1
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Kase AM, Gleba J, Miller JL, Miller E, Petit J, Barrett MT, Zhou Y, Parent EE, Cai H, Knight JA, Orme J, Reynolds J, Durham WF, Metz TM, Meurice N, Edenfield B, Alasonyalilar Demirer A, Bilgili A, Hickman PG, Pawlush ML, Marlow L, Wickland DP, Tan W, Copland JA. Patient-Derived Tumor Xenograft Study with CDK4/6 Inhibitor Plus AKT Inhibitor for the Management of Metastatic Castration-Resistant Prostate Cancer. Mol Cancer Ther 2024; 23:823-835. [PMID: 38442920 DOI: 10.1158/1535-7163.mct-23-0296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 12/04/2023] [Accepted: 02/28/2024] [Indexed: 03/07/2024]
Abstract
Metastatic castration-resistant prostate cancer (mCRPC) is an aggressive malignancy with poor outcomes. To investigate novel therapeutic strategies, we characterized three new metastatic prostate cancer patient derived-tumor xenograft (PDTX) models and developed 3D spheroids from each to investigate molecular targeted therapy combinations including CDK4/6 inhibitors (CDK4/6i) with AKT inhibitors (ATKi). Metastatic prostate cancer tissue was collected and three PDTX models were established and characterized using whole-exome sequencing. PDTX 3D spheroids were developed from these three PDTXs to show resistance patterns and test novel molecular-targeted therapies. CDK4/6i's were combined with AKTi's to assess synergistic antitumor response to prove our hypothesis that blockade of AKT overcomes drug resistance to CDK4/6i. This combination was evaluated in PDTX three-dimensional (3D) spheroids and in vivo experiments with responses measured by tumor volumes, PSA, and Ga-68 PSMA-11 PET-CT imaging. We demonstrated CDK4/6i's with AKTi's possess synergistic antitumor activity in three mCRPC PDTX models. These models have multiple unique pathogenic and deleterious genomic alterations with resistance to single-agent CDK4/6i's. Despite this, combination therapy with AKTi's was able to overcome resistance mechanisms. The IHC and Western blot analysis confirmed on target effects, whereas tumor volume, serum PSA ELISA, and radionuclide imaging demonstrated response to therapy with statistically significant SUV differences seen with Ga-68 PSMA-11 PET-CT. These preclinical data demonstrating antitumor synergy by overcoming single-agent CDK 4/6i as well as AKTi drug resistance provide the rational for a clinical trial combining a CDK4/6i with an AKTi in patients with mCRPC whose tumor expresses wild-type retinoblastoma 1.
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Affiliation(s)
- Adam M Kase
- Division of Hematology-Oncology, Mayo Clinic Jacksonville, Florida
| | - Justyna Gleba
- Cancer Biology Department, Mayo Clinic Jacksonville, Florida
| | - James L Miller
- Cancer Biology Department, Mayo Clinic Jacksonville, Florida
| | - Erin Miller
- Cancer Biology Department, Mayo Clinic Jacksonville, Florida
| | - Joachim Petit
- Division of Hematology-Oncology, Mayo Clinic Scottsdale, Arizona
| | | | - Yumei Zhou
- Division of Hematology-Oncology, Mayo Clinic Scottsdale, Arizona
| | | | - Hancheng Cai
- Radiology Department, Mayo Clinic Jacksonville, Florida
| | - Joshua A Knight
- Cancer Biology Department, Mayo Clinic Jacksonville, Florida
| | - Jacob Orme
- Division of Hematology-Oncology, Mayo Clinic Rochester, Minnesota
| | - Jordan Reynolds
- Department of Laboratory Medicine and Pathology, Mayo Clinic Jacksonville, Florida
| | | | - Thomas M Metz
- Charles River Discovery Research Services Germany, Freiburg, Germany
| | - Nathalie Meurice
- Division of Hematology-Oncology, Mayo Clinic Scottsdale, Arizona
| | | | | | - Ahmet Bilgili
- Cancer Biology Department, Mayo Clinic Jacksonville, Florida
| | | | | | - Laura Marlow
- Cancer Biology Department, Mayo Clinic Jacksonville, Florida
| | - Daniel P Wickland
- Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic Jacksonville, Florida
| | - Winston Tan
- Division of Hematology-Oncology, Mayo Clinic Jacksonville, Florida
| | - John A Copland
- Cancer Biology Department, Mayo Clinic Jacksonville, Florida
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2
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Hynds RE, Huebner A, Pearce DR, Hill MS, Akarca AU, Moore DA, Ward S, Gowers KHC, Karasaki T, Al Bakir M, Wilson GA, Pich O, Martínez-Ruiz C, Hossain ASMM, Pearce SP, Sivakumar M, Ben Aissa A, Grönroos E, Chandrasekharan D, Kolluri KK, Towns R, Wang K, Cook DE, Bosshard-Carter L, Naceur-Lombardelli C, Rowan AJ, Veeriah S, Litchfield K, Crosbie PAJ, Dive C, Quezada SA, Janes SM, Jamal-Hanjani M, Marafioti T, McGranahan N, Swanton C. Representation of genomic intratumor heterogeneity in multi-region non-small cell lung cancer patient-derived xenograft models. Nat Commun 2024; 15:4653. [PMID: 38821942 PMCID: PMC11143323 DOI: 10.1038/s41467-024-47547-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 03/28/2024] [Indexed: 06/02/2024] Open
Abstract
Patient-derived xenograft (PDX) models are widely used in cancer research. To investigate the genomic fidelity of non-small cell lung cancer PDX models, we established 48 PDX models from 22 patients enrolled in the TRACERx study. Multi-region tumor sampling increased successful PDX engraftment and most models were histologically similar to their parent tumor. Whole-exome sequencing enabled comparison of tumors and PDX models and we provide an adapted mouse reference genome for improved removal of NOD scid gamma (NSG) mouse-derived reads from sequencing data. PDX model establishment caused a genomic bottleneck, with models often representing a single tumor subclone. While distinct tumor subclones were represented in independent models from the same tumor, individual PDX models did not fully recapitulate intratumor heterogeneity. On-going genomic evolution in mice contributed modestly to the genomic distance between tumors and PDX models. Our study highlights the importance of considering primary tumor heterogeneity when using PDX models and emphasizes the benefit of comprehensive tumor sampling.
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Affiliation(s)
- Robert E Hynds
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK.
- Epithelial Cell Biology in ENT Research Group (EpiCENTR), Developmental Biology and Cancer, Great Ormond Street University College London Institute of Child Health, London, UK.
| | - Ariana Huebner
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - David R Pearce
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Mark S Hill
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Ayse U Akarca
- Department of Cellular Pathology, University College London Hospitals, London, UK
| | - David A Moore
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Department of Cellular Pathology, University College London Hospitals, London, UK
| | - Sophia Ward
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Advanced Sequencing Facility, The Francis Crick Institute, London, UK
| | - Kate H C Gowers
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Takahiro Karasaki
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
| | - Maise Al Bakir
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Gareth A Wilson
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Oriol Pich
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Carlos Martínez-Ruiz
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - A S Md Mukarram Hossain
- Cancer Research UK National Biomarker Centre, University of Manchester, Manchester, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University of Manchester, Manchester, UK
| | - Simon P Pearce
- Cancer Research UK National Biomarker Centre, University of Manchester, Manchester, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University of Manchester, Manchester, UK
| | - Monica Sivakumar
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Department of Cellular Pathology, University College London Hospitals, London, UK
| | - Assma Ben Aissa
- Cancer Immunology Unit, Research Department of Haematology, University College London Cancer Institute, London, UK
| | - Eva Grönroos
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Deepak Chandrasekharan
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Krishna K Kolluri
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Rebecca Towns
- Biological Services Unit, University College London, London, UK
| | - Kaiwen Wang
- School of Medicine, University of Leeds, Leeds, UK
| | - Daniel E Cook
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Leticia Bosshard-Carter
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | | | - Andrew J Rowan
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Selvaraju Veeriah
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Kevin Litchfield
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Tumour Immunogenomics and Immunosurveillance Laboratory, University College London Cancer Institute, London, UK
| | - Philip A J Crosbie
- Cancer Research UK Lung Cancer Centre of Excellence, University of Manchester, Manchester, UK
- Division of Infection, Immunity and Respiratory Medicine, University of Manchester, Manchester, UK
| | - Caroline Dive
- Cancer Research UK National Biomarker Centre, University of Manchester, Manchester, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University of Manchester, Manchester, UK
| | - Sergio A Quezada
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Immunology Unit, Research Department of Haematology, University College London Cancer Institute, London, UK
| | - Sam M Janes
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Mariam Jamal-Hanjani
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
- Department of Oncology, University College London Hospitals, London, UK
| | - Teresa Marafioti
- Department of Cellular Pathology, University College London Hospitals, London, UK
| | - Nicholas McGranahan
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
| | - Charles Swanton
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK.
- Department of Oncology, University College London Hospitals, London, UK.
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3
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Sunil HS, O'Donnell KA. Capturing heterogeneity in PDX models: representation matters. Nat Commun 2024; 15:4652. [PMID: 38821926 PMCID: PMC11143235 DOI: 10.1038/s41467-024-47607-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 04/05/2024] [Indexed: 06/02/2024] Open
Affiliation(s)
- Hari Shankar Sunil
- Department of Molecular Biology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Kathryn A O'Donnell
- Department of Molecular Biology, UT Southwestern Medical Center, Dallas, TX, USA.
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA.
- Hamon Center for Regenerative Science and Medicine, UT Southwestern Medical Center, Dallas, TX, USA.
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4
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Sánchez-Ramírez D, Mendoza-Rodríguez MG, Alemán OR, Candanedo-González FA, Rodríguez-Sosa M, Montesinos-Montesinos JJ, Salcedo M, Brito-Toledo I, Vaca-Paniagua F, Terrazas LI. Impact of STAT-signaling pathway on cancer-associated fibroblasts in colorectal cancer and its role in immunosuppression. World J Gastrointest Oncol 2024; 16:1705-1724. [PMID: 38764833 PMCID: PMC11099434 DOI: 10.4251/wjgo.v16.i5.1705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 02/28/2024] [Accepted: 04/01/2024] [Indexed: 05/09/2024] Open
Abstract
Colorectal cancer (CRC) remains one of the most commonly diagnosed and deadliest types of cancer worldwide. CRC displays a desmoplastic reaction (DR) that has been inversely associated with poor prognosis; less DR is associated with a better prognosis. This reaction generates excessive connective tissue, in which cancer-associated fibroblasts (CAFs) are critical cells that form a part of the tumor microenvironment. CAFs are directly involved in tumorigenesis through different mechanisms. However, their role in immunosuppression in CRC is not well understood, and the precise role of signal transducers and activators of transcription (STATs) in mediating CAF activity in CRC remains unclear. Among the myriad chemical and biological factors that affect CAFs, different cytokines mediate their function by activating STAT signaling pathways. Thus, the harmful effects of CAFs in favoring tumor growth and invasion may be modulated using STAT inhibitors. Here, we analyze the impact of different STATs on CAF activity and their immunoregulatory role.
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Affiliation(s)
- Damián Sánchez-Ramírez
- Unidad de Investigacion en Biomedicina, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autonoma de Mexico, Tlalnepantla 54090, Estado de Mexico, Mexico
| | - Mónica G Mendoza-Rodríguez
- Unidad de Investigacion en Biomedicina, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autonoma de Mexico, Tlalnepantla 54090, Estado de Mexico, Mexico
| | - Omar R Alemán
- Department of Biology, Facultad de Quimica, Universidad Nacional Autonoma de Mexico, Ciudad Universitaria, Mexico City 04510, Mexico
| | - Fernando A Candanedo-González
- Department of Pathology, National Medical Center Century XXI, Instituto Mexicano del Seguro Social, Mexico City 06720, Mexico
| | - Miriam Rodríguez-Sosa
- Unidad de Investigacion en Biomedicina, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autonoma de Mexico, Tlalnepantla 54090, Estado de Mexico, Mexico
| | - Juan José Montesinos-Montesinos
- Laboratorio de Células Troncales Mesenquimales, Unidad de Investigación Médica en Enfermedades Oncológicas, Hospital de Oncología Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City 06720, Mexico
| | - Mauricio Salcedo
- Unidad de Investigacion en Biomedicina y Oncologia Genomica, Instituto Mexciano del Seguro Social, Mexico City 07300, Mexico
| | - Ismael Brito-Toledo
- Servicio de Colon y Recto, Hospital de Oncología Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City 06720, Mexico
| | - Felipe Vaca-Paniagua
- Unidad de Investigacion en Biomedicina, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autonoma de Mexico, Tlalnepantla 54090, Estado de Mexico, Mexico
- Laboratorio Nacional en Salud, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla 54090, Estado de Mexico, Mexico
| | - Luis I Terrazas
- Unidad de Investigacion en Biomedicina, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autonoma de Mexico, Tlalnepantla 54090, Estado de Mexico, Mexico
- Laboratorio Nacional en Salud, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla 54090, Estado de Mexico, Mexico
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5
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Wu G, Zaker A, Ebrahimi A, Tripathi S, Mer AS. Text-mining-based feature selection for anticancer drug response prediction. BIOINFORMATICS ADVANCES 2024; 4:vbae047. [PMID: 38606185 PMCID: PMC11009020 DOI: 10.1093/bioadv/vbae047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 03/09/2024] [Accepted: 03/22/2024] [Indexed: 04/13/2024]
Abstract
Motivation Predicting anticancer treatment response from baseline genomic data is a critical obstacle in personalized medicine. Machine learning methods are commonly used for predicting drug response from gene expression data. In the process of constructing these machine learning models, one of the most significant challenges is identifying appropriate features among a massive number of genes. Results In this study, we utilize features (genes) extracted using the text-mining of scientific literatures. Using two independent cancer pharmacogenomic datasets, we demonstrate that text-mining-based features outperform traditional feature selection techniques in machine learning tasks. In addition, our analysis reveals that text-mining feature-based machine learning models trained on in vitro data also perform well when predicting the response of in vivo cancer models. Our results demonstrate that text-mining-based feature selection is an easy to implement approach that is suitable for building machine learning models for anticancer drug response prediction. Availability and implementation https://github.com/merlab/text_features.
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Affiliation(s)
- Grace Wu
- Division of Engineering Science, University of Toronto, Toronto, M5S2E4, Canada
| | - Arvin Zaker
- Department of Biochemistry, Microbiology & Immunology, University of Ottawa, Ottawa, K1H8M5, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, K1H8M5, Canada
| | - Amirhosein Ebrahimi
- Department of Biochemistry, Microbiology & Immunology, University of Ottawa, Ottawa, K1H8M5, Canada
| | - Shivanshi Tripathi
- Department of Biochemistry, Microbiology & Immunology, University of Ottawa, Ottawa, K1H8M5, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, K1H8M5, Canada
| | - Arvind Singh Mer
- Department of Biochemistry, Microbiology & Immunology, University of Ottawa, Ottawa, K1H8M5, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, K1H8M5, Canada
- School of Electrical Engineering & Computer Science, University of Ottawa, Ottawa, K1N6N5, Canada
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6
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Audet-Delage Y, St-Louis C, Minarrieta L, McGuirk S, Kurreal I, Annis MG, Mer AS, Siegel PM, St-Pierre J. Spatiotemporal modeling of chemoresistance evolution in breast tumors uncovers dependencies on SLC38A7 and SLC46A1. Cell Rep 2023; 42:113191. [PMID: 37792528 DOI: 10.1016/j.celrep.2023.113191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 08/17/2023] [Accepted: 09/15/2023] [Indexed: 10/06/2023] Open
Abstract
In solid tumors, drug concentrations decrease with distance from blood vessels. However, cellular adaptations accompanying the gradated exposure of cancer cells to drugs are largely unknown. Here, we modeled the spatiotemporal changes promoting chemotherapy resistance in breast cancer. Using pairwise cell competition assays at each step during the acquisition of chemoresistance, we reveal an important priming phase that renders cancer cells previously exposed to sublethal drug concentrations refractory to dose escalation. Therapy-resistant cells throughout the concentration gradient display higher expression of the solute carriers SLC38A7 and SLC46A1 and elevated intracellular concentrations of their associated metabolites. Reduced levels of SLC38A7 and SLC46A1 diminish the proliferative potential of cancer cells, and elevated expression of these SLCs in breast tumors from patients correlates with reduced survival. Our work provides mechanistic evidence to support dose-intensive treatment modalities for patients with solid tumors and reveals two members of the SLC family as potential actionable targets.
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Affiliation(s)
- Yannick Audet-Delage
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Catherine St-Louis
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Lucía Minarrieta
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Shawn McGuirk
- Department of Biochemistry, McGill University, Montréal, QC H3G 1Y6, Canada; Goodman Cancer Institute, McGill University, Montréal, QC H3A 1A3, Canada
| | - Irwin Kurreal
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Matthew G Annis
- Goodman Cancer Institute, McGill University, Montréal, QC H3A 1A3, Canada; Department of Medicine, McGill University, Montréal, QC H4A 3J1, Canada
| | - Arvind Singh Mer
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Peter M Siegel
- Goodman Cancer Institute, McGill University, Montréal, QC H3A 1A3, Canada; Department of Medicine, McGill University, Montréal, QC H4A 3J1, Canada
| | - Julie St-Pierre
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada; Department of Biochemistry, McGill University, Montréal, QC H3G 1Y6, Canada.
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7
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Kim H, Aliar K, Tharmapalan P, McCloskey CW, Kuttanamkuzhi A, Grünwald BT, Palomero L, Mahendralingam MJ, Waas M, Mer AS, Elliott MJ, Zhang B, Al-Zahrani KN, Langille ER, Parsons M, Narala S, Hofer S, Waterhouse PD, Hakem R, Haibe-Kains B, Kislinger T, Schramek D, Cescon DW, Pujana MA, Berman HK, Khokha R. Differential DNA damage repair and PARP inhibitor vulnerability of the mammary epithelial lineages. Cell Rep 2023; 42:113256. [PMID: 37847590 DOI: 10.1016/j.celrep.2023.113256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 09/02/2023] [Accepted: 09/28/2023] [Indexed: 10/19/2023] Open
Abstract
It is widely assumed that all normal somatic cells can equally perform homologous recombination (HR) and non-homologous end joining in the DNA damage response (DDR). Here, we show that the DDR in normal mammary gland inherently depends on the epithelial cell lineage identity. Bioinformatics, post-irradiation DNA damage repair kinetics, and clonogenic assays demonstrated luminal lineage exhibiting a more pronounced DDR and HR repair compared to the basal lineage. Consequently, basal progenitors were far more sensitive to poly(ADP-ribose) polymerase inhibitors (PARPis) in both mouse and human mammary epithelium. Furthermore, PARPi sensitivity of murine and human breast cancer cell lines as well as patient-derived xenografts correlated with their molecular resemblance to the mammary progenitor lineages. Thus, mammary epithelial cells are intrinsically divergent in their DNA damage repair capacity and PARPi vulnerability, potentially influencing the clinical utility of this targeted therapy.
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Affiliation(s)
- Hyeyeon Kim
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Kazeera Aliar
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Pirashaanthy Tharmapalan
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Curtis W McCloskey
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada
| | | | - Barbara T Grünwald
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Luis Palomero
- ProCURE, Catalan Institute of Oncology, Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet del Llobregat, 08908 Barcelona, Catalonia, Spain
| | - Mathepan J Mahendralingam
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Matthew Waas
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Arvind S Mer
- Department of Biochemistry, Microbiology & Immunology, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Mitchell J Elliott
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Bowen Zhang
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Khalid N Al-Zahrani
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada
| | - Ellen R Langille
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Michael Parsons
- Department of Biochemistry, Microbiology & Immunology, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Swami Narala
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Stefan Hofer
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Paul D Waterhouse
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Razqallah Hakem
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5G 2N2, Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Thomas Kislinger
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Daniel Schramek
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - David W Cescon
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Miquel A Pujana
- ProCURE, Catalan Institute of Oncology, Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet del Llobregat, 08908 Barcelona, Catalonia, Spain
| | - Hal K Berman
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Rama Khokha
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5G 2N2, Canada.
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8
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El Zawily A, Vizeacoumar FS, Dahiya R, Banerjee SL, Bhanumathy KK, Elhasasna H, Hanover G, Sharpe JC, Sanchez MG, Greidanus P, Stacey RG, Moon KM, Alexandrov I, Himanen JP, Nikolov DB, Fonge H, White AP, Foster LJ, Wang B, Toosi BM, Bisson N, Mirzabekov TA, Vizeacoumar FJ, Freywald A. A Multipronged Unbiased Strategy Guides the Development of an Anti-EGFR/EPHA2-Bispecific Antibody for Combination Cancer Therapy. Clin Cancer Res 2023; 29:2686-2701. [PMID: 36976175 PMCID: PMC10345963 DOI: 10.1158/1078-0432.ccr-22-2535] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 12/26/2022] [Accepted: 03/01/2023] [Indexed: 03/29/2023]
Abstract
PURPOSE Accumulating analyses of pro-oncogenic molecular mechanisms triggered a rapid development of targeted cancer therapies. Although many of these treatments produce impressive initial responses, eventual resistance onset is practically unavoidable. One of the main approaches for preventing this refractory condition relies on the implementation of combination therapies. This includes dual-specificity reagents that affect both of their targets with a high level of selectivity. Unfortunately, selection of target combinations for these treatments is often confounded by limitations in our understanding of tumor biology. Here, we describe and validate a multipronged unbiased strategy for predicting optimal co-targets for bispecific therapeutics. EXPERIMENTAL DESIGN Our strategy integrates ex vivo genome-wide loss-of-function screening, BioID interactome profiling, and gene expression analysis of patient data to identify the best fit co-targets. Final validation of selected target combinations is done in tumorsphere cultures and xenograft models. RESULTS Integration of our experimental approaches unambiguously pointed toward EGFR and EPHA2 tyrosine kinase receptors as molecules of choice for co-targeting in multiple tumor types. Following this lead, we generated a human bispecific anti-EGFR/EPHA2 antibody that, as predicted, very effectively suppresses tumor growth compared with its prototype anti-EGFR therapeutic antibody, cetuximab. CONCLUSIONS Our work not only presents a new bispecific antibody with a high potential for being developed into clinically relevant biologics, but more importantly, successfully validates a novel unbiased strategy for selecting biologically optimal target combinations. This is of a significant translational relevance, as such multifaceted unbiased approaches are likely to augment the development of effective combination therapies for cancer treatment. See related commentary by Kumar, p. 2570.
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Affiliation(s)
- Amr El Zawily
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Royal University Hospital, Saskatoon, Saskatchewan, Canada
- Department of Biology, College of Liberal Arts and Sciences, University of Iowa, Iowa City, Iowa
| | - Frederick S. Vizeacoumar
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Royal University Hospital, Saskatoon, Saskatchewan, Canada
| | - Renuka Dahiya
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Royal University Hospital, Saskatoon, Saskatchewan, Canada
| | - Sara L. Banerjee
- Department of Molecular Biology, Medical Biochemistry and Pathology, PROTEO and Centre de recherche du Centre Hospitalier Universitaire (CHU) de Quebec-Université Laval, Division Oncologie, Québec, Canada
| | - Kalpana K. Bhanumathy
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Royal University Hospital, Saskatoon, Saskatchewan, Canada
| | - Hussain Elhasasna
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Royal University Hospital, Saskatoon, Saskatchewan, Canada
| | - Glinton Hanover
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Royal University Hospital, Saskatoon, Saskatchewan, Canada
- Department of Biochemistry, Microbiology and Immunology, University of Saskatchewan, Health Sciences, Saskatoon, Saskatchewan, Canada
| | - Jessica C. Sharpe
- Department of Small Animal Clinical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, Canada
| | - Malkon G. Sanchez
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Royal University Hospital, Saskatoon, Saskatchewan, Canada
- Department of Biochemistry, Microbiology and Immunology, University of Saskatchewan, Health Sciences, Saskatoon, Saskatchewan, Canada
| | - Paul Greidanus
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Royal University Hospital, Saskatoon, Saskatchewan, Canada
- Department of Biochemistry, Microbiology and Immunology, University of Saskatchewan, Health Sciences, Saskatoon, Saskatchewan, Canada
| | - R. Greg Stacey
- Michael Smith Laboratories and Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Kyung-Mee Moon
- Michael Smith Laboratories and Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, British Columbia, Canada
| | | | - Juha P. Himanen
- Department of Biochemistry, University of Turku, Turku, Finland
| | - Dimitar B. Nikolov
- Structural Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Humphrey Fonge
- Department of Medical Imaging, College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
- Department of Medical Imaging, Royal University Hospital, Saskatoon, Saskatchewan, Canada
| | - Aaron P. White
- Department of Biochemistry, Microbiology and Immunology, University of Saskatchewan, Health Sciences, Saskatoon, Saskatchewan, Canada
- Vaccine and Infectious Disease Organization-International Vaccine Centre, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Leonard J. Foster
- Michael Smith Laboratories and Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Bingcheng Wang
- Division of Cancer Biology, Department of Medicine, MetroHealth Medical Center, and Case Western Reserve University School of Medicine, Case Comprehensive Cancer Center, Cleveland, Ohio
| | - Behzad M. Toosi
- Department of Small Animal Clinical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, Canada
| | - Nicolas Bisson
- Department of Molecular Biology, Medical Biochemistry and Pathology, PROTEO and Centre de recherche du Centre Hospitalier Universitaire (CHU) de Quebec-Université Laval, Division Oncologie, Québec, Canada
| | | | - Franco J. Vizeacoumar
- Cancer Research, Saskatchewan Cancer Agency and Division of Oncology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Andrew Freywald
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Royal University Hospital, Saskatoon, Saskatchewan, Canada
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9
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Lawrence MG, Taylor RA, Cuffe GB, Ang LS, Clark AK, Goode DL, Porter LH, Le Magnen C, Navone NM, Schalken JA, Wang Y, van Weerden WM, Corey E, Isaacs JT, Nelson PS, Risbridger GP. The future of patient-derived xenografts in prostate cancer research. Nat Rev Urol 2023; 20:371-384. [PMID: 36650259 PMCID: PMC10789487 DOI: 10.1038/s41585-022-00706-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/09/2022] [Indexed: 01/19/2023]
Abstract
Patient-derived xenografts (PDXs) are generated by engrafting human tumours into mice. Serially transplantable PDXs are used to study tumour biology and test therapeutics, linking the laboratory to the clinic. Although few prostate cancer PDXs are available in large repositories, over 330 prostate cancer PDXs have been established, spanning broad clinical stages, genotypes and phenotypes. Nevertheless, more PDXs are needed to reflect patient diversity, and to study new treatments and emerging mechanisms of resistance. We can maximize the use of PDXs by exchanging models and datasets, and by depositing PDXs into biorepositories, but we must address the impediments to accessing PDXs, such as institutional, ethical and legal agreements. Through collaboration, researchers will gain greater access to PDXs representing diverse features of prostate cancer.
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Affiliation(s)
- Mitchell G Lawrence
- Department of Anatomy and Developmental Biology, Monash University, Clayton, Victoria, Australia.
- Melbourne Urological Research Alliance, Monash Biomedicine Discovery Institute, Clayton, Victoria, Australia.
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia.
- Cabrini Institute, Cabrini Health, Malvern, Victoria, Australia.
| | - Renea A Taylor
- Melbourne Urological Research Alliance, Monash Biomedicine Discovery Institute, Clayton, Victoria, Australia
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
- Cabrini Institute, Cabrini Health, Malvern, Victoria, Australia
- Department of Physiology, Monash University, Clayton, Victoria, Australia
| | - Georgia B Cuffe
- Department of Anatomy and Developmental Biology, Monash University, Clayton, Victoria, Australia
| | - Lisa S Ang
- Divisions of Human Biology and Clinical Research, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Ashlee K Clark
- Department of Anatomy and Developmental Biology, Monash University, Clayton, Victoria, Australia
- Department of Urology, Radboud University Medical Center, Nijmegen, Netherlands
| | - David L Goode
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Laura H Porter
- Department of Anatomy and Developmental Biology, Monash University, Clayton, Victoria, Australia
| | - Clémentine Le Magnen
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
- Department of Urology, University Hospital Basel, Basel, Switzerland
- Department of Biomedicine, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Nora M Navone
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- David H. Koch Center for Applied Research of Genitourinary Cancers, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jack A Schalken
- Department of Urology, Radboud University Medical Center, Nijmegen, Netherlands
| | - Yuzhuo Wang
- Vancouver Prostate Centre, Vancouver, British Columbia, Canada
- Department of Urologic Sciences, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Experimental Therapeutics, BC Cancer Agency, Vancouver, British Columbia, Canada
| | | | - Eva Corey
- Department of Urology, University of Washington, Seattle, WA, USA
| | - John T Isaacs
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center (SKCCC), Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Pharmacology and Molecular Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Urology, James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Peter S Nelson
- Divisions of Human Biology and Clinical Research, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Urology, University of Washington, Seattle, WA, USA
| | - Gail P Risbridger
- Department of Anatomy and Developmental Biology, Monash University, Clayton, Victoria, Australia.
- Melbourne Urological Research Alliance, Monash Biomedicine Discovery Institute, Clayton, Victoria, Australia.
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia.
- Cabrini Institute, Cabrini Health, Malvern, Victoria, Australia.
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10
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Liu Y, Wu W, Cai C, Zhang H, Shen H, Han Y. Patient-derived xenograft models in cancer therapy: technologies and applications. Signal Transduct Target Ther 2023; 8:160. [PMID: 37045827 PMCID: PMC10097874 DOI: 10.1038/s41392-023-01419-2] [Citation(s) in RCA: 47] [Impact Index Per Article: 47.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 03/21/2023] [Indexed: 04/14/2023] Open
Abstract
Patient-derived xenograft (PDX) models, in which tumor tissues from patients are implanted into immunocompromised or humanized mice, have shown superiority in recapitulating the characteristics of cancer, such as the spatial structure of cancer and the intratumor heterogeneity of cancer. Moreover, PDX models retain the genomic features of patients across different stages, subtypes, and diversified treatment backgrounds. Optimized PDX engraftment procedures and modern technologies such as multi-omics and deep learning have enabled a more comprehensive depiction of the PDX molecular landscape and boosted the utilization of PDX models. These irreplaceable advantages make PDX models an ideal choice in cancer treatment studies, such as preclinical trials of novel drugs, validating novel drug combinations, screening drug-sensitive patients, and exploring drug resistance mechanisms. In this review, we gave an overview of the history of PDX models and the process of PDX model establishment. Subsequently, the review presents the strengths and weaknesses of PDX models and highlights the integration of novel technologies in PDX model research. Finally, we delineated the broad application of PDX models in chemotherapy, targeted therapy, immunotherapy, and other novel therapies.
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Affiliation(s)
- Yihan Liu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, P.R. China
| | - Wantao Wu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, P.R. China
| | - Changjing Cai
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, P.R. China
| | - Hao Zhang
- Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Hong Shen
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, P.R. China.
| | - Ying Han
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, P.R. China.
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11
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Partin A, Brettin T, Zhu Y, Dolezal JM, Kochanny S, Pearson AT, Shukla M, Evrard YA, Doroshow JH, Stevens RL. Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images. Front Med (Lausanne) 2023; 10:1058919. [PMID: 36960342 PMCID: PMC10027779 DOI: 10.3389/fmed.2023.1058919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 02/10/2023] [Indexed: 03/09/2023] Open
Abstract
Patient-derived xenografts (PDXs) are an appealing platform for preclinical drug studies. A primary challenge in modeling drug response prediction (DRP) with PDXs and neural networks (NNs) is the limited number of drug response samples. We investigate multimodal neural network (MM-Net) and data augmentation for DRP in PDXs. The MM-Net learns to predict response using drug descriptors, gene expressions (GE), and histology whole-slide images (WSIs). We explore whether combining WSIs with GE improves predictions as compared with models that use GE alone. We propose two data augmentation methods which allow us training multimodal and unimodal NNs without changing architectures with a single larger dataset: 1) combine single-drug and drug-pair treatments by homogenizing drug representations, and 2) augment drug-pairs which doubles the sample size of all drug-pair samples. Unimodal NNs which use GE are compared to assess the contribution of data augmentation. The NN that uses the original and the augmented drug-pair treatments as well as single-drug treatments outperforms NNs that ignore either the augmented drug-pairs or the single-drug treatments. In assessing the multimodal learning based on the MCC metric, MM-Net outperforms all the baselines. Our results show that data augmentation and integration of histology images with GE can improve prediction performance of drug response in PDXs.
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Affiliation(s)
- Alexander Partin
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Thomas Brettin
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Yitan Zhu
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - James M. Dolezal
- Section of Hematology/Oncology, Department of Medicine, University of Chicago Medical Center, Chicago, IL, United States
| | - Sara Kochanny
- Section of Hematology/Oncology, Department of Medicine, University of Chicago Medical Center, Chicago, IL, United States
| | - Alexander T. Pearson
- Section of Hematology/Oncology, Department of Medicine, University of Chicago Medical Center, Chicago, IL, United States
| | - Maulik Shukla
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Yvonne A. Evrard
- Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD, United States
| | - James H. Doroshow
- Division of Cancer Therapeutics and Diagnosis, National Cancer Institute, Bethesda, MD, United States
| | - Rick L. Stevens
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
- Department of Computer Science, The University of Chicago, Chicago, IL, United States
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12
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Liu J, Liu J, Meng C, Gu Q, Huang C, Liu F, Xia C. NRF2 and FXR dual signaling pathways cooperatively regulate the effects of oleanolic acid on cholestatic liver injury. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2023; 108:154529. [PMID: 36343550 DOI: 10.1016/j.phymed.2022.154529] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 10/21/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Previous studies have shown that the anti-cholestatic effect of oleanolic acid (OA) is associated with FXR and NRF2. However, how the two signaling pathways cooperate to regulate the anti-cholestatic effect of OA remains unclear. PURPOSE This study aimed to further demonstrate the effect of OA on alpha-naphthyl isothiocyanate (ANIT)-induced cholestatic liver injury and the interaction mechanism between NRF2 and FXR signaling pathways in maintaining bile acid homeostasis. METHODS Gene knockout animals and cell models, metabolomics analysis, and co-immunoprecipitation were used to investigate the mechanism of OA against cholestatic liver injury. RESULTS The effect of OA against ANIT-induced liver injury in rats was dramatically reduced after Nrf2 gene knockdown. With the silencing of Fxr, the hepatoprotective effect of OA was weakened, but it still effectively alleviated cholestatic liver injury in rats. In L02 cells, OA can up-regulate the levels of NRF2, FXR, BSEP and UGT1A1, and reduce the expression of CYP7A1. Silencing of NRF2 or FXR significantly attenuated the protective effect of OA on ANIT-induced L02 cell injury and its regulation on downstream target genes, and the influence of NRF2 gene silencing on OA appeared to be greater. The NRF2 activator sulforaphane, and the FXR activator GW4064 both remarkably promoted NRF2 binding to P300 and FXR to RXRα, but reduced β-catenin binding to P300 and β-catenin binding to FXR. CONCLUSION The effect of OA on cholestatic liver injury is closely related to the simultaneous activation of NRF2 and FXR dual signaling pathways, in which NRF2 signaling pathway plays a more important role. The dual signaling pathways of NRF2 and FXR cooperatively regulate bile acid metabolic homeostasis through the interaction mechanism with β-catenin/P300.
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Affiliation(s)
- Jianming Liu
- Clinical Pharmacology Institute, Pharmaceutical School, Nanchang University, Nanchang 330031, P. R. China; Department of Pharmacy, The First Affiliated Hospital of Nanchang University, Nanchang 330006, P. R. China
| | - Jiawei Liu
- Clinical Pharmacology Institute, Pharmaceutical School, Nanchang University, Nanchang 330031, P. R. China
| | - Chao Meng
- Clinical Pharmacology Institute, Pharmaceutical School, Nanchang University, Nanchang 330031, P. R. China
| | - Qi Gu
- Clinical Pharmacology Institute, Pharmaceutical School, Nanchang University, Nanchang 330031, P. R. China
| | - Chao Huang
- Clinical Pharmacology Institute, Pharmaceutical School, Nanchang University, Nanchang 330031, P. R. China
| | - Fanglan Liu
- Clinical Pharmacology Institute, Pharmaceutical School, Nanchang University, Nanchang 330031, P. R. China; Jiangxi Key Laboratory of Clinical Pharmacokinetics, Nanchang 330031, P. R. China
| | - Chunhua Xia
- Clinical Pharmacology Institute, Pharmaceutical School, Nanchang University, Nanchang 330031, P. R. China; Jiangxi Key Laboratory of Clinical Pharmacokinetics, Nanchang 330031, P. R. China.
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13
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Woo XY, Srivastava A, Mack PC, Graber JH, Sanderson BJ, Lloyd MW, Chen M, Domanskyi S, Gandour-Edwards R, Tsai RA, Keck J, Cheng M, Bundy M, Jocoy EL, Riess JW, Holland W, Grubb SC, Peterson JG, Stafford GA, Paisie C, Neuhauser SB, Karuturi RKM, George J, Simons AK, Chavaree M, Tepper CG, Goodwin N, Airhart SD, Lara PN, Openshaw TH, Liu ET, Gandara DR, Bult CJ. A Genomically and Clinically Annotated Patient-Derived Xenograft Resource for Preclinical Research in Non-Small Cell Lung Cancer. Cancer Res 2022; 82:4126-4138. [PMID: 36069866 PMCID: PMC9664138 DOI: 10.1158/0008-5472.can-22-0948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 06/22/2022] [Accepted: 09/01/2022] [Indexed: 12/14/2022]
Abstract
Patient-derived xenograft (PDX) models are an effective preclinical in vivo platform for testing the efficacy of novel drugs and drug combinations for cancer therapeutics. Here we describe a repository of 79 genomically and clinically annotated lung cancer PDXs available from The Jackson Laboratory that have been extensively characterized for histopathologic features, mutational profiles, gene expression, and copy-number aberrations. Most of the PDXs are models of non-small cell lung cancer (NSCLC), including 37 lung adenocarcinoma (LUAD) and 33 lung squamous cell carcinoma (LUSC) models. Other lung cancer models in the repository include four small cell carcinomas, two large cell neuroendocrine carcinomas, two adenosquamous carcinomas, and one pleomorphic carcinoma. Models with both de novo and acquired resistance to targeted therapies with tyrosine kinase inhibitors are available in the collection. The genomic profiles of the LUAD and LUSC PDX models are consistent with those observed in patient tumors from The Cancer Genome Atlas and previously characterized gene expression-based molecular subtypes. Clinically relevant mutations identified in the original patient tumors were confirmed in engrafted PDX tumors. Treatment studies performed in a subset of the models recapitulated the responses expected on the basis of the observed genomic profiles. These models therefore serve as a valuable preclinical platform for translational cancer research. SIGNIFICANCE Patient-derived xenografts of lung cancer retain key features observed in the originating patient tumors and show expected responses to treatment with standard-of-care agents, providing experimentally tractable and reproducible models for preclinical investigations.
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Affiliation(s)
- Xing Yi Woo
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA,Current affiliation: Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Anuj Srivastava
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA
| | - Philip C. Mack
- University of California Davis Comprehensive Cancer Center, Sacramento, California, USA,Current affiliation: Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Joel H. Graber
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine, USA,Current affiliation: MDI Biological Laboratory, Bar Harbor, Maine, USA
| | - Brian J. Sanderson
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA
| | - Michael W. Lloyd
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine, USA
| | - Mandy Chen
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine, USA
| | - Sergii Domanskyi
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine, USA
| | | | - Rebekah A. Tsai
- University of California Davis Comprehensive Cancer Center, Sacramento, California, USA
| | - James Keck
- The Jackson Laboratory, Sacramento, California, USA
| | | | | | | | - Jonathan W. Riess
- University of California Davis Comprehensive Cancer Center, Sacramento, California, USA
| | - William Holland
- University of California Davis Comprehensive Cancer Center, Sacramento, California, USA
| | - Stephen C. Grubb
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine, USA
| | - James G. Peterson
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine, USA
| | - Grace A. Stafford
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine, USA
| | - Carolyn Paisie
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA
| | | | | | - Joshy George
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA
| | - Allen K. Simons
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine, USA
| | - Margaret Chavaree
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine, USA,Eastern Maine Medical Center, Lafayette Family Cancer Center, Brewer, Maine, USA
| | - Clifford G. Tepper
- University of California Davis Comprehensive Cancer Center, Sacramento, California, USA
| | - Neal Goodwin
- The Jackson Laboratory, Sacramento, California, USA,Current affiliation: Teknova, Hollister, California USA
| | - Susan D. Airhart
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine, USA
| | - Primo N. Lara
- University of California Davis Comprehensive Cancer Center, Sacramento, California, USA
| | - Thomas H. Openshaw
- Eastern Maine Medical Center, Lafayette Family Cancer Center, Brewer, Maine, USA,Current affiliation: Cape Cod Hospital, Hyannis, Massachusetts, USA
| | - Edison T. Liu
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine, USA
| | - David R. Gandara
- University of California Davis Comprehensive Cancer Center, Sacramento, California, USA
| | - Carol J. Bult
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine, USA,Corresponding author: Carol J. Bult, The Jackson Laboratory, 600 Main Street, RL13, Bar Harbor, ME 04609; (tel) 207-288-6324,
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14
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Huang L, Wang J, Fang B, Meric-Bernstam F, Roth JA, Ha MJ. CombPDX: a unified statistical framework for evaluating drug synergism in patient-derived xenografts. Sci Rep 2022; 12:12984. [PMID: 35906256 PMCID: PMC9338066 DOI: 10.1038/s41598-022-16933-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 07/18/2022] [Indexed: 12/14/2022] Open
Abstract
Anticancer combination therapy has been developed to increase efficacy by enhancing synergy. Patient-derived xenografts (PDXs) have emerged as reliable preclinical models to develop effective treatments in translational cancer research. However, most PDX combination study designs focus on single dose levels, and dose-response surface models are not appropriate for testing synergism. We propose a comprehensive statistical framework to assess joint action of drug combinations from PDX tumor growth curve data. We provide various metrics and robust statistical inference procedures that locally (at a fixed time) and globally (across time) access combination effects under classical drug interaction models. Integrating genomic and pharmacological profiles in non-small-cell lung cancer (NSCLC), we have shown the utilities of combPDX in discovering effective therapeutic combinations and relevant biological mechanisms. We provide an interactive web server, combPDX ( https://licaih.shinyapps.io/CombPDX/ ), to analyze PDX tumor growth curve data and perform power analyses.
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Affiliation(s)
- Licai Huang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
- Quantitative Sciences Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, 77030, USA
| | - Jing Wang
- Departments of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Bingliang Fang
- Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Funda Meric-Bernstam
- Department of Investigational Cancer Therapeutics, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Jack A Roth
- Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Min Jin Ha
- Department of Biostatistics, Graduate School of Public Health, Yonsei University, Seoul, South Korea.
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15
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Li Y, Huang Y, Cheng H, Xu F, Qi R, Dai B, Yang Y, Tu Z, Peng L, Zhang Z. Discovery of BRAF/HDAC Dual Inhibitors Suppressing Proliferation of Human Colorectal Cancer Cells. Front Chem 2022; 10:910353. [PMID: 35936102 PMCID: PMC9354042 DOI: 10.3389/fchem.2022.910353] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 05/30/2022] [Indexed: 11/13/2022] Open
Abstract
The combination of histone deacetylase inhibitor and BRAF inhibitor (BRAFi) has been shown to enhance the antineoplastic effect and reduce the progress of BRAFi resistance. In this study, a series of (thiazol-5-yl)pyrimidin-2-yl)amino)-N-hydroxyalkanamide derivatives were designed and synthesized as novel dual inhibitors of BRAF and HDACs using a pharmacophore hybrid strategy. In particular, compound 14b possessed potent activities against BRAF, HDAC1, and HDAC6 enzymes. It potently suppressed the proliferation of HT-29 cells harboring BRAFV600E mutation as well as HCT116 cells with wild-type BRAF. The dual inhibition against BRAF and HDAC downstream proteins was validated in both cells. Collectively, the results support 14b as a promising lead molecule for further development and a useful tool for studying the effects of BRAF/HDAC dual inhibitors.
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Affiliation(s)
- Yingjun Li
- Academy for Advanced Interdisciplinary Studies and Department of Chemistry, Southern University of Science and Technology, Shenzhen, China
- *Correspondence: Yingjun Li, ; Zhang Zhang, ; Lijie Peng,
| | - Yongjun Huang
- International Cooperative Laboratory of Traditional Chinese Medicine Modernization and Innovative Drug Development of Chinese Ministry of Education (MOE), Guangzhou City Key Laboratory of Precision Chemical Drug Development, School of Pharmacy, Jinan University, Guangzhou, China
| | - Huimin Cheng
- XtalPi Inc., (Shenzhen Jingtai Technology Co., Ltd.), Shenzhen, China
| | - Fang Xu
- International Cooperative Laboratory of Traditional Chinese Medicine Modernization and Innovative Drug Development of Chinese Ministry of Education (MOE), Guangzhou City Key Laboratory of Precision Chemical Drug Development, School of Pharmacy, Jinan University, Guangzhou, China
| | - Ruxi Qi
- Cryo-EM Center, Southern University of Science and Technology, Shenzhen, China
| | - Botao Dai
- Academy for Advanced Interdisciplinary Studies and Department of Chemistry, Southern University of Science and Technology, Shenzhen, China
| | - Yujian Yang
- Academy for Advanced Interdisciplinary Studies and Department of Chemistry, Southern University of Science and Technology, Shenzhen, China
| | - Zhengchao Tu
- Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Lijie Peng
- International Cooperative Laboratory of Traditional Chinese Medicine Modernization and Innovative Drug Development of Chinese Ministry of Education (MOE), Guangzhou City Key Laboratory of Precision Chemical Drug Development, School of Pharmacy, Jinan University, Guangzhou, China
- *Correspondence: Yingjun Li, ; Zhang Zhang, ; Lijie Peng,
| | - Zhang Zhang
- International Cooperative Laboratory of Traditional Chinese Medicine Modernization and Innovative Drug Development of Chinese Ministry of Education (MOE), Guangzhou City Key Laboratory of Precision Chemical Drug Development, School of Pharmacy, Jinan University, Guangzhou, China
- *Correspondence: Yingjun Li, ; Zhang Zhang, ; Lijie Peng,
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16
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Ba-Alawi W, Kadambat Nair S, Li B, Mammoliti A, Smirnov P, Mer AS, Penn LZ, Haibe-Kains B. Bimodal gene expression in cancer patients provides interpretable biomarkers for drug sensitivity. Cancer Res 2022; 82:2378-2387. [PMID: 35536872 DOI: 10.1158/0008-5472.can-21-2395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 02/24/2022] [Accepted: 05/06/2022] [Indexed: 11/16/2022]
Abstract
Identifying biomarkers predictive of cancer cell response to drug treatment constitutes one of the main challenges in precision oncology. Recent large-scale cancer pharmacogenomic studies have opened new avenues of research to develop predictive biomarkers by profiling thousands of human cancer cell lines at the molecular level and screening them with hundreds of approved drugs and experimental chemical compounds. Many studies have leveraged these data to build predictive models of response using various statistical and machine learning methods. However, a common pitfall to these methods is the lack of interpretability as to how they make predictions, hindering the clinical translation of these models. To alleviate this issue, we used the recent logic modeling approach to develop a new machine learning pipeline that explores the space of bimodally expressed genes in multiple large in vitro pharmacogenomic studies and builds multivariate, nonlinear, yet interpretable logic-based models predictive of drug response. The performance of this approach was showcased in a compendium of the three largest in vitro pharmacogenomic data sets to build robust and interpretable models for 101 drugs that span 17 drug classes with high validation rates in independent datasets. These results along with in vivo and clinical validation, support a better translation of gene expression biomarkers between model systems using bimodal gene expression.
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Affiliation(s)
| | | | - Bo Li
- University of Toronto, Toronto, Canada
| | | | | | | | - Linda Z Penn
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada
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17
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Fayzullina D, Tsibulnikov S, Stempen M, Schroeder BA, Kumar N, Kharwar RK, Acharya A, Timashev P, Ulasov I. Novel Targeted Therapeutic Strategies for Ewing Sarcoma. Cancers (Basel) 2022; 14:cancers14081988. [PMID: 35454895 PMCID: PMC9032664 DOI: 10.3390/cancers14081988] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/03/2022] [Accepted: 04/11/2022] [Indexed: 02/06/2023] Open
Abstract
Simple Summary Ewing sarcoma is an uncommon cancer that arises in mesenchymal tissues and represents the second most widespread malignant bone neoplasm after osteosarcoma in children. Therapy has increased the 5-year survival rate in the last 40 years, although the recurrence rate has remained high. There is an immediate and unmet need for the development of novel Ewing sarcoma therapies. We offer new prospective targets for the therapy of Ewing sarcoma. The EWSR1/FLI1 fusion protein, which is identified in 85–90% of Ewing sarcoma tumors, and its direct targets are given special focus in this study. Experimantal therapy that targets multiple signaling pathways activated during ES progression, alone or in combination with existing regimens, may become the new standard of care for Ewing sarcoma patients, improving patient survival. Abstract Ewing sarcoma (ES) is an uncommon cancer that arises in mesenchymal tissues and represents the second most widespread malignant bone neoplasm after osteosarcoma in children. Amplifications in genomic, proteomic, and metabolism are characteristics of sarcoma, and targeting altered cancer cell molecular processes has been proposed as the latest promising strategy to fight cancer. Recent technological advancements have elucidated some of the underlying oncogenic characteristics of Ewing sarcoma. Offering new insights into the physiological basis for this phenomenon, our current review examines the dynamics of ES signaling as it related to both ES and the microenvironment by integrating genomic and proteomic analyses. An extensive survey of the literature was performed to compile the findings. We have also highlighted recent and ongoing studies integrating metabolomics and genomics aimed at better understanding the complex interactions as to how ES adapts to changing biochemical changes within the tumor microenvironment.
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Affiliation(s)
- Daria Fayzullina
- Group of Experimental Biotherapy and Diagnostic, Department of Advanced Materials, Institute for Regenerative Medicine, Sechenov First Moscow State Medical University, Moscow 119991, Russia
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow 119991, Russia; (D.F.); (S.T.); (M.S.); (P.T.)
| | - Sergey Tsibulnikov
- Group of Experimental Biotherapy and Diagnostic, Department of Advanced Materials, Institute for Regenerative Medicine, Sechenov First Moscow State Medical University, Moscow 119991, Russia
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow 119991, Russia; (D.F.); (S.T.); (M.S.); (P.T.)
| | - Mikhail Stempen
- Group of Experimental Biotherapy and Diagnostic, Department of Advanced Materials, Institute for Regenerative Medicine, Sechenov First Moscow State Medical University, Moscow 119991, Russia
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow 119991, Russia; (D.F.); (S.T.); (M.S.); (P.T.)
| | - Brett A. Schroeder
- National Cancer Institute, National Institutes of Health, Bethesda, MD 20814, USA;
| | - Naveen Kumar
- Tumor Immunology Lab, Department of Zoology, Institute of Science, Banaras Hindu University, Varanasi 221005, India; (N.K.); (A.A.)
| | - Rajesh Kumar Kharwar
- Endocrine Research Lab, Department of Zoology, Kutir Post Graduate College, Chakkey, Jaunpur 222146, India;
| | - Arbind Acharya
- Tumor Immunology Lab, Department of Zoology, Institute of Science, Banaras Hindu University, Varanasi 221005, India; (N.K.); (A.A.)
| | - Peter Timashev
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow 119991, Russia; (D.F.); (S.T.); (M.S.); (P.T.)
- Department of Advanced Materials, Institute for Regenerative Medicine, Sechenov First Moscow State Medical University, Moscow 119991, Russia
| | - Ilya Ulasov
- Group of Experimental Biotherapy and Diagnostic, Department of Advanced Materials, Institute for Regenerative Medicine, Sechenov First Moscow State Medical University, Moscow 119991, Russia
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow 119991, Russia; (D.F.); (S.T.); (M.S.); (P.T.)
- Correspondence:
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18
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Patient-derived tumor models are attractive tools to repurpose drugs for ovarian cancer treatment: Pre-clinical updates. Oncotarget 2022; 13:553-575. [PMID: 35359749 PMCID: PMC8959092 DOI: 10.18632/oncotarget.28220] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 03/08/2022] [Indexed: 11/29/2022] Open
Abstract
Despite advances in understanding of ovarian cancer biology, the progress in translation of research findings into new therapies is still slow. It is associated in part with limitations of commonly used cancer models such as cell lines and genetically engineered mouse models that lack proper representation of diversity and complexity of actual human tumors. In addition, the development of de novo anticancer drugs is a lengthy and expensive process. A promising alternative to new drug development is repurposing existing FDA-approved drugs without primary oncological purpose. These approved agents have known pharmacokinetics, pharmacodynamics, and toxicology and could be approved as anticancer drugs quicker and at lower cost. To successfully translate repurposed drugs to clinical application, an intermediate step of pre-clinical animal studies is required. To address challenges associated with reliability of tumor models for pre-clinical studies, there has been an increase in development of patient-derived xenografts (PDXs), which retain key characteristics of the original patient’s tumor, including histologic, biologic, and genetic features. The expansion and utilization of clinically and molecularly annotated PDX models derived from different ovarian cancer subtypes could substantially aid development of new therapies or rapid approval of repurposed drugs to improve treatment options for ovarian cancer patients.
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19
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Shi M, Wang Y, Lin D, Wang Y. Patient-derived xenograft models of neuroendocrine prostate cancer. Cancer Lett 2022; 525:160-169. [PMID: 34767925 DOI: 10.1016/j.canlet.2021.11.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Revised: 11/02/2021] [Accepted: 11/03/2021] [Indexed: 12/21/2022]
Abstract
In recent years, patient-derived xenografts (PDXs) have attracted much attention as clinically relevant models for basic and translational cancer research. PDXs retain the principal histopathological and molecular heterogeneity of their donor tumors and remain stable across passages. These characteristics allow PDXs to offer a reliable platform for better understanding cancer biology, discovering biomarkers and therapeutic targets, and developing novel therapies. A growing interest in generating neuroendocrine prostate cancer (NEPC) PDX models has been demonstrated, and such models have proven useful in several areas. This review provides a comprehensive summary of currently available NEPC PDX collections, encompassing 1) primary or secondary sites where patient samples were collected, 2) donor patients' treatment histories, 3) morphological features (i.e., small cell and large cell), and 4) genomic alterations. We also highlight suitable models for various research purposes, including identifying therapeutic targets and evaluating drug responses in models with specific genomic backgrounds. Finally, we provide perspectives on the current knowledge gaps and shed light on future applications and improvements of NEPC PDXs.
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Affiliation(s)
- Mingchen Shi
- Vancouver Prostate Centre, Vancouver, BC, Canada; Department of Urologic Sciences, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada; Department of Experimental Therapeutics, BC Cancer Agency, Vancouver, BC, Canada
| | - Yu Wang
- Vancouver Prostate Centre, Vancouver, BC, Canada; Department of Urologic Sciences, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada; Department of Experimental Therapeutics, BC Cancer Agency, Vancouver, BC, Canada
| | - Dong Lin
- Vancouver Prostate Centre, Vancouver, BC, Canada; Department of Urologic Sciences, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada; Department of Experimental Therapeutics, BC Cancer Agency, Vancouver, BC, Canada
| | - Yuzhuo Wang
- Vancouver Prostate Centre, Vancouver, BC, Canada; Department of Urologic Sciences, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada; Department of Experimental Therapeutics, BC Cancer Agency, Vancouver, BC, Canada.
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20
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Ortmann J, Rampášek L, Tai E, Mer AS, Shi R, Stewart EL, Mascaux C, Fares A, Pham NA, Beri G, Eeles C, Tkachuk D, Ho C, Sakashita S, Weiss J, Jiang X, Liu G, Cescon DW, O'Brien CA, Guo S, Tsao MS, Haibe-Kains B, Goldenberg A. Assessing therapy response in patient-derived xenografts. Sci Transl Med 2021; 13:eabf4969. [PMID: 34788078 DOI: 10.1126/scitranslmed.abf4969] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Janosch Ortmann
- Département AOTI, Université du Québec à Montréal, Montréal, QC H2X3X2, Canada.,Group for Research in Decision Analysis (GERAD), Montreal, QC H3T1J4, Canada
| | - Ladislav Rampášek
- Department of Computer Science, University of Toronto, Toronto, ON M5S2E4, Canada.,Vector Institute for Artificial Intelligence, Toronto, ON M5G1M1, Canada.,Hospital for Sick Children, Toronto, ON M5G1X8, Canada
| | - Elijah Tai
- Department of Computer Science, University of Toronto, Toronto, ON M5S2E4, Canada
| | - Arvind Singh Mer
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON M5G1L7, Canada
| | - Ruoshi Shi
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - Erin L Stewart
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - Celine Mascaux
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada.,Pulmonology Department, Hôpitaux Universitaires de Strasbourg, 67200 Strasbourg, France.,Laboratory of Molecular Mechanisms of the Stress Response and Pathologies, INSERM U1113, 3 Avenue Molière, 67200 Strasbourg, France
| | - Aline Fares
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - Nhu-An Pham
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - Gangesh Beri
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - Christopher Eeles
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - Denis Tkachuk
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - Chantal Ho
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - Shingo Sakashita
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - Jessica Weiss
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - Xiaoqian Jiang
- Crown Bioscience Taicang Inc., No.6 Beijing West Road, Taicang, Jiangsu 215400, P. R. China
| | - Geoffrey Liu
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - David W Cescon
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - Catherine A O'Brien
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON M5G1L7, Canada.,Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S1A8, Canada.,Department of Physiology, University of Toronto, Toronto, ON M5G1L7, Canada.,Department of Surgery, Toronto General Hospital, Toronto, ON M5G2C4, Canada
| | - Sheng Guo
- Crown Bioscience Taicang Inc., No.6 Beijing West Road, Taicang, Jiangsu 215400, P. R. China
| | - Ming-Sound Tsao
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - Benjamin Haibe-Kains
- Department of Computer Science, University of Toronto, Toronto, ON M5S2E4, Canada.,Vector Institute for Artificial Intelligence, Toronto, ON M5G1M1, Canada.,Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON M5G1L7, Canada.,Ontario Institute for Cancer Research, Toronto, ON M5G1L7, Canada
| | - Anna Goldenberg
- Department of Computer Science, University of Toronto, Toronto, ON M5S2E4, Canada.,Vector Institute for Artificial Intelligence, Toronto, ON M5G1M1, Canada.,Hospital for Sick Children, Toronto, ON M5G1X8, Canada.,CIFAR, Toronto, ON M5G1M1, Canada
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21
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Orchestrating and sharing large multimodal data for transparent and reproducible research. Nat Commun 2021; 12:5797. [PMID: 34608132 PMCID: PMC8490371 DOI: 10.1038/s41467-021-25974-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 09/08/2021] [Indexed: 11/08/2022] Open
Abstract
Reproducibility is essential to open science, as there is limited relevance for findings that can not be reproduced by independent research groups, regardless of its validity. It is therefore crucial for scientists to describe their experiments in sufficient detail so they can be reproduced, scrutinized, challenged, and built upon. However, the intrinsic complexity and continuous growth of biomedical data makes it increasingly difficult to process, analyze, and share with the community in a FAIR (findable, accessible, interoperable, and reusable) manner. To overcome these issues, we created a cloud-based platform called ORCESTRA ( orcestra.ca ), which provides a flexible framework for the reproducible processing of multimodal biomedical data. It enables processing of clinical, genomic and perturbation profiles of cancer samples through automated processing pipelines that are user-customizable. ORCESTRA creates integrated and fully documented data objects with persistent identifiers (DOI) and manages multiple dataset versions, which can be shared for future studies.
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22
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Liu Z, Liu J, Liu X, Wang X, Xie Q, Zhang X, Kong X, He M, Yang Y, Deng X, Yang L, Qi Y, Li J, Liu Y, Yuan L, Diao L, He F, Li D. CTR-DB, an omnibus for patient-derived gene expression signatures correlated with cancer drug response. Nucleic Acids Res 2021; 50:D1184-D1199. [PMID: 34570230 PMCID: PMC8728209 DOI: 10.1093/nar/gkab860] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 09/08/2021] [Accepted: 09/15/2021] [Indexed: 12/26/2022] Open
Abstract
To date, only some cancer patients can benefit from chemotherapy and targeted therapy. Drug resistance continues to be a major and challenging problem facing current cancer research. Rapidly accumulated patient-derived clinical transcriptomic data with cancer drug response bring opportunities for exploring molecular determinants of drug response, but meanwhile pose challenges for data management, integration, and reuse. Here we present the Cancer Treatment Response gene signature DataBase (CTR-DB, http://ctrdb.ncpsb.org.cn/), a unique database for basic and clinical researchers to access, integrate, and reuse clinical transcriptomes with cancer drug response. CTR-DB has collected and uniformly reprocessed 83 patient-derived pre-treatment transcriptomic source datasets with manually curated cancer drug response information, involving 28 histological cancer types, 123 drugs, and 5139 patient samples. These data are browsable, searchable, and downloadable. Moreover, CTR-DB supports single-dataset exploration (including differential gene expression, receiver operating characteristic curve, functional enrichment, sensitizing drug search, and tumor microenvironment analyses), and multiple-dataset combination and comparison, as well as biomarker validation function, which provide insights into the drug resistance mechanism, predictive biomarker discovery and validation, drug combination, and resistance mechanism heterogeneity.
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Affiliation(s)
- Zhongyang Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.,College of Chemistry and Environmental Science, Hebei University, Baoding 071002, China
| | - Jiale Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Xinyue Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Xun Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Qiaosheng Xie
- Department of Radiation Oncology, China-Japan Friendship Hospital, Beijing 100029, China
| | - Xinlei Zhang
- Beijing Geneworks Technology Co., Ltd., Beijing 100101, China
| | - Xiangya Kong
- Beijing Geneworks Technology Co., Ltd., Beijing 100101, China
| | - Mengqi He
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Yuting Yang
- Department of Immunology, Medical College of Qingdao University, Qingdao 266071, China
| | - Xinru Deng
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Lele Yang
- College of Chemistry and Environmental Science, Hebei University, Baoding 071002, China
| | - Yaning Qi
- College of Chemistry and Environmental Science, Hebei University, Baoding 071002, China
| | - Jiajun Li
- College of Chemistry and Environmental Science, Hebei University, Baoding 071002, China
| | - Yuan Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Liying Yuan
- College of Chemistry and Environmental Science, Hebei University, Baoding 071002, China
| | - Lihong Diao
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Fuchu He
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Dong Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.,College of Chemistry and Environmental Science, Hebei University, Baoding 071002, China
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23
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Sharifi-Noghabi H, Jahangiri-Tazehkand S, Smirnov P, Hon C, Mammoliti A, Nair SK, Mer AS, Ester M, Haibe-Kains B. Drug sensitivity prediction from cell line-based pharmacogenomics data: guidelines for developing machine learning models. Brief Bioinform 2021; 22:6348324. [PMID: 34382071 DOI: 10.1093/bib/bbab294] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 06/29/2021] [Accepted: 07/10/2021] [Indexed: 11/13/2022] Open
Abstract
The goal of precision oncology is to tailor treatment for patients individually using the genomic profile of their tumors. Pharmacogenomics datasets such as cancer cell lines are among the most valuable resources for drug sensitivity prediction, a crucial task of precision oncology. Machine learning methods have been employed to predict drug sensitivity based on the multiple omics data available for large panels of cancer cell lines. However, there are no comprehensive guidelines on how to properly train and validate such machine learning models for drug sensitivity prediction. In this paper, we introduce a set of guidelines for different aspects of training gene expression-based predictors using cell line datasets. These guidelines provide extensive analysis of the generalization of drug sensitivity predictors and challenge many current practices in the community including the choice of training dataset and measure of drug sensitivity. The application of these guidelines in future studies will enable the development of more robust preclinical biomarkers.
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Affiliation(s)
- Hossein Sharifi-Noghabi
- School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada.,Vancouver Prostate Center, Vancouver, British Columbia, Canada.,Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Soheil Jahangiri-Tazehkand
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,University of Toronto, Toronto, Ontario, Canada
| | - Petr Smirnov
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,University of Toronto, Toronto, Ontario, Canada
| | - Casey Hon
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,University of Toronto, Toronto, Ontario, Canada
| | - Anthony Mammoliti
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,University of Toronto, Toronto, Ontario, Canada
| | | | - Arvind Singh Mer
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,University of Toronto, Toronto, Ontario, Canada
| | - Martin Ester
- School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada.,Vancouver Prostate Center, Vancouver, British Columbia, Canada
| | - Benjamin Haibe-Kains
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,University of Toronto, Toronto, Ontario, Canada
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24
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Huang Z, Su Q, Li W, Ren H, Huang H, Wang A. Suppressed mitochondrial respiration via NOX5-mediated redox imbalance contributes to the antitumor activity of anlotinib in oral squamous cell carcinoma. J Genet Genomics 2021; 48:582-594. [PMID: 34373220 DOI: 10.1016/j.jgg.2021.06.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 06/19/2021] [Accepted: 06/23/2021] [Indexed: 02/04/2023]
Abstract
Anlotinib, a novel multitarget tyrosine kinase inhibitor, has shown promising results in the management of various carcinomas. This study aimed to investigate the antitumor activity of anlotinib in oral squamous cell carcinoma (OSCC) and the underlying molecular mechanism. A retrospective clinical study revealed that anlotinib improved the median progression-free survival (mPFS) and median overall survival (mOS) of patients with recurrent and metastatic (R/M) OSCC, respectively. Functional studies revealed that anlotinib markedly inhibited in vitro proliferation of OSCC cells and impeded in vivo tumor growth of OSCC patient-derived xenograft models. Mechanistically, RNA-sequencing identified that oxidative stress, oxidative phosphorylation and AKT/mTOR signaling were involved in anlotinib-treated OSCC cells. Anlotinib upregulated NADPH oxidase 5 (NOX5) expression, elevated reactive oxygen species (ROS) production, impaired mitochondrial respiration, and promoted apoptosis. Moreover, anlotinb also inhibited phospho-Akt (p-AKT) expression and elevated p-eIF2α expression in OSCC cells. NOX5 knockdown attenuated these inhibitory effects and cytotoxicity in anlotinib-treated OSCC cells. Collectively, we demonstrated that anlotinib monotherapy demonstrated favorable anticancer activity and manageable toxicities in patients with R/M OSCC. The antitumor activity of anlotinib in OSCC may be mainly involved in the suppression of mitochondrial respiration via NOX5-mediated redox imbalance and the AKT/eIF2α pathway.
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Affiliation(s)
- Zhexun Huang
- Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong 510080, China
| | - Qiao Su
- Animal Experiment Center, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong 510080, China
| | - Wuguo Li
- Animal Experiment Center, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong 510080, China
| | - Hui Ren
- Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong 510080, China
| | - Huiqiang Huang
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong 510060, China; State Key Laboratory of Oncology in South China, Guangzhou, Guangdong 510060, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong 510060, China
| | - Anxun Wang
- Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong 510080, China.
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25
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Rafique R, Islam SR, Kazi JU. Machine learning in the prediction of cancer therapy. Comput Struct Biotechnol J 2021; 19:4003-4017. [PMID: 34377366 PMCID: PMC8321893 DOI: 10.1016/j.csbj.2021.07.003] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 07/06/2021] [Accepted: 07/07/2021] [Indexed: 12/15/2022] Open
Abstract
Resistance to therapy remains a major cause of cancer treatment failures, resulting in many cancer-related deaths. Resistance can occur at any time during the treatment, even at the beginning. The current treatment plan is dependent mainly on cancer subtypes and the presence of genetic mutations. Evidently, the presence of a genetic mutation does not always predict the therapeutic response and can vary for different cancer subtypes. Therefore, there is an unmet need for predictive models to match a cancer patient with a specific drug or drug combination. Recent advancements in predictive models using artificial intelligence have shown great promise in preclinical settings. However, despite massive improvements in computational power, building clinically useable models remains challenging due to a lack of clinically meaningful pharmacogenomic data. In this review, we provide an overview of recent advancements in therapeutic response prediction using machine learning, which is the most widely used branch of artificial intelligence. We describe the basics of machine learning algorithms, illustrate their use, and highlight the current challenges in therapy response prediction for clinical practice.
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Affiliation(s)
| | - S.M. Riazul Islam
- Department of Computer Science and Engineering, Sejong University, Seoul, South Korea
| | - Julhash U. Kazi
- Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden
- Lund Stem Cell Center, Department of Laboratory Medicine, Lund University, Lund, Sweden
- Corresponding author at: Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Medicon village Building 404:C3, Scheelevägen 8, 22363 Lund, Sweden.
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26
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Tanoli Z, Vähä-Koskela M, Aittokallio T. Artificial intelligence, machine learning, and drug repurposing in cancer. Expert Opin Drug Discov 2021; 16:977-989. [PMID: 33543671 DOI: 10.1080/17460441.2021.1883585] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Introduction: Drug repurposing provides a cost-effective strategy to re-use approved drugs for new medical indications. Several machine learning (ML) and artificial intelligence (AI) approaches have been developed for systematic identification of drug repurposing leads based on big data resources, hence further accelerating and de-risking the drug development process by computational means.Areas covered: The authors focus on supervised ML and AI methods that make use of publicly available databases and information resources. While most of the example applications are in the field of anticancer drug therapies, the methods and resources reviewed are widely applicable also to other indications including COVID-19 treatment. A particular emphasis is placed on the use of comprehensive target activity profiles that enable a systematic repurposing process by extending the target profile of drugs to include potent off-targets with therapeutic potential for a new indication.Expert opinion: The scarcity of clinical patient data and the current focus on genetic aberrations as primary drug targets may limit the performance of anticancer drug repurposing approaches that rely solely on genomics-based information. Functional testing of cancer patient cells exposed to a large number of targeted therapies and their combinations provides an additional source of repurposing information for tissue-aware AI approaches.
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Affiliation(s)
- Ziaurrehman Tanoli
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLife, University of Helsinki, Helsinki, Finland
| | - Markus Vähä-Koskela
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLife, University of Helsinki, Helsinki, Finland
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLife, University of Helsinki, Helsinki, Finland.,Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital, Oslo, Norway.,Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, Oslo, Norway
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27
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Woo XY, Giordano J, Srivastava A, Zhao ZM, Lloyd MW, de Bruijn R, Suh YS, Patidar R, Chen L, Scherer S, Bailey MH, Yang CH, Cortes-Sanchez E, Xi Y, Wang J, Wickramasinghe J, Kossenkov AV, Rebecca VW, Sun H, Mashl RJ, Davies SR, Jeon R, Frech C, Randjelovic J, Rosains J, Galimi F, Bertotti A, Lafferty A, O’Farrell AC, Modave E, Lambrechts D, ter Brugge P, Serra V, Marangoni E, El Botty R, Kim H, Kim JI, Yang HK, Lee C, Dean DA, Davis-Dusenbery B, Evrard YA, Doroshow JH, Welm AL, Welm BE, Lewis MT, Fang B, Roth JA, Meric-Bernstam F, Herlyn M, Davies MA, Ding L, Li S, Govindan R, Isella C, Moscow JA, Trusolino L, Byrne AT, Jonkers J, Bult CJ, Medico E, Chuang JH. Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts. Nat Genet 2021; 53:86-99. [PMID: 33414553 PMCID: PMC7808565 DOI: 10.1038/s41588-020-00750-6] [Citation(s) in RCA: 109] [Impact Index Per Article: 36.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Accepted: 11/18/2020] [Indexed: 02/03/2023]
Abstract
Patient-derived xenografts (PDXs) are resected human tumors engrafted into mice for preclinical studies and therapeutic testing. It has been proposed that the mouse host affects tumor evolution during PDX engraftment and propagation, affecting the accuracy of PDX modeling of human cancer. Here, we exhaustively analyze copy number alterations (CNAs) in 1,451 PDX and matched patient tumor (PT) samples from 509 PDX models. CNA inferences based on DNA sequencing and microarray data displayed substantially higher resolution and dynamic range than gene expression-based inferences, and they also showed strong CNA conservation from PTs through late-passage PDXs. CNA recurrence analysis of 130 colorectal and breast PT/PDX-early/PDX-late trios confirmed high-resolution CNA retention. We observed no significant enrichment of cancer-related genes in PDX-specific CNAs across models. Moreover, CNA differences between patient and PDX tumors were comparable to variations in multiregion samples within patients. Our study demonstrates the lack of systematic copy number evolution driven by the PDX mouse host.
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Grants
- NC/T001267/1 National Centre for the Replacement, Refinement and Reduction of Animals in Research
- P30 CA016672 NCI NIH HHS
- 29567 Cancer Research UK
- U54 CA233223 NCI NIH HHS
- P30 CA034196 NCI NIH HHS
- P01 CA114046 NCI NIH HHS
- HHSN261201400008C NCI NIH HHS
- P30 CA091842 NCI NIH HHS
- U24 CA224067 NCI NIH HHS
- P50 CA196510 NCI NIH HHS
- U54 CA224070 NCI NIH HHS
- U54 CA224076 NCI NIH HHS
- U54 CA224065 NCI NIH HHS
- U54 CA233306 NCI NIH HHS
- P30 CA010815 NCI NIH HHS
- U24 CA204781 NCI NIH HHS
- U54 CA224083 NCI NIH HHS
- HHSN261201500003C NCI NIH HHS
- HHSN261200800001C NCI NIH HHS
- T32 HG008962 NHGRI NIH HHS
- R50 CA211199 NCI NIH HHS
- P30 CA125123 NCI NIH HHS
- P50 CA070907 NCI NIH HHS
- HHSN261201500003I NCI NIH HHS
- HHSN261200800001E NCI NIH HHS
- P30 CA042014 NCI NIH HHS
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
- KWF Kankerbestrijding (Dutch Cancer Society)
- Oncode Institute
- Fondazione AIRC under 5 per Mille 2018 - ID. 21091 EU H2020 Research and Innovation Programme, grant agreement no. 731105 European Research Council Consolidator Grant 724748
- EU H2020 Research and Innovation Programme, grant Agreement No. 754923
- EU H2020 Research and Innovation Programme, grant agreement no. 731105 ISCIII - Miguel Servet program CP14/00228 GHD-Pink/FERO Foundation grant
- Fondazione Piemontese per la Ricerca sul Cancro-ONLUS 5 per mille Ministero della Salute 2015
- Korean Health Industry Development Institute HI13C2148
- Korean Health Industry Development Institute HI13C2148 The First Affiliated Hospital of Xi’an Jiaotong University Ewha Womans University Research Grant
- CPRIT RP170691
- SCU | Ignatian Center for Jesuit Education, Santa Clara University
- Breast Cancer Research Foundation (BCRF)
- Fashion Footwear Charitable Foundation of New York The Foundation for Barnes-Jewish Hospital’s Cancer Frontier Fund
- My First AIRC Grant 19047
- Fondazione AIRC under 5 per Mille 2018 - ID. 21091 AIRC Investigator Grants 18532 and 20697 AIRC/CRUK/FC AECC Accelerator Award 22795 Fondazione Piemontese per la Ricerca sul Cancro-ONLUS 5 per mille Ministero della Salute 2015, 2014, 2016 EU H2020 Research and Innovation Programme, grant Agreement No. 754923 EU H2020 Research and Innovation Programme, grant agreement no. 731105
- Science Foundation Ireland (SFI)
- EU H2020 Research and Innovation Programme, grant agreement no. 731105 EU H2020 Research and Innovation Programme, grant Agreement No. 754923 Irish Health Research Board grant ILP-POR-2019-066
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Netherlands Organisation for Scientific Research)
- EU H2020 Research and Innovation Programme, grant agreement no. 731105 European Research Council (ERC) Synergy project CombatCancer Oncode Institute
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Affiliation(s)
- Xing Yi Woo
- grid.249880.f0000 0004 0374 0039The Jackson Laboratory for Genomic Medicine, Farmington, CT USA
| | - Jessica Giordano
- grid.7605.40000 0001 2336 6580Department of Oncology, University of Turin, Turin, Italy ,grid.419555.90000 0004 1759 7675Candiolo Cancer Institute, FPO-IRCCS, Turin, Italy
| | - Anuj Srivastava
- grid.249880.f0000 0004 0374 0039The Jackson Laboratory for Genomic Medicine, Farmington, CT USA
| | - Zi-Ming Zhao
- grid.249880.f0000 0004 0374 0039The Jackson Laboratory for Genomic Medicine, Farmington, CT USA
| | - Michael W. Lloyd
- grid.249880.f0000 0004 0374 0039The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME USA
| | - Roebi de Bruijn
- grid.430814.aNetherlands Cancer Institute, Amsterdam, the Netherlands
| | - Yun-Suhk Suh
- grid.31501.360000 0004 0470 5905College of Medicine, Seoul National University, Seoul, Republic of Korea
| | - Rajesh Patidar
- grid.418021.e0000 0004 0535 8394Frederick National Laboratory for Cancer Research, Frederick, MD USA
| | - Li Chen
- grid.418021.e0000 0004 0535 8394Frederick National Laboratory for Cancer Research, Frederick, MD USA
| | - Sandra Scherer
- grid.223827.e0000 0001 2193 0096Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT USA
| | - Matthew H. Bailey
- grid.223827.e0000 0001 2193 0096Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT USA ,grid.223827.e0000 0001 2193 0096Department of Human Genetics, University of Utah, Salt Lake City, UT USA
| | - Chieh-Hsiang Yang
- grid.223827.e0000 0001 2193 0096Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT USA
| | - Emilio Cortes-Sanchez
- grid.223827.e0000 0001 2193 0096Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT USA
| | - Yuanxin Xi
- grid.240145.60000 0001 2291 4776Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Jing Wang
- grid.240145.60000 0001 2291 4776Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | | | | | - Vito W. Rebecca
- grid.251075.40000 0001 1956 6678The Wistar Institute, Philadelphia, PA USA
| | - Hua Sun
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO USA
| | - R. Jay Mashl
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO USA
| | - Sherri R. Davies
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO USA
| | - Ryan Jeon
- grid.492568.4Seven Bridges Genomics, Charlestown, MA USA
| | | | | | | | - Francesco Galimi
- grid.7605.40000 0001 2336 6580Department of Oncology, University of Turin, Turin, Italy ,grid.419555.90000 0004 1759 7675Candiolo Cancer Institute, FPO-IRCCS, Turin, Italy
| | - Andrea Bertotti
- grid.7605.40000 0001 2336 6580Department of Oncology, University of Turin, Turin, Italy ,grid.419555.90000 0004 1759 7675Candiolo Cancer Institute, FPO-IRCCS, Turin, Italy
| | - Adam Lafferty
- grid.4912.e0000 0004 0488 7120Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Alice C. O’Farrell
- grid.4912.e0000 0004 0488 7120Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Elodie Modave
- grid.5596.f0000 0001 0668 7884Center for Cancer Biology, VIB, Leuven, Belgium ,grid.5596.f0000 0001 0668 7884Laboratory of Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Diether Lambrechts
- grid.5596.f0000 0001 0668 7884Center for Cancer Biology, VIB, Leuven, Belgium ,grid.5596.f0000 0001 0668 7884Laboratory of Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Petra ter Brugge
- grid.430814.aNetherlands Cancer Institute, Amsterdam, the Netherlands
| | - Violeta Serra
- grid.411083.f0000 0001 0675 8654Vall d´Hebron Institute of Oncology, Barcelona, Spain
| | - Elisabetta Marangoni
- grid.418596.70000 0004 0639 6384Department of Translational Research, Institut Curie, PSL Research University, Paris, France
| | - Rania El Botty
- grid.418596.70000 0004 0639 6384Department of Translational Research, Institut Curie, PSL Research University, Paris, France
| | - Hyunsoo Kim
- grid.249880.f0000 0004 0374 0039The Jackson Laboratory for Genomic Medicine, Farmington, CT USA
| | - Jong-Il Kim
- grid.31501.360000 0004 0470 5905College of Medicine, Seoul National University, Seoul, Republic of Korea
| | - Han-Kwang Yang
- grid.31501.360000 0004 0470 5905College of Medicine, Seoul National University, Seoul, Republic of Korea
| | - Charles Lee
- grid.249880.f0000 0004 0374 0039The Jackson Laboratory for Genomic Medicine, Farmington, CT USA ,grid.452438.cPrecision Medicine Center, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, People’s Republic of China ,grid.255649.90000 0001 2171 7754Department of Life Sciences, Ewha Womans University, Seoul, Republic of Korea
| | - Dennis A. Dean
- grid.492568.4Seven Bridges Genomics, Charlestown, MA USA
| | | | - Yvonne A. Evrard
- grid.418021.e0000 0004 0535 8394Frederick National Laboratory for Cancer Research, Frederick, MD USA
| | - James H. Doroshow
- grid.48336.3a0000 0004 1936 8075Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD USA
| | - Alana L. Welm
- grid.223827.e0000 0001 2193 0096Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT USA
| | - Bryan E. Welm
- grid.223827.e0000 0001 2193 0096Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT USA ,grid.223827.e0000 0001 2193 0096Department of Surgery, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT USA
| | - Michael T. Lewis
- grid.39382.330000 0001 2160 926XLester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX USA
| | - Bingliang Fang
- grid.240145.60000 0001 2291 4776Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Jack A. Roth
- grid.240145.60000 0001 2291 4776Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Funda Meric-Bernstam
- grid.240145.60000 0001 2291 4776Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Meenhard Herlyn
- grid.251075.40000 0001 1956 6678The Wistar Institute, Philadelphia, PA USA
| | - Michael A. Davies
- grid.240145.60000 0001 2291 4776Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Li Ding
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO USA
| | - Shunqiang Li
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO USA
| | - Ramaswamy Govindan
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO USA
| | - Claudio Isella
- grid.7605.40000 0001 2336 6580Department of Oncology, University of Turin, Turin, Italy ,grid.419555.90000 0004 1759 7675Candiolo Cancer Institute, FPO-IRCCS, Turin, Italy
| | - Jeffrey A. Moscow
- grid.48336.3a0000 0004 1936 8075Investigational Drug Branch, National Cancer Institute, Bethesda, MD USA
| | - Livio Trusolino
- grid.7605.40000 0001 2336 6580Department of Oncology, University of Turin, Turin, Italy ,grid.419555.90000 0004 1759 7675Candiolo Cancer Institute, FPO-IRCCS, Turin, Italy
| | - Annette T. Byrne
- grid.4912.e0000 0004 0488 7120Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Jos Jonkers
- grid.430814.aNetherlands Cancer Institute, Amsterdam, the Netherlands
| | - Carol J. Bult
- grid.249880.f0000 0004 0374 0039The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME USA
| | - Enzo Medico
- grid.7605.40000 0001 2336 6580Department of Oncology, University of Turin, Turin, Italy ,grid.419555.90000 0004 1759 7675Candiolo Cancer Institute, FPO-IRCCS, Turin, Italy
| | - Jeffrey H. Chuang
- grid.249880.f0000 0004 0374 0039The Jackson Laboratory for Genomic Medicine, Farmington, CT USA
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28
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Mateo L, Duran-Frigola M, Gris-Oliver A, Palafox M, Scaltriti M, Razavi P, Chandarlapaty S, Arribas J, Bellet M, Serra V, Aloy P. Personalized cancer therapy prioritization based on driver alteration co-occurrence patterns. Genome Med 2020; 12:78. [PMID: 32907621 PMCID: PMC7488324 DOI: 10.1186/s13073-020-00774-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 08/11/2020] [Indexed: 12/19/2022] Open
Abstract
Identification of actionable genomic vulnerabilities is key to precision oncology. Utilizing a large-scale drug screening in patient-derived xenografts, we uncover driver gene alteration connections, derive driver co-occurrence (DCO) networks, and relate these to drug sensitivity. Our collection of 53 drug-response predictors attains an average balanced accuracy of 58% in a cross-validation setting, rising to 66% for a subset of high-confidence predictions. We experimentally validated 12 out of 14 predictions in mice and adapted our strategy to obtain drug-response models from patients’ progression-free survival data. Our strategy reveals links between oncogenic alterations, increasing the clinical impact of genomic profiling.
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Affiliation(s)
- Lidia Mateo
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Miquel Duran-Frigola
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Albert Gris-Oliver
- Experimental Therapeutics Group, Vall d'Hebron Institute of Oncology, Barcelona, Catalonia, Spain
| | - Marta Palafox
- Experimental Therapeutics Group, Vall d'Hebron Institute of Oncology, Barcelona, Catalonia, Spain
| | - Maurizio Scaltriti
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center (MSKCC), New York, NY, 10065, USA.,Department of Pathology, MSKCC, New York, NY, 10065, USA
| | - Pedram Razavi
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center (MSKCC), New York, NY, 10065, USA.,Breast Medicine Service, Department of Medicine, MSKCC and Weill-Cornell Medical College, New York, NY, 10065, USA
| | - Sarat Chandarlapaty
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center (MSKCC), New York, NY, 10065, USA.,Breast Medicine Service, Department of Medicine, MSKCC and Weill-Cornell Medical College, New York, NY, 10065, USA
| | - Joaquin Arribas
- Growth Factors Laboratory, Vall d'Hebron Institute of Oncology, Barcelona, Catalonia, Spain.,Department of Biochemistry and Molecular Biology, Universitat Autònoma de Barcelona, Bellaterra, Catalonia, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.,CIBERONC, Barcelona, Spain
| | - Meritxell Bellet
- Breast Cancer Group, Vall d'Hebron Institute of Oncology, Barcelona, Catalonia, Spain.,Department of Medical Oncology, Hospital Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain
| | - Violeta Serra
- Experimental Therapeutics Group, Vall d'Hebron Institute of Oncology, Barcelona, Catalonia, Spain.,CIBERONC, Barcelona, Spain
| | - Patrick Aloy
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain. .,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.
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29
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Evrard YA, Srivastava A, Randjelovic J, Doroshow JH, Dean DA, Morris JS, Chuang JH. Systematic Establishment of Robustness and Standards in Patient-Derived Xenograft Experiments and Analysis. Cancer Res 2020; 80:2286-2297. [PMID: 32152150 PMCID: PMC7272270 DOI: 10.1158/0008-5472.can-19-3101] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 01/16/2020] [Accepted: 03/04/2020] [Indexed: 12/30/2022]
Abstract
Patient-derived xenografts (PDX) are tumor-in-mouse models for cancer. PDX collections, such as the NCI PDXNet, are powerful resources for preclinical therapeutic testing. However, variations in experimental and analysis procedures have limited interpretability. To determine the robustness of PDX studies, the PDXNet tested temozolomide drug response for three prevalidated PDX models (sensitive, resistant, and intermediate) across four blinded PDX Development and Trial Centers using independently selected standard operating procedures. Each PDTC was able to correctly identify the sensitive, resistant, and intermediate models, and statistical evaluations were concordant across all groups. We also developed and benchmarked optimized PDX informatics pipelines, and these yielded robust assessments across xenograft biological replicates. These studies show that PDX drug responses and sequence results are reproducible across diverse experimental protocols. In addition, we share the range of experimental procedures that maintained robustness, as well as standardized cloud-based workflows for PDX exome-sequencing and RNA-sequencing analyses and for evaluating growth. SIGNIFICANCE: The PDXNet Consortium shows that PDX drug responses and sequencing results are reproducible across diverse experimental protocols, establishing the potential for multisite preclinical studies to translate into clinical trials.
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Affiliation(s)
- Yvonne A Evrard
- Leidos Biomedical Research, Inc, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - Anuj Srivastava
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut
| | | | - James H Doroshow
- Division of Cancer Treatment and Diagnosis, NCI, NIH, Bethesda, Maryland
| | | | - Jeffrey S Morris
- The University of Texas M.D. Anderson Cancer Center, Houston, Texas
| | - Jeffrey H Chuang
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut.
- University of Connecticut Health Center, Farmington, Connecticut
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30
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Caroli J, Dori M, Bicciato S. Computational Methods for the Integrative Analysis of Genomics and Pharmacological Data. Front Oncol 2020; 10:185. [PMID: 32175273 PMCID: PMC7056894 DOI: 10.3389/fonc.2020.00185] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 02/03/2020] [Indexed: 01/22/2023] Open
Abstract
Since the pioneering NCI-60 panel of the late'80's, several major screenings of genetic profiling and drug testing in cancer cell lines have been conducted to investigate how genetic backgrounds and transcriptional patterns shape cancer's response to therapy and to identify disease-specific genes associated with drug response. Historically, pharmacogenomics screenings have been largely heterogeneous in terms of investigated cell lines, assay technologies, number of compounds, type and quality of genomic data, and methods for their computational analysis. The analysis of this enormous and heterogeneous amount of data required the development of computational methods for the integration of genomic profiles with drug responses across multiple screenings. Here, we will review the computational tools that have been developed to integrate cancer cell lines' genomic profiles and sensitivity to small molecule perturbations obtained from different screenings.
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Affiliation(s)
- Jimmy Caroli
- Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Martina Dori
- Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Silvio Bicciato
- Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy
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31
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Meehan TF. Know Thy PDX Model. Cancer Res 2019; 79:4324-4325. [PMID: 31481418 DOI: 10.1158/0008-5472.can-19-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Accepted: 07/02/2019] [Indexed: 11/16/2022]
Abstract
Patient-derived tumor xenograft (PDX) models are frequently used to study cancer mechanisms and potential therapeutics, however, differences in tumor evolution between models and patients have called into question their clinical relevance. In this issue, Mer and colleagues describe the Xenograft Visualization and Analysis (Xeva) software tool that empowers pharmacogenomic analysis through integration of PDX model tumor-drug response with genetic data. By performing the largest PDX model meta-analysis of its kind, the authors demonstrate PDX models are robust platforms for cancer treatment studies. With a clear need for more integrative studies, Xeva is well placed to make more important contributions to pharmacogenomic discovery.See related article by Mer et al., p. 4539.
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Affiliation(s)
- Terrence F Meehan
- Mouse Informatics Coordinator, European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom.
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