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Molecular pathways: extracting medical knowledge from high-throughput genomic data
Theodore C Goldstein1, Evan O Paull, Matthew J Ellis
1Department of Biomolecular Engineering, University of California, Santa Cruz, California 95064, USA.
Abstract:
High-throughput genomic data that measures RNA expression, DNA copy number, mutation status, and protein levels provide us with insights into the molecular pathway structure of cancer. Genomic lesions (amplifications, deletions, mutations) and epigenetic modifications disrupt biochemical cellular pathways. Although the number of possible lesions is vast, different genomic alterations may result in concordant expression and pathway activities, producing common tumor subtypes that share similar phenotypic outcomes. How can these data be translated into medical knowledge that provides prognostic and predictive information? First-generation mRNA expression signatures such as Genomic Health's Oncotype DX already provide prognostic information, but do not provide therapeutic guidance beyond the current standard of care, which is often inadequate in high-risk patients. Rather than building molecular signatures based on gene expression levels, evidence is growing that signatures based on higher-level quantities such as from genetic pathways may provide important prognostic and diagnostic cues. We provide examples of how activities for molecular entities can be predicted from pathway analysis and how the composite of all such activities, referred to here as the "activitome," helps connect genomic events to clinical factors to predict the drivers of poor outcome.
Insights
Cancer molecular pathway analysis reveals that pathway activity signatures, not just gene expression, can predict patient outcomes. This "activitome" approach connects genomic events to clinical factors for better cancer prognostics.
Area of Science:
- Oncology
- Genomics
- Systems Biology
Background:
- High-throughput genomic data (RNA expression, DNA copy number, mutation status, protein levels) offer insights into cancer's molecular pathways.
- Genomic lesions and epigenetic modifications disrupt cellular pathways, leading to common tumor subtypes with similar phenotypes despite diverse alterations.
Purpose of the Study:
- To explore how high-throughput genomic data can be translated into medical knowledge for prognostic and predictive cancer information.
- To investigate the utility of pathway-based signatures over gene expression signatures for improved cancer diagnostics and prognostics.
Main Methods:
- Analysis of high-throughput genomic data to understand molecular pathway structure in cancer.
- Prediction of molecular entity activities using pathway analysis.
- Development of the "activitome" concept, a composite of molecular activities, to link genomic events with clinical factors.
Main Results:
- Genomic alterations can lead to concordant pathway activities and common tumor subtypes.
- Pathway activity signatures show potential for providing prognostic and diagnostic cues.
- The "activitome" framework connects genomic events to clinical factors to identify drivers of poor outcomes.
Conclusions:
- Pathway-based signatures, or the "activitome," offer a promising approach for cancer prognostics and therapeutics.
- This approach moves beyond traditional gene expression signatures to leverage higher-level biological information.
- Translating genomic insights into clinical knowledge through pathway analysis can improve patient care for high-risk individuals.
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