Continuous therapy response references for BCR::ABL1 monitoring in pediatric chronic myeloid leukemia.

Response to tyrosine kinase inhibitor (TKI) therapy in patients with chronic myeloid leukemia (CML) is monitored by quantification of BCR::ABL1 transcript levels. Milestones for assessing optimal treatment response have been defined in adult CML patients and are applied to children and adolescents although it is questionable whether transferability to pediatric patients is appropriate regarding genetic and clinical differences. Therefore, we analyzed the molecular response kinetics to TKI therapy in 129 pediatric CML patients and investigated whether response assessment based on continuous references can support an early individual therapy adjustment. We applied a moving quantiles approach to establish a high-resolution response target curve and contrasted the median responses in all patients with the median of the ideal target curve obtained from a subgroup of optimal responders. The high-resolution response target curve of the optimal responder group presents a valuable tool for continuous therapy monitoring of individual pediatric CML patients in addition to the fixed milestones. By further comparing BCR::ABL1 transcript levels with BCR::ABL1 fusion gene copy numbers, it is also possible to model the differential dynamics of BCR::ABL1 expression and cell number under therapy. The developed methodology can be transferred to other biomarkers for continuous therapy monitoring.

Data integration between clinical research and patient care: A framework for context-depending data sharing and in silico predictions.

The transfer of new insights from basic or clinical research into clinical routine is usually a lengthy and time-consuming process. Conversely, there are still many barriers to directly provide and use routine data in the context of basic and clinical research. In particular, no coherent software solution is available that allows a convenient and immediate bidirectional transfer of data between concrete treatment contexts and research settings. Here, we present a generic framework that integrates health data (e.g., clinical, molecular) and computational analytics (e.g., model predictions, statistical evaluations, visualizations) into a clinical software solution which simultaneously supports both patient-specific healthcare decisions and research efforts, while also adhering to the requirements for data protection and data quality. Specifically, our work is based on a recently established generic data management concept, for which we designed and implemented a web-based software framework that integrates data analysis, visualization as well as computer simulation and model prediction with audit trail functionality and a regulation-compliant pseudonymization service. Within the front-end application, we established two tailored views: a clinical (i.e., treatment context) perspective focusing on patient-specific data visualization, analysis and outcome prediction and a research perspective focusing on the exploration of pseudonymized data. We illustrate the application of our generic framework by two use-cases from the field of haematology/oncology. Our implementation demonstrates the feasibility of an integrated generation and backward propagation of data analysis results and model predictions at an individual patient level into clinical decision-making processes while enabling seamless integration into a clinical information system or an electronic health record.

Modelling of immune response in chronic myeloid leukemia patients suggests potential for treatment reduction prior to cessation.

Introduction: Discontinuation of tyrosine kinase inhibitor (TKI) treatment is emerging as the main therapy goal for Chronic Myeloid Leukemia (CML) patients. The DESTINY trial showed that TKI dose reduction prior to cessation can lead to an increased number of patients achieving sustained treatment free remission (TFR). However, there has been no systematic investigation to evaluate how dose reduction regimens can further improve the success of TKI stop trials. Methods: Here, we apply an established mathematical model of CML therapy to investigate different TKI dose reduction schemes prior to therapy cessation and evaluate them with respect to the total amount of drug used and the expected TFR success. Results: Our systematic analysis confirms clinical findings that the overall time of TKI treatment is a major determinant of TFR success, while highlighting that lower dose TKI treatment for the same duration is equally sufficient for many patients. Our results further suggest that a stepwise dose reduction prior to TKI cessation can increase the success rate of TFR, while substantially reducing the amount of administered TKI. Discussion: Our findings illustrate the potential of dose reduction schemes prior to treatment cessation and suggest corresponding and clinically testable strategies that are applicable to many CML patients.

A ROS-dependent mechanism promotes CDK2 phosphorylation to drive progression through S phase.

Reactive oxygen species (ROS) at the right concentration promote cell proliferation in cell culture, stem cells, and model organisms. However, the mystery of how ROS signaling is coordinated with cell cycle progression and integrated into the cell cycle control machinery on the molecular level remains unsolved. Here, we report increasing levels of mitochondrial ROS during the cell cycle in human cell lines that target cyclin-dependent kinase 2 (CDK2). Chemical and metabolic interferences with ROS production decrease T-loop phosphorylation on CDK2 and so impede its full activation and thus its efficient DNA replication. ROS regulate CDK2 activity through the oxidation of a conserved cysteine residue near the T-loop, which prevents the binding of the T-loop phosphatase KAP. Together, our data reveal how mitochondrial metabolism is coupled with DNA replication and cell cycle progression via ROS, thereby demonstrating how KAP activity toward CDKs can be cell cycle regulated.

How to predict relapse in leukemia using time series data: A comparative in silico study.

Risk stratification and treatment decisions for leukemia patients are regularly based on clinical markers determined at diagnosis, while measurements on system dynamics are often neglected. However, there is increasing evidence that linking quantitative time-course information to disease outcomes can improve the predictions for patient-specific treatment responses. We designed a synthetic experiment simulating response kinetics of 5,000 patients to compare different computational methods with respect to their ability to accurately predict relapse for chronic and acute myeloid leukemia treatment. Technically, we used clinical reference data to first fit a model and then generate de novo model simulations of individual patients' time courses for which we can systematically tune data quality (i.e. measurement error) and quantity (i.e. number of measurements). Based hereon, we compared the prediction accuracy of three different computational methods, namely mechanistic models, generalized linear models, and deep neural networks that have been fitted to the reference data. Reaching prediction accuracies between 60 and close to 100%, our results indicate that data quality has a higher impact on prediction accuracy than the specific choice of the particular method. We further show that adapted treatment and measurement schemes can considerably improve the prediction accuracy by 10 to 20%. Our proof-of-principle study highlights how computational methods and optimized data acquisition strategies can improve risk assessment and treatment of leukemia patients.

Mast Cells Occupy Stable Clonal Territories in Adult Steady-State Skin.

Mast cells (MCs) are tissue-resident hematopoietic cells intensely studied for their role as effectors in allergic immune responses. Yolk sac-derived embryonic MCs first populate tissues and are later replaced by definitive MCs. We show that definitive MC progenitors expand locally in skin and form clonal colonies that cover stable territories. In MC-deficient skin, colonies grow by proliferation of MCs at the border of the clonal territory. Clonal growth ceases at common borders of neighboring colonies. In steady state, colony self-renewal is independent of bone marrow contribution, and the clonal architecture remains fixed if not disturbed by skin inflammation. Inflammatory cues increase MC density setpoint, stimulating the influx of new progenitors from the bone marrow as well as proliferation of skin-resident cells. The expanding new arrivals disrespect territories of preexisting MC clones. We conclude that during a limited window early in development, definitive MC precursors efficiently enter the skin, expand, and self-maintain, occupying stable territories. In adulthood, circulating progenitors, excluded from steady-state skin, are recruited only into inflamed skin where they clonally expand alongside proliferating skin-resident MCs, disorganizing the original architecture of clonal territories.

Continuous mitotic activity of primitive hematopoietic stem cells in adult mice.

The proliferative activity of aging hematopoietic stem cells (HSCs) is controversially discussed. Inducible fluorescent histone 2B fusion protein (H2B-FP) transgenic mice are important tools for tracking the mitotic history of murine HSCs in label dilution experiments. A recent study proposed that primitive HSCs symmetrically divide only four times to then enter permanent quiescence. We observed that background fluorescence due to leaky H2B-FP expression, occurring in all H2B-FP transgenes independent of label induction, accumulated with age in HSCs with high repopulation potential. We argue that this background had been misinterpreted as stable retention of induced label. We found cell division-independent half-lives of H2B-FPs to be short, which had led to overestimation of HSC divisional activity. Our data do not support abrupt entry of HSCs into permanent quiescence or sudden loss of regeneration potential after four divisions, but show that primitive HSCs of adult mice continue to cycle rarely.

Statistical and Mathematical Modeling of Spatiotemporal Dynamics of Stem Cells.

Statistical and mathematical modeling are crucial to describe, interpret, compare, and predict the behavior of complex biological systems including the organization of hematopoietic stem and progenitor cells in the bone marrow environment. The current prominence of high-resolution and live-cell imaging data provides an unprecedented opportunity to study the spatiotemporal dynamics of these cells within their stem cell niche and learn more about aberrant, but also unperturbed, normal hematopoiesis. However, this requires careful quantitative statistical analysis of the spatial and temporal behavior of cells and the interaction with their microenvironment. Moreover, such quantification is a prerequisite for the construction of hypothesis-driven mathematical models that can provide mechanistic explanations by generating spatiotemporal dynamics that can be directly compared to experimental observations. Here, we provide a brief overview of statistical methods in analyzing spatial distribution of cells, cell motility, cell shapes, and cellular genealogies. We also describe cell-based modeling formalisms that allow researchers to simulate emergent behavior in a multicellular system based on a set of hypothesized mechanisms. Together, these methods provide a quantitative workflow for the analytic and synthetic study of the spatiotemporal behavior of hematopoietic stem and progenitor cells.

Paracrine mechanisms in early differentiation of human pluripotent stem cells: Insights from a mathematical model.

With their capability to self-renew and differentiate into derivatives of all three germ layers, human pluripotent stem cells (hPSCs) offer a unique model to study aspects of human development in vitro. Directed differentiation towards mesendodermal lineages is a complex process, involving transition through a primitive streak (PS)-like stage. We have recently shown PS-like patterning from hPSCs into definitive endoderm, cardiac as well as presomitic mesoderm by only modulating the bulk cell density and the concentration of the GSK3 inhibitor CHIR99021, a potent activator of the WNT pathway. The patterning process is modulated by a complex paracrine network, whose identity and mechanistic consequences are poorly understood. To study the underlying dynamics, we here applied mathematical modeling based on ordinary differential equations. We compared time-course data of early hPSC differentiation to increasingly complex model structures with incremental numbers of paracrine factors. Model simulations suggest at least three paracrine factors being required to recapitulate the experimentally observed differentiation kinetics. Feedback mechanisms from both undifferentiated and differentiated cells turned out to be crucial. Evidence from double knock-down experiments and secreted protein enrichment allowed us to hypothesize on the identity of two of the three predicted factors. From a practical perspective, the mathematical model predicts optimal settings for directing lineage-specific differentiation. This opens new avenues for rational stem cell bioprocessing in more advanced culture systems, e.g. in perfusion-fed bioreactors enabling cell therapies.

Metabolism is the tie: The Bertalanffy-type cancer growth model as common denominator of various modelling approaches.

Cancer or tumour growth has been addressed from a variety of mathematical modelling perspectives in the past. Examples are single variable growth models, reaction diffusion models, compartment models, individual cell-based models, clonal competition models, to name only a few. In this paper, we show that the so called Bertalanffy-type growth model is a macroscopic model variant that can be conceived as an optimal condensed modelling approach that to a high degree preserves complexity with respect to the aforementioned more complex modelling variants. The derivation of the Bertalanffy-type model is crucially based on features of metabolism. Therefore, this model contains a shape parameter that can be interpreted as a resource utilisation efficiency. This shape parameter reflects features that are usually captured in much more complex models. To be specific, the shape parameter is related to morphological structures of tumours, which in turn depend on metabolic conditions. We, furthermore, show that a single variable variant of the Bertalanffy-type model can straightforwardly be extended to a multiclonal competition model. Since competition is crucially based on available shared or clone-specific resources, the metabolism-based approach is an obvious candidate to capture clonal competition. Depending on the specific context, metabolic reprogramming or other oncogene driven changes either lead to a suppression of cancer cells or to an improved competition resulting in outgrowth of tumours. The parametrisation of the Bertalanffy-type growth model allows to account for this observed variety of cancer characteristics. The shape parameter, conceived as a classifier for healthy and oncogenic phenotypes, supplies a link to survival and evolutionary stability concepts discussed in demographic studies, such as opportunistic versus equilibrium strategies.

Quantitative Cell Cycle Analysis Based on an Endogenous All-in-One Reporter for Cell Tracking and Classification.

Cell cycle kinetics are crucial to cell fate decisions. Although live imaging has provided extensive insights into this relationship at the single-cell level, the limited number of fluorescent markers that can be used in a single experiment has hindered efforts to link the dynamics of individual proteins responsible for decision making directly to cell cycle progression. Here, we present fluorescently tagged endogenous proliferating cell nuclear antigen (PCNA) as an all-in-one cell cycle reporter that allows simultaneous analysis of cell cycle progression, including the transition into quiescence, and the dynamics of individual fate determinants. We also provide an image analysis pipeline for automated segmentation, tracking, and classification of all cell cycle phases. Combining the all-in-one reporter with labeled endogenous cyclin D1 and p21 as prime examples of cell-cycle-regulated fate determinants, we show how cell cycle and quantitative protein dynamics can be simultaneously extracted to gain insights into G1 phase regulation and responses to perturbations.

The bulk of the hematopoietic stem cell population is dispensable for murine steady-state and stress hematopoiesis.

Long-term repopulating (LT) hematopoietic stem cells (HSCs) are the most undifferentiated cells at the top of the hematopoietic hierarchy. The regulation of HSC pool size and its contribution to hematopoiesis are incompletely understood. We depleted hematopoietic stem and progenitor cells (HSPCs) in adult mice in situ and found that LT-HSCs recovered from initially very low levels (<1%) to below 10% of normal numbers but not more, whereas progenitor cells substantially recovered shortly after depletion. In spite of the persistent and massive reduction of LT-HSCs, steady-state hematopoiesis was unaffected and residual HSCs remained quiescent. Hematopoietic stress, although reported to recruit quiescent HSCs into cycle, was well tolerated by HSPC-depleted mice and did not induce expansion of the small LT-HSC compartment. Only upon 5-fluorouracil treatment was HSPC-depleted bone marrow compromised in reconstituting hematopoiesis, demonstrating that HSCs and early progenitors are crucial to compensate myeloablation. Hence, a contracted HSC compartment cannot recover in situ to its original size, and normal steady-state blood cell generation is sustained with <10% of normal LT-HSC numbers without increased contribution of the few residual cells.

Dissecting mechanisms of mouse embryonic stem cells heterogeneity through a model-based analysis of transcription factor dynamics.

Pluripotent mouse embryonic stem cells (mESCs) show heterogeneous expression levels of transcription factors (TFs) involved in pluripotency regulation, among them Nanog and Rex1. The expression of both TFs can change dynamically between states of high and low activity, correlating with the cells' capacity for self-renewal. Stochastic fluctuations as well as sustained oscillations in gene expression are possible mechanisms to explain this behaviour, but the lack of suitable data hampered their clear distinction. Here, we present a systems biology approach in which novel experimental data on TF heterogeneity is complemented by an agent-based model of mESC self-renewal. Because the model accounts for intracellular interactions, cell divisions and heredity structures, it allows for evaluating the consistency of the proposed mechanisms with data on population growth and on TF dynamics after cell sorting. Our model-based analysis revealed that a bistable, noise-driven network model fulfils the minimal requirements to consistently explain Nanog and Rex1 expression dynamics in heterogeneous and sorted mESC populations. Moreover, we studied the impact of TF-related proliferation capacities on the frequency of state transitions and demonstrate that cellular genealogies can provide insights into the heredity structures of mESCs.

Image-based quantification and mathematical modeling of spatial heterogeneity in ESC colonies.

Pluripotent embryonic stem cells (ESCs) have the potential to differentiate into cells of all three germ layers. This unique property has been extensively studied on the intracellular, transcriptional level. However, ESCs typically form clusters of cells with distinct size and shape, and establish spatial structures that are vital for the maintenance of pluripotency. Even though it is recognized that the cells' arrangement and local interactions play a role in fate decision processes, the relations between transcriptional and spatial patterns have not yet been studied. We present a systems biology approach which combines live-cell imaging, quantitative image analysis, and multiscale, mathematical modeling of ESC growth. In particular, we develop quantitative measures of the morphology and of the spatial clustering of ESCs with different expression levels and apply them to images of both in vitro and in silico cultures. Using the same measures, we are able to compare model scenarios with different assumptions on cell-cell adhesions and intercellular feedback mechanisms directly with experimental data. Applying our methodology to microscopy images of cultured ESCs, we demonstrate that the emerging colonies are highly variable regarding both morphological and spatial fluorescence patterns. Moreover, we can show that most ESC colonies contain only one cluster of cells with high self-renewing capacity. These cells are preferentially located in the interior of a colony structure. The integrated approach combining image analysis with mathematical modeling allows us to reveal potential transcription factor related cellular and intercellular mechanisms behind the emergence of observed patterns that cannot be derived from images directly.

Elucidating functional heterogeneity in hematopoietic progenitor cells: a combined experimental and modeling approach.

A detailed understanding of the mechanisms maintaining the hierarchical balance of cell types in hematopoiesis will be important for the therapeutic manipulation of normal and leukemic cells. Mathematical modeling is expected to make an important contribution to this area, but the iterative development of increasingly accurate models will rely on repeated validation using experimental data of sufficient resolution to distinguish between alternative model scenarios. The multipotent hematopoietic progenitor FDCP-Mix cells maintain a hierarchy from self-renewal to post-mitotic differentiation in vitro and are accessible to detailed analysis. Here, we report the development of a combined mathematical modeling and experimental approach to study the principles underlying heterogeneity in FDCP-Mix cultures. We adapt a single-cell based model of hematopoiesis to the conditions of cell culture and describe an association between proliferative history and phenotype of FDCP-Mix cells. While data derived from population studies are incapable of distinguishing between three mechanistically different model scenarios, statistical analysis of single cell tracking data provides a resolution sufficient to select one of them. This scenario favors differences between granulocytic and monocytic lineage with respect to their proliferative behavior and death rates as a mechanistic explanation for the observed heterogeneity. Our results demonstrate the power of a combined experimental/modeling approach in which single cell fate analysis is the key to revealing regulatory principles at the cellular level.