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Cancer precision medicine aims to identify the best course of treatment for an individual. To achieve this goal, two important questions include predicting the response of an individual to a treatment strategy and identifying molecular markers that determine the response. The rapid growth of large publicly available databases containing clinical and molecular characteristics of cancer-derived samples paired with their response to single or multiple drugs, has enabled the development of computational models to answer these questions. In recent years, various deep learning models have been proposed to predict the response to polytherapy and monotherapies. However, selecting among all available options or developing new models for a particular study requires careful considerations and best practices to avoid various pitfalls. In this chapter, and drawing from our own studies, we will discuss various important points for choosing, utilizing, and developing such deep learning tools.

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Anticancer Monotherapy and Polytherapy Drug Response Prediction Using Deep Learning: Guidelines and Best Practices

  • 实验方案

  • 发表日期：

    2025年

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  • Amin Emad,
  • David Earl Hostallero

摘要

Cancer precision medicine aims to identify the best course of treatment for an individual. To achieve this goal, two important questions include predicting the response of an individual to a treatment strategy and identifying molecular markers that determine the response. The rapid growth of large publicly available databases containing clinical and molecular characteristics of cancer-derived samples paired with their response to single or multiple drugs, has enabled the development of computational models to answer these questions. In recent years, various deep learning models have been proposed to predict the response to polytherapy and monotherapies. However, selecting among all available options or developing new models for a particular study requires careful considerations and best practices to avoid various pitfalls. In this chapter, and drawing from our own studies, we will discuss various important points for choosing, utilizing, and developing such deep learning tools.