Detecting Personalized Determinants During Drug Treatment from Omics Big Data

Area of Science:

• Oncology
• Genomics
• Computational Biology

Background:

• Personalized medicine in cancer relies on targeted therapy, matching the right drug to the right patient.
• Genetics, epigenetics, and metagenomics offer individual-specific biological determinants for predicting therapy responses.
• These determinants require reproducible features observed across specific groups, not universally.

Purpose of the Study:

• To review advanced drug therapies utilizing genetics, epigenetics, and metagenomics for personalized medicine.
• To summarize computational methods for analyzing omics data within a personalized biological context.
• To focus on metagenomics, discussing its current data, methods, and opportunities in personalized medicine.

Main Methods:

• Review of literature on targeted therapies and omics data.
• Summary of computational approaches for analyzing genetics, epigenetics, and metagenomics data.
• Focus on high-throughput sequencing technologies and temporal-spatial experimental protocols generating omics big data.

Main Results:

• Genetics, epigenetics, and metagenomics provide key determinants for personalized drug treatment.
• Computational methods are crucial for analyzing omics big data to identify personalized biological features.
• Metagenomics presents significant opportunities for advancing personalized cancer medicine.

Conclusions:

• Detecting personalized determinants from omics data transitions precision medicine from concept to application.
• Advancements in biotechnology, data resources, and analytical approaches will drive future progress in personalized medicine.
• The integration of omics data is essential for realizing the full potential of personalized medicine in oncology.