Temporal clonal developments.

<p>(A) & (B) Simulation without heterogeneity between the clones (<i>σ</i> = 0) for 2 years and till monoclonality is reached (~8 years), respectively. (C) Simulation with no aging effect (<i>α</i> = 0) and a simulated heterogeneity of the differentiation rate <i>d</i>_<i>i</i> (<i>σ</i> > 0). (D) Average time to reach monoclonality on a logarithmic scale in years vs. the clonal heterogeneity defined by <i>σ</i>. (E) Simulation of an aging effect (<i>α</i> > 0) and no difference in the differentiation rates <i>d</i>_<i>i</i> between the clones (<i>σ</i> = 0). (F) Shows the change in the average time to reach monoclonality depending on the aging effect <i>α</i>.</p

Model Based Analysis of Clonal Developments Allows for Early Detection of Monoclonal Conversion and Leukemia

<div><p>The availability of several methods to unambiguously mark individual cells has strongly fostered the understanding of clonal developments in hematopoiesis and other stem cell driven regenerative tissues. While cellular barcoding is the method of choice for experimental studies, patients that underwent gene therapy carry a unique insertional mark within the transplanted cells originating from the integration of the retroviral vector. Close monitoring of such patients allows accessing their clonal dynamics, however, the early detection of events that predict monoclonal conversion and potentially the onset of leukemia are beneficial for treatment. We developed a simple mathematical model of a self-stabilizing hematopoietic stem cell population to generate a wide range of possible clonal developments, reproducing typical, experimentally and clinically observed scenarios. We use the resulting model scenarios to suggest and test a set of statistical measures that should allow for an interpretation and classification of relevant clonal dynamics. Apart from the assessment of several established diversity indices we suggest a measure that quantifies the extension to which the increase in the size of one clone is attributed to the total loss in the size of all other clones. By evaluating the change in relative clone sizes between consecutive measurements, the suggested measure, referred to as <i>maximum relative clonal expansion</i> (<i>mRCE</i>), proves to be highly sensitive in the detection of rapidly expanding cell clones prior to their dominant manifestation. This predictive potential places the <i>mRCE</i> as a suitable means for the early recognition of leukemogenesis especially in gene therapy patients that are closely monitored. Our model based approach illustrates how simulation studies can actively support the design and evaluation of preclinical strategies for the analysis and risk evaluation of clonal developments.</p></div

Scale representation of <i>mRCE</i> measurement.

<p><i>ΣΔ</i>⁻ denotes the sum of all shrinking clones and <i>maxΔ</i>⁺ the clone with the highest expansion between two consecutive points in time. Only the case of one dominantly expanding clone balances the scale and indicates a high risk for rapid monoclonal conversion (<i>mRCE</i> = 1).</p

Performance comparison of <i>mRCE</i> vs. classical indices.

<p>(A) Time courses of <i>mRCE</i> values of 10 individual runs of pathological and healthy simulations. (B) The plot shows the distribution of the <i>mRCE</i> values for healthy (blue) and pathological (black) contributions (circles). These distributions are used to fit a binomial glm (lines indicate the probability for the occurrence of pathological (black) and physiological (blue) scenarios). (C) AUC comparison of different measures. (D) Positive likelihood ratio comparison over time of different.</p

MAGPIE: Simplifying access and execution of computational models in the life sciences

Over the past decades, quantitative methods linking theory and observation became increasingly important in many areas of life science. Subsequently, a large number of mathematical and computational models has been developed. The BioModels database alone lists more than 140,000 Systems Biology Markup Language (SBML) models. However, while the exchange within specific model classes has been supported by standardisation and database efforts, the generic application and especially the re-use of models is still limited by practical issues such as easy and straight forward model execution. MAGPIE, a Modeling and Analysis Generic Platform with Integrated Evaluation, closes this gap by providing a software platform for both, publishing and executing computational models without restrictions on the programming language, thereby combining a maximum on flexibility for programmers with easy handling for non-technical users. MAGPIE goes beyond classical SBML platforms by including all models, independent of the underlying programming language, ranging from simple script models to complex data integration and computations. We demonstrate the versatility of MAGPIE using four prototypic example cases. We also outline the potential of MAGPIE to improve transparency and reproducibility of computational models in life sciences. A demo server is available at magpie.imb.medizin.tu-dresden.de

Clonal developments described by classical indices.

<p>(A) Time courses of the Simpson and Shannon index as well as species Richness for a non-mutated scenario (clonal pattern in inset). For reference, the size of the largest clone is given by the dotted line. (B) Similar time courses based on a mutation scenario (as provided in the inset). The mutation event occurs after ~3 months.</p

Clonal reconstruction from co-occurrence of vector integration sites accurately quantifies expanding clones in vivo

High transduction rates of viral vectors in gene therapies (GT) and experimental hematopoiesis ensure a high frequency of gene delivery, although multiple integration events can occur in the same cell. Therefore, tracing of integration sites (IS) leads to mis-quantification of the true clonal spectrum and limits safety considerations in GT. Hence, we use correlations between repeated measurements of IS abundances to estimate their mutual similarity and identify clusters of co-occurring IS, for which we assume a clonal origin. We evaluate the performance, robustness and specificity of our methodology using clonal simulations. The reconstruction methods, implemented and provided as an R-package, are further applied to experimental clonal mixes and preclinical models of hematopoietic GT. Our results demonstrate that clonal reconstruction from IS data allows to overcome systematic biases in the clonal quantification as an essential prerequisite for the assessment of safety and long-term efficacy of GT involving integrative vectors