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Published on: April 11, 2016
Network Approaches for Precision Oncology
1Ontario Institute for Cancer Research, University of Toronto, Toronto, ON, Canada. shraddha.pai@utoronto.ca.
Abstract:
The growth of multi-omic tumour profile datasets along with knowledge of genome regulatory networks has created an unprecedented opportunity to advance precision oncology. Achieving this goal requires computational methods that can make sense of and combine heterogeneous data sources. Interpretability and integration of prior knowledge is of particular relevance for genomic models to minimize ungeneralizable models, promote rational treatment design, and make use of sparse genetic mutation data. While networks have long been used to capture genomic interactions at the levels of genes, proteins, and pathways, the use of networks in precision oncology is relatively new. In this chapter, I provide an introduction to network-based approaches used to integrate multi-modal data sources for patient stratification and patient classification. There is a particular emphasis on methods using patient similarity networks (PSNs) as part of the design. I separately discuss strategies for inferring driver mutations from individual patient mutation data. Finally, I discuss challenges and opportunities the field will need to overcome to achieve its full potential, with an outlook towards a clinic of the future.
Insights
Network-based computational methods are advancing precision oncology by integrating multi-omic tumor data. Patient similarity networks (PSNs) are key for stratifying and classifying patients, improving treatment design.
Area of Science:
- Computational biology
- Genomics
- Oncology
Background:
- Multi-omic tumor profiling and genomic regulatory networks offer new avenues for precision oncology.
- Integrating heterogeneous data sources computationally is crucial for advancing personalized cancer treatments.
- Genomic models benefit from interpretability and prior knowledge integration to minimize ungeneralizable models and optimize treatment strategies.
Purpose of the Study:
- To introduce network-based approaches for integrating multi-modal data in precision oncology.
- To highlight the application of patient similarity networks (PSNs) for patient stratification and classification.
- To discuss methods for inferring driver mutations and future challenges in the field.
Main Methods:
- Utilizing network-based approaches to integrate diverse data sources.
- Employing patient similarity networks (PSNs) for patient stratification and classification.
- Developing strategies for inferring driver mutations from individual patient mutation data.
Main Results:
- Network-based methods enable the integration of multi-modal data for improved patient stratification.
- Patient similarity networks (PSNs) show promise in classifying patients for tailored treatments.
- Strategies for driver mutation inference are discussed in the context of sparse genetic data.
Conclusions:
- Network-based approaches, particularly PSNs, are vital for advancing precision oncology.
- Effective integration of multi-omic data and prior knowledge is essential for rational treatment design.
- Overcoming current challenges will pave the way for future clinical applications in personalized cancer care.
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