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Use of Longitudinal Serum Analysis and Machine Learning to Develop a Classifier for Cancer Early Detection

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Serum/Plasma Proteomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2628))

Abstract

Early detection of solid tumors through a simple screening process, such as the proteomic analysis of biofluids, has the potential to significantly alter the management and outcomes of cancers. The application of advanced targeted proteomics measurements and data analysis strategies to uniformly collected serum or plasma samples would enable longitudinal studies of cancer risk, progression, and response to therapy that have the potential to significantly reduce cancer burden in general. In this article, we describe a generalizable workflow combining robust, multiplexed targeted proteomics measurements applied to longitudinal samples from the Department of Defense Serum Repository with a Random Forest machine learning method for developing and initially evaluating the performance of candidate biomarker panels for early detection of cancers. The effectiveness of this approach was demonstrated in a cohort of 175 head and neck squamous cell carcinoma patients. The outlined protocols include methods for sample preparation, instrument analysis, and data analysis and interpretation using this workflow.

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Acknowledgments

This work was supported by Federal Award No. HU0001-16-2-0014 (Subaward No. 3879, to K.D. Rodland and T. Liu). The authors thank the clinical and laboratory staff at the Uniformed Services University of the Health Sciences and Pacific Northwest National Laboratory (PNNL). Portions of the research were performed in the Environmental Molecular Sciences Laboratory (grid.436923.9), a US Department of Energy (DOE) Office of Biological and Environmental Research national scientific user facility on the PNNL campus. PNNL is a multiprogram national laboratory operated by Battelle for the DOE under contract no. DE-AC05-76RL01830. The contents of this publication are the sole responsibility of the author(s) and do not necessarily reflect the views, opinions, or policies of the Uniformed Services University of the Health Sciences; the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc.; the Department of Defense; or the Departments of the Army, Navy, or Air Force. Mention of trade names, commercial products, or organizations does not imply endorsement by the US Government.

Author information

Authors and Affiliations

  1. Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA

    Rashmi Madda, Vladislav A. Petyuk, Yi-Ting Wang, Tujin Shi, Karin D. Rodland & Tao Liu

  2. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA

    Craig D. Shriver

  3. John P. Murtha Cancer Center, Uniformed Services University of the Health Sciences, Walter Reed National Military Medical Center, Bethesda, MD, USA

    Craig D. Shriver

  4. Department of Cell Developmental and Cancer Biology, Oregon Health and Science University, Portland, OR, USA

    Karin D. Rodland

Authors
  1. Rashmi Madda
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  2. Vladislav A. Petyuk
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  3. Yi-Ting Wang
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  4. Tujin Shi
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  5. Craig D. Shriver
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  6. Karin D. Rodland
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  7. Tao Liu
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Corresponding author

Correspondence to Tao Liu .

Editor information

Editors and Affiliations

  1. Molecular Proteomics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia

    David W. Greening

  2. Department of Biochemistry and Chemistry, School of Agriculture, Biomedicine and Environment, La Trobe Institute for Molecular Science, Melbourne, VIC, Australia

    Richard J. Simpson

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© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Madda, R. et al. (2023). Use of Longitudinal Serum Analysis and Machine Learning to Develop a Classifier for Cancer Early Detection. In: Greening, D.W., Simpson, R.J. (eds) Serum/Plasma Proteomics. Methods in Molecular Biology, vol 2628. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2978-9_33

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  • DOI: https://doi.org/10.1007/978-1-0716-2978-9_33

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2977-2

  • Online ISBN: 978-1-0716-2978-9

  • eBook Packages: Springer Protocols

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