Every which way? On predicting tumor evolution using cancer progression models

Dı́az-Uriarte, Ramón; Vega, Claudia Vasallo

doi:10.1101/371039

Cited by 8 publications

(17 citation statements)

References 69 publications

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“…In other words, while evolutionary constraints may be difficult to learn (Diaz-Uriarte, 2018), this does not necessarily imply that predictability cannot be estimated reliably (which is a simpler task addressed and well approximated by the subsetting scheme). The robust estimation conferred by our subsetting scheme partly explains the different conclusion of our study as compared to the previous one (Diaz-Uriarte and Vasallo, 2018), which reports that cancer evolution can be unpredictable for many datasets. In addition, the former study uses the ‘Lines of Descent’ (Szendro et al , 2013), instead of the SSWM assumption employed here, such that different evolutionary regimes are analyzed.…”

Section: Discussioncontrasting

confidence: 52%

Estimating the predictability of cancer evolution

et al. 2019

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Motivation How predictable is the evolution of cancer? This fundamental question is of immense relevance for the diagnosis, prognosis and treatment of cancer. Evolutionary biologists have approached the question of predictability based on the underlying fitness landscape. However, empirical fitness landscapes of tumor cells are impossible to determine in vivo. Thus, in order to quantify the predictability of cancer evolution, alternative approaches are required that circumvent the need for fitness landscapes. Results We developed a computational method based on conjunctive Bayesian networks (CBNs) to quantify the predictability of cancer evolution directly from mutational data, without the need for measuring or estimating fitness. Using simulated data derived from >200 different fitness landscapes, we show that our CBN-based notion of evolutionary predictability strongly correlates with the classical notion of predictability based on fitness landscapes under the strong selection weak mutation assumption. The statistical framework enables robust and scalable quantification of evolutionary predictability. We applied our approach to driver mutation data from the TCGA and the MSK-IMPACT clinical cohorts to systematically compare the predictability of 15 different cancer types. We found that cancer evolution is remarkably predictable as only a small fraction of evolutionary trajectories are feasible during cancer progression. Availability and implementation https://github.com/cbg-ethz/predictability\_of\_cancer\_evolution Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Discussioncontrasting

confidence: 52%

Estimating the predictability of cancer evolution

et al. 2019

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“…Simulated data were taken from [27], where complete details are provided. In summary, simulations were conducted under evolutionary scenarios that differed in the number of genes, the type of fitness landscape, the initial population size, and the mutation rates.…”

Section: Simulated Evolutionary Processes and Samplingmentioning

confidence: 99%

“…Fitness landscapes were of three types: representable, local maxima or Rough Mount Fuji (RMF). The three types of fitness landscapes incorporate increasing departures from the assumptions of most CPMs: for the representable fitness landscapes a DAG of restrictions exists with the same accessible genotypes and accessible mutational paths; for local maxima fitness landscapes the set of accessible genotypes can be represented by a DAG of restrictions, but there are local fitness maxima and the fitness graph has missing paths [27]; the genotype with all genes mutated might or might not be the genotype with largest fitness. The RMF fitness landscapes [22,33,50] usually have multiple local fitness maxima and considerable reciprocal sign epistasis so not even the set of accessible genotypes can be represented by a DAG of restrictions.…”

Section: Simulated Evolutionary Processes and Samplingmentioning

confidence: 99%

“…General overviews of all the methods used here, except for MHN, are available in [8,9,26,27] and detailed descriptions are given in the original references for each procedure (MHN [57], CBN [34,35], MCCBN [49], OT [23,61], CAPRI [16,56], CAPRESE [52]). Briefly, CBN, MCCBN, OT, CAPRESE, and CAPRI try to identify restrictions in the order of accumulation of mutations from cross-sectional data.…”

Section: Transition Probabilities From Cpmsmentioning

confidence: 99%

“…For all of these methods, since a DAG of restrictions determines a fitness graph (see Fig. 1 A-B and [26,27]), we can obtain the set of possible descendants for a genotype directly from this fitness graph.…”

Section: Transition Probabilities From Cpmsmentioning

confidence: 99%

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Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next?

Díaz-Colunga

Dı́az-Uriarte

2020

Preprint

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Accurate prediction of tumor progression would be helpful for adaptive therapy and precision medicine. Cancer progression models (CPMs) can be used to infer dependencies in mutation accumulation from cross-sectional data and provide predictions of tumor progression paths. But their performance when predicting the complete evolutionary paths is limited by violations of assumptions and the size of available data sets. Instead of predicting full tumor progression paths, we can focus on short-term predictions, more relevant for diagnostic and therapeutic purposes. Here we examine if five distinct CPMs can be used to answer the question 'Given that a genotype with n mutations has been observed, what genotype with n + 1 mutations is next in the path of tumor progression' or, shortly, 'What genotype comes next'. Using simulated data we find that under specific combinations of genotype and fitness landscape characteristics CPMs can provide predictions of short-term evolution that closely match the true probabilities, and that some genotype characteristics (fitness and probability of being a local fitness maximum) can be much more relevant than global features. Thus, CPMs can provide short-term predictions even when global, long-term predictions are not possible because fitness landscape- and evolutionary model-specific assumptions are violated. When good performance is possible, we observe significant variation in the quality of predictions of different methods. Genotype-specific and global fitness landscape characteristics are required to determine which method provides best results in each case. Application of these methods to 25 cancer data sets shows that their use is hampered by lack of the information needed to make principled decisions about method choice and what predictions to trust. Fruitful use of these methods for short-term predictions requires adapting their use to local genotype characteristics and obtaining reliable indicators of performance, as well clarifying the interpretation of their results when key assumptions do not hold.

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Inferring Tumor Progression in Large Datasets

Neyshabouri

Jun

Lagergren

2020

Preprint

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Identification of mutations of the genes that give cancer a selective advantage is an important step towards research and clinical objectives. As such, there has been a growing interest in developing methods for identification of driver genes and their temporal order within a single patient (intra-tumor) as well as across a cohort of patients (inter-tumor). In this paper, we develop a probabilistic model for tumor progression, in which the driver genes are clustered into several ordered driver pathways. We develop an efficient inference algorithm that exhibits favorable scalability to the number of genes and samples compared to a previously introduced ILP-based method. Adopting a probabilistic approach also allows principled approaches to model selection and uncertainty quantification. Using a large set of experiments on synthetic datasets, we demonstrate our superior performance compared to the ILP-based method. We also analyze two biological datasets of colorectal and glioblastoma cancers. We emphasize that while the ILP-based method puts many seemingly passenger genes in the driver pathways, our algorithm keeps focused on truly driver genes and outputs more accurate models for cancer progression. Author summaryCancer is a disease caused by the accumulation of somatic mutations in the genome. This process is mainly driven by mutations in certain genes that give the harboring cells some selective advantage. The rather few driver genes are usually masked amongst an abundance of so-called passenger mutations. Identification of the driver genes and the temporal order in which the mutations occur is of great importance towards research and clinical objectives. In this paper, we introduce a probabilistic model for cancer progression and devise an efficient inference algorithm to train the model. We show that our method scales favorably to large datasets and provides superior performance compared to an ILP-based counterpart on a wide set of synthetic data simulations. Our Bayesian approach also allows for systematic model selection and confidence quantification procedures in contrast to the previous non-probabilistic progression models. We also study two large datasets on colorectal and glioblastoma cancers and validate our inferred model in comparison to the ILP-based method.

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Every which way? On predicting tumor evolution using cancer progression models

Cited by 8 publications

References 69 publications

Estimating the predictability of cancer evolution

Estimating the predictability of cancer evolution

Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next?

Inferring Tumor Progression in Large Datasets

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