The Next Breakthrough in Precision Medicine: OMICs-Based Approaches and Computational Work
By Shrey Kohli, Ph.D.
Despite remarkable scientific advances and the plethora of scientific information collected in medicine, specific and targeted therapies remain challenging. The study of most disease processes occurs after manifestation of symptoms, which limits innovation of interventions to halt disease progression. Beyond this, development of therapy resistance may lead to disease relapse and therefore limit the therapeutic efficacy. The complexity of most diseases means involvement of multiple cell types, which makes it difficult to develop therapies specific enough to avoid adverse outcomes. Therefore, approaches to develop personalized and precision medicine are urgently needed.
The coronavirus pandemic challenged the traditional working style of scientists. These difficult circumstances forced us to think out of the box and invent efficient approaches to be productive. Computational work proved to be an asset in these challenging times and enabled us to explore understudied avenues for personalized medicine.
The latest advancement in single-cell omics-based approaches combined with artificial intelligence to construct a molecular disease map enables us to better understand disease trajectories. A boom in single-cell transcriptomics-based approaches provides us with comprehensive cell atlas of healthy and diseased organs. These atlases reveal the complexity and heterogeneity of cell types and their plasticity. Single-cell epigenetic studies provide additional information on chromatin accessibility, DNA methylation, histone modifications, and organization.
This information enabled us to understand the spatio-temporal association of the molecular makeup that regulates disease processes and cellular communication network. The latest proteomics technologies involve mass-cytometer and lipidomics-based approaches, which revealed key insights of functionally relevant nodes in the disease map. However, paramount is to integrate these approaches with other artificial-intelligence-based approaches. This would include analysis of multi-center disease cohorts, high-content imaging, and patient-derived experimental disease models such as organ-on-a-chip approaches.
Combining approaches would result in scale up of the information available from omics to clinical applications. Handling and integration of such large molecular datasets will require sophisticated computational pipelines and machine-learning approaches. Such predictive computational models will lay the groundwork for advanced personalized disease models.
Computational approaches have classically been used to identify molecular structures and their interactions. Current algorithms enable us to dock computationally-designed chemical structures on targets and explore novel therapeutic approaches. Although these findings typically need fusion of computational and experimental efforts, they can largely reduce the energy to screen multi-compound libraries.
The potential of digital work and molecular networks integrating with artificial intelligence is unprecedented and needs to be unlocked. The current advancements set the stage of such combined data analysis but require further development of dedicated pipelines to implement this in routine clinical care.