Literature DB >> 31157772

A Melanoma Patient-Derived Xenograft Model.

Min Xiao1, Vito W Rebecca2, Meenhard Herlyn1.   

Abstract

Accumulating evidence suggests that molecular and biological properties differ in melanoma cells grown in traditional two-dimensional tissue culture vessels versus in vivo in human patients. This is due to the bottleneck selection of clonal populations of melanoma cells that can robustly grow in vitro in the absence of physiological conditions. Further, responses to therapy in two-dimensional tissue cultures overall do not faithfully reflect responses to therapy in melanoma patients, with the majority of clinical trials failing to show the efficacy of therapeutic combinations shown to be effective in vitro. Although xenografting of melanoma cells into mice provides the physiological in vivo context absent from two-dimensional tissue culture assays, the melanoma cells used for engraftment have already undergone bottleneck selection for cells that could grow under two-dimensional conditions when the cell line was established. The irreversible alterations that occur as a consequence of the bottleneck include changes in growth and invasion properties, as well as the loss of specific subpopulations. Therefore, models that better recapitulate the human condition in vivo may better predict therapeutic strategies that effectively increase the overall survival of patients with metastatic melanoma. The patient-derived xenograft (PDX) technique involves the direct implantation of tumor cells from the human patient to a mouse recipient. In this manner, tumor cells are consistently grown under physiological stresses in vivo and never undergo the two-dimensional bottleneck, which preserves the molecular and biological properties present when the tumor was in the human patient. Notable, PDX models derived from organ sites of metastases (i.e., brain) display similar metastatic capacity, while PDX models derived from therapy naive patients and patients with acquired resistance to therapy (i.e., BRAF/MEK inhibitor therapy) display similar sensitivity to therapy.

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Year:  2019        PMID: 31157772      PMCID: PMC6986799          DOI: 10.3791/59508

Source DB:  PubMed          Journal:  J Vis Exp        ISSN: 1940-087X            Impact factor:   1.355


  10 in total

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2.  High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response.

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Journal:  Nat Med       Date:  2015-10-19       Impact factor: 53.440

3.  A Comprehensive Patient-Derived Xenograft Collection Representing the Heterogeneity of Melanoma.

Authors:  Clemens Krepler; Katrin Sproesser; Patricia Brafford; Marilda Beqiri; Bradley Garman; Min Xiao; Batool Shannan; Andrea Watters; Michela Perego; Gao Zhang; Adina Vultur; Xiangfan Yin; Qin Liu; Ioannis N Anastopoulos; Bradley Wubbenhorst; Melissa A Wilson; Wei Xu; Giorgos Karakousis; Michael Feldman; Xiaowei Xu; Ravi Amaravadi; Tara C Gangadhar; David E Elder; Lauren E Haydu; Jennifer A Wargo; Michael A Davies; Yiling Lu; Gordon B Mills; Dennie T Frederick; Michal Barzily-Rokni; Keith T Flaherty; Dave S Hoon; Michael Guarino; Joseph J Bennett; Randall W Ryan; Nicholas J Petrelli; Carol L Shields; Mizue Terai; Takami Sato; Andrew E Aplin; Alexander Roesch; David Darr; Steve Angus; Rakesh Kumar; Ensar Halilovic; Giordano Caponigro; Sebastien Jeay; Jens Wuerthner; Annette Walter; Matthias Ocker; Matthew B Boxer; Lynn Schuchter; Katherine L Nathanson; Meenhard Herlyn
Journal:  Cell Rep       Date:  2017-11-14       Impact factor: 9.423

4.  Genetic and Genomic Characterization of 462 Melanoma Patient-Derived Xenografts, Tumor Biopsies, and Cell Lines.

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Journal:  Cell Rep       Date:  2017-11-14       Impact factor: 9.423

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6.  PDX-MI: Minimal Information for Patient-Derived Tumor Xenograft Models.

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7.  Recovery of phospho-ERK activity allows melanoma cells to escape from BRAF inhibitor therapy.

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8.  Long-Term Outcomes in Patients With BRAF V600-Mutant Metastatic Melanoma Who Received Dabrafenib Combined With Trametinib.

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Authors:  Manuel Hidalgo; Frederic Amant; Andrew V Biankin; Eva Budinská; Annette T Byrne; Carlos Caldas; Robert B Clarke; Steven de Jong; Jos Jonkers; Gunhild Mari Mælandsmo; Sergio Roman-Roman; Joan Seoane; Livio Trusolino; Alberto Villanueva
Journal:  Cancer Discov       Date:  2014-07-15       Impact factor: 39.397

  10 in total
  4 in total

Review 1.  The Challenging Melanoma Landscape: From Early Drug Discovery to Clinical Approval.

Authors:  Mariana Matias; Jacinta O Pinho; Maria João Penetra; Gonçalo Campos; Catarina Pinto Reis; Maria Manuela Gaspar
Journal:  Cells       Date:  2021-11-09       Impact factor: 6.600

Review 2.  Melanoma models for the next generation of therapies.

Authors:  E Elizabeth Patton; Kristen L Mueller; David J Adams; Niroshana Anandasabapathy; Andrew E Aplin; Corine Bertolotto; Marcus Bosenberg; Craig J Ceol; Christin E Burd; Ping Chi; Meenhard Herlyn; Sheri L Holmen; Florian A Karreth; Charles K Kaufman; Shaheen Khan; Sebastian Kobold; Eleonora Leucci; Carmit Levy; David B Lombard; Amanda W Lund; Kerrie L Marie; Jean-Christophe Marine; Richard Marais; Martin McMahon; Carla Daniela Robles-Espinoza; Ze'ev A Ronai; Yardena Samuels; Maria S Soengas; Jessie Villanueva; Ashani T Weeraratna; Richard M White; Iwei Yeh; Jiyue Zhu; Leonard I Zon; Marc S Hurlbert; Glenn Merlino
Journal:  Cancer Cell       Date:  2021-02-04       Impact factor: 31.743

3.  Brain Tumor Biobank Development for Precision Medicine: Role of the Neurosurgeon.

Authors:  Emilie Darrigues; Benjamin W Elberson; Annick De Loose; Madison P Lee; Ebonye Green; Ashley M Benton; Ladye G Sink; Hayden Scott; Murat Gokden; John D Day; Analiz Rodriguez
Journal:  Front Oncol       Date:  2021-04-26       Impact factor: 6.244

4.  3D-Printed Collagen Scaffolds Promote Maintenance of Cryopreserved Patients-Derived Melanoma Explants.

Authors:  Yun-Mi Jeong; ChulHwan Bang; MinJi Park; Sun Shin; Seokhwan Yun; Chul Min Kim; GaHee Jeong; Yeun-Jun Chung; Won-Soo Yun; Ji Hyun Lee; Songwan Jin
Journal:  Cells       Date:  2021-03-07       Impact factor: 6.600

  4 in total

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