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Cancer evolution software tools | Phylogenomics data analysis

Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. Source text: Shahrabi Farahani and Lagergren, 2013.

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TumourSimulator
Simulates a model for tumour evolution that shows how short-range dispersal and cell turnover can account for rapid cell mixing inside the tumour. TumourSimulator model shows that even a small selective advantage of a single cell within a large tumour allows the descendants of that cell to replace the precursor mass in a clinically relevant time frame. It has also been demonstrated that the same mechanisms can be responsible for the rapid onset of resistance to chemotherapy. This model not only provides insights into spatial and temporal aspects of tumour growth, but also suggests that targeting short-range cellular migratory activity could have marked effects on tumour growth rates. The source code of this model can be downloaded for local use. An interactive version for MS Windows is available and allows the user to set the parameters of the simulation and set up video recording.
CT-CBN / Continuous Time Conjunctive Bayesian Networks
A specific probabilistic graphical model for the accumulation of mutations and their interdependencies. The Bayesian network models cancer progression by an explicit unobservable accumulation process in time that is separated from the observable but error-prone detection of mutations. Model parameters are estimated by an expectation-maximization algorithm and the underlying interaction graph is obtained by a simulated annealing procedure.
deconstructSigs
Allows the identification of mutational signatures within a single tumor sample. The deconstructSigs approach determines the linear combination of pre-defined signatures that most accurately reconstructs the mutational profile of a single tumor sample. It uses a multiple linear regression model with the caveat that any coefficient must be greater than 0, as negative contributions make no biological sense. Application of deconstructSigs identifies samples with DNA repair deficiencies and reveals distinct and dynamic mutational processes molding the cancer genome in esophageal adenocarcinoma compared to squamous cell carcinomas. deconstructSigs confers the ability to define mutational processes driven by environmental exposures, DNA repair abnormalities, and mutagenic processes in individual tumors with implications for precision cancer medicine.
TiMEx
A generative probabilistic model for detecting patterns of various degrees of mutual exclusivity across genetic alterations, which can indicate pathways involved in cancer progression. TiMEx explicitly accounts for the temporal interplay between the waiting times to alterations and the observation time. In simulation studies, we show that our model outperforms previous methods for detecting mutual exclusivity. On large-scale biological datasets, TiMEx identifies gene groups with strong functional biological relevance, while also proposing new candidates for biological validation. TiMEx possesses several advantages over previous methods, including a novel generative probabilistic model of tumorigenesis, direct estimation of the probability of mutual exclusivity interaction, computational efficiency and high sensitivity in detecting gene groups involving low-frequency alterations.
BML / Bayesian Mutation Landscape
Reconstructs evolutionary paths and ancestral genotypes from sequenced tumor samples. BML first estimates the probability P(g) that a particular combination of mutations (denoted by genotype g) reaches fixation in a cell population that has evolved from a normal cell genotype and will eventually attain a tumor cell genotype. BML uses both observed tumor samples and imputed evolutionary paths to estimate P(g). The evolutionary probabilities are represented by a Bayesian network (up to an overall normalizing factor) that is optimized for the best choice of imputed paths. Once a Bayesian network is selected, a recursive algorithm is used to infer the likely Evolutionary Progression Paths (EPP). This software package is freely available for download.
Rtreemix
Offers an environment for estimating the mutagenetic trees mixture models from cross-sectional data and using them for various predictions. Rtreemix includes functions for fitting the trees mixture models, likelihood computations, model comparisons, waiting time estimations, stability analysis, etc. It takes advantage of the high-level interface, the statistical tools and the large amount of data that R and Bioconductor projects provide. For estimating mixture models, the package builds up on efficient C/C++ code provided by a modified version of the Mtreemix software, which we made independent of the LEDA package in order to provide a free R package. Rtreemix implements the main functionality of Mtreemix for model fitting and adds new functions for estimating genetic progression scores with corresponding confidence intervals and for performing model analysis.
SIApopr / Simulating Infinite-Allele populations
Simulates homogeneous and inhomogeneous stochastic branching processes under a very flexible set of assumptions. SIApopr simulates clonal evolution with the emergence of driver and passenger mutations under the infinite-allele assumption. It adapts the Stochastic Simulation Algorithm (SSA) to simulate an infinite-allele branching process where mutant cells are of a unique type each and have random variables representing their birth and death rates.
SCHISM / SubClonal Hierarchy Inference from Somatic Mutations
Reconstructs tumor subclonal phylogenies using somatic mutation cellularities in patient's tumor sample(s). SCHISM combines information about somatic mutation cellularity (aka mutation cancer cell fraction) across all tumor sample(s) available from a patient in a hypothesis testing framework to identify the statistical support for the lineage relationship between each pair of mutations or mutation clusters. The results of the hypothesis test are represented as Cluster Order Precedence Violation (CPOV) matrix which informs the subsequent step in SCHISM and ensures compliance of candidate tree topologies with lineage precedence rule. Next, an implementation of genetic algorithm (GA) explores the space of tree topologies and returns a prioritized list of candidate subclonal phylogenetic trees, most compatible with observed cellularity data.
TO-DAG / Timed Oncogenetic Directed Acyclic Graphs
Deduces from cross-sectional data of genetic alterations in tumor patients the causal dependencies and the waiting times among these genetic events. From matrices with genetic events and patient samples as rows and columns, respectively, TO-DAG generates a probabilistic graph model whose nodes represent genetic events and oriented edges between nodes indicate the presence and the direction of a causal dependency between the nodes. A direct acyclic graph, i.e. a graph with no directed cycles, has been specifically chosen as model of putative causal dependencies, as genetic alterations are assumed to be irreversible events. Two parameters define an edge: (i) its probability estimated from the frequency of occurrence of the genetic events represented by the nodes and its conditional probability, and (ii) the waiting time, i.e. the time elapsing from the occurrence of a mutation to the occurrence of another one that is conditionally dependent on it.
Unmix
Performs mixture type separation on a tumor sample. Unmix approach uses unmixing of tumor samples to assist in phylogenetic inference of cancer progression pathways. This unmixing method adapts the geometric approach of Ehrlich and Full to represent unmixing as the problem of placing a polytope of minimum size around a point set representing expression states of tumors. It then uses the inferred amounts by which the components are shared by different tumors to perform phylogenetic inference. The method thus follows a similar intuition to that of the prior cell-by-cell phylogenetic methods, assuming that cell states commonly found in the same tumors are likely to lie on common progression pathways.
tumopp
Allows tumor growth simulation in C++. tumopp offers many setting options so that simulations can be carried out under various settings. Setting options include how the cell division rate is determined, how daughter cells are placed, and how driver mutations are treated. Furthermore, to account for the cell cycle, a gamma function has been introduced for the waiting time involved in cell division. tumopp also allows simulations in a hexagonal lattice. Using tumopp, it was investigated how model settings affect the growth curve and intratumor heterogeneity (ITH) pattern. It was found that, even under neutrality (with no driver mutations), tumopp produced dramatically variable patterns of ITH and tumor morphology, from tumors in which cells with different genetic background are well intermixed to irregular shapes of tumors with a cluster of closely related cells. This result suggests a caveat in analyzing ITH data with simulations with limited settings, and tumopp will be useful to explore ITH patterns in various conditions.
CAPRI / CAncer PRogression Inference
Obsolete
Relies on a scoring method based on a probabilistic theory developed by Suppes, coupled with bootstrap and maximum likelihood inference. The resulting algorithm is efficient, achieves high accuracy and has good complexity, also, in terms of convergence properties. CAPRI performs especially well in the presence of noise in the data, and with limited sample sizes. Moreover CAPRI, in contrast to other approaches, robustly reconstructs different types of confluent trajectories despite irregularities in the data.
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