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History, Challenges, and Future Directions of Bioinformatics in Biomarker Development
By Yu Liang, Ph.D., Director of Clinical Biomarkers, Calithera Biosciences
When genetic codes pass the messages into RNA, most of the fidelity is maintained, so the implicated functions and biologies at the RNA level are primarily reflected from a 2-dimensional profile, that is, what genes are expressed and how much they are expressed, or a 3-dimensional profile with time added on top of these 2 dimensions. Because transcription of genes represents how a genome responds to extrinsic and intrinsic stimuli, the demand for bioinformatics in this phase expanded towards using statistical procedures to decipher the gene expression profiles, that is, to identify genes with similar patterns of expression, functions these genes possess, and biological pathways they belong to.
Moving forward, it is desirable for bioinformatics to utilize artificial intelligence and develop powerful ML, continuing to improve and build diagnostic, prognostic, or predictive devices in precision medicine
Compendium of sequences and their computationally predicted or experimentally verified functions continued to grow during this time.
The statistical component of the bioinformatics activities became the most critical pillar, so false discovery rate and data over fitting can be properly controlled during meta-analysis and data-mining. A few biomarkers identified in this period through genome-wide expression profiling were successfully developed into actionable targets for therapeutics or medical devices. Ever-improving cutting-edge technologies continued to provide faster data delivery, higher throughput, and broader coverage, along with falling costs. However, the deluge of data and scientific discoveries in biomarkers did not translate into a comparable number of final qualified biomarkers that are used in practice. This highlights the inseparable role of bioinformatics in biomarker development and the importance of exerting statistical rigor during this process.
Increasing volume of data and complexity of analytics demands automation of processing. Using algorithms to speed up data processing and using machine learning (ML) to facilitate finding patterns and modeling prediction became an emerging trend. These are valuable for biomarker discovery within one or few platforms in exploratory settings initially, but their scope needed to be further expanded in the current era of the history of bioinformatics because of two factors. The first factor started with the emergence of systems biology. Systems biology is a discipline using computational and mathematical analyses and modeling as a platform to holistically interrogate complex biological systems. Data and analyses needed to be integrated from not only just within the same cells and same tissues, but also the crosstalk between cells and tissues, for example, interactions between cells, between tissues, between cells and their matrix, and between microbiota and its host organism. This systems approach was expected to provide insights for biomarker discovery in a more robust and mechanistically relevant fashion. Along the path of biomarker-driven development of devices, one big challenge to bioinformatics will be creating the capacity to integrate information beyond systems biology, namely systems medicine. Systems medicine covers how a human body interacts with the environment, and the information would likely be reflected from all the medical records, laboratory data, disease history, and response to prior treatments, etc.
The second factor is represented by the great success in treating cancer using immune checkpoint inhibitors (ICI) in the past decade. ICI is a type of immunotherapy with impressive efficacy but only in a minority of patients, sometimes 10 to 30 percent depending on the tumor types. Increased expression of the ligand of the drug target enriches patients who would respond but only in certain settings. Incorporating multiple biomarkers such as quantity and distribution of subsets of immune cells, mutation burden could increase the predicting power of the markers, but the effectiveness is still uncertain, and the list of candidates is still growing. Moving forward, it is desirable for bioinformatics to utilize artificial intelligence and develop powerful ML, continuing to improve and build diagnostic, prognostic, or predictive devices in precision medicine.
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