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Dive into the research topics where Timothy Beck is active.

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Featured researches published by Timothy Beck.


Nature Genetics | 2015

Spatial genomic heterogeneity within localized, multifocal prostate cancer

Paul C. Boutros; Michael Fraser; Nicholas J. Harding; Richard de Borja; Dominique Trudel; Emilie Lalonde; Alice Meng; Pablo H. Hennings-Yeomans; Andrew McPherson; Veronica Y. Sabelnykova; Amin Zia; Natalie S. Fox; Julie Livingstone; Yu Jia Shiah; Jianxin Wang; Timothy Beck; Cherry Have; Taryne Chong; Michelle Sam; Jeremy Johns; Lee Timms; Nicholas Buchner; Ada Wong; John D. Watson; Trent T. Simmons; Christine P'ng; Gaetano Zafarana; Francis Nguyen; Xuemei Luo; Kenneth C. Chu

Herein we provide a detailed molecular analysis of the spatial heterogeneity of clinically localized, multifocal prostate cancer to delineate new oncogenes or tumor suppressors. We initially determined the copy number aberration (CNA) profiles of 74 patients with index tumors of Gleason score 7. Of these, 5 patients were subjected to whole-genome sequencing using DNA quantities achievable in diagnostic biopsies, with detailed spatial sampling of 23 distinct tumor regions to assess intraprostatic heterogeneity in focal genomics. Multifocal tumors are highly heterogeneous for single-nucleotide variants (SNVs), CNAs and genomic rearrangements. We identified and validated a new recurrent amplification of MYCL, which is associated with TP53 deletion and unique profiles of DNA damage and transcriptional dysregulation. Moreover, we demonstrate divergent tumor evolution in multifocal cancer and, in some cases, tumors of independent clonal origin. These data represent the first systematic relation of intraprostatic genomic heterogeneity to predicted clinical outcome and inform the development of novel biomarkers that reflect individual prognosis.


Nature | 2016

A renewed model of pancreatic cancer evolution based on genomic rearrangement patterns.

Faiyaz Notta; Michelle Chan-Seng-Yue; Mathieu Lemire; Yilong Li; Gavin Wilson; Ashton A. Connor; Robert E. Denroche; Sheng Ben Liang; Andrew M.K. Brown; Jaeseung C. Kim; Tao Wang; Jared T. Simpson; Timothy Beck; Ayelet Borgida; Nicholas Buchner; Dianne Chadwick; Sara Hafezi-Bakhtiari; John E. Dick; Lawrence E. Heisler; Michael A. Hollingsworth; Emin Ibrahimov; Gun Ho Jang; Jeremy Johns; Lars G T Jorgensen; Calvin Law; Olga Ludkovski; Ilinca Lungu; Karen Ng; Danielle Pasternack; Gloria M. Petersen

Pancreatic cancer, a highly aggressive tumour type with uniformly poor prognosis, exemplifies the classically held view of stepwise cancer development. The current model of tumorigenesis, based on analyses of precursor lesions, termed pancreatic intraepithelial neoplasm (PanINs) lesions, makes two predictions: first, that pancreatic cancer develops through a particular sequence of genetic alterations (KRAS, followed by CDKN2A, then TP53 and SMAD4); and second, that the evolutionary trajectory of pancreatic cancer progression is gradual because each alteration is acquired independently. A shortcoming of this model is that clonally expanded precursor lesions do not always belong to the tumour lineage, indicating that the evolutionary trajectory of the tumour lineage and precursor lesions can be divergent. This prevailing model of tumorigenesis has contributed to the clinical notion that pancreatic cancer evolves slowly and presents at a late stage. However, the propensity for this disease to rapidly metastasize and the inability to improve patient outcomes, despite efforts aimed at early detection, suggest that pancreatic cancer progression is not gradual. Here, using newly developed informatics tools, we tracked changes in DNA copy number and their associated rearrangements in tumour-enriched genomes and found that pancreatic cancer tumorigenesis is neither gradual nor follows the accepted mutation order. Two-thirds of tumours harbour complex rearrangement patterns associated with mitotic errors, consistent with punctuated equilibrium as the principal evolutionary trajectory. In a subset of cases, the consequence of such errors is the simultaneous, rather than sequential, knockout of canonical preneoplastic genetic drivers that are likely to set-off invasive cancer growth. These findings challenge the current progression model of pancreatic cancer and provide insights into the mutational processes that give rise to these aggressive tumours.


Nature Communications | 2015

A comprehensive assessment of somatic mutation detection in cancer using whole-genome sequencing

Tyler Alioto; Ivo Buchhalter; Sophia Derdak; Barbara Hutter; Matthew Eldridge; Eivind Hovig; Lawrence E. Heisler; Timothy Beck; Jared T. Simpson; Laurie Tonon; Anne Sophie Sertier; Ann Marie Patch; Natalie Jäger; Philip Ginsbach; Ruben M. Drews; Nagarajan Paramasivam; Rolf Kabbe; Sasithorn Chotewutmontri; Nicolle Diessl; Christopher Previti; Sabine Schmidt; Benedikt Brors; Lars Feuerbach; Michael Heinold; Susanne Gröbner; Andrey Korshunov; Patrick Tarpey; Adam Butler; Jonathan Hinton; David Jones

As whole-genome sequencing for cancer genome analysis becomes a clinical tool, a full understanding of the variables affecting sequencing analysis output is required. Here using tumour-normal sample pairs from two different types of cancer, chronic lymphocytic leukaemia and medulloblastoma, we conduct a benchmarking exercise within the context of the International Cancer Genome Consortium. We compare sequencing methods, analysis pipelines and validation methods. We show that using PCR-free methods and increasing sequencing depth to ∼100 × shows benefits, as long as the tumour:control coverage ratio remains balanced. We observe widely varying mutation call rates and low concordance among analysis pipelines, reflecting the artefact-prone nature of the raw data and lack of standards for dealing with the artefacts. However, we show that, using the benchmark mutation set we have created, many issues are in fact easy to remedy and have an immediate positive impact on mutation detection accuracy.


Nature | 2017

Genomic hallmarks of localized, non-indolent prostate cancer

Michael Fraser; Veronica Y. Sabelnykova; Takafumi N. Yamaguchi; Lawrence E. Heisler; Julie Livingstone; Vincent Huang; Yu Jia Shiah; Fouad Yousif; Xihui Lin; Andre P. Masella; Natalie S. Fox; Michael Xie; Stephenie D. Prokopec; Alejandro Berlin; Emilie Lalonde; Musaddeque Ahmed; Dominique Trudel; Xuemei Luo; Timothy Beck; Alice Meng; Junyan Zhang; Alister D'Costa; Robert E. Denroche; Haiying Kong; Shadrielle Melijah G. Espiritu; Melvin Lee Kiang Chua; Ada Wong; Taryne Chong; Michelle Sam; Jeremy Johns

Prostate tumours are highly variable in their response to therapies, but clinically available prognostic factors can explain only a fraction of this heterogeneity. Here we analysed 200 whole-genome sequences and 277 additional whole-exome sequences from localized, non-indolent prostate tumours with similar clinical risk profiles, and carried out RNA and methylation analyses in a subset. These tumours had a paucity of clinically actionable single nucleotide variants, unlike those seen in metastatic disease. Rather, a significant proportion of tumours harboured recurrent non-coding aberrations, large-scale genomic rearrangements, and alterations in which an inversion repressed transcription within its boundaries. Local hypermutation events were frequent, and correlated with specific genomic profiles. Numerous molecular aberrations were prognostic for disease recurrence, including several DNA methylation events, and a signature comprised of these aberrations outperformed well-described prognostic biomarkers. We suggest that intensified treatment of genomically aggressive localized prostate cancer may improve cure rates.


International Journal of Cancer | 2014

Identification of genes expressed by immune cells of the colon that are regulated by colorectal cancer-associated variants

Vanya Peltekova; Mathieu Lemire; Aamer Mahmood Qazi; Syed H. Zaidi; Quang M. Trinh; Ryszard Bielecki; Marianne Rogers; Lyndsey Hodgson; Mike Wang; David J. A. D'Souza; Sasan Zandi; Taryne Chong; Jennifer Y. Y. Kwan; Krystian Kozak; Richard de Borja; Lee Timms; Jagadish Rangrej; Milica Volar; Michelle Chan-Seng-Yue; Timothy Beck; Colleen Ash; Shawna Lee; Jianxin Wang; Paul C. Boutros; Lincoln Stein; John E. Dick; Robert Gryfe; John D. McPherson; Brent W. Zanke; Aaron Pollett

A locus on human chromosome 11q23 tagged by marker rs3802842 was associated with colorectal cancer (CRC) in a genome‐wide association study; this finding has been replicated in case–control studies worldwide. In order to identify biologic factors at this locus that are related to the etiopathology of CRC, we used microarray‐based target selection methods, coupled to next‐generation sequencing, to study 103 kb at the 11q23 locus. We genotyped 369 putative variants from 1,030 patients with CRC (cases) and 1,061 individuals without CRC (controls) from the Ontario Familial Colorectal Cancer Registry. Two previously uncharacterized genes, COLCA1 and COLCA2, were found to be co‐regulated genes that are transcribed from opposite strands. Expression levels of COLCA1 and COLCA2 transcripts correlate with rs3802842 genotypes. In colon tissues, COLCA1 co‐localizes with crystalloid granules of eosinophils and granular organelles of mast cells, neutrophils, macrophages, dendritic cells and differentiated myeloid‐derived cell lines. COLCA2 is present in the cytoplasm of normal epithelial, immune and other cell lineages, as well as tumor cells. Tissue microarray analysis demonstrates the association of rs3802842 with lymphocyte density in the lamina propria (p = 0.014) and levels of COLCA1 in the lamina propria (p = 0.00016) and COLCA2 (tumor cells, p = 0.0041 and lamina propria, p = 6 × 10–5). In conclusion, genetic, expression and immunohistochemical data implicate COLCA1 and COLCA2 in the pathogenesis of colon cancer. Histologic analyses indicate the involvement of immune pathways.


bioRxiv | 2014

A comprehensive multicenter comparison of whole genome sequencing pipelines using a uniform tumor-normal sample pair

Ivo Buchhalter; Barbara Hutter; Tyler Alioto; Timothy Beck; Paul C. Boutros; Benedikt Brors; Adam Butler; Sasithorn Chotewutmontri; Robert E. Denroche; Sophia Derdak; Nicolle Diessl; Lars Feuerbach; Akihiro Fujimoto; Susanne Gröbner; Marta Gut; Nicholas J. Harding; Michael Heinold; Lawrence E. Heisler; Jonathan Hinton; Natalie Jäger; David Jones; Rolf Kabbe; Andrey Korshunov; John D. McPherson; Andrew Menzies; Hidewaki Nakagawa; Christopher Previti; Keiran Raine; Paolo Ribeca; Sabine Schmidt

As next-generation sequencing becomes a clinical tool, a full understanding of the variables affecting sequencing analysis output is required. Through the International Cancer Genome Consortium (ICGC), we compared sequencing pipelines at five independent centers (CNAG, DKFZ, OICR, RIKEN and WTSI) using a single tumor-blood DNA pair. Analyses by each center and with one standardized algorithm revealed significant discrepancies. Although most pipelines performed well for coding mutations, library preparation methods and sequencing coverage metrics clearly influenced downstream results. PCR-free methods showed reduced GC-bias and more even coverage. Increasing sequencing depth to ∼100x (two- to three-fold higher than current standards) showed a benefit, as long as the tumor:control coverage ratio remained balanced. To become part of routine clinical care, high-throughput sequencing must be globally compatible and comparable. This benchmarking exercise has highlighted several fundamental parameters to consider in this regard, which will allow for better optimization and planning of both basic and translational studies.


bioRxiv | 2014

A Comprehensive Assessment of Somatic Mutation Calling in Cancer Genomes

Tyler Alioto; Sophia Derdak; Timothy Beck; Paul C. Boutros; Lawrence Bower; Ivo Buchhalter; Matthew Eldridge; Nicholas J. Harding; Lawrence E. Heisler; Eivind Hovig; David T. W. Jones; Andy G. Lynch; Sigve Nakken; Paolo Ribeca; Anne-Sophie Sertier; Jared T. Simpson; Paul T. Spellman; Patrick Tarpey; Laurie Tonon; Daniel Vodák; Takafumi N. Yamaguchi; Sergi Beltran Agullo; Marc Dabad; Robert E. Denroche; Philip Ginsbach; Simon Heath; Emanuele Raineri; Charlotte L Anderson; Benedikt Brors; Ruben M. Drews

The emergence of next generation DNA sequencing technology is enabling high-resolution cancer genome analysis. Large-scale projects like the International Cancer Genome Consortium (ICGC) are systematically scanning cancer genomes to identify recurrent somatic mutations. Second generation DNA sequencing, however, is still an evolving technology and procedures, both experimental and analytical, are constantly changing. Thus the research community is still defining a set of best practices for cancer genome data analysis, with no single protocol emerging to fulfil this role. Here we describe an extensive benchmark exercise to identify and resolve issues of somatic mutation calling. Whole genome sequence datasets comprising tumor-normal pairs from two different types of cancer, chronic lymphocytic leukaemia and medulloblastoma, were shared within the ICGC and submissions of somatic mutation calls were compared to verified mutations and to each other. Varying strategies to call mutations, incomplete awareness of sources of artefacts, and even lack of agreement on what constitutes an artefact or real mutation manifested in widely varying mutation call rates and somewhat low concordance among submissions. We conclude that somatic mutation calling remains an unsolved problem. However, we have identified many issues that are easy to remedy that are presented here. Our study highlights critical issues that need to be addressed before this valuable technology can be routinely used to inform clinical decision-making. Abbreviations and Definitions SSM Somatic Single-base Mutations or Simple Somatic Mutations, refers to a somatic single base change SIM Somatic Insertion/deletion Mutation CNV Copy Number Variant SV Structural Variant SNP Single Nucleotide Polymorphisms, refers to a single base variable position in the germline with a frequency of > 1% in the general population CLL Chronic Lymphocytic Leukaemia MB Medulloblastoma ICGC International Cancer Genome Consortium BM Benchmark aligner = mapper, these terms are used interchangeably


Epigenetics & Chromatin | 2016

Quantitative analysis of ChIP-seq data uncovers dynamic and sustained H3K4me3 and H3K27me3 modulation in cancer cells under hypoxia

Michiel E. Adriaens; Peggy Prickaerts; Michelle Chan-Seng-Yue; Twan van den Beucken; V.E.H. Dahlmans; Lars Eijssen; Timothy Beck; Bradly G. Wouters; Jan Willem Voncken; Chris T. Evelo

BackgroundA comprehensive assessment of the epigenetic dynamics in cancer cells is the key to understanding the molecular mechanisms underlying cancer and to improving cancer diagnostics, prognostics and treatment. By combining genome-wide ChIP-seq epigenomics and microarray transcriptomics, we studied the effects of oxygen deprivation and subsequent reoxygenation on histone 3 trimethylation of lysine 4 (H3K4me3) and lysine 27 (H3K27me3) in a breast cancer cell line, serving as a model for abnormal oxygenation in solid tumors. A priori, epigenetic markings and gene expression levels not only are expected to vary greatly between hypoxic and normoxic conditions, but also display a large degree of heterogeneity across the cell population. Where traditionally ChIP-seq data are often treated as dichotomous data, the model and experiment here necessitate a quantitative, data-driven analysis of both datasets.ResultsWe first identified genomic regions with sustained epigenetic markings, which provided a sample-specific reference enabling quantitative ChIP-seq data analysis. Sustained H3K27me3 marking was located around centromeres and intergenic regions, while sustained H3K4me3 marking is associated with genes involved in RNA binding, translation and protein transport and localization. Dynamic marking with both H3K4me3 and H3K27me3 (hypoxia-induced bivalency) was found in CpG-rich regions at loci encoding factors that control developmental processes, congruent with observations in embryonic stem cells.ConclusionsIn silico-identified epigenetically sustained and dynamic genomic regions were confirmed through ChIP-PCR in vitro, and obtained results are corroborated by published data and current insights regarding epigenetic regulation.


BMC Research Notes | 2015

A cancer cell-line titration series for evaluating somatic classification.

Robert E. Denroche; Laura Mullen; Lee Timms; Timothy Beck; Christina K. Yung; Lincoln Stein; John D. McPherson; Andrew M.K. Brown

BackgroundAccurate detection of somatic single nucleotide variants and small insertions and deletions from DNA sequencing experiments of tumour-normal pairs is a challenging task. Tumour samples are often contaminated with normal cells confounding the available evidence for the somatic variants. Furthermore, tumours are heterogeneous so sub-clonal variants are observed at reduced allele frequencies. We present here a cell-line titration series dataset that can be used to evaluate somatic variant calling pipelines with the goal of reliably calling true somatic mutations at low allele frequencies.ResultsCell-line DNA was mixed with matched normal DNA at 8 different ratios to generate samples with known tumour cellularities, and exome sequenced on Illumina HiSeq to depths of >300×. The data was processed with several different variant calling pipelines and verification experiments were performed to assay >1500 somatic variant candidates using Ion Torrent PGM as an orthogonal technology. By examining the variants called at varying cellularities and depths of coverage, we show that the best performing pipelines are able to maintain a high level of precision at any cellularity. In addition, we estimate the number of true somatic variants undetected as cellularity and coverage decrease.ConclusionsOur cell-line titration series dataset, along with the associated verification results, was effective for this evaluation and will serve as a valuable dataset for future somatic calling algorithm development. The data is available for further analysis at the European Genome-phenome Archive under accession number EGAS00001001016. Data access requires registration through the International Cancer Genome Consortium’s Data Access Compliance Office (ICGC DACO).


Genome Research | 2018

Complex rearrangements and oncogene amplifications revealed by long-read DNA and RNA sequencing of a breast cancer cell line

Maria Nattestad; Sara Goodwin; Karen Ng; Timour Baslan; Fritz J. Sedlazeck; Philipp Rescheneder; Tyler Garvin; Han Fang; James Gurtowski; Elizabeth Hutton; Elizabeth Tseng; Chen-Shan Chin; Timothy Beck; Yogi Sundaravadanam; Melissa Kramer; Eric Antoniou; John D. McPherson; James Hicks; W. Richard McCombie; Michael C. Schatz

The SK-BR-3 cell line is one of the most important models for HER2+ breast cancers, which affect one in five breast cancer patients. SK-BR-3 is known to be highly rearranged, although much of the variation is in complex and repetitive regions that may be underreported. Addressing this, we sequenced SK-BR-3 using long-read single molecule sequencing from Pacific Biosciences and develop one of the most detailed maps of structural variations (SVs) in a cancer genome available, with nearly 20,000 variants present, most of which were missed by short-read sequencing. Surrounding the important ERBB2 oncogene (also known as HER2), we discover a complex sequence of nested duplications and translocations, suggesting a punctuated progression. Full-length transcriptome sequencing further revealed several novel gene fusions within the nested genomic variants. Combining long-read genome and transcriptome sequencing enables an in-depth analysis of how SVs disrupt the genome and sheds new light on the complex mechanisms involved in cancer genome evolution.

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Lee Timms

Ontario Institute for Cancer Research

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Michelle Chan-Seng-Yue

Ontario Institute for Cancer Research

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Michelle Sam

Ontario Institute for Cancer Research

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Paul C. Boutros

Ontario Institute for Cancer Research

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Richard de Borja

Ontario Institute for Cancer Research

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Taryne Chong

Ontario Institute for Cancer Research

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Jeremy Johns

Ontario Institute for Cancer Research

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Lincoln Stein

Ontario Institute for Cancer Research

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Michael Fraser

Princess Margaret Cancer Centre

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