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

Abstract

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 maximum relative clonal expansion (mRCE), proves to be highly sensitive in the detection of rapidly expanding cell clones prior to their dominant manifestation. This predictive potential places the mRCE 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.

Clonal selection and asymmetric distribution of human leukemia in murine xenografts revealed by cellular barcoding

“…Clonal markers can be cell-intrinsic (eg, genetic mutations, immunoglobulin heavy chain rearrangements), induced by cell manipulation (eg, vector integration, barcodes) or by the experimental design (eg, limiting dilution or single cell-sorting strategies), and should be defined based on the particular experimental question. 14,15 In the present study, we use cellular barcodes to mark and trace patient-derived leukemia cells. We use the term clone to refer to a group of cells that carry the same barcode and therefore derive from the same ancestor, which we call the "leukemiapropagating cell" (LPC).…”

Subclonal mutation selection in mouse lymphomagenesis identifies known cancer loci and suggests novel candidates

“…Early-stage samples from the time course had a flat profile of subclonal mutations with the majority represented by a single read/DNA fragment, whereas later stage lymphoma samples are dominated by outgrowth of up to 20 clonal integrations. Entropy was employed as a description of the clonality of integrations within each sample (based on the prior use of the Shannon entropy to estimate clonal outgrowth of T lymphoma 12 and in mathematical models of leukemia 13 ). Entropy calculations from the 50 most clonal integrations yielded high scores for early-stage samples and low scores for advanced-stage lymphoma.…”

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

“…The MAGPIE demo server is equipped with several test models. Clonal Leukemia is a single cell based model implemented in R focussing on the impact of different population-based properties to influence competition between healthy and cancer cells [ 22 ]. After creating a project ( Fig 3A ), a new job with corresponding parameters can be requested ( Fig 3C ).…”