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Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material
Published on: August 1, 2018
Multi-omic profiling in breast cancer: utility for advancing diagnostics and clinical care
Emna El Gazzah1,2, Scott Parker1,2, Mariaelena Pierobon1,2
1School of Systems Biology, George Mason University, Manassas, VA, USA.
Introduction:
Breast cancer remains a major global health challenge. While advances in precision oncology have contributed to improvements in patient outcomes and provided a deeper understanding of the biological mechanisms that drive the disease, historically, research and patients' allocation to treatment have heavily relied on single-omic approaches, analyzing individual molecular dimensions such as genomics, transcriptomics, or proteomics. While these have provided deep insights into breast cancer biology, they often fail to offer a complete understanding of the disease's complex molecular landscape.
Areas Covered:
In this review, the authors explore the recent advancements in multi-omic research in the realm of breast cancer and use clinical data to show how multi-omic integration can offer a more holistic understanding of the molecular alterations and their functional consequences underlying breast cancer.
Expert Opinion:
The overall developments in multi-omic research and AI are expected to complement precision diagnostics through potentially refining prognostic models, and treatment selection. Overcoming challenges such as cost, data complexity, and lack of standardization is crucial for unlocking the full potential of multi-omics and AI in breast cancer patient care to enable the advancement of personalized treatments and improve patient outcomes.
Insights
Multi-omic research offers a comprehensive view of breast cancer molecular landscapes, improving diagnostics and treatment selection. Integrating multi-omics and AI is key to advancing personalized breast cancer care and patient outcomes.
Area of Science:
- Oncology
- Genomics
- Proteomics
- Transcriptomics
Background:
- Breast cancer is a significant global health issue.
- Single-omic approaches provide limited understanding of complex breast cancer biology.
- Precision oncology has improved outcomes but relies on fragmented molecular data.
Purpose of the Study:
- To review advancements in multi-omic research for breast cancer.
- To demonstrate how multi-omic integration enhances understanding of molecular alterations and consequences.
- To highlight the role of multi-omics and AI in precision diagnostics and treatment selection.
Main Methods:
- Review of recent multi-omic research in breast cancer.
- Analysis of clinical data to illustrate multi-omic integration benefits.
- Exploration of Artificial Intelligence (AI) applications in multi-omic data analysis.
Main Results:
- Multi-omic integration provides a holistic view of breast cancer molecular drivers.
- Multi-omics combined with AI can refine prognostic models and treatment strategies.
- Overcoming challenges like cost and data complexity is essential for clinical implementation.
Conclusions:
- Multi-omic research and AI are crucial for advancing personalized breast cancer treatments.
- Enhanced understanding through multi-omics leads to improved patient outcomes.
- Standardization and data integration are vital for realizing the full potential of multi-omics in oncology.
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