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

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

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)579-592
Number of pages14
JournalMethods in molecular biology (Clifton, N.J.)
StatePublished - 2023
Externally publishedYes


  • Cancer
  • Longitudinal analysis
  • Machine learning
  • Mass spectrometry
  • Targeted proteomics


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