Article

# Clonal evolution in cancer. Nature

Division of Molecular Pathology, The Institute of Cancer Research, Brookes Lawley Building, 15 Cotswold Road, Sutton, Surrey SM2 5NG, UK.
(Impact Factor: 41.46). 01/2012; 481(7381):306-13. DOI: 10.1038/nature10762
Source: PubMed

ABSTRACT

Cancers evolve by a reiterative process of clonal expansion, genetic diversification and clonal selection within the adaptive landscapes of tissue ecosystems. The dynamics are complex, with highly variable patterns of genetic diversity and resulting clonal architecture. Therapeutic intervention may destroy cancer clones and erode their habitats, but it can also inadvertently provide a potent selective pressure for the expansion of resistant variants. The inherently Darwinian character of cancer is the primary reason for this therapeutic failure, but it may also hold the key to more effective control.

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Available from: Carlo Maley, Mar 24, 2015
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• "On the one hand, tumor progression can be viewed as a sequential selection for fitter or dominant clones; on the other hand, tumors with greater genetic clonal diversity have a high probability of generating mutant cells, driving the transformation from the non-tumor to the tumor state[9,10]. Cancer evolution is a reiterative process, which consists of clonal expansion, genetic diversification, and clonal selection, in the adaptive landscapes of tissue ecosystems[11]. Biodiversity is defined as the ''variation of life at all levels of biological organization "[12], which not only involves the number of species, but also the number of individuals within each species. Diversity can be measured by Simpson's diversity index (SDI), which takes into account the number of species and the abundance of each species. "
##### Article: Diversity of Gene Expression in Hepatocellular Carcinoma Cells
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ABSTRACT: Understanding tumor diversity has been a long-lasting and challenging question for researchers in the field of cancer heterogeneity or tumor evolution. Studies have reported that compared to normal cells, there is a higher genetic diversity in tumor cells, while higher genetic diversity is associated with higher progression risks of tumor. We thus hypothesized that tumor diversity also holds true at the gene expression level. To test this hypothesis, we used t-test to compare the means of Simpson’s diversity index for gene expression (SDIG) between tumor and non-tumor samples. We found that the mean SDIG in tumor tissues is significantly higher than that in the non-tumor or normal tissues (P < 0.05) for most datasets. We also combined microarrays and next-generation sequencing data for validation. This cross-platform and cross-experimental validation greatly increased the reliability of our results.
Full-text · Article · Jan 2016 · Genomics Proteomics & Bioinformatics
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• "At the molecular and cellular levels, cancer is an evolutionary process [1] [2] [3] [4] driven by random mutational events [5] [6] [7] [8] responsible for genetic diversification which typically arises via waves of clonal and sub-clonal expansions [9] [10], operating over an adaptive fitness landscape in which Darwinian selection favors highly proliferative cell phenotypes which in turn drive rapid tumor growth [11] [12] [13]. The tumor environment should be viewed as a complex Darwinian adaptive eco-system consisting of cell types which have evolved over many years [1]. "
##### Article: An evolutionary model of tumor cell kinetics and the emergence of molecular heterogeneity driving Gompertzian growth
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ABSTRACT: A cell-molecular based evolutionary model of tumor development driven by a stochastic Moran birth-death process is developed, where each cell carries molecular information represented by a four-digit binary string, used to differentiate cells into 16 molecular types. The binary string value determines cell fitness, with lower fit cells (e.g. 0000) defined as healthy phenotypes, and higher fit cells (e.g. 1111) defined as malignant phenotypes. At each step of the birth-death process, the two phenotypic sub-populations compete in a prisoner's dilemma evolutionary game with healthy cells (cooperators) competing with cancer cells (defectors). Fitness and birth-death rates are defined via the prisoner's dilemma payoff matrix. Cells are able undergo two types of stochastic point mutations passed to the daughter cell's binary string during birth: passenger mutations (conferring no fitness advantage) and driver mutations (increasing cell fitness). Dynamic phylogenetic trees show clonal expansions of cancer cell sub-populations from an initial malignant cell. The tumor growth equation states that the growth rate is proportional to the logarithm of cellular heterogeneity, here measured using the Shannon entropy of the distribution of binary sequences in the tumor cell population. Nonconstant tumor growth rates, (exponential growth during sub-clinical range of the tumor and subsequent slowed growth during tumor saturation) are associated with a Gompertzian growth curve, an emergent feature of the model explained here using simple statistical mechanics principles related to the degree of functional coupling of the cell states. Dosing strategies at early stage development, mid-stage (clinical stage), and late stage development of the tumor are compared, showing therapy is most effective during the sub-clinical stage, before the cancer subpopulation is selected for growth.
Full-text · Article · Dec 2015
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• "The disease progresses through a series of clonal expansions that result in tumor persistence and growth, and ultimately the ability to invade surrounding tissues and metastasize to distant organs. As shown in Fig. 1(c), the evolution trajectories inherent to cancer progression are complex and branching [17]. Due to the obvious necessity for timely treatment, it is not typically feasible to collect time series data to study human cancer progression [28]. "
##### Article: A Novel Regularized Principal Graph Learning Framework on Explicit Graph Representation
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ABSTRACT: Many scientific datasets are of high dimension, and the analysis usually requires visual manipulation by retaining the most important structures of data. Principal curve is a widely used approach for this purpose. However, many existing methods work only for data with structures that are not self-intersected, which is quite restrictive for real applications. A few methods can overcome the above problem, but they either require complicated human-made rules for a specific task with lack of convergence guarantee and adaption flexibility to different tasks, or cannot obtain explicit structures of data. To address these issues, we develop a new regularized principal graph learning framework that captures the local information of the underlying graph structure based on reversed graph embedding. As showcases, models that can learn a spanning tree or a weighted undirected $\ell_1$ graph are proposed, and a new learning algorithm is developed that learns a set of principal points and a graph structure from data, simultaneously. The new algorithm is simple with guaranteed convergence. We then extend the proposed framework to deal with large-scale data. Experimental results on various synthetic and six real world datasets show that the proposed method compares favorably with baselines and can uncover the underlying structure correctly.
Full-text · Article · Dec 2015