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Patient-Derived Xenograft Models of Human Cancer [electronic resource] / edited by Yuzhuo Wang, Dong Lin, Peter W. Gout.

Medverkande: Materialtyp: TextSerie: Molecular and Translational MedicineUtgivningsuppgift: Cham : Springer International Publishing : Imprint: Humana Press, 2017Beskrivning: XI, 204 p. 27 illus., 26 illus. in color. online resourceInnehållstyp:
  • text
Medietyp:
  • computer
Bärartyp:
  • online resource
ISBN:
  • 9783319558257
Ämnen: Fler format: Printed edition:: Ingen titelDDK-klassifikation:
  • 616.994 23
Library of Congress (LC) klassifikationskod:
  • RC254-282
Onlineresurser:
Innehåll:
Introduction -- History and Evolution of PDX Cancer Models -- Establishment and Characterization of PDX Models -- Methods of Generating PDX Models -- Fidelity and Stability of PDX Models -- Applications of PDX Models -- Studies of Cancer Heterogeneity -- PDX and Cancer Stem Cells -- Modeling Cancer Metastasis -- Modeling Drug Resistance -- PDX and Biomarker Development -- PDX and Preclinical Drug Screening -- Co-clinical Trials and Personalized Cancer Therapy/Precision Medicine -- Epilogue -- Future Perspective.
I: Springer eBooksSammanfattning: This book provides a comprehensive, state-of-the-art review of PDX cancer models. In separately produced chapters, the history and evolution of PDX models is reviewed, methods of PDX model development are compared in detail, characteristics of available established models are presented, current applications are summarized and new perspectives about use of PDX models are proposed. Each chapter is written by a world-renowned expert who is conducting cutting-edge research in the field. Each of the subsections provide a comprehensive review of existing literature addressing the particular topic followed by a conclusive paragraph detailing future directions. Extensive illustrations make this an interactive text. Patient-Derived Xenograft Models of Human Cancer will serve as a highly useful resource for researchers and clinicians dealing with, or interested in, this important topic. It will provide a concise yet comprehensive summary of the current status of the field that will help guide preclinical and clinical applications as well as stimulate investigative efforts. This book will propagate innovative concepts and prompt the development of ground-breaking technological solutions in this field.
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Introduction -- History and Evolution of PDX Cancer Models -- Establishment and Characterization of PDX Models -- Methods of Generating PDX Models -- Fidelity and Stability of PDX Models -- Applications of PDX Models -- Studies of Cancer Heterogeneity -- PDX and Cancer Stem Cells -- Modeling Cancer Metastasis -- Modeling Drug Resistance -- PDX and Biomarker Development -- PDX and Preclinical Drug Screening -- Co-clinical Trials and Personalized Cancer Therapy/Precision Medicine -- Epilogue -- Future Perspective.

This book provides a comprehensive, state-of-the-art review of PDX cancer models. In separately produced chapters, the history and evolution of PDX models is reviewed, methods of PDX model development are compared in detail, characteristics of available established models are presented, current applications are summarized and new perspectives about use of PDX models are proposed. Each chapter is written by a world-renowned expert who is conducting cutting-edge research in the field. Each of the subsections provide a comprehensive review of existing literature addressing the particular topic followed by a conclusive paragraph detailing future directions. Extensive illustrations make this an interactive text. Patient-Derived Xenograft Models of Human Cancer will serve as a highly useful resource for researchers and clinicians dealing with, or interested in, this important topic. It will provide a concise yet comprehensive summary of the current status of the field that will help guide preclinical and clinical applications as well as stimulate investigative efforts. This book will propagate innovative concepts and prompt the development of ground-breaking technological solutions in this field.

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