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REVIEW article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1351584
This article is part of the Research Topic Community Series in Biomarkers in the Era of Cancer Immunotherapy: Zooming in from Periphery to Tumor Microenvironment, Volume II View all 14 articles

An overview of statistical methods for biomarkers relevant to early clinical development of cancer immunotherapies

Provisionally accepted
David Dejardin David Dejardin 1*Anton Kraxner Anton Kraxner 2Emilie Schindler Emilie Schindler 2Nicolas Städler Nicolas Städler 2Marcel Wolbers Marcel Wolbers 1
  • 1 Product Development, Data Science, Roche (Switzerland), Basel, Switzerland
  • 2 Oncology, Roche Pharma Research and Early Development, Roche Innovation Center, Basel, Switzerland

The final, formatted version of the article will be published soon.

    Over the last decade, a new paradigm for cancer therapies has emerged which leverages the immune system to act against the tumor. The novel mechanism of action of these immunotherapies has also introduced new challenges to drug development. Biomarkers play a key role in several areas of early clinical development of immunotherapies including the demonstration of mechanism of action, dose finding and dose optimization, mitigation and prevention of adverse reactions, and patient enrichment and indication prioritization. We discuss statistical principles and methods for establishing the prognostic, predictive aspect of a (set of) biomarker and for linking the change in biomarkers to clinical efficacy in the context of early development studies.The methods discussed are meant to avoid bias and produce robust and reproducible conclusions. This review is targeted to drug developers and data scientists interested in the strategic usage and analysis of biomarkers in the context of immunotherapies.

    Keywords: biomarkers, statistical methods, Validation, Immunotherapy, Prognostic model, predictive model

    Received: 06 Dec 2023; Accepted: 29 Jul 2024.

    Copyright: © 2024 Dejardin, Kraxner, Schindler, Städler and Wolbers. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: David Dejardin, Product Development, Data Science, Roche (Switzerland), Basel, Switzerland

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.