Precision Medicine Research Research Articles

Precision Medicine in Colorectal Cancer: Targeted Therapies and Biomarker Insights.

The current review delves into the transformative role of precision medicine in addressing Colorectal Cancer [CRC], a pressing global health challenge. It examines closely signalling pathways, genetic and epigenetic modifications, and microsatellite in-stability. The primary focus is on elucidating biomarkers revolutionizing CRC diagnosis and treatment. Genetic biomarkers encompass non-coding RNA, epigenetic markers, TP53 mutations, and KRAS, NRAS, and BRAF gene alterations. Targeted therapies, including anti-EGFR, anti-VEGF, immune checkpoint inhibitors, and HER2-targeted treatments, are explored along with their mechanisms and clinical applications. Additionally, we highlight the importance of utilizing personalized treatment strategies by employing molecular pro-filing and genetic testing. These approaches facilitate the identification of appropriate pa-tients for targeted therapies. Clinical trials supporting these treatments are presented, em-phasizing response rates and survival outcomes. Detailed exploration of resistance mecha-nisms to targeted therapies and strategies to overcome resistance is also provided, paving the way for more effective regimens. Directions for future research in precision medicine, such as biomarkers, combinations, and liquid biopsy in the oncology field, are described. However, the precise application of precision medicine in CRC comes with questions such as costs, considerations of ethical factors, and the need to sensitize patients. Nonetheless, based on a meticulous analysis of several aspects, precision medicine finds itself equipped with the ability to enhance the patients' prognosis ,thereby, lessening the global oncologic burden of CRC and provide important information to the clinician-scientist and policy-maker that might benefit from the ongoing development of precision medicine.

A national framework for transition to precision medicine

Precision medicine (PM) is transforming healthcare by offering tailored interventions that address individual variability, transforming patient care and outcomes. PM is based on providing health-oriented services according to genetic characteristics, individual and family medical history, lifestyle, place of residence, and other personalized characteristics. This study aims to establish an appropriate framework for implementing PM in Iran. First, the global transition framework to PM was drawn by a systematic review, and then a framework for transition to PM in Iran was drawn by a case study through semi-structured interviews, an expert panel, and an analytic hierarchy process (AHP) questionnaire. The statistical sample of the study comprised PM specialists, researchers, and patients whose PM plays a significant role in their diagnosis and treatment. The sampling method was non-random with a combination of purposive and snowball techniques. The results from the systematic review show that for the transition to PM, we must first move from common medicine to stratified medicine and then PM. Moving toward PM requires strong economic, social, political, institutional, industrial, and, most importantly, technological infrastructures. These infrastructures will vary from country to country. In general, coexistence between the health system and PM technologies did not exist in the beginning, but it will emerge with its development. The resistance of the health system to accepting PM will gradually decrease. Furthermore, the government plays a key role in the early phases, while market and PM demand become more prominent during the development. New health actors will also develop PM, and out-of-date actors will be deleted or replaced. But moving toward PM is slightly different in Iran, particularly in the middle phases of transition.

Standard operating procedure combined with comprehensive quality control system for multiple LC-MS platforms urinary proteomics

Urinary proteomics is emerging as a potent tool for detecting sensitive and non-invasive biomarkers. At present, the comparability of urinary proteomics data across diverse liquid chromatography−mass spectrometry (LC-MS) platforms remains an area that requires investigation. In this study, we conduct a comprehensive evaluation of urinary proteome across multiple LC-MS platforms. To systematically analyze and assess the quality of large-scale urinary proteomics data, we develop a comprehensive quality control (QC) system named MSCohort, which extracted 81 metrics for individual experiment and the whole cohort quality evaluation. Additionally, we present a standard operating procedure (SOP) for high-throughput urinary proteome analysis based on MSCohort QC system. Our study involves 20 LC-MS platforms and reveals that, when combined with a comprehensive QC system and a unified SOP, the data generated by data-independent acquisition (DIA) workflow in urine QC samples exhibit high robustness, sensitivity, and reproducibility across multiple LC-MS platforms. Furthermore, we apply this SOP to hybrid benchmarking samples and clinical colorectal cancer (CRC) urinary proteome including 527 experiments. Across three different LC-MS platforms, the analyses report high quantitative reproducibility and consistent disease patterns. This work lays the groundwork for large-scale clinical urinary proteomics studies spanning multiple platforms, paving the way for precision medicine research.

Automated Laser-Assisted Single-Cell Sorting for Cell Functional and RNA Sequencing.

Accurate and efficient sorting of single target cells is crucial for downstream single-cell analysis, such as RNA sequencing, to uncover cellular heterogeneity and functional characteristics. However, conventional single-cell sorting techniques, such as manual micromanipulation or fluorescence-activated cell sorting, do not match current demands and are limited by low throughput, low sorting efficiency and precision, or limited cell viability. Here, we report an automated, highly efficient single-cell sorter, integrating laser-induced forward transfer (LIFT) with a high-throughput picoliter micropore array. The micropore array was surface-functionalized to manipulate liquid surface tension, facilitating the formation of single-cell picoliter droplets in the micropores to realize automated and highly efficient (>80%) single-cell isolation. Using an in-house built microscopic system, rare target cells were identified and automatically retrieved by LIFT with precise sorting efficiency (about 100%) for downstream single-cell analysis while maintaining high cell viability (about 80%). As a case demonstration, we demonstrated the accurate sorting of rare transfected PC-9 cells and post-transfection cell culture, minimizing cell loss and the risk of contamination. Furthermore, we performed single-cell RNA sequencing and showed that high-quality single-cell transcriptome information was efficiently and reliably obtained during cell sorting, preventing additional costs due to low sorting accuracy. The single-cell sorter will become invaluable for single-cell analysis, laying the foundation for multiomics analysis and precision medicine research.

State-of-the-Art Liver Cancer Organoids: Modeling Cancer Stem Cell Heterogeneity for Personalized Treatment.

Liver cancer poses a global health challenge with limited therapeutic options. Notably, the limited success of current therapies in patients with primary liver cancers (PLCs) may be attributed to the high heterogeneity of both hepatocellular carcinoma (HCCs) and intrahepatic cholangiocarcinoma (iCCAs). This heterogeneity evolves over time as tumor-initiating stem cells, or cancer stem cells (CSCs), undergo (epi)genetic alterations or encounter microenvironmental changes within the tumor microenvironment. These modifications enable CSCs to exhibit plasticity, differentiating into various resistant tumor cell types. Addressing this challenge requires urgent efforts to develop personalized treatments guided by biomarkers, with a specific focus on targeting CSCs. The lack of effective precision treatments for PLCs is partly due to the scarcity of ex vivo preclinical models that accurately capture the complexity of CSC-related tumors and can predict therapeutic responses. Fortunately, recent advancements in the establishment of patient-derived liver cancer cell lines and organoids have opened new avenues for precision medicine research. Notably, patient-derived organoid (PDO) cultures have demonstrated self-assembly and self-renewal capabilities, retaining essential characteristics of their respective in vivo tissues, including both inter- and intratumoral heterogeneities. The emergence of PDOs derived from PLCs serves as patient avatars, enabling preclinical investigations for patient stratification, screening of anticancer drugs, efficacy testing, and thereby advancing the field of precision medicine. This review offers a comprehensive summary of the advancements in constructing PLC-derived PDO models. Emphasis is placed on the role of CSCs, which not only contribute significantly to the establishment of PDO cultures but also faithfully capture tumor heterogeneity and the ensuing development of therapy resistance. The exploration of PDOs' benefits in personalized medicine research is undertaken, including a discussion of their limitations, particularly in terms of culture conditions, reproducibility, and scalability.

The interplay of sex and genotype in disease associations: a comprehensive network analysis in the UK Biobank

BackgroundDisease comorbidities and longer-term complications, arising from biologically related associations across phenotypes, can lead to increased risk of severe health outcomes. Given that many diseases exhibit sex-specific differences in their genetics, our objective was to determine whether genotype-by-sex (GxS) interactions similarly influence cross-phenotype associations. Through comparison of sex-stratified disease-disease networks (DDNs)—where nodes represent diseases and edges represent their relationships—we investigate sex differences in patterns of polygenicity and pleiotropy between diseases.ResultsUsing UK Biobank summary statistics, we built male- and female-specific DDNs for 103 diseases. This revealed that male and female diseasomes have similar topology and central diseases (e.g., hypertensive, chronic respiratory, and thyroid-based disorders), yet some phenotypes exhibit sex-specific influence in cross-phenotype associations. Multiple sclerosis and osteoarthritis are central only in the female DDN, while cardiometabolic diseases and skin cancer are more prominent in the male DDN. Edge comparison indicated similar shared genetics between the two graphs relative to a random model of disease association, though notable discrepancies in embedding distances and clustering patterns imply a more expansive genetic influence on multimorbidity risk for females than males. Analysis of pleiotropic contributions of two sexually-dimorphic single-nucleotide polymorphisms related to thyroid disorders further validated a distinct genetic architecture across sexes that influences associations, confirmed through examination of corresponding gene expression profiles from the GTEx Portal.ConclusionsOur analysis affirms the presence of GxS interactions in cross-phenotype associations, emphasizing the need to investigate the role of sex in disease onset and its importance in biomedical discovery and precision medicine research.

Bridging the gap: understanding the perspective of healthcare professional students towards precision medicine in a Nigerian tertiary institution (a cross-sectional study)

BackgroundIndividuals often respond differently to medications, giving rise to the field of precision medicine (PM), which focuses on tailoring treatments to individual genetic, environmental, and lifestyle factors. This study examined the level of comfort healthcare professional students have with their knowledge of precision medicine, alongside their attitudes and perceptions toward precision medicine, at a tertiary institution in Nigeria.MethodsA cross-sectional questionnaire-based study was conducted among healthcare professional students (400–600 level) at the University of Nigeria Nsukka between January and March 2024. The data were analyzed via IBM Statistical Product and Service Solutions (SPSS) for Windows version 27. Descriptive analyses (frequency, percentage, mean, and standard deviation) and chi-square tests were used to summarize and compare the variables. Statistical significance was set at p < 0.05.ResultsA total of 431 healthcare professional students participated in this study. Fewer than half (n = 200, 46.4%) were pharmacy students, and the majority were within the age range of 21–25 years (n = 288, 66.8%). Nearly half (n = 206, 47.8%) reported having information about precision medicine from the internet, and the majority (n = 341, 79.1%) expressed having an interest in a career involving research in precision medicine. More than half of the students (n = 240, 55.7%) were comfortable with their knowledge of precision medicine and had favourable attitudes (n = 236, 54.8%). Additionally, more than half had positive perceptions of ethical concerns (n = 216, 50.1%) and education in precision medicine (n = 239, 55.5%). Gender, age, department, level of study, awareness of PM, and interest in a career involving research were significantly associated with students’ knowledge, attitudes, and perceptions of precision medicine (p < 0.001).ConclusionHealthcare professional students were comfortable with their knowledge of PM and, in addition, had favourable attitudes and positive perceptions toward the use of precision medicine.

A data management system for precision medicine.

Precision, or personalised medicine has advanced requirements for medical data management systems (MedDMSs). MedDMS for precision medicine should be able to process hundreds of parameters from multiple sites, be adaptable while remaining in sync at multiple locations, real-time syncing to analytics and be compliant with international privacy legislation. This paper describes the LogiqSuite software solution, aimed to support a precision medicine solution at the patient care (LogiqCare), research (LogiqScience) and data science (LogiqAnalytics) level. LogiqSuite is certified and compliant with international medical data and privacy legislations. This paper evaluates a MedDMS in five types of use cases for precision medicine, ranging from data collection to algorithm development and from implementation to integration with real-world data. The MedDMS is evaluated in seven precision medicine data science projects in prehospital triage, cardiovascular disease, pulmonology, and oncology. The P4O2 consortium uses the MedDMS as an electronic case report form (eCRF) that allows real-time data management and analytics in long covid and pulmonary diseases. In an acute myeloid leukaemia, study data from different sources were integrated to facilitate easy descriptive analytics for various research questions. In the AIDPATH project, LogiqCare is used to process patient data, while LogiqScience is used for pseudonymous CAR-T cell production for cancer treatment. In both these oncological projects the data in LogiqAnalytics is also used to facilitate machine learning to develop new prediction models for clinical-decision support (CDS). The MedDMS is also evaluated for real-time recording of CDS data from U-Prevent for cardiovascular risk management and from the Stroke Triage App for prehospital triage. The MedDMS is discussed in relation to other solutions for privacy-by-design, integrated data stewardship and real-time data analytics in precision medicine. LogiqSuite is used for multi-centre research study data registrations and monitoring, data analytics in interdisciplinary consortia, design of new machine learning / artificial intelligence (AI) algorithms, development of new or updated prediction models, integration of care with advanced therapy production, and real-world data monitoring in using CDS tools. The integrated MedDMS application supports data management for care and research in precision medicine.

Whole-Exome Analysis and Osteosarcoma: A Game Still Open.

Osteosarcoma (OS) is the most prevalent malignant bone tumor in adolescents and young adults. OS cells grow in a permissive local microenvironment which modulates their behavior and facilitates all steps in tumor development (e.g., proliferation/quiescence, invasion/migration, and drug resistance) and contributes to their intrinsic heterogeneity. The lung parenchyma is the most common metastatic site in OS, and metastatic foci are frequently associated with a poor clinical outcome. Although multiple factors may be responsible for the disease, including genetic mutations (e.g., Rb and p53), the molecular mechanism of development of OS remains unclear, and the conventional treatment for OS is still based on a sequential approach that combines chemotherapy and surgery. Also, despite the increase in clinical trials, the survival rates for OS have not improved. Non-specific targeting therapies thus show poor therapeutic effects, along with side effects at high doses. For these reasons, many efforts have been made to characterize the complex genome of OS thanks to the whole-exome analysis, with the aim of identifying predictive biomarkers to give these patients a better therapeutic option. This review aims to summarize and discuss the main recent advances in OS molecular research for precision medicine.

Engaging Australian healthcare consumers to determine priorities and consensus for precision medicine approaches to detect non-communicable disease in early life: a modified Delphi study

ObjectivesResearch to develop early screening tools to determine an individual’s risk of developing adult-onset disease is a growing field. Expectant parents may find themselves with an option in the future...

Nothing about Us without Us in Precision Medicine: A Call to Reframe Disability Difference in Genetics and Genomics.

Sixty-one million Americans and approximately a billion people worldwide live with some form of disability that limits one or more major life activities. The field of precision medicine continues to grapple with how to best serve disability communities. In this paper, we suggest that precision medicine faces an ethical tension between its goal to treat or cure disabling conditions and views that consider disability as a marginalized identity. We appeal to the concepts of recognition justice and distributive justice to argue that the ELSI community should take a more proactive role in promoting disability inclusion in precision medicine's practice and research. We also highlight two priorities for the ELSI community moving forward: facilitating greater collaboration between genetics and genomic professionals and disability communities and advocating for inclusive research design and disability accommodations in the research process.

Genomics and Health Data Governance in Africa: Democratize the Use of Big Data and Popularize Public Engagement.

Effectively addressing ethical issues in precision medicine research in Africa requires a holistic social contract that integrates biomedical knowledge with local cultural values and Indigenous knowledge systems. Drawing on African epistemologies such as ubuntu and ujamaa and on our collective experiences in genomics and big data research for sickle cell disease, hearing impairment, and fragile X syndrome and the project Public Understanding of Big Data in Genomics Medicine in Africa, we envision a transformative shift in health research data governance in Africa that could help create a sense of shared responsibility between all stakeholders in genomics and data-driven health research in Africa. This shift includes proposing a social contract for genomics and data science in health research that is grounded in African communitarianism such as solidarity, shared decision-making, and reciprocity. We make several recommendations for a social contract for genomics and data science in health, including the coproduction of genomics knowledge with study communities, power sharing between stakeholders, public education on the ethical and social implications of genetics and data science, benefit sharing, giving voice to data subjects through dynamic consent, and democratizing data access to allow wide access by all research stakeholders. Achieving this would require adopting participatory approaches to genomics and data governance.

Your Heart Can't See What Sneakers You Are Wearing: Exercise Training Load in Endurance Athletes Is Inadequately Quantified in Sports Cardiology: A Systematic Review.

Training load may be an important factor underlying the (patho-)physiologic cardiovascular adaptations from endurance exercise. Yet, quantifying training load remains challenging due to the complexity of its components (Frequency, Intensity, Time, and Type [FITT]). In this systematic review we evaluate how training load has been quantified in sports cardiology studies and provide recommendations for how this can be improved. A comprehensive search was conducted across PubMed and EMBASE up to October 2024. Studies involving "sports cardiology," "training load," and "endurance sport" were included. Data extraction included study characteristics, training load assessment methods, cardiovascular outcomes, and athlete profiles. A total of 62 studies with 1,060,700 participants were included in our review. The majority of studies (59.7%) focused on exercise-induced cardiac remodelling, with other topics being cardiac arrhythmias (12.9%), cardiac autonomic adaptation (3.2%), exercise dose-response (6.5%), and coronary heart disease (17.7%). Training load was primarily quantified by questionnaires (58.1%), whereas heart rate monitoring, a more objective measure, was used in only 1.6% of the studies. All studies reported exercise type, but only 19.4% measured all FITT components. There is a lack of uniformity in the assessment of key FITT variables to quantify training load within the field of sports cardiology, with many studies relying on subjective or incomplete methods. As cardiology moves into the precision medicine era, researchers and clinicians should seek to obtain objective training load information from their athletes according to the FITT framework, and data from use of objective wearable devices represent the optimal way to do this.

Lung cancer organoid-based drug evaluation models and new drug development application trends.

Lung cancer is a malignant tumor with high incidence and mortality rates in both men and women worldwide. Although anticancer drugs are prescribed to treat lung cancer patients, individual responses to these drugs vary, making it crucial to identify the most suitable treatment for each patient. Therefore, it is necessary to develop an anticancer drug efficacy prediction model that can analyze drug efficacy before patient treatment and establish personalized treatment strategies. Unlike two-dimensional (2D) cultured lung cancer cells, lung cancer organoid (LCO) models have a three-dimensional (3D) structure that effectively mimics the characteristics and heterogeneity of lung cancer cells. Lung cancer patient-derived organoids (PDOs) also have the advantage of recapitulating histological and genetic characteristics similar to those of patient tissues under in vitro conditions. Due to these advantages, LCO models are utilized in various fields, including cancer research, and precision medicine, and are especially employed in various new drug development processes, such as targeted therapies and immunotherapy. LCO models demonstrate potential applications in precision medicine and new drug development research. This review discusses the various methods for implementing LCO models, LCO-based anticancer drug efficacy analysis models, and new trends in lung cancer-targeted drug development.

Informatics innovation to provide return of value to participant communities in the All of Us Research Program.

The All of Us Research Program harnesses advances in technology, science, and engagement for precision medicine research. We describe informatics innovations which support that goal and return value to the participant cohort and community. Research data from the All of Us Research Program are available to authorized users on the All of Us Researcher Workbench. We describe the technical infrastructure that enables data access and usage for researchers. Participants are considered partners. To ensure return of value, we outline participant access to information. The All of Us Research Hub allows broad access to data, regardless of background. The innovations described are rooted in the program's core values: participation is open and reflects the diversity of the United States; participants are partners and have access to their information; transparency, security, and privacy are of the highest importance; data are broadly accessible; and the program promotes positive change. We assess research impact and reflect on how All of Us can increase existing return of value to participant communities through future informatics advancements. The program will continue to support efforts to ensure equitable access to data and return of value to participants. Looking ahead, we invite the community to join us. All of Us research findings can change clinical care, inform guidelines, and set a new bar for data sharing. The ultimate return of value is better care for all.

Investigating the Role of B Cell Receptors in Pathogen Recognition and Immune Response Activation: Implications for Infection Control and Therapeutic Development

B cell receptors (BCRs) are critical components of the adaptive immune system, enabling precise pathogen recognition and the activation of immune responses. This research examines the molecular and genetic mechanisms underlying BCR function, focusing on their role in detecting, binding, and neutralizing infectious agents. By exploring antigen-BCR interactions, we aim to elucidate how BCRs achieve their remarkable specificity and affinity, ultimately shaping the strength and duration of immune responses. Through advanced immunological assays, flow cytometry, and bioinformatics, this study characterizes the diversity of BCR expression across various infectious models. Genetic processes such as somatic hypermutation and clonal selection are analyzed to understand how BCR repertoires adapt to rapidly evolving pathogens. Predictive models developed using machine learning identify biomarkers within BCR pathways, providing insights into acute infections, chronic diseases, and autoimmune conditions. The findings highlight variations in BCR function that influence immune resilience and susceptibility to infections. These insights inform vaccine development, antibody engineering, and therapeutic strategies targeting BCR pathways. This research underscores the potential of BCRs as biomarkers and therapeutic targets, paving the way for innovative approaches in infection control and immune modulation, ultimately advancing precision medicine and adaptive immunity research.

Age-Related Hearing Impairment: Genome and Blood Methylome Data Integration Reveals Candidate Epigenetic Biomarkers.

Age-related hearing impairment (ARHI) is a major planetary health burden that is in need of precision medicine for prevention, diagnosis, and treatment. The present study was set out to identify candidate epigenetic markers for ARHI. Associations of genetically predicted DNA methylation levels with ARHI risk were evaluated using two sets of blood DNA methylation genetic prediction models in 147,997 cases and 575,269 controls of European descent. A total of 1314 CpG sites (CpGs) were significantly associated with ARHI risk at a false discovery rate (FDR) <0.05, including 12 putatively causal CpGs based on fine-mapping analysis. Measured methylation levels of 247 of the associated CpGs were significantly correlated with measured expression levels of 127 nearby genes in blood at an FDR <0.05. A total of 37 CpGs and their 18 nearby genes showed consistent association directions for the methylation-gene expression-ARHI risk pathway. Importantly, three genes (PEX6, TCF19, and SPTBN1) were enriched in auditory disease categories. Our results indicate that specific CpGs may modulate ARHI risk by regulating the expression of candidate ARHI target genes. Future precision medicine and biomarker development research on ARHI are called for.

Organoid models of breast cancer in precision medicine and translational research.

One of the most famous and heterogeneous cancers worldwide is breast cancer (BC). Owing to differences in the gene expression profiles and clinical features of distinct BC subtypes, different treatments are prescribed for patients. However, even with more thorough pathological evaluations of tumors than in the past, available treatments do not perform equally well for all individuals. Precision medicine is a new approach that considers the effects of patients' genes, lifestyle, and environment to choose the right treatment for an individual patient. As a powerful tool, the organoid culture system can maintain the morphological and genetic characteristics of patients' tumors. Evidence also shows that organoids have high predictive value for patient treatment. In this review, a variety of BC studies performed on organoid culture systems are evaluated. Additionally, the potential of using organoid models in BC translational research, especially in immunotherapy, drug screening, and precision medicine, has been reported.

Biobanking with genetics shapes precision medicine and global health.

Precision medicine provides patients with access to personally tailored treatments based on individual-level data. However, developing personalized therapies requires analyses with substantial statistical power to map genetic and epidemiologic associations that ultimately create models informing clinical decisions. As one solution, biobanks have emerged as large-scale, longitudinal cohort studies with long-term storage of biological specimens and health information, including electronic health records and participant survey responses. By providing access to individual-level data for genotype-phenotype mapping efforts, pharmacogenomic studies, polygenic risk score assessments and rare variant analyses, biobanks support ongoing and future precision medicine research. Notably, due in part to the geographical enrichment of biobanks in Western Europe and North America, European ancestries have become disproportionately over-represented in precision medicine research. Herein, we provide a genetics-focused review of biobanks from around the world that are in pursuit of supporting precision medicine. We discuss the limitations of their designs, ongoing efforts to diversify genomics research and strategies to maximize the benefits of research leveraging biobanks for all.

Model Organoids: Integrated Frameworks for the Next Frontier of Healthcare Advancements.

The morphogenetic events leading to tissue formation can be recapitulated using organoids, which allows studying new diseases and modelling personalized medicines. In this review, culture systems comparable to human organs are presented, these organoids are created from pluripotent stem cells or adult stem cells. The efficient and reproducible models of human tissues are discussed for biobanking, precision medicine and basic research. Mechanisms used by these model systems with an overview of models from human cells are also covered. As human physiology is different from animals, culture conditions and tissue limits often become challenging. Organoids offer novel approaches for such cases with rapid screening, transplantation studies and in immunotherapy. Discrepancies with large datasets can be handled with an integrated framework of artificial intelligence or AI and machine learning. An attempt has been made to show the improved effectiveness, simplified iterations, along with image analysis that are possible from this synergy. AI-assisted organoids have the potential to transform healthcare by improving disease understanding and accelerating clinical decision-making through personalized and precision medicine.