Toward multiomics-based next-generation diagnostics for precision medicine

The Natural History of Hereditary Colorectal Cancer Syndromes: From Phenotype to Genotype? Where Do We Stand and What Does the Future Hold?

Applying the concept of a "natural history" to hereditary colorectal cancer is an interesting exercise because the way the syndromes are approached has changed so drastically. However, the exercise is instructive as it forces us to think in depth about where we are, where we have been, and, most helpfully, about where we may be going. In this article the diagnosis, along with endoscopic and surgical management of hereditary colorectal cancer are discussed in the context of their history and the changes in genomics and technology that have occurred over the last one hundred years.

A review of disease risk prediction methods and applications in the omics era

Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.

Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine

Mounting evidence has highlighted the implementation of big data handling and management in the healthcare industry to improve the clinical services. Various private and public companies have generated, stored, and analyzed different types of big healthcare data, such as omics data, clinical data, electronic health records, personal health records, and sensing data with the aim to move in the direction of precision medicine. Additionally, with the advancement in technologies, researchers are curious to extract the potential involvement of artificial intelligence and machine learning on big healthcare data to enhance the quality of patient's lives. However, seeking solutions from big healthcare data requires proper management, storage, and analysis, which imposes hinderances associated with big data handling. Herein, we briefly discuss the implication of big data handling and the role of artificial intelligence in precision medicine. Further, we also highlighted the potential of artificial intelligence in integrating and analyzing the big data that offer personalized treatment. In addition, we briefly discuss the applications of artificial intelligence in personalized treatment, especially in neurological diseases. Lastly, we discuss the challenges and limitations imposed by artificial intelligence in big data management and analysis to hinder precision medicine.

Multiomic technologies for analyses of inborn errors of immunity: from snapshot of the average cell to dynamic temporal picture at single-cell resolution

Advances in DNA sequencing technology have significantly impacted human genetics; they have enabled the analysis of genetic causes of rare diseases, which are usually pathogenic variants in a single gene at the nucleotide sequence level. However, since the quantity of data regarding the relationship between genotype and phenotype is insufficient to diagnose some rare immune diseases definitively, genetic information alone cannot help obtain a mechanistic understanding of the disease etiology. For such cases, exploring the molecular phenotype using multiomic analyses could be the approach of choice. In this review, we first overview current technologies for multiomic analysis, particularly focusing on RNA and protein profiling of bulk cell ensembles. We then discuss the measurement modality and granularity issue because it is critical to design multiomic experiments properly. Next, we illustrate the importance of bioimaging by describing our experience with the analysis of an autoinflammatory disease, cryopyrin-associated periodic fever syndrome, which could be caused by low-frequency somatic mosaicism and cannot be well characterized only by multiomic snapshot analyses of an ensemble of many immune cells. We found it powerful to complement the multiomic data with bioimaging data that can provide us with indispensable time-specific dynamic information of every single cell in the "immune cell society." Because we now have many measurement tools in different modalities and granularity to tackle the etiology of rare hereditary immune diseases, we might gain a deeper understanding of the pathogenic mechanisms of these diseases by taking full advantage of these tools in an integrated manner.

Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis

While cost-effective high-throughput technologies provide an increasing amount of data, the analyses of single layers of data seldom provide causal relations. Multi-omics data integration strategies across different cellular function levels, including genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offer unparalleled opportunities to understand the underlying biology of complex diseases, such as cancer. We review some of the most frequently used data integration methods and outline research areas where multi-omics significantly benefit our understanding of the process and outcome of the malignant transformation. We discuss algorithmic frameworks developed to reveal cancer subtypes, disease mechanisms, and methods for identifying driver genomic alterations and consider the significance of multi-omics in tumor classifications, diagnostics, and prognostications. We provide a comprehensive summary of each omics strategy's most recent advances within the clinical context and discuss the main challenges facing their clinical implementations. Despite its unparalleled advantages, multi-omics data integration is slow to enter everyday clinics. One major obstacle is the uneven maturity of different omics approaches and the growing gap between generating large volumes of data compared to data processing capacity. Progressive initiatives to enforce the standardization of sample processing and analytical pipelines, multidisciplinary training of experts for data analysis and interpretation are vital to facilitate the translatability of theoretical findings.

"Omics" in traumatic brain injury: novel approaches to a complex disease

BACKGROUND

To date, there is neither any pharmacological treatment with efficacy in traumatic brain injury (TBI) nor any method to halt the disease progress. This is due to an incomplete understanding of the vast complexity of the biological cascades and failure to appreciate the diversity of secondary injury mechanisms in TBI. In recent years, techniques for high-throughput characterization and quantification of biological molecules that include genomics, proteomics, and metabolomics have evolved and referred to as omics.

METHODS

In this narrative review, we highlight how omics technology can be applied to potentiate diagnostics and prognostication as well as to advance our understanding of injury mechanisms in TBI.

RESULTS

The omics platforms provide possibilities to study function, dynamics, and alterations of molecular pathways of normal and TBI disease states. Through advanced bioinformatics, large datasets of molecular information from small biological samples can be analyzed in detail and provide valuable knowledge of pathophysiological mechanisms, to include in prognostic modeling when connected to clinically relevant data. In such a complex disease as TBI, omics enables broad categories of studies from gene compositions associated with susceptibility to secondary injury or poor outcome, to potential alterations in metabolites following TBI.

CONCLUSION

The field of omics in TBI research is rapidly evolving. The recent data and novel methods reviewed herein may form the basis for improved precision medicine approaches, development of pharmacological approaches, and individualization of therapeutic efforts by implementing mathematical "big data" predictive modeling in the near future.