1
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Gürgen D, Becker M, Dahlmann M, Flechsig S, Schaeffeler E, Büttner FA, Schmees C, Bohnert R, Bedke J, Schwab M, Wendler JJ, Schostak M, Jandrig B, Walther W, Hoffmann J. A Molecularly Characterized Preclinical Platform of Subcutaneous Renal Cell Carcinoma (RCC) Patient-Derived Xenograft Models to Evaluate Novel Treatment Strategies. Front Oncol 2022; 12:889789. [PMID: 35800063 PMCID: PMC9254864 DOI: 10.3389/fonc.2022.889789] [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: 03/04/2022] [Accepted: 04/26/2022] [Indexed: 11/13/2022] Open
Abstract
Renal cell carcinoma (RCC) is a kidney cancer with an onset mainly during the sixth or seventh decade of the patient’s life. Patients with advanced, metastasized RCC have a poor prognosis. The majority of patients develop treatment resistance towards Standard of Care (SoC) drugs within months. Tyrosine kinase inhibitors (TKIs) are the backbone of first-line therapy and have been partnered with an immune checkpoint inhibitor (ICI) recently. Despite the most recent progress, the development of novel therapies targeting acquired TKI resistance mechanisms in advanced and metastatic RCC remains a high medical need. Preclinical models with high translational relevance can significantly support the development of novel personalized therapies. It has been demonstrated that patient-derived xenograft (PDX) models represent an essential tool for the preclinical evaluation of novel targeted therapies and their combinations. In the present project, we established and molecularly characterized a comprehensive panel of subcutaneous RCC PDX models with well-conserved molecular and pathological features over multiple passages. Drug screening towards four SoC drugs targeting the vascular endothelial growth factor (VEGF) and PI3K/mTOR pathway revealed individual and heterogeneous response profiles in those models, very similar to observations in patients. As unique features, our cohort includes PDX models from metastatic disease and multi-tumor regions from one patient, allowing extended studies on intra-tumor heterogeneity (ITH). The PDX models are further used as basis for developing corresponding in vitro cell culture models enabling advanced high-throughput drug screening in a personalized context. PDX models were subjected to next-generation sequencing (NGS). Characterization of cancer-relevant features including driver mutations or cellular processes was performed using mutational and gene expression data in order to identify potential biomarker or treatment targets in RCC. In summary, we report a newly established and molecularly characterized panel of RCC PDX models with high relevance for translational preclinical research.
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Affiliation(s)
- Dennis Gürgen
- Experimental Pharmacology and Oncology Berlin-Buch GmbH (EPO), Berlin, Germany
- *Correspondence: Dennis Gürgen, ; orcid.org/0000-0001-9241-6537
| | - Michael Becker
- Experimental Pharmacology and Oncology Berlin-Buch GmbH (EPO), Berlin, Germany
| | - Mathias Dahlmann
- Experimental Pharmacology and Oncology Berlin-Buch GmbH (EPO), Berlin, Germany
| | - Susanne Flechsig
- Experimental Pharmacology and Oncology Berlin-Buch GmbH (EPO), Berlin, Germany
| | - Elke Schaeffeler
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
- Cluster of Excellence iFIT (EXC 2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK), Partner Site Tübingen, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Florian A. Büttner
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
| | - Christian Schmees
- Natural and Medical Sciences Institute (NMI) at the University of Tübingen, Reutlingen, Germany
| | - Regina Bohnert
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
| | - Jens Bedke
- German Cancer Consortium (DKTK), Partner Site Tübingen, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Urology, University Hospital Tübingen, Tübingen, Germany
| | - Matthias Schwab
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
- Cluster of Excellence iFIT (EXC 2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK), Partner Site Tübingen, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Departments of Clinical Pharmacology, and Pharmacy and Biochemistry, University of Tübingen, Tübingen, Germany
| | - Johann J. Wendler
- Department of Urology, University Medical Center Magdeburg, Magdeburg, Germany
| | - Martin Schostak
- Department of Urology, University Medical Center Magdeburg, Magdeburg, Germany
| | - Burkhard Jandrig
- Department of Urology, University Medical Center Magdeburg, Magdeburg, Germany
| | - Wolfgang Walther
- Experimental Pharmacology and Oncology Berlin-Buch GmbH (EPO), Berlin, Germany
- Experimental and Clinical Research Center (ECRC) Charité Universitätsmedizin Berlin, Berlin, Germany
- Max-Delbrück-Centrum für Molekulare Medizin (MDC), Berlin, Germany
| | - Jens Hoffmann
- Experimental Pharmacology and Oncology Berlin-Buch GmbH (EPO), Berlin, Germany
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2
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Oswald E, Bug D, Grote A, Lashuk K, Bouteldja N, Lenhard D, Löhr A, Behnke A, Knauff V, Edinger A, Klingner K, Gaedicke S, Niedermann G, Merhof D, Feuerhake F, Schueler J. Immune cell infiltration pattern in non-small cell lung cancer PDX models is a model immanent feature and correlates with a distinct molecular and phenotypic make-up. J Immunother Cancer 2022; 10:jitc-2021-004412. [PMID: 35483746 PMCID: PMC9052060 DOI: 10.1136/jitc-2021-004412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2022] [Indexed: 11/04/2022] Open
Abstract
BACKGROUND The field of cancer immunology is rapidly moving towards innovative therapeutic strategies, resulting in the need for robust and predictive preclinical platforms reflecting the immunological response to cancer. Well characterized preclinical models are essential for the development of predictive biomarkers in the oncology as well as the immune-oncology space. In the current study, gold standard preclinical models are being refined and combined with novel image analysis tools to meet those requirements. METHODS A panel of 14 non-small cell lung cancer patient-derived xenograft models (NSCLC PDX) was propagated in humanized NOD/Shi-scid/IL-2Rnull mice. The models were comprehensively characterized for relevant phenotypic and molecular features, including flow cytometry, immunohistochemistry, histology, whole exome sequencing and cytokine secretion. RESULTS Models reflecting hot (>5% tumor-infiltrating lymphocytes/TILs) as opposed to cold tumors (<5% TILs) significantly differed regarding their cytokine profiles, molecular genetic aberrations, stroma content, and programmed cell death ligand-1 status. Treatment experiments including anti cytotoxic T-lymphocyte-associated protein 4, anti-programmed cell death 1 or the combination thereof across all 14 models in the single mouse trial format showed distinctive tumor growth response and spatial immune cell patterns as monitored by computerized analysis of digitized whole-slide images. Image analysis provided for the first time qualitative evaluation of the extent to which PDX models retain the histological features from their original human donors. CONCLUSIONS Deep phenotyping of PDX models in a humanized setting by combinations of computational pathology, immunohistochemistry, flow cytometry and proteomics enables the exhaustive analysis of innovative preclinical models and paves the way towards the development of translational biomarkers for immuno-oncology drugs.
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Affiliation(s)
- Eva Oswald
- Charles River Discovery Research Services Gemany GmbH, Charles River Laboratories Inc, Freiburg, Germany
| | - Daniel Bug
- Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany
| | - Anne Grote
- Department of Pathology, Hannover Medical School, Hannover, Germany
| | - Kanstantsin Lashuk
- Charles River Discovery Research Services Gemany GmbH, Charles River Laboratories Inc, Freiburg, Germany
| | - Nassim Bouteldja
- Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany
| | - Dorothee Lenhard
- Charles River Discovery Research Services Gemany GmbH, Charles River Laboratories Inc, Freiburg, Germany
| | - Anne Löhr
- Charles River Discovery Research Services Gemany GmbH, Charles River Laboratories Inc, Freiburg, Germany
| | - Anke Behnke
- Charles River Discovery Research Services Gemany GmbH, Charles River Laboratories Inc, Freiburg, Germany
| | - Volker Knauff
- Charles River Discovery Research Services Gemany GmbH, Charles River Laboratories Inc, Freiburg, Germany
| | - Anna Edinger
- Charles River Discovery Research Services Gemany GmbH, Charles River Laboratories Inc, Freiburg, Germany
| | - Kerstin Klingner
- Charles River Discovery Research Services Gemany GmbH, Charles River Laboratories Inc, Freiburg, Germany
| | - Simone Gaedicke
- Department of Radiation Oncology, Medical Center-University of Freiburg, Freiburg, Germany
| | - Gabriele Niedermann
- Department of Radiation Oncology, Medical Center-University of Freiburg, Freiburg, Germany.,German Cancer Consortium, Heidelberg, Germany
| | - Dorit Merhof
- Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany
| | | | - Julia Schueler
- Charles River Discovery Research Services Gemany GmbH, Charles River Laboratories Inc, Freiburg, Germany
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3
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Ali Z, Vildevall M, Rodriguez GV, Tandiono D, Vamvakaris I, Evangelou G, Lolas G, Syrigos KN, Villanueva A, Wick M, Omar S, Erkstam A, Schueler J, Fahlgren A, Jensen LD. Zebrafish patient-derived xenograft models predict lymph node involvement and treatment outcome in non-small cell lung cancer. J Exp Clin Cancer Res 2022; 41:58. [PMID: 35139880 PMCID: PMC8827197 DOI: 10.1186/s13046-022-02280-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 01/31/2022] [Indexed: 11/25/2022] Open
Abstract
Background Accurate predictions of tumor dissemination risks and medical treatment outcomes are critical to personalize therapy. Patient-derived xenograft (PDX) models in mice have demonstrated high accuracy in predicting therapeutic outcomes, but methods for predicting tumor invasiveness and early stages of vascular/lymphatic dissemination are still lacking. Here we show that a zebrafish tumor xenograft (ZTX) platform based on implantation of PDX tissue fragments recapitulate both treatment outcome and tumor invasiveness/dissemination in patients, within an assay time of only 3 days. Methods Using a panel of 39 non-small cell lung cancer PDX models, we developed a combined mouse-zebrafish PDX platform based on direct implantation of cryopreserved PDX tissue fragments into zebrafish embryos, without the need for pre-culturing or expansion. Clinical proof-of-principle was established by direct implantation of tumor samples from four patients. Results The resulting ZTX models responded to Erlotinib and Paclitaxel, with similar potency as in mouse-PDX models and the patients themselves, and resistant tumors similarly failed to respond to these drugs in the ZTX system. Drug response was coupled to elevated expression of EGFR, Mdm2, Ptch1 and Tsc1 (Erlotinib), or Nras and Ptch1 (Paclitaxel) and reduced expression of Egfr, Erbb2 and Foxa (Paclitaxel). Importantly, ZTX models retained the invasive phenotypes of the tumors and predicted lymph node involvement of the patients with 91% sensitivity and 62% specificity, which was superior to clinically used tests. The biopsies from all four patient tested implanted successfully, and treatment outcome and dissemination were quantified for all patients in only 3 days. Conclusions We conclude that the ZTX platform provide a fast, accurate, and clinically relevant system for evaluation of treatment outcome and invasion/dissemination of PDX models, providing an attractive platform for combined mouse-zebrafish PDX trials and personalized medicine. Supplementary Information The online version contains supplementary material available at 10.1186/s13046-022-02280-x.
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Affiliation(s)
| | | | | | | | | | - Georgios Evangelou
- 3rd Department of Internal Medicine and Laboratory, National & Kapodistrian University of Athens, Athens, Greece
| | - Georgios Lolas
- 3rd Department of Internal Medicine and Laboratory, National & Kapodistrian University of Athens, Athens, Greece.,InCELLiA P.C, Athens, Greece
| | - Konstantinos N Syrigos
- 3rd Department of Internal Medicine and Laboratory, National & Kapodistrian University of Athens, Athens, Greece
| | - Alberto Villanueva
- Program Against Cancer Therapeutic Resistance (ProCURE), Catalan Institute of Oncology (ICO), Bellvitge Institute for Biomedical Research (IDIBELL), Oncobell Program, L'Hospitalet del Llobregat, Barcelona, Catalonia, Spain.,Xenopat S.L., Parc Cientific de Barcelona (PCB), Barcelona, Spain
| | | | - Shenga Omar
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Campus US, Entrance 68, Pl. 08, SE-58185, Linköping, Sweden
| | | | | | - Anna Fahlgren
- BioReperia AB, Linköping, Sweden.,Division of Cell Biology, Department of Biomedical and Clinical Sciences, Linköping University, Linöping, Sweden
| | - Lasse D Jensen
- BioReperia AB, Linköping, Sweden. .,Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Campus US, Entrance 68, Pl. 08, SE-58185, Linköping, Sweden.
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4
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Cooley LS, Rudewicz J, Souleyreau W, Emanuelli A, Alvarez-Arenas A, Clarke K, Falciani F, Dufies M, Lambrechts D, Modave E, Chalopin-Fillot D, Pineau R, Ambrosetti D, Bernhard JC, Ravaud A, Négrier S, Ferrero JM, Pagès G, Benzekry S, Nikolski M, Bikfalvi A. Experimental and computational modeling for signature and biomarker discovery of renal cell carcinoma progression. Mol Cancer 2021; 20:136. [PMID: 34670568 PMCID: PMC8527701 DOI: 10.1186/s12943-021-01416-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 08/30/2021] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Renal Cell Carcinoma (RCC) is difficult to treat with 5-year survival rate of 10% in metastatic patients. Main reasons of therapy failure are lack of validated biomarkers and scarce knowledge of the biological processes occurring during RCC progression. Thus, the investigation of mechanisms regulating RCC progression is fundamental to improve RCC therapy. METHODS In order to identify molecular markers and gene processes involved in the steps of RCC progression, we generated several cell lines of higher aggressiveness by serially passaging mouse renal cancer RENCA cells in mice and, concomitantly, performed functional genomics analysis of the cells. Multiple cell lines depicting the major steps of tumor progression (including primary tumor growth, survival in the blood circulation and metastatic spread) were generated and analyzed by large-scale transcriptome, genome and methylome analyses. Furthermore, we performed clinical correlations of our datasets. Finally we conducted a computational analysis for predicting the time to relapse based on our molecular data. RESULTS Through in vivo passaging, RENCA cells showed increased aggressiveness by reducing mice survival, enhancing primary tumor growth and lung metastases formation. In addition, transcriptome and methylome analyses showed distinct clustering of the cell lines without genomic variation. Distinct signatures of tumor aggressiveness were revealed and validated in different patient cohorts. In particular, we identified SAA2 and CFB as soluble prognostic and predictive biomarkers of the therapeutic response. Machine learning and mathematical modeling confirmed the importance of CFB and SAA2 together, which had the highest impact on distant metastasis-free survival. From these data sets, a computational model predicting tumor progression and relapse was developed and validated. These results are of great translational significance. CONCLUSION A combination of experimental and mathematical modeling was able to generate meaningful data for the prediction of the clinical evolution of RCC.
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Affiliation(s)
- Lindsay S Cooley
- University of Bordeaux, LAMC, Pessac, France
- INSERM U1029, Pessac, France
| | - Justine Rudewicz
- University of Bordeaux, LAMC, Pessac, France
- INSERM U1029, Pessac, France
- Bordeaux Bioinformatics Center, CBiB, University of Bordeaux, Bordeaux, France
| | | | - Andrea Emanuelli
- University of Bordeaux, LAMC, Pessac, France
- INSERM U1029, Pessac, France
| | - Arturo Alvarez-Arenas
- Mathematical Modeling for Oncology Team, Inria Bordeaux Sud-Ouest, Talence, France
- Department of Mathematics, Mathematical Oncology Laboratory (MOLAB), Universidad de Castilla-La Mancha, Ciudad Real, Spain
| | - Kim Clarke
- University of Liverpool, Institute of Systems, Molecular and Integrative Biology, Liverpool, UK
| | - Francesco Falciani
- University of Liverpool, Institute of Systems, Molecular and Integrative Biology, Liverpool, UK
| | - Maeva Dufies
- Centre Scientifique de Monaco, Biomedical Department, Principality of Monaco, Monaco
- University Côte d'Azur, Institute for Research on Cancer and Aging of Nice (IRCAN), CNRS UMR 7284; INSERM U1081, Centre Antoine Lacassagne, Nice, France
| | | | - Elodie Modave
- VIB-KU Leuven Center for Cancer Biology, Leuven, Belgium
| | - Domitille Chalopin-Fillot
- Bordeaux Bioinformatics Center, CBiB, University of Bordeaux, Bordeaux, France
- University of Bordeaux, IBGC, Bordeaux, France
| | - Raphael Pineau
- University of Bordeaux, "Service Commun des Animaleries", Bordeaux, France
| | - Damien Ambrosetti
- Centre Hospitalier Universitaire (CHU) de Nice, Hôpital Pasteur, Central laboratory of Pathology, Nice, France
| | | | - Alain Ravaud
- Centre Hospitalier Universitaire (CHU) de Bordeaux, service d'oncologie médicale, Bordeaux, France
| | | | - Jean-Marc Ferrero
- Centre Antoine Lacassagne, Clinical Research Department, Nice, France
| | - Gilles Pagès
- Centre Scientifique de Monaco, Biomedical Department, Principality of Monaco, Monaco
- University Côte d'Azur, Institute for Research on Cancer and Aging of Nice (IRCAN), CNRS UMR 7284; INSERM U1081, Centre Antoine Lacassagne, Nice, France
| | - Sebastien Benzekry
- Mathematical Modeling for Oncology Team, Inria Bordeaux Sud-Ouest, Talence, France
- COMPO team-project, Inria Sophia Antipolis and CRCM, Inserm U1068, CNRS UMR7258, Aix-Marseille University UM105, Institut Paoli-Calmettes, Marseille, France
| | - Macha Nikolski
- Bordeaux Bioinformatics Center, CBiB, University of Bordeaux, Bordeaux, France
- University of Bordeaux, IBGC, Bordeaux, France
| | - Andreas Bikfalvi
- University of Bordeaux, LAMC, Pessac, France.
- INSERM U1029, Pessac, France.
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5
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Tracey AT, Murray KS, Coleman JA, Kim K. Patient-Derived Xenograft Models in Urological Malignancies: Urothelial Cell Carcinoma and Renal Cell Carcinoma. Cancers (Basel) 2020; 12:cancers12020439. [PMID: 32069881 PMCID: PMC7072311 DOI: 10.3390/cancers12020439] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Revised: 02/03/2020] [Accepted: 02/10/2020] [Indexed: 12/17/2022] Open
Abstract
The engraftment of human tumor tissues into immunodeficient host mice to generate patient-derived xenograft (PDX) models has become increasingly utilized for many types of cancers. By capturing the unique genomic and molecular properties of the parental tumor, PDX models enable analysis of patient-specific clinical responses. PDX models are an important platform to address the contribution of inter-tumoral heterogeneity to therapeutic sensitivity, tumor evolution, and the mechanisms of treatment resistance. With the increasingly important role played by targeted therapies in urological malignancies, the establishment of representative PDX models can contribute to improved facilitation and adoption of precision medicine. In this review of the evolving role of the PDX in urothelial cancer and kidney cancer, we discuss the essential elements of successful graft development, effective translational application, and future directions for clinical models.
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Affiliation(s)
- Andrew T. Tracey
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (A.T.T.); (J.A.C.)
| | - Katie S. Murray
- Department of Surgery, Division of Urology, University of Missouri, Columbia, MO 65211, USA;
| | - Jonathan A. Coleman
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (A.T.T.); (J.A.C.)
| | - Kwanghee Kim
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Correspondence: ; Tel.: +1-646-422-4432
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6
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Fifield AL, Hanavan PD, Faigel DO, Sergienko E, Bobkov A, Meurice N, Petit JL, Polito A, Caulfield TR, Castle EP, Copland JA, Mukhopadhyay D, Pal K, Dutta SK, Luo H, Ho TH, Lake DF. Molecular Inhibitor of QSOX1 Suppresses Tumor Growth In Vivo. Mol Cancer Ther 2019; 19:112-122. [PMID: 31575656 DOI: 10.1158/1535-7163.mct-19-0233] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 07/01/2019] [Accepted: 09/24/2019] [Indexed: 11/16/2022]
Abstract
Quiescin sulfhydryl oxidase 1 (QSOX1) is an enzyme overexpressed by many different tumor types. QSOX1 catalyzes the formation of disulfide bonds in proteins. Because short hairpin knockdowns (KD) of QSOX1 have been shown to suppress tumor growth and invasion in vitro and in vivo, we hypothesized that chemical compounds inhibiting QSOX1 enzymatic activity would also suppress tumor growth, invasion, and metastasis. High throughput screening using a QSOX1-based enzymatic assay revealed multiple potential QSOX1 inhibitors. One of the inhibitors, known as "SBI-183," suppresses tumor cell growth in a Matrigel-based spheroid assay and inhibits invasion in a modified Boyden chamber, but does not affect viability of nonmalignant cells. Oral administration of SBI-183 inhibits tumor growth in 2 independent human xenograft mouse models of renal cell carcinoma. We conclude that SBI-183 warrants further exploration as a useful tool for understanding QSOX1 biology and as a potential novel anticancer agent in tumors that overexpress QSOX1.
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Affiliation(s)
- Amber L Fifield
- School of Life Sciences, Arizona State University, Tempe, Arizona
| | | | - Douglas O Faigel
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Mayo Clinic, Phoenix, Arizona
| | - Eduard Sergienko
- Assay Development, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, California
| | - Andrey Bobkov
- Assay Development, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, California
| | | | | | - Alysia Polito
- Hematology/Oncology, Mayo Clinic, Scottsdale, Arizona
| | - Thomas R Caulfield
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida.,Mayo Graduate School, Neurobiology of Disease, Mayo Clinic, Jacksonville, Florida.,Department of Cancer Biology, Mayo Clinic, Jacksonville, Florida.,Health Sciences Research, Division of Biomedical Statistics & Informatics, Mayo Clinic, Jacksonville, Florida.,Center for Individualized Medicine, Mayo Clinic, Jacksonville, Florida
| | - Erik P Castle
- Department of Urology, Mayo Clinic, Phoenix, Arizona
| | - John A Copland
- Department of Cancer Biology, Mayo Clinic, Jacksonville, Florida
| | | | - Krishnendu Pal
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Jacksonville, Florida
| | - Shamit K Dutta
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Jacksonville, Florida
| | - Huijun Luo
- Division of Hematology/Oncology, Mayo Clinic, Phoenix, Arizona
| | - Thai H Ho
- Division of Hematology/Oncology, Mayo Clinic, Phoenix, Arizona.
| | - Douglas F Lake
- School of Life Sciences, Arizona State University, Tempe, Arizona.
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7
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Aguilar-Mahecha A, Joseph S, Cavallone L, Buchanan M, Krzemien U, Batist G, Basik M. Precision Medicine Tools to Guide Therapy and Monitor Response to Treatment in a HER-2+ Gastric Cancer Patient: Case Report. Front Oncol 2019; 9:698. [PMID: 31448226 PMCID: PMC6691136 DOI: 10.3389/fonc.2019.00698] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Accepted: 07/15/2019] [Indexed: 12/18/2022] Open
Abstract
Trastuzumab, has played a major role in improving treatment outcomes in HER-2 positive gastric cancer. However, once there is disease progression there is a paucity of evidence for second line therapy. Patient-derived xenografts (PDXs) in combination with liquid biopsies can help guide individual therapeutic decisions and have now started to be studied. In the present case we established a PDX model from a metastatic HER-2+ gastric cancer patient and after the first engraftment passage we performed a mouse clinical trial to test T-DM1 as an alternative therapy for the patient. The PDX tumor response served as a guide to administer T-DM1 therapy to the patient who responded to treatment before relapsing 6 months later. Throughout out the clinical follow up of the patient, ctDNA levels of HER-2 copy number and a PIK3CA mutation were monitored and we found their correlation with drug response and disease progression to outperform that of CEA levels. This study highlights the utility of applying precision medicine tools combining PDX models to guide therapy with circulating tumor DNA (ctDNA) to monitor treatment response and disease progression.
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Affiliation(s)
| | - Sarah Joseph
- Segal Cancer Center, Jewish General Hospital, Montreal, QC, Canada
| | - Luca Cavallone
- Department of Oncology, Lady Davis Institute, McGill University, Montreal, QC, Canada
| | - Marguerite Buchanan
- Department of Oncology, Lady Davis Institute, McGill University, Montreal, QC, Canada
| | - Urszula Krzemien
- Department of Oncology, Lady Davis Institute, McGill University, Montreal, QC, Canada
| | - Gerald Batist
- Department of Oncology, Lady Davis Institute, McGill University, Montreal, QC, Canada.,Segal Cancer Center, Jewish General Hospital, Montreal, QC, Canada
| | - Mark Basik
- Department of Oncology, Lady Davis Institute, McGill University, Montreal, QC, Canada.,Department of Surgery, Jewish General Hospital, Montreal, QC, Canada
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