Precision Medicine: What Challenges Are We Facing?

Empowering Healthcare through Precision Medicine: Unveiling the Nexus of Social Factors and Trust

This study investigated the impact of social factors on the acceptance of precision medicine (PM) using a quantitative survey grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The findings revealed that social influence has a significantly positive effect on PM acceptance, while the influence of social media is found to be insignificant. Performance expectancy emerged as the most influential factor, demonstrating a significant relationship with PM acceptance. Trust plays a crucial moderating role, mitigating the impact of social factors on PM acceptance. While exploring the mediating effects of trust, we identified a significant mediation effect for social influence and performance expectancy on PM acceptance. However, the mediation effect of social media influence is insignificant. These findings highlight the importance of trust in shaping decisions regarding PM acceptance. These findings have significant implications for healthcare practitioners and policymakers aiming to promote the adoption of precision medicine in clinical practice.

Genomics and Transcriptomics: The Powerful Technologies in Precision Medicine

In a clinical trial, people with the same disease can show different responses after treatment with the same drug and exactly under the same conditions. Some of them may improve, some may not show any response, and occasionally side effects may be observed. In other words, people with the same disease process under the same therapeutic conditions may have different responses. Today, some diseases are resistant to conventional (standard) treatment procedures. Why do people with the same disease show different responses to the treatment with the same drug? This is primarily due to differences in molecular pathways (especially genetic variations) associated with the disease. On the other hand, designing and delivery of a new drug is a time-consuming and costly process, so any mistake in any stage of this process can have irreparable consequences for pharmaceutical companies and consumer patients. Therefore, we can achieve more accurate and reliable treatments by acquiring precise insight into different aspects of precision medicine including genomics and transcriptomics. The aim of this paper is to address the role of genomics and transcriptomics in precision medicine.

Precision Medicine: Steps along the Road to Combat Human Cancer

The diagnosis and treatment of diseases such as cancer is becoming more accurate and specialized with the advent of precision medicine techniques, research and treatments. Reaching down to the cellular and even sub-cellular level, diagnostic tests can pinpoint specific, individual information from each patient, and guide providers to a more accurate plan of treatment. With this advanced knowledge, researchers and providers can better gauge the effectiveness of drugs, radiation, and other therapies, which is bound to lead to a more accurate, if not more positive, prognosis. As precision medicine becomes more established, new techniques, equipment, materials and testing methods will be required. Herein, we will examine the recent innovations in assays, devices and software, along with next generation sequencing in genomics diagnostics which are in use or are being developed for personalized medicine. So as to avoid duplication and produce the fullest possible benefit, all involved must be strongly encouraged to collaborate, across national borders, public and private sectors, science, medicine and academia alike. In this paper we will offer recommendations for tools, research and development, along with ideas for implementation. We plan to begin with discussion of the lessons learned to date, and the current research on pharmacogenomics. Given the steady stream of advances in imaging mass spectrometry and nanoLC-MS/MS, and use of genomic, proteomic and metabolomics biomarkers to distinguish healthy tissue from diseased cells, there is great potential to utilize pharmacogenomics to tailor a drug or drugs to a particular cohort of patients. Such efforts very well may bring increased hope for small groups of non-responders and those who have demonstrated adverse reactions to current treatments.

RETRACTED: CGVD: a genomic variation database for Chinese populations

Precision medicine calls upon deeper coverage of population-based sequencing and thorough gene-content and phenotype-based analysis, which lead to a population-associated genomic variation map or database. The Chinese Genomic Variation Database (CGVD; https://bigd.big.ac.cn/cgvd/) is such a database that has combined 48.30 million (M) SNVs and 5.77 M small indels, identified from 991 Chinese individuals of the Chinese Academy of Sciences Precision Medicine Initiative Project (CASPMI) and 301 Chinese individuals of the 1000 Genomes Project (1KGP). The CASPMI project includes whole-genome sequencing data (WGS, 25–30×) from ∼1000 healthy individuals of the CASPMI cohort. To facilitate the usage of such variations for pharmacogenomics studies, star-allele frequencies of the drug-related genes in the CASPMI and 1KGP populations are calculated and provided in CGVD. As one of the important database resources in BIG Data Center, CGVD will continue to collect more genomic variations and to curate structural and functional annotations to support population-based healthcare projects and studies in China and worldwide.

Difficulties and challenges in the development of precision medicine

The rapid development of precision medicine is introducing a new era of significance in medicine. However, attaining precision medicine is an ambitious goal that is bound to encounter some challenges. Here, we have put forward some difficulties or questions that should be addressed by the progress in this field. The proposed issues include the long road to precision medicine for all types of diseases as the unknown domains of the human genome hinder the development of precision medicine. The challenges in the acquisition and analysis of large amounts of omics data, including difficulties in the establishment of a library of biological samples and large-scale data analysis, as well as the challenges of informed consent and medical ethics in precision medicine, must be overcome to attain the goals of precision medicine. To date, precision medicine programs have accomplished many preliminary achievements and will help to drive a dramatic revolution in clinical practices for the medical community. Through these advances, the diagnosis and treatment of many diseases will achieve many breakthroughs. This project is just beginning and requires a great deal of time and money. Precision medicine also requires extensive collaboration. Ultimately, these difficulties can be overcome. We should realize that precision medicine is good for patients, but there is still a long way to go.

Machine Learning Models for Genetic Risk Assessment of Infants with Non-syndromic Orofacial Cleft

The isolated type of orofacial cleft, termed non-syndromic cleft lip with or without cleft palate (NSCL/P), is the second most common birth defect in China, with Asians having the highest incidence in the world. NSCL/P involves multiple genes and complex interactions between genetic and environmental factors, imposing difficulty for the genetic assessment of the unborn fetus carrying multiple NSCL/P-susceptible variants. Although genome-wide association studies (GWAS) have uncovered dozens of single nucleotide polymorphism (SNP) loci in different ethnic populations, the genetic diagnostic effectiveness of these SNPs requires further experimental validation in Chinese populations before a diagnostic panel or a predictive model covering multiple SNPs can be built. In this study, we collected blood samples from control and NSCL/P infants in Han and Uyghur Chinese populations to validate the diagnostic effectiveness of 43 candidate SNPs previously detected using GWAS. We then built predictive models with the validated SNPs using different machine learning algorithms and evaluated their prediction performance. Our results showed that logistic regression had the best performance for risk assessment according to the area under curve. Notably, defective variants in MTHFR and RBP4, two genes involved in folic acid and vitamin A biosynthesis, were found to have high contributions to NSCL/P incidence based on feature importance evaluation with logistic regression. This is consistent with the notion that folic acid and vitamin A are both essential nutritional supplements for pregnant women to reduce the risk of conceiving an NSCL/P baby. Moreover, we observed a lower predictive power in Uyghur than in Han cases, likely due to differences in genetic background between these two ethnic populations. Thus, our study highlights the urgency to generate the HapMap for Uyghur population and perform resequencing-based screening of Uyghur-specific NSCL/P markers.

Male Infertility, Precision Medicine and Systems Proteomics

Precision medicine (PM) is an approach that has the power to create the best effect and safety of medicine and treatment with the least side effects for each person. PM is very helpful as sometimes due to inaccurate or late diagnosis or toxicities of the drugs irreversible side effect for patient's health are generated. This seemingly new and emerging science is also effective in preventing disease, due to differences in the genes, environment, and lifestyles of any particular person. PM can be a prominent criterion in infertility research. To achieve this goal, there should be information from a healthy human body, including genetic and molecular information. A PM is an evolution in health care, which is very helpful even economically. The guarantor of the PM success is the examination of the molecular profile of the patient, including genes, proteins, metabolites, etc. Therefore, genomics, proteomics, and metabolomics-based techniques are very important in this regard. Unfortunately, despite extensive studies on PM practice in various fields, male infertility has remained unresponsive. Given that around 20% of couples around the world suffer from infertility, and almost half of them are related to men's problems, the PM approach has a high potential for male infertility. In this study, with the help of proteomics and metabolomics, PM information on male infertility was explored.

Clinical multi-omics strategies for the effective cancer management

Cancer is a global health issue as a multi-factorial complex disease, and early detection and novel therapeutic strategies are required for more effective cancer management. With the development of systemic analytical -omics strategies, the therapeutic approach and study of the molecular mechanisms of carcinogenesis and cancer progression have moved from hypothesis-driven targeted investigations to data-driven untargeted investigations focusing on the integrated diagnosis, treatment, and prevention of cancer in individual patients. Predictive, preventive, and personalized medicine (PPPM) is a promising new approach to reduce the burden of cancer and facilitate more accurate prognosis, diagnosis, as well as effective treatment. Here we review the fundamentals of, and new developments in, -omics technologies, together with the key role of a variety of practical -omics strategies in PPPM for cancer treatment and diagnosis.

BIOLOGICAL SIGNIFICANCE

In this review, a comprehensive and critical overview of the systematic strategy for predictive, preventive, and personalized medicine (PPPM) for cancer disease was described in a view of cancer prognostic prediction, diagnostics, and prevention as well as cancer therapy and drug responses. We have discussed multi-dimensional data obtained from various resources and integration of multisciplinary -omics strategies with computational method which could contribute the more effective PPPM for cancer. This review has provided the novel insights of the current applications of each and combined -omics technologies, which showed their powerful potential for the establishment of PPPM for cancer.

Personalized or Precision Medicine? The Example of Cystic Fibrosis

The advent of the knowledge on human genetics, by the identification of disease-associated variants, culminated in the understanding of human variability. With the genetic knowledge, the specificity of the clinical phenotype and the drug response of each individual were understood. Using the cystic fibrosis (CF) as an example, the new terms that emerged such as personalized medicine and precision medicine can be characterized. The genetic knowledge in CF is broad and the presence of a monogenic disease caused by mutations in the CFTR gene enables the phenotype–genotype association studies (including the response to drugs), considering the wide clinical and laboratory spectrum dependent on the mutual action of genotype, environment, and lifestyle. Regarding the CF disease, personalized medicine is the treatment directed at the symptoms, and this treatment is adjusted depending on the patient’s phenotype. However, more recently, the term precision medicine began to be widely used, although its correct application and understanding are still vague and poorly characterized. In precision medicine, we understand the individual as a response to the interrelation between environment, lifestyle, and genetic factors, which enabled the advent of new therapeutic models, such as conventional drugs adjustment by individual patient dosage and drug type and response, development of new drugs (read through, broker, enhancer, stabilizer, and amplifier compounds), genome editing by homologous recombination, zinc finger nucleases, TALEN (transcription activator-like effector nuclease), CRISPR-Cas9 (clustered regularly interspaced short palindromic repeats-CRISPR-associated endonuclease 9), and gene therapy. Thus, we introduced the terms personalized medicine and precision medicine based on the CF.

GSA: Genome Sequence Archive<sup/>

With the rapid development of sequencing technologies towards higher throughput and lower cost, sequence data are generated at an unprecedentedly explosive rate. To provide an efficient and easy-to-use platform for managing huge sequence data, here we present Genome Sequence Archive (GSA; http://bigd.big.ac.cn/gsa or http://gsa.big.ac.cn), a data repository for archiving raw sequence data. In compliance with data standards and structures of the International Nucleotide Sequence Database Collaboration (INSDC), GSA adopts four data objects (BioProject, BioSample, Experiment, and Run) for data organization, accepts raw sequence reads produced by a variety of sequencing platforms, stores both sequence reads and metadata submitted from all over the world, and makes all these data publicly available to worldwide scientific communities. In the era of big data, GSA is not only an important complement to existing INSDC members by alleviating the increasing burdens of handling sequence data deluge, but also takes the significant responsibility for global big data archive and provides free unrestricted access to all publicly available data in support of research activities throughout the world.