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Multi-Omic Integration of Blood-Based Tumor-Associated Genomic and Lipidomic Profiles Using Machine Learning Models

Shikai Fang1, Shandian Zhe2, Hui-Ming Lin3,4

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|July 25, 2023
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Summary

This study combined genomic and lipid data to predict outcomes in advanced prostate cancer. Machine learning models accurately identified patients likely to respond to treatment or experience poor prognosis.

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Area of Science:

  • Oncology
  • Genomics
  • Lipidomics
  • Machine Learning

Background:

  • Advanced prostate cancer, including metastatic hormone-sensitive (mHSPC) and castrate-resistant (mCRPC) forms, presents complex prognostic and predictive challenges.
  • Identifying reliable biomarkers for patient stratification and treatment response is crucial for improving clinical outcomes.

Purpose of the Study:

  • To develop and validate a multifeature classifier integrating plasma-derived genomic alterations and lipid features for predicting clinical outcomes in mHSPC and mCRPC.
  • To assess the prognostic and predictive accuracy of a multi-omic approach in a longitudinal cohort of advanced prostate cancer patients.

Main Methods:

  • A cohort of 71 mHSPC and 144 mCRPC patients was analyzed over 11 years.
  • Plasma-based genomic alterations (120 genes) and lipidomic species (772) were profiled.
  • Machine learning models, including logistic regression, Gaussian process regression, and support vector machines, were employed to build multi-omic classifiers.

Main Results:

  • Specific ceramides (d18:1/14:0, d18:1/17:0), CHEK2 mutations, AR amplification, and RB1 deletion were key predictors.
  • The multi-omic models achieved AUC scores ranging from 0.638 to 0.751 for various outcome predictions in mHSPC and mCRPC.
  • The integrated multi-omic approach demonstrated superior predictive performance compared to models using fewer features.

Conclusions:

  • Machine learning incorporating multi-omic features significantly enhances prediction accuracy for metastatic prostate cancer outcomes.
  • This approach holds promise for personalized medicine in advanced prostate cancer.
  • Further validation in independent datasets is warranted.