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Pancreatic Neoplasms: HELP
Articles by Deena M. A. Gendoo
Based on 1 article published since 2009
(Why 1 article?)
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Between 2009 and 2019, Deena M. A. Gendoo wrote the following article about Pancreatic Neoplasms.
 
+ Citations + Abstracts
1 Article Whole genomes define concordance of matched primary, xenograft, and organoid models of pancreas cancer. 2019

Gendoo, Deena M A / Denroche, Robert E / Zhang, Amy / Radulovich, Nikolina / Jang, Gun Ho / Lemire, Mathieu / Fischer, Sandra / Chadwick, Dianne / Lungu, Ilinca M / Ibrahimov, Emin / Cao, Ping-Jiang / Stein, Lincoln D / Wilson, Julie M / Bartlett, John M S / Tsao, Ming-Sound / Dhani, Neesha / Hedley, David / Gallinger, Steven / Haibe-Kains, Benjamin. ·Centre for Computational Biology, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom. · School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom. · PanCuRx Translational Research Initiative, Ontario Institute of Cancer Research (OICR), Toronto, Ontario, Canada. · Informatics and Bio-computing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada. · Princess Margaret Living Biobank Core, University Health Network, Toronto, Ontario, Canada. · Department of Statistical Science, University of Toronto, Toronto, Ontario, Canada. · Department of Pathology, University Health Network, University of Toronto, Toronto, Ontario, Canada. · UHN Program in BioSpecimen Sciences, Department of Pathology, University Health Network, Toronto, Ontario, Canada. · Transformative Pathology, Ontario Institute for Cancer Research, Toronto, Ontario, Canada. · Division of Medical Oncology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada. · Molecular Genetics Department, University of Toronto, Toronto, Ontario, Canada. · Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada. · Hepatobiliary/Pancreatic Surgical Oncology Program, University Health Network, Toronto, Ontario, Canada. · Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada. · Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. · Vector Institute, Toronto, Ontario, Canada. ·PLoS Comput Biol · Pubmed #30629588.

ABSTRACT: Pancreatic ductal adenocarcinoma (PDAC) has the worst prognosis among solid malignancies and improved therapeutic strategies are needed to improve outcomes. Patient-derived xenografts (PDX) and patient-derived organoids (PDO) serve as promising tools to identify new drugs with therapeutic potential in PDAC. For these preclinical disease models to be effective, they should both recapitulate the molecular heterogeneity of PDAC and validate patient-specific therapeutic sensitivities. To date however, deep characterization of the molecular heterogeneity of PDAC PDX and PDO models and comparison with matched human tumour remains largely unaddressed at the whole genome level. We conducted a comprehensive assessment of the genetic landscape of 16 whole-genome pairs of tumours and matched PDX, from primary PDAC and liver metastasis, including a unique cohort of 5 'trios' of matched primary tumour, PDX, and PDO. We developed a pipeline to score concordance between PDAC models and their paired human tumours for genomic events, including mutations, structural variations, and copy number variations. Tumour-model comparisons of mutations displayed single-gene concordance across major PDAC driver genes, but relatively poor agreement across the greater mutational load. Genome-wide and chromosome-centric analysis of structural variation (SV) events highlights previously unrecognized concordance across chromosomes that demonstrate clustered SV events. We found that polyploidy presented a major challenge when assessing copy number changes; however, ploidy-corrected copy number states suggest good agreement between donor-model pairs. Collectively, our investigations highlight that while PDXs and PDOs may serve as tractable and transplantable systems for probing the molecular properties of PDAC, these models may best serve selective analyses across different levels of genomic complexity.