CAPRI: efficient inference of cancer progression models from cross-sectional data

Ramazzotti, Daniele; Caravagna, Giulio; Loohuis, Loes Olde; Graudenzi, Alex; Korsunsky, Ilya; Mauri, Giancarlo; Mishra, Bud

doi:10.1093/bioinformatics/btv296

Cited by 87 publications

(186 citation statements)

References 52 publications

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“…However, more advanced Bayesian models commonly use variants of Markov chain Monte Carlo (MCMC) sampling, which is a statistical technique for exploring the ranges of possible tree models and evolutionary parameters but at a much greater computational cost than maximum likelihood methods 90,119 . The recurring theme of trade-offs between more realistic and more computationally tractable models has inspired a great deal of research into more exotic algorithmic techniques in this domain 91,120,121 .…”

Section: Variations On Tumour Phylogeneticsmentioning

confidence: 99%

The evolution of tumour phylogenetics: principles and practice

2017

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Rapid advances in high-throughput sequencing and a growing realization of the importance of evolutionary theory to cancer genomics have led to a proliferation of phylogenetic studies of tumour progression. These studies have yielded not only new insights but also a plethora of experimental approaches, sometimes reaching conflicting or poorly supported conclusions. Here, we consider this body of work in light of the key computational principles underpinning phylogenetic inference, with the goal of providing practical guidance on the design and analysis of scientifically rigorous tumour phylogeny studies. We survey the range of methods and tools available to the researcher, their key applications, and the various unsolved problems, closing with a perspective on the prospects and broader implications of this field.

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Section: Variations On Tumour Phylogeneticsmentioning

confidence: 99%

The evolution of tumour phylogenetics: principles and practice

2017

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“…TRaIT is a computational framework that combines Suppes' probabilistic causation [38] with information theory to infer the temporal ordering of mutations that accumulate during tumour growth, as an extension of our previous work [13][14][15][16][17][18]. The framework comprises 4 algorithms (EDMONDS, GABOW, CHOW-LIU and PRIM) designed to model different types of progressions (expressivity) and integrate various types of data, still maintaining a low burden of computational complexity (Figures 1 and 2 -see Methods for the algorithmic details).…”

Section: Resultsmentioning

confidence: 99%

“…Consistently with earlier works of us [13][14][15][16][17][18], we here approach the third problem ("mutational ordering") from two types of data: multi-region bulk and single-cell sequencing.…”

Section: Introductionmentioning

confidence: 80%

Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data

Ramazzotti

Graudenzi

Sano

et al. 2017

Preprint

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Phylogenetic techniques quantify intra-tumor heterogeneity by deconvolving either clonal or mutational trees from multi-sample sequencing data of individual tumors. Most of these methods rely on the well-known infinite sites assumption, and are limited to process either multi-region or single-cell sequencing data. Here, we improve over those methods with TRaIT, a unified statistical framework for the inference of the accumulation order of multiple types of genomic alterations driving tumor development. TRaIT supports both multi-region and single-cell sequencing data, and output mutational graphs accounting for violations of the infinite sites assumption due to convergent evolution, and other complex phenomena that cannot be detected with phylogenetic tools. Our method displays better accuracy, performance and robustness to noise and small sample size than state-of-the-art phylogenetic methods. We show with single-cell data from breast cancer and multi-region data from colorectal cancer that TRaIT can quantify the extent of intra-tumor heterogeneity and generate new testable experimental hypotheses.

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“…TOMC revealed that tumor suppressor genes tend to be mutated ahead of oncogenes, which are considered as important events for key functional loss and gain during tumorigenesis. A larger workflow approach was used to develop CAPRI [21], which generates acyclic graphs to capture branched, independent and confluent evolution via bootstrapping, shrinkage, maximum likelihood, and regularization. Caravagna et al [22] reported on the PiCnIc pipeline, which incorporates CAPRI, and is a versatile, modular, and customizable pipeline to extract ensemble-level progression models from cross-sectional sequenced cancer genomes.…”

Section: Introductionmentioning

confidence: 99%

Progression inference for somatic mutations in cancer

Peterson

Kovyrshina

2017

Heliyon

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Computational methods were employed to determine progression inference of genomic alterations in commonly occurring cancers. Using cross-sectional TCGA data, we computed evolutionary trajectories involving selectivity relationships among pairs of gene-specific genomic alterations such as somatic mutations, deletions, amplifications, downregulation, and upregulation among the top 20 driver genes associated with each cancer. Results indicate that the majority of hierarchies involved TP53, PIK3CA, ERBB2, APC, KRAS, EGFR, IDH1, VHL, etc. Research into the order and accumulation of genomic alterations among cancer driver genes will ever-increase as the costs of nextgen sequencing subside, and personalized/precision medicine incorporates whole-genome scans into the diagnosis and treatment of cancer.

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CAPRI: efficient inference of cancer progression models from cross-sectional data

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 87 publications

References 52 publications

The evolution of tumour phylogenetics: principles and practice

The evolution of tumour phylogenetics: principles and practice

Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data

Progression inference for somatic mutations in cancer

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