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Precision medicine in the era of multi-omics: can the data tsunami guide rational treatment decision?
M Aldea1, L Friboulet2, S Apcher2
1Department of Medical Oncology, Gustave Roussy, Villejuif; PRISM, INSERM, Gustave Roussy, Villejuif.
Abstract:
Precision medicine for cancer is rapidly moving to an approach that integrates multiple dimensions of the biology in order to model mechanisms of cancer progression in each patient. The discovery of multiple drivers per tumor challenges medical decision that faces several treatment options. Drug sensitivity depends on the actionability of the target, its clonal or subclonal origin and coexisting genomic alterations. Sequencing has revealed a large diversity of drivers emerging at treatment failure, which are potential targets for clinical trials or drug repurposing. To effectively prioritize therapies, it is essential to rank genomic alterations based on their proven actionability. Moving beyond primary drivers, the future of precision medicine necessitates acknowledging the intricate spatial and temporal heterogeneity inherent in cancer. The advent of abundant complex biological data will make artificial intelligence algorithms indispensable for thorough analysis. Here, we will discuss the advancements brought by the use of high-throughput genomics, the advantages and limitations of precision medicine studies and future perspectives in this field.
Insights
Precision medicine integrates multi-dimensional biology to model cancer progression. Ranking genomic alterations by actionability is key for effective cancer therapy, especially with emerging drivers.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Precision medicine in cancer increasingly integrates multi-dimensional biological data.
- Identifying multiple tumor drivers complicates treatment decisions and necessitates advanced analytical approaches.
Purpose of the Study:
- To discuss advancements in high-throughput genomics for precision cancer medicine.
- To explore the advantages, limitations, and future perspectives of precision medicine studies.
Main Methods:
- Review of high-throughput genomic data integration.
- Analysis of drug sensitivity based on target actionability, clonal origin, and genomic alterations.
Main Results:
- Genomic sequencing reveals diverse drivers emerging at treatment failure, offering targets for clinical trials and drug repurposing.
- Prioritizing therapies requires ranking genomic alterations by proven actionability.
Conclusions:
- Future precision medicine must address cancer's spatial and temporal heterogeneity.
- Artificial intelligence is essential for analyzing complex biological data in precision oncology.
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