PICNIC: an algorithm to predict absolute allelic copy number variation with microarray cancer data

Greenman, Chris; Bignell, Graham R.; Butler, Adam; Edkins, Sarah; Hinton, Jon; Beare, Dave; Swamy, Sajani; Santarius, Thomas; Chen, Lina; Widaa, Sara; Futreal, P. Andy; Stratton, Michael R.

doi:10.1093/biostatistics/kxp045

Cited by 186 publications

(205 citation statements)

References 39 publications

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“…The standard ASCAT workflow and default parameters described in the software manual were used for all analyses. 21 as of 06/10/13 was used for all analyses. For all analyses PICNIC was run using only the tumour array.…”

Section: Copy Number Analysismentioning

confidence: 99%

PyClone: statistical inference of clonal population structure in cancer

Roth

Khattra²,

Yap³

et al. 2014

Nat Methods

903

1,052

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We introduce a novel statistical method, PyClone, for inference of clonal population structures in cancers. PyClone is a Bayesian clustering method for grouping sets of deeply sequenced somatic mutations into putative clonal clusters while estimating their cellular prevalences and accounting for allelic imbalances introduced by segmental copy number changes and normal cell contamination. Single cell sequencing validation demonstrates that PyClone infers accurate clustering of mutations that co-occur in individual cells.Human cancer progresses under Darwinian evolution where (epi)genetic variation alters molecular phenotypes in individual cells 1 . Consequently, tumours at diagnosis often consist of multiple, genotypically distinct cell populations ( Supplementary Fig. 1) 2 . These populations, referred to as clones, are related through a phylogeny and act as substrates for selection in tumour micro-environments or with therapeutic intervention 2, 3 . The prevalence of a particular clone measured over time or in anatomic space is a reflection of its growth and proliferative fitness. Thus, ascertaining the dynamic prevalence of clones can identify precise genetic determinants of phenotypes such as acquisition of metastatic potential or chemotherapeutic resistance.In this contribution, we provide a statistical model for analysis of deeply sequenced (coverage >1000x) mutations to identify and quantify clonal populations in tumours, which extends to modelling mutations measured in multiple samples from the same patient. Our approach uses the measurement of allelic prevalence to estimate the proportion of tumour ♣

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Section: Copy Number Analysismentioning

confidence: 99%

PyClone: statistical inference of clonal population structure in cancer

Roth

Khattra²,

Yap³

et al. 2014

Nat Methods

903

1,052

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“…Copy number events -Copy number variants (CNVs) are called with the PICNIC 44 algorithm using the human genome build 38 as the reference. CNVs might be wild type, deletion or amplification of certain segments in a chromosome.…”

Section: Molecular Characterisationmentioning

confidence: 99%

A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction

Wang

Guan

et al. 2017

Preprint

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In the last decade advances in genomics, uptake of targeted therapies, and the advent of personalized treatments have fueled a dramatic change in cancer care. However, the effectiveness of most targeted therapies is short lived, as tumors evolve and develop resistance. Combinations of drugs offer the potential to overcome resistance. The space of possible combinations is vast, and significant advances are required to effectively find optimal treatment regimens tailored to a patient's tumor. DREAM and AstraZeneca hosted a Challenge open to the scientific community aimed at computational prediction of synergistic drug combinations and predictive biomarkers associated to these combinations. We released a data set comprising ~11,500 experimentally tested drug combinations, coupled to deep molecular characterization of the respective 85 cancer cell lines. Among 150 submitted approaches, those that incorporated prior knowledge of putative drug targets showed superior performance predicting drug synergy across independent data. Genomic features of best-performing models revealed putative mechanisms of drug synergy for multiple drugs in combination with PI3K/AKT pathway inhibitors.All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

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“…Interpreted data generally contain segmented copy number profiles where breakpoints along the chromosomes have been defined in each individual tumor using segmentation methods such as circular binary segmentation (CBS) (Venkatraman and Olshen 2007),GLAD (Hupe et al 2004), and others (Picard et al 2005;Wang et al 2005;Day et al 2007;Ben-Yaacov and Eldar 2008). Recent methods for measuring and determining segments of absolute allele-specific copy number from SNP arrays provide a more accurate description of allelic gains and losses and loss of heterozygosity in cancer genomes (LaFramboise et al 2005;Bengtsson et al 2010;Greenman et al 2010;Van Loo et al 2010). …”

Section: Scnas and Structural Rearrangementsmentioning

confidence: 99%

Making sense of cancer genomic data

Chin¹,

Hahn²,

Getz³

et al. 2011

Genes Dev.

316

241

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High-throughput tools for nucleic acid characterization now provide the means to conduct comprehensive analyses of all somatic alterations in the cancer genomes. Both large-scale and focused efforts have identified new targets of translational potential. The deluge of information that emerges from these genome-scale investigations has stimulated a parallel development of new analytical frameworks and tools. The complexity of somatic genomic alterations in cancer genomes also requires the development of robust methods for the interrogation of the function of genes identified by these genomics efforts. Here we provide an overview of the current state of cancer genomics, appraise the current portals and tools for accessing and analyzing cancer genomic data, and discuss emerging approaches to exploring the functions of somatically altered genes in cancer.

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PICNIC: an algorithm to predict absolute allelic copy number variation with microarray cancer data

Cited by 186 publications

References 39 publications

PyClone: statistical inference of clonal population structure in cancer

PyClone: statistical inference of clonal population structure in cancer

A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction

Making sense of cancer genomic data

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