Cell lineage analysis of acute leukemia relapse uncovers the role of replication-rate heterogeneity and microsatellite instability

Identification of pre-leukaemic haematopoietic stem cells in acute leukaemia

In acute myeloid leukemia (AML), the cell of origin, nature and biological consequences of initiating lesions and order of subsequent mutations remain poorly understood, as AML is typically diagnosed without observation of a pre-leukemic phase. Here, highly purified hematopoietic stem cells (HSC), progenitor and mature cell fractions from the blood of AML patients were found to contain recurrent DNMT3a mutations (DNMT3a^mut) at high allele frequency, but without coincident NPM1 mutations (NPM1c) present in AML blasts. DNMT3a^mut-bearing HSC exhibited multilineage repopulation advantage over non-mutated HSC in xenografts, establishing their identity as pre-leukemic-HSC (preL-HSC). preL-HSC were found in remission samples indicating that they survive chemotherapy. Thus DNMT3a^mut arises early in AML evolution, likely in HSC, leading to a clonally expanded pool of preL-HSC from which AML evolves. Our findings provide a paradigm for the detection and treatment of pre-leukemic clones before the acquisition of additional genetic lesions engenders greater therapeutic resistance.

Phylogenetic analysis of multiprobe fluorescence in situ hybridization data from tumor cell populations

Motivation: Development and progression of solid tumors can be attributed to a process of mutations, which typically includes changes in the number of copies of genes or genomic regions. Although comparisons of cells within single tumors show extensive heterogeneity, recurring features of their evolutionary process may be discerned by comparing multiple regions or cells of a tumor. A useful source of data for studying likely progression of individual tumors is fluorescence in situ hybridization (FISH), which allows one to count copy numbers of several genes in hundreds of single cells. Novel algorithms for interpreting such data phylogenetically are needed, however, to reconstruct likely evolutionary trajectories from states of single cells and facilitate analysis of tumor evolution.

Results: In this article, we develop phylogenetic methods to infer likely models of tumor progression using FISH copy number data and apply them to a study of FISH data from two cancer types. Statistical analyses of topological characteristics of the tree-based model provide insights into likely tumor progression pathways consistent with the prior literature. Furthermore, tree statistics from the resulting phylogenies can be used as features for prediction methods. This results in improved accuracy, relative to unstructured gene copy number data, at predicting tumor state and future metastasis.

Availability: Source code for software that does FISH tree building (FISHtrees) and the data on cervical and breast cancer examined here are available at ftp://ftp.ncbi.nlm.nih.gov/pub/FISHtrees.

Contact:sachowdh@andrew.cmu.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Comparing algorithms that reconstruct cell lineage trees utilizing information on microsatellite mutations

Organism cells proliferate and die to build, maintain, renew and repair it. The cellular history of an organism up to any point in time can be captured by a cell lineage tree in which vertices represent all organism cells, past and present, and directed edges represent progeny relations among them. The root represents the fertilized egg, and the leaves represent extant and dead cells. Somatic mutations accumulated during cell division endow each organism cell with a genomic signature that is unique with a very high probability. Distances between such genomic signatures can be used to reconstruct an organism's cell lineage tree. Cell populations possess unique features that are absent or rare in organism populations (e.g., the presence of stem cells and a small number of generations since the zygote) and do not undergo sexual reproduction, hence the reconstruction of cell lineage trees calls for careful examination and adaptation of the standard tools of population genetics. Our lab developed a method for reconstructing cell lineage trees by examining only mutations in highly variable microsatellite loci (MS, also called short tandem repeats, STR). In this study we use experimental data on somatic mutations in MS of individual cells in human and mice in order to validate and quantify the utility of known lineage tree reconstruction algorithms in this context. We employed extensive measurements of somatic mutations in individual cells which were isolated from healthy and diseased tissues of mice and humans. The validation was done by analyzing the ability to infer known and clear biological scenarios. In general, we found that if the biological scenario is simple, almost all algorithms tested can infer it. Another somewhat surprising conclusion is that the best algorithm among those tested is Neighbor Joining where the distance measure used is normalized absolute distance. We include our full dataset in Tables S1, S2, S3, S4, S5 to enable further analysis of this data by others.

Single-cell sequencing-based technologies will revolutionize whole-organism science

The unabated progress in next-generation sequencing technologies is fostering a wave of new genomics, epigenomics, transcriptomics and proteomics technologies. These sequencing-based technologies are increasingly being targeted to individual cells, which will allow many new and longstanding questions to be addressed. For example, single-cell genomics will help to uncover cell lineage relationships; single-cell transcriptomics will supplant the coarse notion of marker-based cell types; and single-cell epigenomics and proteomics will allow the functional states of individual cells to be analysed. These technologies will become integrated within a decade or so, enabling high-throughput, multi-dimensional analyses of individual cells that will produce detailed knowledge of the cell lineage trees of higher organisms, including humans. Such studies will have important implications for both basic biological research and medicine.

Preimplantation genetic diagnosis in genomic regions with duplications and pseudogenes: long-range PCR in the single-cell assay

Long-range PCR is generally employed for the analysis of disease-causing mutations in genes with homologous pseudogene copies. However, long-range PCR is challenging when performed on single cells, as in preimplantation genetic diagnosis (PGD) of monogenic disorders. PGD on single cells requires concurrent analysis of a mutation together with multiple linked polymorphic markers from closely related family members to prevent misdiagnosis. In PGD cases involving childless de novo mutation carriers, linkage cannot be performed based on family members but rather must first be identified in single gametes. This can be an especially difficult task if the mutation to be assayed lies in a duplicated genomic region because gene-specific long-range PCR must be coupled with short-range PCR analysis of genetic markers on single cells. Here, we describe a novel method by which accurate PGD of pseudogene-homologous mutations can be achieved. Essentially, we performed whole genome amplification on single sperm or blastomeres followed by haplotype construction and long-range PCR-based mutation analysis. This original and universal strategy was used to establish allelic association for two different mutations in genes with one or more pseudogene copies (IKBKG and PKD1). The method was also sensitive enough to detect unexpected germline mosaicism in one mutation carrier.

Stem cell clonality -- theoretical concepts, experimental techniques, and clinical challenges

Here we report highlights of discussions and results presented at an International Workshop on Concepts and Models of Stem Cell Organization held on July 16th and 17th, 2012 in Dresden, Germany. The goal of the workshop was to undertake a systematic survey of state-of-the-art methods and results of clonality studies of tissue regeneration and maintenance with a particular emphasis on the hematopoietic system. The meeting was the 6th in a series of similar conceptual workshops, termed StemCellMathLab,(2) all of which have had the general objective of using an interdisciplinary approach to discuss specific aspects of stem cell biology. The StemCellMathLab 2012, which was jointly organized by the Institute for Medical Informatics and Biometry, Medical Faculty Carl Gustav Carus, Dresden University of Technology and the Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, brought together 32 scientists from 8 countries, with scientific backgrounds in medicine, cell biology, virology, physics, computer sciences, bioinformatics and mathematics. The workshop focused on the following questions: (1) How heterogeneous are stem cells and their progeny? and (2) What are the characteristic differences in the clonal dynamics between physiological and pathophysiological situations? In discussing these questions, particular emphasis was placed on (a) the methods for quantifying clones and their dynamics in experimental and clinical settings and (b) general concepts and models for their description. In this workshop summary we start with an introduction to the current state of clonality research and a proposal for clearly defined terminology. Major topics of discussion include clonal heterogeneity in unperturbed tissues, clonal dynamics due to physiological and pathophysiological pressures and conceptual and technical issues of clone quantification. We conclude that an interactive cross-disciplinary approach to research in this field will continue to promote a conceptual understanding of tissue organization.