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

Area of Science:

• Computational biology
• Genomics
• Pharmacology

Background:

• Cancer precision medicine seeks personalized treatments by predicting patient response.
• Large datasets of clinical and molecular cancer data are crucial for developing predictive models.
• Deep learning models show promise in predicting drug responses in cancer therapy.

Purpose of the Study:

• To guide the selection, utilization, and development of deep learning models for cancer precision medicine.
• To highlight best practices and potential pitfalls in applying computational models to predict treatment response.
• To identify molecular markers that determine individual responses to cancer therapies.

Main Methods:

• Review of existing deep learning models for predicting monotherapy and polytherapy response.
• Discussion of considerations for choosing and developing computational models.
• Analysis of publicly available clinical and molecular cancer datasets.

Main Results:

• Deep learning models can predict individual responses to cancer treatments.
• Identification of molecular markers is key to personalized therapy.
• Careful model selection and development are essential for reliable predictions.

Conclusions:

• Effective use of deep learning in cancer precision medicine requires adherence to best practices.
• Computational models offer powerful tools for predicting treatment efficacy and guiding personalized oncology.
• Further research is needed to refine deep learning applications in identifying predictive biomarkers.