mimicking the spatial sampling of tumour cells, J-SPACE returns the related phylogenetic model, and allows one to generate synthetic reads from several Next-Generation Sequencing (NGS) platforms, via the ART read simulator. The results are finally returned in standard FASTA, FASTQ, SAM, ALN and Newick file formats.

Conclusion

J-SPACE is designed to efficiently simulate the heterogeneous behaviour of a large number of cancer cells and produces a rich set of outputs. Our framework is useful to investigate the emergent spatial dynamics of cancer subpopulations, as well as to assess the impact of incomplete sampling and of experiment-specific errors. Importantly, the output of J-SPACE is designed to allow the performance assessment of downstream bioinformatics pipelines processing NGS data. J-SPACE is freely available at: https://github.com/BIMIB-DISCo/J-Space.jl.

Acute Myeloid Leukaemia Drives Metabolic Changes in the Bone Marrow Niche

Acute myeloid leukaemia (AML) is a highly proliferative cancer characterised by infiltration of immature haematopoietic cells in the bone marrow (BM). AML predominantly affects older people and outcomes, particularly in this difficult to treat population remain poor, in part due to inadequate response to therapy, and treatment toxicity. Normal haematopoiesis is supported by numerous support cells within the BM microenvironment or niche, including adipocytes, stromal cells and endothelial cells. In steady state haematopoiesis, haematopoietic stem cells (HSCs) primarily acquire ATP through glycolysis. However, during stress-responses HSCs rapidly transition to oxidative phosphorylation, enabled by mitochondrial plasticity. Historically it was thought that cancer cells preferentially used glycolysis for ATP production, however recently it has become evident that many cancers, including AML primarily use the TCA cycle and oxidative phosphorylation for rapid proliferation. AML cells hijack the stress-response pathways of their non-malignant counterparts, utilising mitochondrial changes to drive expansion. In addition, amino acids are also utilised by leukaemic stem cells to aid their metabolic output. Together, these processes allow AML cells to maximise their ATP production, using multiple metabolites and fuelling rapid cell turnover which is a hallmark of the disease. This review of AML derived changes in the BM niche, which enable enhanced metabolism, will consider the important pathways and discuss future challenges with a view to understanding how AML cells are able to hijack metabolic pathways and how we may elucidate new targets for potential therapies.

Revisiting the outstanding questions in cancer nanomedicine with a future outlook

The field of cancer nanomedicine has been fueled by the expectation of mitigating the inefficiencies and life-threatening side effects of conventional chemotherapy. Nanomedicine proposes to utilize the unique nanoscale properties of nanoparticles to address the most pressing questions in cancer treatment and diagnosis. The approval of nano-based products in the 1990s inspired scientific explorations in this direction. However, despite significant progress in the understanding of nanoscale properties, there are only very few success stories in terms of substantial increase in clinical efficacy and overall patient survival. All existing paradigms such as the concept of enhanced permeability and retention (EPR), the stealth effect and immunocompatibility of nanomedicine have been questioned in recent times. In this review we critically examine impediments posed by biological factors to the clinical success of nanomedicine. We put forth current observations on critical outstanding questions in nanomedicine. We also provide the promising side of cancer nanomedicine as we move forward in nanomedicine research. This would provide a future direction for research in nanomedicine and inspire ongoing investigations.

Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

Systems Modeling to Quantify Safety Risks in Early Drug Development: Using Bifurcation Analysis and Agent-Based Modeling as Examples

Quantitative Systems Toxicology (QST) models, recapitulating pharmacokinetics and mechanism of action together with the organic response at multiple levels of biological organization, can provide predictions on the magnitude of injury and recovery dynamics to support study design and decision-making during drug development. Here, we highlight the application of QST models to predict toxicities of cancer treatments, such as cytopenia(s) and gastrointestinal adverse effects, where narrow therapeutic indexes need to be actively managed. The importance of bifurcation analysis is demonstrated in QST models of hematologic toxicity to understand how different regions of the parameter space generate different behaviors following cancer treatment, which results in asymptotically stable predictions, yet highly irregular for specific schedules, or oscillating predictions of blood cell levels. In addition, an agent-based model of the intestinal crypt was used to simulate how the spatial location of the injury within the crypt affects the villus disruption severity. We discuss the value of QST modeling approaches to support drug development and how they align with technological advances impacting trial design including patient selection, dose/regimen selection, and ultimately patient safety.

Quantifying Drug-Induced Bone Marrow Toxicity Using a Novel Haematopoiesis Systems Pharmacology Model

Haematological toxicity associated with cancer therapeutics is monitored by changes in blood cell count, and their primary effect is on proliferative progenitors in the bone marrow. Using observations in rat bone marrow and blood, we characterize a mathematical model that comprises cell proliferation and differentiation of the full haematopoietic phylogeny, with interacting feedback loops between lineages in homeostasis as well as following carboplatin exposure. We accurately predicted the temporal dynamics of several mature cell types related to carboplatin-induced bone marrow toxicity and identified novel insights into haematopoiesis. Our model confirms a significant degree of plasticity within bone marrow cells, with the number and type of both early progenitors and circulating cells affecting cell balance, via feedback mechanisms, through fate decisions of the multipotent progenitors. We also demonstrated cross-species translation of our predictions to patients, applying the same core model structure and considering differences in drug-dependent and physiology-dependent parameters.

Understanding Hematological Toxicities Using Mathematical Modeling

Balancing antitumor efficacy with toxicity is a significant challenge, and drug-induced myelosuppression is a common dose-limiting toxicity of cancer treatments. Mathematical modeling has proven to be a powerful ally in this field, scaling results from animal models to humans, and designing optimized treatment regimens. Here we outline existing mathematical approaches for studying bone marrow toxicity, identify gaps in current understanding, and make future recommendations to advance this vital field of safety research further.

Three dimensional de novo micro bone marrow and its versatile application in drug screening and regenerative medicine

The finding that bone marrow hosts several types of multipotent stem cell has prompted extensive research aimed at regenerating organs and building models to elucidate the mechanisms of diseases. Conventional research depends on the use of two-dimensional (2D) bone marrow systems, which imposes several obstacles. The development of 3D bone marrow systems with appropriate molecules and materials however, is now showing promising results. In this review, we discuss the advantages of 3D bone marrow systems over 2D systems and then point out various factors that can enhance the 3D systems. The intensive research on 3D bone marrow systems has revealed multiple important clinical applications including disease modeling, drug screening, regenerative medicine, etc. We also discuss some possible future directions in the 3D bone marrow research field.

Simulation predicts IGFBP2-HIF1α interaction drives glioblastoma growth

Tremendous strides have been made in improving patients’ survival from cancer with one glaring exception: brain cancer. Glioblastoma is the most common, aggressive and highly malignant type of primary brain tumor. The average overall survival remains less than 1 year. Notably, cancer patients with obesity and diabetes have worse outcomes and accelerated progression of glioblastoma. The root cause of this accelerated progression has been hypothesized to involve the insulin signaling pathway. However, while the process of invasive glioblastoma progression has been extensively studied macroscopically, it has not yet been well characterized with regards to intracellular insulin signaling. In this study we connect for the first time microscale insulin signaling activity with macroscale glioblastoma growth through the use of computational modeling. Results of the model suggest a novel observation: feedback from IGFBP2 to HIF1α is integral to the sustained growth of glioblastoma. Our study suggests that downstream signaling from IGFI to HIF1α, which has been the target of many insulin signaling drugs in clinical trials, plays a smaller role in overall tumor growth. These predictions strongly suggest redirecting the focus of glioma drug candidates on controlling the feedback between IGFBP2 and HIF1α.

Current treatment for glioblastoma patients is limited to nonspecific methods: surgery followed by a combination of radio- and chemotherapy. With these methods, glioma patient survival is less than one year post-diagnosis. Targeting specific protein signaling pathways offers potentially more potent therapies. One promising potential target is the insulin signaling pathway, which is known to contribute to glioblastoma progression. However, drugs targeting this pathway have shown mixed results in clinical trials, and the detailed mechanisms of how the insulin signaling pathway promotes glioblastoma growth remain to be elucidated. Here, we developed a computational model of insulin signaling in glioblastoma in order to study this pathway’s role in tumor progression. Using the model, we systematically test contributions of different insulin signaling protein interactions on glioblastoma growth. Our model highlights a key driver for the growth of glioblastoma: IGFBP2-HIF1α feedback. This interaction provides a target that could open the door for new therapies in glioma and other solid tumors.