Graph Models of Oncogenesis with an Application to Melanoma

Radmacher, Michael D.; Simon, Richard; Desper, Richard; Taetle, Raymond; Schäffer, Alejandro A.; Nelson, Mark A.

doi:10.1006/jtbi.2001.2395

Cited by 34 publications

(39 citation statements)

References 23 publications

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“…Tumors progress through a sequential series of genetic alterations, but the order of these alterations can vary among tumors and even among different compartments of the same tumor [8], [9], [10], [14], [17], [18], [19], [20]. This observation has prompted the development of statistical methods that generalize the assumption of a linear order in different ways [21], [22], [23], [24], [25], [26]. Most of these models have been applied to CGH data from various cancer types but not yet to detailed cancer sequencing data.…”

Section: Introductionmentioning

confidence: 99%

The Temporal Order of Genetic and Pathway Alterations in Tumorigenesis

et al. 2011

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Cancer evolves through the accumulation of mutations, but the order in which mutations occur is poorly understood. Inference of a temporal ordering on the level of genes is challenging because clinically and histologically identical tumors often have few mutated genes in common. This heterogeneity may at least in part be due to mutations in different genes having similar phenotypic effects by acting in the same functional pathway. We estimate the constraints on the order in which alterations accumulate during cancer progression from cross-sectional mutation data using a probabilistic graphical model termed Hidden Conjunctive Bayesian Network (H-CBN). The possible orders are analyzed on the level of genes and, after mapping genes to functional pathways, also on the pathway level. We find stronger evidence for pathway order constraints than for gene order constraints, indicating that temporal ordering results from selective pressure acting at the pathway level. The accumulation of changes in core pathways differs among cancer types, yet a common feature is that progression appears to begin with mutations in genes that regulate apoptosis pathways and to conclude with mutations in genes involved in invasion pathways. H-CBN models provide a quantitative and intuitive model of tumorigenesis showing that the genetic events can be linked to the phenotypic progression on the level of pathways.

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Section: Introductionmentioning

confidence: 99%

The Temporal Order of Genetic and Pathway Alterations in Tumorigenesis

et al. 2011

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“…Some of these methods used tree models [5-7], later extended to acyclic networks [8-10]. These evolutionary models enable recognition of aberrations that occur at early stages of cancer; often referred to as 'primary', they are suspected of being cancer drivers.…”

Section: Introductionmentioning

confidence: 99%

Large-scale analysis of chromosomal aberrations in cancer karyotypes reveals two distinct paths to aneuploidy

et al. 2011

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BackgroundChromosomal aneuploidy, that is to say the gain or loss of chromosomes, is the most common abnormality in cancer. While certain aberrations, most commonly translocations, are known to be strongly associated with specific cancers and contribute to their formation, most aberrations appear to be non-specific and arbitrary, and do not have a clear effect. The understanding of chromosomal aneuploidy and its role in tumorigenesis is a fundamental open problem in cancer biology.ResultsWe report on a systematic study of the characteristics of chromosomal aberrations in cancers, using over 15,000 karyotypes and 62 cancer classes in the Mitelman Database. Remarkably, we discovered a very high co-occurrence rate of chromosome gains with other chromosome gains, and of losses with losses. Gains and losses rarely show significant co-occurrence. This finding was consistent across cancer classes and was confirmed on an independent comparative genomic hybridization dataset of cancer samples. The results of our analysis are available for further investigation via an accompanying website.ConclusionsThe broad generality and the intricate characteristics of the dichotomy of aneuploidy, ranging across numerous tumor classes, are revealed here rigorously for the first time using statistical analyses of large-scale datasets. Our finding suggests that aneuploid cancer cells may use extra chromosome gain or loss events to restore a balance in their altered protein ratios, needed for maintaining their cellular fitness.

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“…A special case of the D-CBN, where the poset is a tree, is known as the oncogenetic or mutagenetic tree model (Desper et al, 1999;Beerenwinkel et al, 2005b,c). It has been applied to the somatic evolution of cancer (Radmacher et al, 2001;Rahnenführer et al, 2005) and to the evolution of drug resistance in HIV (Beerenwinkel et al, 2005a). The basic mutagenetic tree model has been extended to a mixture model (Beerenwinkel et al, 2005b) and to account for longitudinal data (Beerenwinkel and Drton, 2007).…”

Section: Introductionmentioning

confidence: 99%

Markov models for accumulating mutations

Beerenwinkel

Sullivant

2009

Biometrika

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Abstract. We introduce and analyze a waiting time model for the accumulation of genetic changes. The continuous time conjunctive Bayesian network is defined by a partially ordered set of mutations and by the rate of fixation of each mutation. The partial order encodes constraints on the order in which mutations can fixate in the population, shedding light on the mutational pathways underlying the evolutionary process. We study a censored version of the model and derive equations for an EM algorithm to perform maximum likelihood estimation of the model parameters. We also show how to select the maximum likelihood poset. The model is applied to genetic data from different cancers and from drug resistant HIV samples, indicating implications for diagnosis and treatment.

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Graph Models of Oncogenesis with an Application to Melanoma

Cited by 34 publications

References 23 publications

The Temporal Order of Genetic and Pathway Alterations in Tumorigenesis

The Temporal Order of Genetic and Pathway Alterations in Tumorigenesis

Large-scale analysis of chromosomal aberrations in cancer karyotypes reveals two distinct paths to aneuploidy

Markov models for accumulating mutations

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