Literature DB >> 24077798

In vivo selection of tumor-specific antibodies.

David Sánchez-Martín1, Laura Sanz, Erkki Ruoslahti, Luis Alvarez-Vallina.   

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Year:  2013        PMID: 24077798      PMCID: PMC3858540          DOI: 10.18632/oncotarget.1407

Source DB:  PubMed          Journal:  Oncotarget        ISSN: 1949-2553


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One of the most promising strategies for the development of effective cancer therapies relies on the targeted delivery of antibody-based therapeutics. Effective tumor targeting with antibodies depends on the identification of new targets, and the optimization of antibody structure [1, 2]; however, the discovery and validation of novel tumor-associated antigens remains challenging [1]. Indeed, the FDA-approved antibodies for cancer indications are directed against a limited number of targets. Often, novel antibodies are raised against well-known antigens, trying to surpass pre-existing ones in favorable properties. This approach can be helpful; for example, the upcoming obinutuzumab seems to outperform the blockbuster rituximab [3], but it doesn't necessarily imply an advance in our understanding of the disease. In contrast, using a functional approach, it is possible to select monoclonal antibodies with a defined biological effect that can lead to the identification of new or less well studied proteins potentially involved in the pathogeny of diseases [4]. This strategy has greater potential for innovation and can be used to address the unmet need in oncology of discovering systemically accessible antigens preferentially overexpressed in the tumor microenvironment. We have recently used a functional approach to identify antibodies able to deliver a payload to the primary tumor in a xenograft mouse model [5]. Using a highly diverse (~3 × 109) combinatorial library of single domain antibodies ensures the presence of antibodies against any potentially relevant target. We performed the selection in vivo—letting the antibody library circulate in the mouse—and selected only those antibodies that met the defined biological effect, without previous knowledge of the target. This unbiased approach can yield new antigens, discriminates against antibodies with unexpected off-target effects; and emphasizes the availability of the epitope in vivo. This latter aspect is sometimes overlooked when selecting antibodies for therapy, as antibodies selected using functional screens in vitro can fail in the clinic because of the in vivo environment is different. One of the antibodies selected with our new strategy recognizes the proteasome activator complex PA28. We found that the expression of the α subunit of PA28 is elevated in primary and metastatic human prostate cancer and used anti-PA28α antibodies to show that PA28 is accessible in mouse xenograft tumors. These results support the use of PA28 as a tumor marker, and potentially, as a target for therapeutic intervention in prostate cancer. We have focused on antibody discovery in tumor-bearing mice; however, this procedure is applicable to any disease for which an animal model exists (be it cancer, neurodegenerative diseases, etc.). Furthermore, the phage display technology can be used to study not only antibodies, but also to investigate any other extracellular protein-protein interactions [6, 7] in vivo, and to map such interaction in the organism where they occur.
  7 in total

Review 1.  Mapping of vascular ZIP codes by phage display.

Authors:  Tambet Teesalu; Kazuki N Sugahara; Erkki Ruoslahti
Journal:  Methods Enzymol       Date:  2012       Impact factor: 1.600

Review 2.  Strategies and challenges for the next generation of therapeutic antibodies.

Authors:  Alain Beck; Thierry Wurch; Christian Bailly; Nathalie Corvaia
Journal:  Nat Rev Immunol       Date:  2010-05       Impact factor: 53.106

3.  General M13 phage display: M13 phage display in identification and characterization of protein-protein interactions.

Authors:  Kirsten Hertveldt; Tim Beliën; Guido Volckaert
Journal:  Methods Mol Biol       Date:  2009

Review 4.  Multivalent antibodies: when design surpasses evolution.

Authors:  Angel M Cuesta; Noelia Sainz-Pastor; Jaume Bonet; Baldomero Oliva; Luis Alvarez-Vallina
Journal:  Trends Biotechnol       Date:  2010-05-04       Impact factor: 19.536

Review 5.  Monoclonal antibodies: versatile platforms for cancer immunotherapy.

Authors:  Louis M Weiner; Rishi Surana; Shangzi Wang
Journal:  Nat Rev Immunol       Date:  2010-05       Impact factor: 53.106

6.  Preclinical activity of the type II CD20 antibody GA101 (obinutuzumab) compared with rituximab and ofatumumab in vitro and in xenograft models.

Authors:  Sylvia Herter; Frank Herting; Olaf Mundigl; Inja Waldhauer; Tina Weinzierl; Tanja Fauti; Gunter Muth; Doris Ziegler-Landesberger; Erwin Van Puijenbroek; Sabine Lang; Minh Ngoc Duong; Lina Reslan; Christian A Gerdes; Thomas Friess; Ute Baer; Helmut Burtscher; Michael Weidner; Charles Dumontet; Pablo Umana; Gerhard Niederfellner; Marina Bacac; Christian Klein
Journal:  Mol Cancer Ther       Date:  2013-07-19       Impact factor: 6.261

7.  Proteasome activator complex PA28 identified as an accessible target in prostate cancer by in vivo selection of human antibodies.

Authors:  David Sánchez-Martín; Jorge Martínez-Torrecuadrada; Tambet Teesalu; Kazuki N Sugahara; Ana Alvarez-Cienfuegos; Pilar Ximénez-Embún; Rodrigo Fernández-Periáñez; M Teresa Martín; Irene Molina-Privado; Isabel Ruppen-Cañás; Ana Blanco-Toribio; Marta Cañamero; Angel M Cuesta; Marta Compte; Leonor Kremer; Carmen Bellas; Vanesa Alonso-Camino; Irene Guijarro-Muñoz; Laura Sanz; Erkki Ruoslahti; Luis Alvarez-Vallina
Journal:  Proc Natl Acad Sci U S A       Date:  2013-08-05       Impact factor: 11.205

  7 in total
  2 in total

Review 1.  Selection strategies for anticancer antibody discovery: searching off the beaten path.

Authors:  David Sánchez-Martín; Morten Dræby Sørensen; Simon Lykkemark; Laura Sanz; Peter Kristensen; Erkki Ruoslahti; Luis Álvarez-Vallina
Journal:  Trends Biotechnol       Date:  2015-03-26       Impact factor: 19.536

Review 2.  Antibodies, Nanobodies, or Aptamers-Which Is Best for Deciphering the Proteomes of Non-Model Species?

Authors:  Poshmaal Dhar; Rasika M Samarasinghe; Sarah Shigdar
Journal:  Int J Mol Sci       Date:  2020-04-03       Impact factor: 5.923

  2 in total

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