Whole genomes define concordance of matched primary, xenograft, and organoid models of pancreas cancer

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Whole genomes define concordance of matched primary, xenograft, and organoid models of pancreas cancer. / 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.

In: PLoS Computational Biology, Vol. 15, No. 1, e1006596, 10.01.2019.

Research output: Contribution to journalArticle

Harvard

Gendoo, DMA, Denroche, RE, Zhang, A, Radulovich, N, Jang, GH, Lemire, M, Fischer, S, Chadwick, D, Lungu, IM, Ibrahimov, E, Cao, P-J, Stein, LD, Wilson, JM, Bartlett, JMS, Tsao, M-S, Dhani, N, Hedley, D, Gallinger, S & Haibe-Kains, B 2019, 'Whole genomes define concordance of matched primary, xenograft, and organoid models of pancreas cancer', PLoS Computational Biology, vol. 15, no. 1, e1006596. https://doi.org/10.1371/journal.pcbi.1006596

APA

Gendoo, D. M. A., Denroche, R. E., Zhang, A., Radulovich, N., Jang, G. H., Lemire, M., Fischer, S., Chadwick, D., Lungu, I. M., Ibrahimov, E., Cao, P-J., Stein, L. D., Wilson, J. M., Bartlett, J. M. S., Tsao, M-S., Dhani, N., Hedley, D., Gallinger, S., & Haibe-Kains, B. (2019). Whole genomes define concordance of matched primary, xenograft, and organoid models of pancreas cancer. PLoS Computational Biology, 15(1), [e1006596]. https://doi.org/10.1371/journal.pcbi.1006596

Vancouver

Author

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. / Whole genomes define concordance of matched primary, xenograft, and organoid models of pancreas cancer. In: PLoS Computational Biology. 2019 ; Vol. 15, No. 1.

Bibtex

@article{04fd99fcf3524859b2c74adfa6de45e8,
title = "Whole genomes define concordance of matched primary, xenograft, and organoid models of pancreas cancer",
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.",
author = "Gendoo, {Deena M. A.} and Denroche, {Robert E} and Amy Zhang and Nikolina Radulovich and Jang, {Gun Ho} and Mathieu Lemire and Sandra Fischer and Dianne Chadwick and Lungu, {Ilinca M} and Emin Ibrahimov and Ping-Jiang Cao and Stein, {Lincoln D} and Wilson, {Julie M} and Bartlett, {John M S} and Ming-Sound Tsao and Neesha Dhani and David Hedley and Steven Gallinger and Benjamin Haibe-Kains",
year = "2019",
month = jan
day = "10",
doi = "10.1371/journal.pcbi.1006596",
language = "English",
volume = "15",
journal = "PLoS Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science (PLOS)",
number = "1",

}

RIS

TY - JOUR

T1 - Whole genomes define concordance of matched primary, xenograft, and organoid models of pancreas cancer

AU - Gendoo, Deena M. A.

AU - Denroche, Robert E

AU - Zhang, Amy

AU - Radulovich, Nikolina

AU - Jang, Gun Ho

AU - Lemire, Mathieu

AU - Fischer, Sandra

AU - Chadwick, Dianne

AU - Lungu, Ilinca M

AU - Ibrahimov, Emin

AU - Cao, Ping-Jiang

AU - Stein, Lincoln D

AU - Wilson, Julie M

AU - Bartlett, John M S

AU - Tsao, Ming-Sound

AU - Dhani, Neesha

AU - Hedley, David

AU - Gallinger, Steven

AU - Haibe-Kains, Benjamin

PY - 2019/1/10

Y1 - 2019/1/10

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85059828530&partnerID=8YFLogxK

U2 - 10.1371/journal.pcbi.1006596

DO - 10.1371/journal.pcbi.1006596

M3 - Article

C2 - 30629588

VL - 15

JO - PLoS Computational Biology

JF - PLoS Computational Biology

SN - 1553-734X

IS - 1

M1 - e1006596

ER -