1
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Cocco E, de Stanchina E. Patient-Derived-Xenografts in Mice: A Preclinical Platform for Cancer Research. Cold Spring Harb Perspect Med 2024; 14:a041381. [PMID: 37696659 PMCID: PMC11216185 DOI: 10.1101/cshperspect.a041381] [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] [Indexed: 09/13/2023]
Abstract
The use of patient-derived xenografts (PDXs) has dramatically improved drug development programs. PDXs (1) reproduce the pathological features and the genomic profile of the parental tumors more precisely than other preclinical models, and (2) more faithfully predict therapy response. However, PDXs have limitations. These include the inability to completely capture tumor heterogeneity and the role of the immune system, the low engraftment efficiency of certain tumor types, and the consequences of the human-host interactions. Recently, the use of novel mouse strains and specialized engraftment techniques has enabled the generation of "humanized" PDXs, partially overcoming such limitations. Importantly, establishing, characterizing, and maintaining PDXs is costly and requires a significant regulatory, administrative, clinical, and laboratory infrastructure. In this review, we will retrace the historical milestones that led to the implementation of PDXs for cancer research, review the most recent innovations in the field, and discuss future avenues to tackle deficiencies that still exist.
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Affiliation(s)
- Emiliano Cocco
- University of Miami, Miller School of Medicine, Department of Biochemistry and Molecular Biology, Sylvester Comprehensive Cancer Center, Miami, Florida 33136, USA
| | - Elisa de Stanchina
- Antitumor Assessment Core Facility, Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA
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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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Liu L, Wu M, Huang A, Gao C, Yang Y, Liu H, Jiang H, Yu L, Huang Y, Wang H. Establishment of a high-fidelity patient-derived xenograft model for cervical cancer enables the evaluation of patient's response to conventional and novel therapies. J Transl Med 2023; 21:611. [PMID: 37689699 PMCID: PMC10492358 DOI: 10.1186/s12967-023-04444-5] [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: 06/08/2023] [Accepted: 08/16/2023] [Indexed: 09/11/2023] Open
Abstract
BACKGROUND Recurrent or metastatic cervical cancer (r/m CC) often has poor prognosis owing to its limited treatment options. The development of novel therapeutic strategies has been hindered by the lack of preclinical models that accurately reflect the biological and genomic heterogeneity of cervical cancer (CC). Herein, we aimed to establish a large patient-derived xenograft (PDX) biobank for CC, evaluate the consistency of the biologic indicators between PDX and primary tumor tissues of patients, and explore its utility for assessing patient's response to conventional and novel therapies. METHODS Sixty-nine fresh CC tumor tissues were implanted directly into immunodeficient mice to establish PDX models. The concordance of the PDX models with their corresponding primary tumors (PTs) was compared based on the clinical pathological features, protein biomarker levels, and genomic features through hematoxylin & eosin staining, immunohistochemistry, and whole exome sequencing, respectively. Moreover, the clinical information of CC patients, RNA transcriptome and immune phenotyping of primary tumors were integrated to identify the potential parameters that could affect the success of xenograft engraftment. Subsequently, PDX model was evaluated for its capacity to mirror patient's response to chemotherapy. Finally, PDX model and PDX-derived organoid (PDXO) were utilized to evaluate the therapeutic efficacy of neratinib and adoptive cell therapy (ACT) combination strategy for CC patients with human epidermal growth factor receptor 2 (HER2) mutation. RESULTS We established a PDX biobank for CC with a success rate of 63.8% (44/69). The primary features of established PDX tumors, including clinicopathological features, the expression levels of protein biomarkers including Ki67, α-smooth muscle actin, and p16, and genomics, were highly consistent with their PTs. Furthermore, xenograft engraftment was likely influenced by the primary tumor size, the presence of follicular helper T cells and the expression of cell adhesion-related genes in primary tumor tissue. The CC derived PDX models were capable of recapitulating the patient's response to chemotherapy. In a PDX model, a novel therapeutic strategy, the combination of ACT and neratinib, was shown to effectively inhibit the growth of PDX tumors derived from CC patients with HER2-mutation. CONCLUSIONS We established by far the largest PDX biobank with a high engraftment rate for CC that preserves the histopathological and genetic characteristics of patient's biopsy samples, recapitulates patient's response to conventional therapy, and is capable of evaluating the efficacy of novel therapeutic modalities for CC.
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Affiliation(s)
- Liting Liu
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Min Wu
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Anni Huang
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chun Gao
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yifan Yang
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hong Liu
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Han Jiang
- Department of Pathogen Biology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Long Yu
- Department of Pathogen Biology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yafei Huang
- Department of Pathogen Biology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Hui Wang
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
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5
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Kayser C, Brauer A, Susanne S, Wandmacher AM. The challenge of making the right choice: patient avatars in the era of cancer immunotherapies. Front Immunol 2023; 14:1237565. [PMID: 37638045 PMCID: PMC10449253 DOI: 10.3389/fimmu.2023.1237565] [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: 06/09/2023] [Accepted: 07/24/2023] [Indexed: 08/29/2023] Open
Abstract
Immunotherapies are a key therapeutic strategy to fight cancer. Diverse approaches are used to activate tumor-directed immunity and to overcome tumor immune escape. The dynamic interplay between tumor cells and their tumor(immune)microenvironment (T(I)ME) poses a major challenge to create appropriate model systems. However, those model systems are needed to gain novel insights into tumor (immune) biology and a prerequisite to accurately develop and test immunotherapeutic approaches which can be successfully translated into clinical application. Several model systems have been established and advanced into so-called patient avatars to mimic the patient´s tumor biology. All models have their advantages but also disadvantages underscoring the necessity to pay attention in defining the rationale and requirements for which the patient avatar will be used. Here, we briefly outline the current state of tumor model systems used for tumor (immune)biological analysis as well as evaluation of immunotherapeutic agents. Finally, we provide a recommendation for further development to make patient avatars a complementary tool for testing and predicting immunotherapeutic strategies for personalization of tumor therapies.
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Affiliation(s)
- Charlotte Kayser
- Group of Inflammatory Carcinogenesis, Institute for Experimental Cancer Research, University Hospital Schleswig-Holstein (UKSH), Kiel University, Kiel, Germany
| | - Annika Brauer
- Group of Inflammatory Carcinogenesis, Institute for Experimental Cancer Research, University Hospital Schleswig-Holstein (UKSH), Kiel University, Kiel, Germany
| | - Sebens Susanne
- Group of Inflammatory Carcinogenesis, Institute for Experimental Cancer Research, University Hospital Schleswig-Holstein (UKSH), Kiel University, Kiel, Germany
| | - Anna Maxi Wandmacher
- Group of Inflammatory Carcinogenesis, Institute for Experimental Cancer Research, University Hospital Schleswig-Holstein (UKSH), Kiel University, Kiel, Germany
- Department of Internal Medicine II, University Hospital Center Schleswig-Holstein, Kiel, Germany
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6
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Chang TG, Cao Y, Shulman ED, Ben-David U, Schäffer AA, Ruppin E. Optimizing cancer immunotherapy response prediction by tumor aneuploidy score and fraction of copy number alterations. NPJ Precis Oncol 2023; 7:54. [PMID: 37270587 DOI: 10.1038/s41698-023-00408-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 05/25/2023] [Indexed: 06/05/2023] Open
Abstract
Identifying patients that are likely to respond to cancer immunotherapy is an important, yet highly challenging clinical need. Using 3139 patients across 17 different cancer types, we comprehensively studied the ability of two common copy-number alteration (CNA) scores-the tumor aneuploidy score (AS) and the fraction of genome single nucleotide polymorphism encompassed by copy-number alterations (FGA)-to predict survival following immunotherapy in both pan-cancer and individual cancer types. First, we show that choice of cutoff during CNA calling significantly influences the predictive power of AS and FGA for patient survival following immunotherapy. Remarkably, by using proper cutoff during CNA calling, AS and FGA can predict pan-cancer survival following immunotherapy for both high-TMB and low-TMB patients. However, at the individual cancer level, our data suggest that the use of AS and FGA for predicting immunotherapy response is currently limited to only a few cancer types. Therefore, larger sample sizes are needed to evaluate the clinical utility of these measures for patient stratification in other cancer types. Finally, we propose a simple, non-parameterized, elbow-point-based method to help determine the cutoff used for calling CNAs.
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Affiliation(s)
- Tian-Gen Chang
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, 20892, USA.
| | - Yingying Cao
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, 20892, USA
| | - Eldad D Shulman
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, 20892, USA
| | - Uri Ben-David
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Alejandro A Schäffer
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, 20892, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, 20892, USA.
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7
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Morikawa A, Li J, Ulintz P, Cheng X, Apfel A, Robinson D, Hopkins A, Kumar-Sinha C, Wu YM, Serhan H, Verbal K, Thomas D, Hayes DF, Chinnaiyan AM, Baladandayuthapani V, Heth J, Soellner MB, Merajver SD, Merrill N. Optimizing Precision Medicine for Breast Cancer Brain Metastases with Functional Drug Response Assessment. CANCER RESEARCH COMMUNICATIONS 2023; 3:1093-1103. [PMID: 37377606 PMCID: PMC10284082 DOI: 10.1158/2767-9764.crc-22-0492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 05/04/2023] [Accepted: 05/25/2023] [Indexed: 06/29/2023]
Abstract
The development of novel therapies for brain metastases is an unmet need. Brain metastases may have unique molecular features that could be explored as therapeutic targets. A better understanding of the drug sensitivity of live cells coupled to molecular analyses will lead to a rational prioritization of therapeutic candidates. We evaluated the molecular profiles of 12 breast cancer brain metastases (BCBM) and matched primary breast tumors to identify potential therapeutic targets. We established six novel patient-derived xenograft (PDX) from BCBM from patients undergoing clinically indicated surgical resection of BCBM and used the PDXs as a drug screening platform to interrogate potential molecular targets. Many of the alterations were conserved in brain metastases compared with the matched primary. We observed differential expressions in the immune-related and metabolism pathways. The PDXs from BCBM captured the potentially targetable molecular alterations in the source brain metastases tumor. The alterations in the PI3K pathway were the most predictive for drug efficacy in the PDXs. The PDXs were also treated with a panel of over 350 drugs and demonstrated high sensitivity to histone deacetylase and proteasome inhibitors. Our study revealed significant differences between the paired BCBM and primary breast tumors with the pathways involved in metabolisms and immune functions. While molecular targeted drug therapy based on genomic profiling of tumors is currently evaluated in clinical trials for patients with brain metastases, a functional precision medicine strategy may complement such an approach by expanding potential therapeutic options, even for BCBM without known targetable molecular alterations. Significance Examining genomic alterations and differentially expressed pathways in brain metastases may inform future therapeutic strategies. This study supports genomically-guided therapy for BCBM and further investigation into incorporating real-time functional evaluation will increase confidence in efficacy estimations during drug development and predictive biomarker assessment for BCBM.
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Affiliation(s)
- Aki Morikawa
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Jinju Li
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | - Peter Ulintz
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Xu Cheng
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Athena Apfel
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Dan Robinson
- Department of Pathology, University of Michigan, Ann Arbor, Michigan
| | - Alex Hopkins
- Department of Pathology, University of Michigan, Ann Arbor, Michigan
| | | | - Yi-Mi Wu
- Department of Pathology, University of Michigan, Ann Arbor, Michigan
| | - Habib Serhan
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Kait Verbal
- Department of Neurosurgery, University of Michigan, Ann Arbor, Michigan
| | - Dafydd Thomas
- Department of Pathology, University of Michigan, Ann Arbor, Michigan
| | - Daniel F. Hayes
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | | | | | - Jason Heth
- Department of Neurosurgery, University of Michigan, Ann Arbor, Michigan
| | | | - Sofia D. Merajver
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Nathan Merrill
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
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8
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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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9
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Devis‐Jauregui L, Vidal A, Plata‐Peña L, Santacana M, García‐Mulero S, Bonifaci N, Noguera‐Delgado E, Ruiz N, Gil M, Dorca E, Llobet FJ, Coll‐Iglesias L, Gassner K, Martinez‐Iniesta M, Rodriguez‐Barrueco R, Barahona M, Marti L, Viñals F, Ponce J, Sanz‐Pamplona R, Piulats JM, Vivancos A, Matias‐Guiu X, Villanueva A, Llobet‐Navas D. Generation and Integrated Analysis of Advanced Patient-Derived Orthoxenograft Models (PDOX) for the Rational Assessment of Targeted Therapies in Endometrial Cancer. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 10:e2204211. [PMID: 36373729 PMCID: PMC9811454 DOI: 10.1002/advs.202204211] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 10/18/2022] [Indexed: 05/19/2023]
Abstract
Clinical management of endometrial cancer (EC) is handicapped by the limited availability of second line treatments and bona fide molecular biomarkers to predict recurrence. These limitations have hampered the treatment of these patients, whose survival rates have not improved over the last four decades. The advent of coordinated studies such as The Cancer Genome Atlas Uterine Corpus Endometrial Carcinoma (TCGA_UCEC) has partially solved this issue, but the lack of proper experimental systems still represents a bottleneck that precludes translational studies from successful clinical testing in EC patients. Within this context, the first study reporting the generation of a collection of endometrioid-EC-patient-derived orthoxenograft (PDOX) mouse models is presented that is believed to overcome these experimental constraints and pave the way toward state-of-the-art precision medicine in EC. The collection of primary tumors and derived PDOXs is characterized through an integrative approach based on transcriptomics, mutational profiles, and morphological analysis; and it is demonstrated that EC tumors engrafted in the mouse uterus retain the main molecular and morphological features from analogous tumor donors. Finally, the molecular properties of these tumors are harnessed to assess the therapeutic potential of trastuzumab, a human epidermal growth factor receptor 2 (HER2) inhibitor with growing interest in EC, using patient-derived organotypic multicellular tumor spheroids and in vivo experiments.
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van den Bosch T, Derks S, Miedema DM. Chromosomal Instability, Selection and Competition: Factors That Shape the Level of Karyotype Intra-Tumor Heterogeneity. Cancers (Basel) 2022; 14:cancers14204986. [PMID: 36291770 PMCID: PMC9600040 DOI: 10.3390/cancers14204986] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 10/07/2022] [Accepted: 10/09/2022] [Indexed: 12/03/2022] Open
Abstract
Simple Summary Each cancer consists of billions of cells. These cells are far from identical; hence, the population of cells that constitute a tumor is heterogeneous. A salient property that varies between cells in a tumor is their karyotype, the number and configuration of the chromosomes. The level of karyotype heterogeneity can be used to predict the survival of a patient. In this review, we describe the processes that shape the level of karyotype heterogeneity in a cancer. Abstract Intra-tumor heterogeneity (ITH) is a pan-cancer predictor of survival, with high ITH being correlated to a dismal prognosis. The level of ITH is, hence, a clinically relevant characteristic of a malignancy. ITH of karyotypes is driven by chromosomal instability (CIN). However, not all new karyotypes generated by CIN are viable or competitive, which limits the amount of ITH. Here, we review the cellular processes and ecological properties that determine karyotype ITH. We propose a framework to understand karyotype ITH, in which cells with new karyotypes emerge through CIN, are selected by cell intrinsic and cell extrinsic selective pressures, and propagate through a cancer in competition with other malignant cells. We further discuss how CIN modulates the cell phenotype and immune microenvironment, and the implications this has for the subsequent selection of karyotypes. Together, we aim to provide a comprehensive overview of the biological processes that shape the level of karyotype heterogeneity.
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Affiliation(s)
- Tom van den Bosch
- Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam and Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centers—Location AMC, 1105 AZ Amsterdam, The Netherlands
- Oncode Institute, 1105 AZ Amsterdam, The Netherlands
| | - Sarah Derks
- Oncode Institute, 1105 AZ Amsterdam, The Netherlands
- Department of Medical Oncology, Amsterdam University Medical Centers—Location VUmc, 1081 HV Amsterdam, The Netherlands
- Correspondence: (S.D.); (D.M.M.)
| | - Daniël M. Miedema
- Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam and Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centers—Location AMC, 1105 AZ Amsterdam, The Netherlands
- Oncode Institute, 1105 AZ Amsterdam, The Netherlands
- Correspondence: (S.D.); (D.M.M.)
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Zanella ER, Grassi E, Trusolino L. Towards precision oncology with patient-derived xenografts. Nat Rev Clin Oncol 2022; 19:719-732. [PMID: 36151307 DOI: 10.1038/s41571-022-00682-6] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/17/2022] [Indexed: 11/09/2022]
Abstract
Under the selective pressure of therapy, tumours dynamically evolve multiple adaptive mechanisms that make static interrogation of genomic alterations insufficient to guide treatment decisions. Clinical research does not enable the assessment of how various regulatory circuits in tumours are affected by therapeutic insults over time and space. Likewise, testing different precision oncology approaches informed by composite and ever-changing molecular information is hard to achieve in patients. Therefore, preclinical models that incorporate the biology and genetics of human cancers, facilitate analyses of complex variables and enable adequate population throughput are needed to pinpoint randomly distributed response predictors. Patient-derived xenograft (PDX) models are dynamic entities in which cancer evolution can be monitored through serial propagation in mice. PDX models can also recapitulate interpatient diversity, thus enabling the identification of response biomarkers and therapeutic targets for molecularly defined tumour subgroups. In this Review, we discuss examples from the past decade of the use of PDX models for precision oncology, from translational research to drug discovery. We elaborate on how and to what extent preclinical observations in PDX models have confirmed and/or anticipated findings in patients. Finally, we illustrate emerging methodological efforts that could broaden the application of PDX models by honing their predictive accuracy or improving their versatility.
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Affiliation(s)
| | - Elena Grassi
- Candiolo Cancer Institute - FPO IRCCS, Candiolo, Italy.,Department of Oncology, University of Torino, Candiolo, Italy
| | - Livio Trusolino
- Candiolo Cancer Institute - FPO IRCCS, Candiolo, Italy. .,Department of Oncology, University of Torino, Candiolo, Italy.
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