Genome-based medicine in Korea: the Korea National Institute of Health infrastructure for precision medicine

This study aimed to investigate awareness, attitudes, and perspectives on precision medicine among health professionals in Korea and to identify issues that need to be addressed before implementing precision medicine. Mixed methods research was applied. For qualitative research, a semi-structured focus group interview was conducted with six health professionals. For quantitative research, a self-reported survey was administered. A total of 542 health professionals participated in the survey, and 526 completed the entire questionnaire. Health professionals showed positive attitudes toward precision medicine. About 95–96% of respondents agreed that precision medicine will be effective in treatment and precise diagnosis, and 69.9% reported that they would participate as study subjects. Meanwhile, they expressed concerns regarding educating patients and health professionals in precision medicine and developing research and data sharing infrastructure. Also, they emphasized the importance of developing precision medicine in an equitable way. Despite varying levels of awareness of precision medicine, the health professionals expressed a willingness to engage in precision medicine research, and recommended that health professionals work closely with policymakers to design precision medicine in a way that can be effectively adopted. Health professionals showed had a positive, but cautious, attitude toward precision medicine. The results of this study suggest areas to be addressed before ushering in precision medicine in Korea.

Background Precision medicine (PM) research and clinical application is moving forward at a rapid pace. To ensure ethical inclusion of all populations in PM, in-depth understanding of diverse communities’ views of PM research and PM implementation is necessary. Methods Semi-structured interviews were conducted to explore perspectives on PM in a tribally managed healthcare organization. Thematic analysis was used to analyze data from 46 interviews. Results Participants described gains in diagnostic efficiency, risk identification for preventable disease, and the advancement of population-specific biomedical research as key benefits of PM. Concerns expressed related to privacy risks associated with data-sharing, overpromising on PM, and managing patient expectations related to PM. Stakeholders encouraged PM implementation to be preceded by health education activities that leverage a range of communication strategies. Conclusion Perspectives described in this study may aid in and should be considered prior to implementation of PM in this and other healthcare systems, especially those serving diverse populations.

A logic model for precision medicine implementation informed by stakeholder views and implementation science

Public participation in precision medicine (PM) research is essential to achieving effective health care but has been impeded by a lack of awareness and basic knowledge. There is a critical need for educational materials that can clearly explain PM to foster involvement. This randomized controlled trial with a posttest-only control group design aims to assess the effects of educational messages delivered through animations relative to live-action videos and leaflets on intentions of involvement in PM research. Knowledge as the moderator and four mediators (engagement, vividness, trustworthiness, and cognitive value) of the intended effects were also evaluated. A total of 326 U.S. adults were sampled from Amazon Mechanical Turk. Among participants with less knowledge about PM, animations produced stronger information-seeking intentions and willingness to participate than leaflets. The effects of three message modalities were not significantly different among average and highly knowledgeable participants. Engagement and vividness mediated the effects of animations relative to two other message modalities. Trustworthiness and cognitive value mediated the effects of animations relative to live-action videos. Overall, animations can be an effective communication strategy to motivate involvement in PM but its effectiveness could decline as knowledge increases. The explanations and implications of the findings were discussed.

Introduction: Precision medicine research investigates the differences in individuals’ genetics, environment, and lifestyle to tailor health prevention and treatment options as part of an emerging model of health care delivery. Advancing precision medicine research will require effective communication across a wide range of scientific and health care disciplines and with research participants who represent diverse segments of the population. Methods: A multidisciplinary group convened over the course of a year and developed precision medicine research case examples to facilitate precision medicine research discussions with communities. Results: A shared definition of precision medicine research as well as six case examples of precision medicine research involving genetic risk, pharmacogenetics, epigenetics, the microbiome, mobile health, and electronic health records were developed. Discussion/Conclusion: The precision medicine research definition and case examples can be used as planning tools to establish a shared understanding of the scope of precision medicine research across multidisciplinary teams and with the diverse communities in which precision medicine research will take place. This shared understanding is vital for successful and equitable progress in precision medicine.

The massive adoption of high-throughput genomics, deep sequencing technologies and big data technologies have made possible the era of precision medicine. However, the volume of data and its complexity remain important challenges for precision medicine research, hindering development in this field. The literature on precision medicine research describes a few platforms to support specific types of studies, but none of these offer researchers the level of customization required to meet their specific needs [1]. Methods: We propose to design and develop a platform able to import and integrate a very large volume of genetics, clinical, demographical and environmental data in a cloud computing infrastructure. In our previous publication, we presented an approach that can customize existing data models to fit any precision medicine research data requirement [1] and the requirement for future large-scale precision medicine platforms, in terms of data extensibility and the scalability of processing on demand. We also proposed a framework to meet the specific requirement of any precision medicine research [2]. In this paper, we describe how this new framework was implemented and trialed by the precision medicine researchers at the Centre Hospitalier Universitaire de l'Université de Montréal (CHUM). Results: The data analysis simulations showed that the random forest algorithm presents better accuracy results. We obtained an F1-Score of 72% for random forest, 69% using linear regression and 62% using the neural network algorithm. Conclusion: The results suggest that the proposed precision medicine data analysis platform allows researchers to configure, prepare the analysis environment and customize the platform data model to their specific research in very optimal delays, at very low cost and with minimal technical skills.

Aim: Precision medicine research recruitment poses challenges. To better understand factors impacting recruitment, this study assessed hypothetical willingness, public opinions of and familiarity with precision medicine research. Materials & methods: Adult attendees (n=942) at the 2017 Minnesota State Fair completed an electronic survey. Results: Few respondents had heard of 'precision medicine' (18%), and familiarity came mostly from media (43%). Fifty-six percent expressed hypothetical willingness to participate in precision medicine research. Significant predictors of willingness were: comfort with unconditional research; perceiving precision medicine research as beneficial, trustworthy and confidential; having a graduate degree; comfort with self- but not family-participation; and familiarity with precision/personalized medicine. Conclusion: This study identified predictors of hypothetical willingness to participate in precision medicine research. Alternative recruitment strategies are needed.

Precision medicine holds promise for improving health care by tailoring disease treatment and prevention efforts to the needs of individual patients. It also raises ethical questions related to equitable distribution of the benefits of precision medicine; data management, including the terms of data ownership, sharing, and security; and, the nature and extent of community engagement in and oversight of research. These questions are particularly salient for minoritized communities that have been harmed by unethical research practices and often deprived the full benefit of advances in medical science. Understanding the perspectives of these communities is essential to the design and conduct of ethical and effective precision medicine research. This study explored perspectives on the acceptability, feasibility, value, and benefits and harms of precision medicine research among Alaska Native and American Indian (ANAI) peoples. We conducted four focus groups with ANAI individuals who receive primary care from a Tribal health organization in Anchorage, Alaska. Participants were willing to engage in precision medicine research provided specific requirements were met. Research must be conducted by the Tribal health organization or another trusted partner, community health priorities must drive the research agenda, and researchers must employ robust data protections to guard against loss of data security and maintain control over data use and access. These requirements work collectively to ensure research benefits and respects Tribal sovereignty. These findings could help inform efforts to design and implement precision medicine research programs tailored to concerns of ANAI peoples.

The completion of the Human Genome Project has unleashed a wealth of human genomics information, but it remains unclear how best to implement this information for the benefit of patients. The standard approach of biomedical research, with researchers pursuing advances in knowledge in the laboratory and, separately, clinicians translating research findings into the clinic as much as decades later, will need to give way to new interdisciplinary models for research in genomic medicine. These models should include scientists and clinicians actively working as teams to study patients and populations recruited in clinical settings and communities to make genomics discoveries-through the combined efforts of data scientists, clinical researchers, epidemiologists, and basic scientists-and to rapidly apply these discoveries in the clinic for the prediction, prevention, diagnosis, prognosis, and treatment of cardiovascular diseases and stroke. The highly publicized US Precision Medicine Initiative, also known as All of Us, is a large-scale program funded by the US National Institutes of Health that will energize these efforts, but several ongoing studies such as the UK Biobank Initiative; the Million Veteran Program; the Electronic Medical Records and Genomics Network; the Kaiser Permanente Research Program on Genes, Environment and Health; and the DiscovEHR collaboration are already providing exemplary models of this kind of interdisciplinary work. In this statement, we outline the opportunities and challenges in broadly implementing new interdisciplinary models in academic medical centers and community settings and bringing the promise of genomics to fruition.

Introduction and Objective: This purpose of this program is to develop and characterize PDXs from multiple organ sites and patients of multiple ethnic backgrounds, and to make these resources available to the scientific community for translational, precision medicine, and health disparity research. Methods: PDXs were directly developed from patient cancer specimens, molecularly characterized with next-generation sequencing, and annotated with clinical information. Various aspects of its applications were tested. Results: UC Davis, working in collaboration with The Jackson Laboratory (JAX), has developed PDX models, reflecting a wide range of tumor types, from 150 of our patients. Each of these models is annotated with clinical and genomic information. The fidelity of these PDXs was validated by the retention of histopathological features and the conservation of genetic aberrations (i.e., 92-97%) of the original patient tumors in bladder cancer PDXs tested. Extensive preclinical studies showed that these PDXs could potentially be used to screen multiple therapeutic agents simultaneously to identify the most efficacious drugs or drug combinations, re-purpose FDA-approved drugs, decipher the mechanisms of primary resistance, decrypt the mechanisms of secondary resistance, guide drug development, and identify biomarkers. For drug repurposing, the EGFR/ERBB2 dual inhibitor lapatinib effectively prolonged the overall survival (OS) of mice carrying ERBB2+ bladder cancer PDXs from 18.4 days to 25.4 days (p=0.007). For screening of targeted therapy in a PDX carrying PI3K, ERBB2 and Src alterations, only a PI3K inhibitor (BEZ235) prolonged the OS (p<0.0001) while lapatinib and a Src inhibitor ponatinib had no effect. For biomarker development, DNA adduct levels correlated with cancer response to alkylating agents and this project has already been translated into a clinical trial. For determination of the mechanisms of secondary resistance, loss of tumor suppressor gene LSP1 expression was associated with secondary resistance to an inhibitor of the PI3K pathway which is commonly mutated in many cancers. With the support of the Minority PDX Development and Trial Center (M-PDTC) U54 grant, UC Davis will continue to establish over 200 PDX models from bladder, lung squamous cell, gastric and hepatocellular carcinoma, with >60% of the specimens coming from minority patients, for health disparity research. Conclusions: PDXs have great potential for cancer translational, precision medicine and health disparity research. The NCI-funded U54 center with minority PDXs is open for collaboration through the PDX Development and Trial Centers Research Network (PDXNet). Citation Format: Chong-Xian Pan, Hongyong Zhang, Ai-Hong Ma, Shuxiong Zeng, Maike Zimmermann, Clifford Tepper, Paul Henderson, Luis Carvajal-Carmona, Regina Gandour-Edwards, Moon Chen, Susan Airhart, Ralph de Vere Whtie. A patient-derived xenograft (PDX) platform for cancer translational, precision medicine and health disparity research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1067.

Precision medicine (PM) has taken center stage in healthcare since the completion of the genomic project. Developed countries have gradually integrated PM into mainstream patient management. However, Nigeria still grapples with wide acceptance, key translational research and implementation of PM. This study sought to explore the knowledge and attitude of PM among pharmacists as key stakeholders in the healthcare team. A cross-sectional study was conducted in selected tertiary hospitals across the country. A 21-item semi-structured questionnaire was administered by hybrid online and physical methods and the results analyzed with Statistical Package for the Social Sciences Version 25. Descriptive statistics were used to summarize the data. A chi-square test was employed to determine the association of knowledge of PM and the sociodemographic characteristics of the study population. A total of 167 hospital pharmacists participated in the study. A high proportion of the participants are familiar with artificial intelligence (91.75%), Pharmacogenomics (84.5%), and precision medicine (61%). Overall, 38.9% of the pharmacists had a good knowledge while 13.2% had a poor knowledge of PM and associated terms. The level of knowledge did not correlate significantly with gender (X 2 = 3.21, p = 0.201), age (X 2 = 5, p = 0.27), marital status (X 2 = 3.21, p = 0.201), and professional level (X 2 = 6.85, p = 0.144). The most important value of precision medicine to hospital pharmacists is the ability to minimize the impact of disease through preventive medicine (49%) while a large portion are pursuing and or actively planning to pursue additional education in precision medicine. There is a highly positive attitude toward the prospect of PM among hospital pharmacists in Nigeria. Education modules in this field are highly recommended as most do not have a holistic knowledge of terms used in PM. Also, more research aimed at translating PM knowledge into clinical practice is recommended.

The field of precision medicine has undergone rapid development over the past three decades, driven by advances in high-throughput molecular profiling, large-scale electronic health data, and computational modeling. The central objective is to refine disease risk prediction, diagnosis, and treatment strategies by incorporating genetic, molecular, environmental, and clinical information into individualized care. However, the effective integration of these heterogeneous data sources presents substantial analytical challenges. The 2026 Precision Medicine session of the Pacific Symposium on Biocomputing (PSB) highlights computational methods that bridge large-scale biological data and intermediate phenotypes, emphasizing approaches that advance mechanistic understanding, risk prediction, and clinical utility. The contributions span multi-modal risk modeling, biomarker discovery, and causal inference frameworks, demonstrating the breadth and depth of research in computational precision medicine.

Background The American Indian and Alaska Native (AI/AN) population is increasing and AI/AN people are living longer than ever before. Although aging-related health issues such as cognitive impairment, dementia, and Alzheimer's disease (AD) are becoming more visible among this population, reliable data on AD prevalence and risk factors among AI/AN people are nearly nonexistent. Concurrently, precision medicine (PM) has demonstrated significant potential for detecting and treating diseases such as AD. For PM to promote health equity for underserved populations, it must not exacerbate existing health disparities and bias in research. There is also little information about preferences among AI/AN people for communicating information regarding AD, PM, or recruitment into clinical trials. Communication barriers and few known facilitators contribute to low rates of AI/AN research participation. This study seeks to address the gaps in AD and PM research among AI/AN communities and promote knowledge of, attitudes towards, and interest and participation in AD-related PM research efforts. Methods We designed a three-armed RCT to determine the effect of a culturally tailored brochure and video compared to non-tailored recruitment materials. Participants were recruited in Rapid City, South Dakota and were required to meet the following eligibility criteria: 1) identify as AI/AN; 2) be able to speak, read, and understand English; 3) be aged 40 or older; and 4) have the cognitive and decisional capacity to consent and sign and date the informed consent document. Results We enrolled 914 in the RCT and 812 have been randomized to a study condition. The mean age is 54 years (standard deviation = 10.3 years); 62% are female. Overall, 22% reported a parent, grandparent or sibling have been diagnosed with AD, and 22% reported a family member with an other type of dementia. One quarter (25%) of participants reported having an undiagnosed memory problem themselves, and 22% reported having a family member with undiagnosed memory problems. Of randomized participants, 743 (72%) enrolled in the research registry. Conclusions This study will inform future recruitment efforts for ADRD-focused clinical trials. Enrollment of AI/AN participants in an Alzheimer's Disease-Precision Medicine (AD-PM) Registry will provide opportunity for future research on this topic in partnership with this population.

Towards computational solutions for precision medicine based big data healthcare system using deep learning models: A review

Precision medicine should aspire to reduce error and improve accuracy in medical and health recommendations by comparison with contemporary practice, while maintaining safety and cost-effectiveness. The etiology, clinical manifestation and prognosis of diseases such as obesity, diabetes, cardiovascular disease, kidney disease and fatty liver disease are heterogeneous. Without standardized reporting, this heterogeneity, combined with the diversity of research tools used in precision medicine studies, makes comparisons across studies and implementation of the findings challenging. Specific recommendations for reporting precision medicine research do not currently exist. The BePRECISE (Better Precision-data Reporting of Evidence from Clinical Intervention Studies & Epidemiology) consortium, comprising 23 experts in precision medicine, cardiometabolic diseases, statistics, editorial and lived experience, conducted a scoping review and participated in a modified Delphi and nominal group technique process to develop guidelines for reporting precision medicine research. The BePRECISE checklist comprises 23 items organized into 5 sections that align with typical sections of a scientific publication. A specific section about health equity serves to encourage precision medicine research to be inclusive of individuals and communities that are traditionally under-represented in clinical research and/or underserved by health systems. Adoption of BePRECISE by investigators, reviewers and editors will facilitate and accelerate equitable clinical implementation of precision medicine.