Detecting Personalized Determinants During Drug Treatment from Omics Big Data
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Published on: September 6, 2017
Lu Wang1, Xiangtian Yu1, Chengming Zhang1
1Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy Science, Shanghai 200031, China.
Backgrounds:
Targeted therapy is the foundation of personalized medicine in cancer, which is often understood as the right patient using the right drug. Thinking from the viewpoint of determinants during personalized drug treatment, the genetics, epigenetics and metagenomics would provide individual-specific biological elements to characterize the personalized responses for therapy.
Methods:
Such personalized determinants should be not only understood as specific to one person, while they should have certain replicate observations in a group of individuals but not all, which actually provide more credible and reproducible personalized biological features. The requirement of detecting personalized determinants is well supported by novel high-throughput sequencing technologies and newly temporal-spatial experimental protocols, which quickly produce the omics big data.
Results:
In this mini-review, we would like to give a brief introduction firstly on the advanced drug or drug-like therapy with genetics, epigenetics and metagenomics, respectively, from the viewpoint of personalized determinants; then summarize the computational methods helpful to analyze the corresponding omics data under the consideration of personalized biological context; and particularly focus on metagenomics to discuss current data, method, and opportunity for personalized medicine.
Conclusion:
Totally, detecting personalized determinants during drug treatment from omics big data will bring the precision medicine or personalized medicine from concept to application. More and more inspiring biotechnologies, data resources, and analytic approaches will benefit All of US in the near future.
Insights
Personalized medicine uses genetics, epigenetics, and metagenomics to identify individual determinants for targeted cancer therapy. Analyzing omics data with advanced computational methods enables precision medicine applications.
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.
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