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Assessment of Tumor Heterogeneity in High-Grade Serous Ovarian Cancer: Mass Cytometry to Understand the Complex Tumor Biology

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Cancer Drug Resistance

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2535))

Abstract

Ovarian cancer (OC) is the most deadly gynecological malignancy worldwide. OC patients undergo debulking surgery followed by platinum/taxane-based chemotherapy; however, despite recent development of new therapeutic approaches based on combination of chemotherapy and innovative targeted-therapies, most of them relapse due to chemoresistance. Many studies have been carried out to decipher the high heterogeneity of ovarian cancer cells that drives tumor treatment failure. Here, we describe our experience in the characterization of ovarian cancer cell subsets through a high-resolution technology in multiparametric analysis, such as mass cytometry (MC).

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Authors and Affiliations

  1. Core Facilities, Istituto Superiore di Sanità, Rome, Italy

    Luca Pasquini

  2. Department of Oncology and Molecular Medicine, Istituto Superiore di Sanità, Rome, Italy

    Roberta Riccioni & Eleonora Petrucci

Authors
  1. Luca Pasquini
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  2. Roberta Riccioni
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  3. Eleonora Petrucci
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Corresponding authors

Correspondence to Luca Pasquini or Eleonora Petrucci .

Editor information

Editors and Affiliations

  1. Department of Oncology and Molecolar Medicine, Istituto Superiore di Sanità, Rome, Italy

    Marta Baiocchi

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© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Pasquini, L., Riccioni, R., Petrucci, E. (2022). Assessment of Tumor Heterogeneity in High-Grade Serous Ovarian Cancer: Mass Cytometry to Understand the Complex Tumor Biology. In: Baiocchi, M. (eds) Cancer Drug Resistance. Methods in Molecular Biology, vol 2535. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2513-2_9

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  • DOI: https://doi.org/10.1007/978-1-0716-2513-2_9

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2512-5

  • Online ISBN: 978-1-0716-2513-2

  • eBook Packages: Springer Protocols

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