44 research outputs found

    Cancer evolution: mathematical models and computational inference.

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    Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy.FM would like to acknowledge the support of The University of Cambridge, Cancer Research UK and Hutchison Whampoa Limited.This is the final published version. It first appeared at http://sysbio.oxfordjournals.org/content/early/2014/10/07/sysbio.syu081.short?rss=1

    The Evolution of Single Cell-derived Colorectal Cancer Cell Lines is Dominated by the Continued Selection of Tumor Specific Genomic Imbalances, Despite Random Chromosomal Instability

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    Intratumor heterogeneity is a major challenge in cancer treatment. To decipher patterns of chromosomal heterogeneity, we analyzed six colorectal cancer cell lines by multiplex interphase FISH (miFISH). The mismatch repair deficient cell lines DLD-1 and HCT116 had the most stable copy numbers, whereas aneuploid cell lines (HT-29, SW480, SW620 and H508) displayed a higher degree of instability. We subsequently assessed the clonal evolution of single cells in two CRC cell lines, SW480 and HT-29, which both have aneuploid karyotypes but different degrees of chromosomal instability. The clonal compositions of the single cell-derived daughter lines, as assessed by miFISH, differed for HT-29 and SW480. Daughters of HT-29 were stable, clonal, with little heterogeneity. Daughters of SW480 were more heterogeneous, with the single cell-derived daughter lines separating into two distinct populations with different ploidy (hyper-diploid and near-triploid), morphology, gene expression and tumorigenicity. To better understand the evolutionary trajectory for the two SW480 populations, we constructed phylogenetic trees which showed ongoing instability in the daughter lines. When analyzing the evolutionary development over time, most single cell-derived daughter lines maintained their major clonal pattern, with the exception of one daughter line that showed a switch involving a loss of APC. Our meticulous analysis of the clonal evolution and composition of these colorectal cancer models shows that all chromosomes are subject to segregation errors, however, specific net genomic imbalances are maintained. Karyotype evolution is driven by the necessity to arrive at and maintain a specific plateau of chromosomal copy numbers as the drivers of carcinogenesis

    Computational strategies for dissecting the high-dimensional complexity of adaptive immune repertoires

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    The adaptive immune system recognizes antigens via an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the quantitative and molecular-level profiling of immune repertoires thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. Several methods for the computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity in order to understand the dynamics of adaptive immunity. Here, we review the current research on (i) diversity, (ii) clustering and network, (iii) phylogenetic and (iv) machine learning methods applied to dissect, quantify and compare the architecture, evolution, and specificity of immune repertoires. We summarize outstanding questions in computational immunology and propose future directions for systems immunology towards coupling AIRR-seq with the computational discovery of immunotherapeutics, vaccines, and immunodiagnostics.Comment: 27 pages, 2 figure

    CNETML: Maximum likelihood inference of phylogeny from copy number profiles of spatio-temporal samples

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    Phylogenetic trees based on copy number alterations (CNAs) for multi-region samples of a single cancer patient are helpful to understand the spatio-temporal evolution of cancers, especially in tumours driven by chromosomal instability. Due to the high cost of deep sequencing data, low-coverage data are more accessible in practice, which only allow the calling of (relative) total copy numbers due to the lower resolution. However, methods to reconstruct sample phylogenies from CNAs often use allele-specific copy numbers and those using total copy number are mostly distance matrix or maximum parsimony methods which do not handle temporal data or estimate mutation rates. In this work, we developed a new maximum likelihood method based on a novel evolutionary model of CNAs, CNETML, to infer phylogenies from spatio-temporal samples taken within a single patient. CNETML is the first program to jointly infer the tree topology, node ages, and mutation rates from total copy numbers when samples were taken at different time points. Our extensive simulations suggest CNETML performed well even on relative copy numbers with subclonal whole genome doubling events and under slight violation of model assumptions. The application of CNETML to real data from Barrett’s esophagus patients also generated consistent results with previous discoveries and novel early CNAs for further investigations

    An Improved Binary Differential Evolution Algorithm to Infer Tumor Phylogenetic Trees

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    CNETML: maximum likelihood inference of phylogeny from copy number profiles of multiple samples

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    Phylogenetic trees based on copy number profiles from multiple samples of a patient are helpful to understand cancer evolution. Here, we develop a new maximum likelihood method, CNETML, to infer phylogenies from such data. CNETML is the first program to jointly infer the tree topology, node ages, and mutation rates from total copy numbers of longitudinal samples. Our extensive simulations suggest CNETML performs well on copy numbers relative to ploidy and under slight violation of model assumptions. The application of CNETML to real data generates results consistent with previous discoveries and provides novel early copy number events for further investigation

    Inferring tumour proliferative organisation from phylogenetic tree measures in a computational model

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    We use a computational modelling approach to explore whether it is possible to infer a solid tumour’s cellular proliferative hierarchy under the assumptions of the cancer stem cell hypothesis and neutral evolution. We focus on inferring the symmetric division probability for cancer stem cells, since this is believed to be a key driver of progression and therapeutic response. Motivated by the advent of multi-region sampling and resulting opportunities to infer tumour evolutionary history, we focus on a suite of statistical measures of the phylogenetic trees resulting from the tumour’s evolution in different regions of parameter space and through time. We find strikingly different patterns in these measures for changing symmetric division probability which hinge on the inclusion of spatial constraints. These results give us a starting point to begin stratifying tumours by this biological parameter and also generate a number of actionable clinical and biological hypotheses including changes during therapy, and through tumour evolution

    Inferring tumour proliferative organisation from phylogenetic tree measures in a computational model

    Get PDF
    We use a computational modelling approach to explore whether it is possible to infer a tumour's cell proliferative hierarchy, under the assumptions of the cancer stem cell hypothesis and neutral evolution. We focus on inferring the symmetric division probability for cancer stem cells in our model, as this is believed to be a key driving parameter of tumour progression and therapeutic response. Given the advent of multi-region sampling, and the opportunities offered by them to understand tumour evolutionary history, we focus on a suite of statistical measures of the phylogenetic trees resulting from the tumour's evolution in different regions of parameter space and through time. We find strikingly different patterns in these measures for changing symmetric division probability which hinge on the inclusion of spatial constraints. These results give us a starting point to begin stratifying tumours by this biological parameter and also generate a number of actionable clinical and biological hypotheses including changes during therapy, and through tumour evolution
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