Christine P'ng
Ontario Institute for Cancer Research
Network
Latest external collaboration on country level. Dive into details by clicking on the dots.
Publication
Featured researches published by Christine P'ng.
Nature Genetics | 2015
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.
Cancer Research | 2011
Wei Shi; Kate Gerster; Nehad M. Alajez; Jasmine Tsang; Levi Waldron; Melania Pintilie; Angela B. Hui; Jenna Sykes; Christine P'ng; Naomi Miller; David R. McCready; Anthony Fyles; Fei-Fei Liu
Several microRNAs have been implicated in human breast cancer but none to date have been validated or utilized consistently in clinical management. MicroRNA-301 (miR-301) overexpression has been implicated as a negative prognostic indicator in lymph node negative (LNN) invasive ductal breast cancer, but its potential functional impact has not been determined. Here we report that in breast cancer cells, miR-301 attenuation decreased cell proliferation, clonogenicity, migration, invasion, tamoxifen resistance, tumor growth, and microvessel density, establishing an important oncogenic role for this gene. Algorithm-based and experimental strategies identified FOXF2, BBC3, PTEN, and COL2A1 as candidate miR-301 targets, all of which were verified as direct targets through luciferase reporter assays. We noted that miR-301 is located in an intron of the SKA2 gene which is responsible for kinetochore assembly, and both genes were found to be coexpressed in primary breast cancer samples. In summary, our findings define miR-301 as a crucial oncogene in human breast cancer that acts through multiple pathways and mechanisms to promote nodal or distant relapses.
Lancet Oncology | 2014
Emilie Lalonde; Adrian Ishkanian; Jenna Sykes; Michael Fraser; Helen Ross-Adams; Nicholas Erho; Mark J. Dunning; Silvia Halim; Alastair D. Lamb; Nathalie C Moon; Gaetano Zafarana; Anne Warren; Xianyue Meng; John Thoms; Michal R Grzadkowski; Alejandro Berlin; Cherry Have; Varune Rohan Ramnarine; Cindy Q. Yao; Chad A. Malloff; Lucia L. Lam; Honglei Xie; Nicholas J. Harding; Denise Y. F. Mak; Kenneth C. Chu; Lauren C. Chong; Dorota H Sendorek; Christine P'ng; Colin Collins; Jeremy A. Squire
BACKGROUND Clinical prognostic groupings for localised prostate cancers are imprecise, with 30-50% of patients recurring after image-guided radiotherapy or radical prostatectomy. We aimed to test combined genomic and microenvironmental indices in prostate cancer to improve risk stratification and complement clinical prognostic factors. METHODS We used DNA-based indices alone or in combination with intra-prostatic hypoxia measurements to develop four prognostic indices in 126 low-risk to intermediate-risk patients (Toronto cohort) who will receive image-guided radiotherapy. We validated these indices in two independent cohorts of 154 (Memorial Sloan Kettering Cancer Center cohort [MSKCC] cohort) and 117 (Cambridge cohort) radical prostatectomy specimens from low-risk to high-risk patients. We applied unsupervised and supervised machine learning techniques to the copy-number profiles of 126 pre-image-guided radiotherapy diagnostic biopsies to develop prognostic signatures. Our primary endpoint was the development of a set of prognostic measures capable of stratifying patients for risk of biochemical relapse 5 years after primary treatment. FINDINGS Biochemical relapse was associated with indices of tumour hypoxia, genomic instability, and genomic subtypes based on multivariate analyses. We identified four genomic subtypes for prostate cancer, which had different 5-year biochemical relapse-free survival. Genomic instability is prognostic for relapse in both image-guided radiotherapy (multivariate analysis hazard ratio [HR] 4·5 [95% CI 2·1-9·8]; p=0·00013; area under the receiver operator curve [AUC] 0·70 [95% CI 0·65-0·76]) and radical prostatectomy (4·0 [1·6-9·7]; p=0·0024; AUC 0·57 [0·52-0·61]) patients with prostate cancer, and its effect is magnified by intratumoral hypoxia (3·8 [1·2-12]; p=0·019; AUC 0·67 [0·61-0·73]). A novel 100-loci DNA signature accurately classified treatment outcome in the MSKCC low-risk to intermediate-risk cohort (multivariate analysis HR 6·1 [95% CI 2·0-19]; p=0·0015; AUC 0·74 [95% CI 0·65-0·83]). In the independent MSKCC and Cambridge cohorts, this signature identified low-risk to high-risk patients who were most likely to fail treatment within 18 months (combined cohorts multivariate analysis HR 2·9 [95% CI 1·4-6·0]; p=0·0039; AUC 0·68 [95% CI 0·63-0·73]), and was better at predicting biochemical relapse than 23 previously published RNA signatures. INTERPRETATION This is the first study of cancer outcome to integrate DNA-based and microenvironment-based failure indices to predict patient outcome. Patients exhibiting these aggressive features after biopsy should be entered into treatment intensification trials. FUNDING Movember Foundation, Prostate Cancer Canada, Ontario Institute for Cancer Research, Canadian Institute for Health Research, NIHR Cambridge Biomedical Research Centre, The University of Cambridge, Cancer Research UK, Cambridge Cancer Charity, Prostate Cancer UK, Hutchison Whampoa Limited, Terry Fox Research Institute, Princess Margaret Cancer Centre Foundation, PMH-Radiation Medicine Program Academic Enrichment Fund, Motorcycle Ride for Dad (Durham), Canadian Cancer Society.
Nature Methods | 2015
Adam D. Ewing; Kathleen E. Houlahan; Yin Hu; Kyle Ellrott; Cristian Caloian; Takafumi N. Yamaguchi; J Christopher Bare; Christine P'ng; Daryl Waggott; Veronica Y. Sabelnykova; Michael R. Kellen; Thea Norman; David Haussler; Stephen H. Friend; Gustavo Stolovitzky; Adam A. Margolin; Joshua M. Stuart; Paul C. Boutros
The detection of somatic mutations from cancer genome sequences is key to understanding the genetic basis of disease progression, patient survival and response to therapy. Benchmarking is needed for tool assessment and improvement but is complicated by a lack of gold standards, by extensive resource requirements and by difficulties in sharing personal genomic information. To resolve these issues, we launched the ICGC-TCGA DREAM Somatic Mutation Calling Challenge, a crowdsourced benchmark of somatic mutation detection algorithms. Here we report the BAMSurgeon tool for simulating cancer genomes and the results of 248 analyses of three in silico tumors created with it. Different algorithms exhibit characteristic error profiles, and, intriguingly, false positives show a trinucleotide profile very similar to one found in human tumors. Although the three simulated tumors differ in sequence contamination (deviation from normal cell sequence) and in subclonality, an ensemble of pipelines outperforms the best individual pipeline in all cases. BAMSurgeon is available at https://github.com/adamewing/bamsurgeon/.
Nature | 2017
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.
Toxicology and Applied Pharmacology | 2012
Cindy Q. Yao; Stephenie D. Prokopec; John D. Watson; Renee Pang; Christine P'ng; Lauren C. Chong; Nicholas J. Harding; Raimo Pohjanvirta; Allan B. Okey; Paul C. Boutros
The biochemical and toxic effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) have been the subject of intense study for decades. It is now clear that essentially all TCDD-induced toxicities are mediated by DNA-protein interactions involving the Aryl Hydrocarbon Receptor (AHR). Nevertheless, it remains unknown which AHR target genes cause TCDD toxicities. Several groups, including our own, have developed rodent model systems to probe these questions. mRNA expression profiling of these model systems has revealed significant inter-species heterogeneity in rodent hepatic responses to TCDD. It has remained unclear if this variability also exists within a species, amongst rodent strains. To resolve this question, we profiled the hepatic transcriptomic response to TCDD of diverse rat strains (L-E, H/W, F344 and Wistar rats) and two lines derived from L-E×H/W crosses, at consistent age, sex, and dosing (100 μg/kg TCDD for 19 h). Using this uniquely consistent dataset, we show that the majority of TCDD-induced alterations in mRNA abundance are strain/line-specific: only 11 genes were affected by TCDD across all strains, including well-known dioxin-responsive genes such as Cyp1a1 and Nqo1. Our analysis identified two novel universally dioxin-responsive genes as well as 4 genes induced by TCDD in dioxin-sensitive rats only. These 6 genes are strong candidates to explain TCDD-related toxicities, so we validated them using 152 animals in time-course (0 to 384 h) and dose-response (0 to 3000 μg/kg) experiments. This study reveals that different rat strains exhibit dramatic transcriptional heterogeneity in their hepatic responses to TCDD and that inter-strain comparisons can help identify candidate toxicity-related genes.
bioRxiv | 2017
Christine P'ng; Jeffrey Green; Lauren C. Chong; Daryl Waggott; Stephenie D. Prokopec; Mehrdad Shamsi; Francis Nguyen; Denise Y. F. Mak; Felix Lam; Marco A. Albuquerque; Ying Wu; Esther Jung; Maud H. W. Starmans; Michelle Chan-Seng-Yue; Cindy Q. Yao; Bianca Liang; Emilie Lalonde; Syed Haider; Nicole A. Simone; Dorota H Sendorek; Kenneth C. Chu; Nathalie C Moon; Natalie S. Fox; Michal R Grzadkowski; Nicholas J. Harding; Clement Fung; Amanda R. Murdoch; Kathleen E. Houlahan; Jianxin Wang; David R. Garcia
We introduce BPG, an easy-to-use framework for generating publication-quality, highly-customizable plots in the R statistical environment. This open-source package includes novel methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it ideal for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for seamless integration with computational pipelines. BPG is available at http://labs.oicr.on.ca/boutros-lab/software/bpg
BMC Genomics | 2014
Ren X. Sun; Lauren C. Chong; Trent T. Simmons; Kathleen E. Houlahan; Stephenie D. Prokopec; John D. Watson; Ivy D. Moffat; Sanna Lensu; Jere Lindén; Christine P'ng; Allan B. Okey; Raimo Pohjanvirta; Paul C. Boutros
BackgroundResearch on the aryl hydrocarbon receptor (AHR) has largely focused on variations in toxic outcomes resulting from its activation by halogenated aromatic hydrocarbons. But the AHR also plays key roles in regulating pathways critical for development, and after decades of research the mechanisms underlying physiological regulation by the AHR remain poorly characterized. Previous studies identified several core genes that respond to xenobiotic AHR ligands across a broad range of species and tissues. However, only limited inferences have been made regarding its role in regulating constitutive gene activity, i.e. in the absence of exogenous ligands. To address this, we profiled transcriptomic variations between AHR-active and AHR-less-active animals in the absence of an exogenous agonist across five tissues, three of which came from rats (hypothalamus, white adipose and liver) and two of which came from mice (kidney and liver). Because AHR status alone has been shown sufficient to alter transcriptomic responses, we reason that by contrasting profiles amongst AHR-variant animals, we may elucidate effects of the AHR on constitutive mRNA abundances.ResultsWe found significantly more overlap in constitutive mRNA abundances amongst tissues within the same species than from tissues between species and identified 13 genes (Agt, Car3, Creg1, Ctsc, E2f6, Enpp1, Gatm, Gstm4, Kcnj8, Me1, Pdk1, Slc35a3, and Sqrdl) that are affected by AHR-status in four of five tissues. One gene, Creg1, was significantly up-regulated in all AHR-less-active animals. We also find greater overlap between tissues at the pathway level than at the gene level, suggesting coherency to the AHR signalling response within these processes. Analysis of regulatory motifs suggests that the AHR mostly mediates transcriptional regulation via direct binding to response elements.ConclusionsThese findings, though preliminary, present a platform for further evaluating the role of the AHR in regulation of constitutive mRNA levels and physiologic function.
bioRxiv | 2018
Paul C. Boutros; Adriana Salcedo; Maxime Tarabichi; Shadrielle Melijah G. Espiritu; Amit G Deshwar; Matei David; Nathan M Wilson; Stefan Dentro; Jeff Wintersinger; Lydia Y Liu; Minjeong Ko; Srinivasan Sivanandan; Hongjiu Zhang; Kaiyi Zhu; Tai-Hsien Ou Yang; John Chilton; Alex Buchanan; Christopher M Lalansingh; Christine P'ng; Catalina V Anghel; Imaad Umar; Bryan Lo; Jared T. Simpson; Joshua M. Stuart; Dimitris Anastassiou; Yuanfang Guan; Adam D. Ewing; Kyle Ellrott; David C. Wedge; Quaid Morris
Tumours evolve through time and space. To infer these evolutionary dynamics for DNA sequencing data, many subclonal reconstruction techniques have been developed and applied to large datasets. Surprisingly, though, there has been no systematic evaluation of these methods, in part due to the complexity of the mathematical and biological questions and the difficulties in creating gold-standards. To fill this gap, we systematically elucidated key algorithmic problems in subclonal reconstruction, and developed mathematically valid quantitative metrics for evaluating them. We then developed approaches to simulate realistic tumour genomes that harbour all known mutation types and processes. Finally, we benchmarked a set of 500 subclonal reconstructions, creating a key resource, and quantified the impact of sequencing read-depth and somatic variant detection strategies on the accuracy of specific subclonal reconstruction approaches. Inference of tumour phylogenies is rapidly becoming standard practice in cancer genome analysis, and this work sets standards for evaluating its accuracy.
International Journal of Radiation Oncology Biology Physics | 2012
Christine P'ng; Emma Ito; Christine How; Andrea Bezjak; Robert G. Bristow; Pam Catton; Anthony Fyles; Mary Gospodarowicz; David A. Jaffray; Shana O. Kelley; Shun Wong; Fei-Fei Liu