Precision Medicine: Transforming Cancer Research through Targeted Therapies

Precision medicine as a framework for disease diagnosis, treatment, and prevention at the molecular level has entered clinical practice. From the start, genetics has been an indispensable tool to understand and stratify the biology of chronic and complex diseases in precision medicine. However, with the advances in biomedical and omics technologies, quantitative proteomics is emerging as a powerful technology complementing genetics. Quantitative proteomics provide insight about the dynamic behaviour of proteins as they represent intermediate phenotypes. They provide direct biological insights into physiological patterns, while genetics accounting for baseline characteristics. Additionally, it opens a wide range of applications in clinical diagnostics, treatment stratification, and drug discovery. In this mini-review, we discuss the current status of quantitative proteomics in precision medicine including the available technologies and common methods to analyze quantitative proteomics data. Furthermore, we highlight the current challenges to put quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data with genomics data for future applications in precision medicine.

Precision medicine, currently a hotspot in mainstream medicine, has been strongly promoted in recent years. With rapid technological development, such as next-generation sequencing, and fierce competition in molecular targeted drug exploitation, precision medicine represents an advance in science and technology; it also fulfills needs in public health care. The clinical translation and application of precision medicine - especially in the prevention and treatment of tumors - is far from satisfactory; however, the aims of precision medicine deserve approval. Thus, this medical approach is currently in its infancy; it has promising prospects, but it needs to overcome numbers of problems and deficiencies. It is expected that in addition to conventional symptoms and signs, precision medicine will define disease in terms of the underlying molecular characteristics and other environmental susceptibility factors. Those expectations should be realized by constructing a novel data network, integrating clinical data from individual patients and personal genomic background with existing research on the molecular makeup of diseases. In addition, multi-omics analysis and multi-discipline collaboration will become crucial elements in precision medicine. Precision medicine deserves strong support, and its development demands directed momentum. We propose three kinds of impetus (research, application and collaboration impetus) for such directed momentum toward promoting precision medicine and accelerating its clinical translation and application.

Unprecedented advances in molecular biology, biomedical imaging and information technology have opened up new avenues towards precision medicine, in which treatments are and will be tailored to specific diagnoses and their unique manifestations in individual patients. In oncology, the application of precision medicine offers tremendous hopes for improving outcomes but it also poses great challenges. Cancer is fundamentally a disease of genetic missteps or misregulation. Cancers are genetically heterogeneous, undergoing constant evolution (or Darwinian Dynamics) as they progress. Not only do biological features differ within primary tumors, but in up to 50% of cancers, metastases de‐differentiate and demonstrate different biological features in different matrices (e.g., bone vs. liver vs. lung). Tissue and serum assays alone cannot sufficiently capture the spatial and temporal diversity of tumor biology. Precise, spatially localized molecularly‐based evaluation of disease that incorporates genomics is essential. Conventional anatomic imaging will continue to be indispensible for localizing cancer and determining where it has spread for both diagnostic and radiation therapy purposes. However, as precision medicine takes hold, the role of imaging in evaluating tumor biology will become even more important. Molecular imaging and the new field of radiogenomics holds great promise for advancing our ability to image tumor biology. Further, the relatively recent enhanced understanding of the human genome is driving transformational changes in cancer research. As we will see from the first two talks of this session, genomics will be the driving factor in cancer research for the foreseeable future. As medical physicists we must consider how that impacts our science ‐ ‐‐which is the application of physics principles to biology and medicine. While we have traditionally focused on rather narrow aspects of physics related to radiation oncology and biomedical imaging, we must ask ourselves how we should evolve in these areas to address the cancer research challenges of the future and “Is it time for us to embrace other aspects of medical physics?” Further, in order for the next generation of medical physicists to participate in cancer research, much less have a significant impact, we must at a minimum consider new dimensions to our educational programs. For example, how can we expect to participate in contemporary cancer research if we have such a limited understanding of molecular genetics and cancer biology that we can' tt communicate effectively with our scientific peers?In this session we will give an overview of precision and genomic medicine with the impact on diagnostic imaging (Hricak), how this will impact the practice of radiation oncology (Jaffray) a general perspective on how we should be addressing issues of research and education in this new era (Hazle).

Artificial Intelligence (AI) has emerged as a transformative force in precision medicine, healthcare analysis, and neonatal surgery, enabling personalized treatment, early disease detection, and optimized clinical decision-making. This survey explores the evolving role of AI in healthcare, focusing on its applications, challenges, and future prospects. AI-driven approaches, including machine learning (ML) and deep learning (DL), have demonstrated remarkable accuracy in medical imaging, genomics, drug discovery, neonatal diagnostics, and patient risk assessment. These technologies enhance diagnostic precision, facilitate predictive analytics, and support real-time monitoring of chronic diseases and neonatal conditions. Precision medicine, which tailors treatments based on an individual’s genetic, environmental, and lifestyle factors, benefits significantly from AI-powered analytics. The integration of AI with electronic health records (EHRs), wearable devices, and biomedical data accelerates early disease identification and personalized therapeutic strategies, including those crucial for neonatal care and surgery. AI models trained on vast healthcare datasets can predict disease progression, recommend targeted therapies, and improve patient outcomes. Furthermore, natural language processing (NLP) enhances clinical documentation, reducing administrative burdens and improving efficiency in healthcare systems. Despite its potential, AI in precision medicine and neonatal surgery faces challenges, including data privacy concerns, model interpretability, and regulatory compliance. Ethical considerations, such as bias in AI models and equitable access to AI-driven healthcare, must be addressed to ensure responsible implementation. Additionally, integrating AI with traditional clinical workflows requires collaboration between healthcare professionals, data scientists, and policymakers. This survey provides a comprehensive analysis of AI applications in precision medicine, healthcare analysis, and neonatal surgery, highlighting key advancements, challenges, and future research directions. As AI continues to evolve, its role in revolutionizing healthcare will expand, paving the way for more efficient, accurate, and patient-centric medical practices. The findings of this survey aim to guide researchers, clinicians, and policymakers in leveraging AI for the next generation of precision healthcare, particularly in neonatal surgical interventions.

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.

Microphysiology systems (MPS), also called organs-on-chips and tissue chips, are miniaturized functional units of organs constructed with multiple cell types under a variety of physical and biochemical environmental cues that complement animal models as part of a new paradigm of drug discovery and development. Biomimetic human liver MPS have evolved from simpler 2D cell models, spheroids and organoids to address the increasing need to understand patient-specific mechanisms of complex and rare diseases, the response to therapeutic treatments, and the absorption, distribution, metabolism, excretion and toxicity of potential therapeutics. The parallel development and application of transdisciplinary technologies, including microfluidic devices, bioprinting, engineered matrix materials, defined physiological and pathophysiological media, patient-derived primary cells, and pluripotent stem cells as well as synthetic biology to engineer cell genes and functions, have created the potential to produce patient-specific, biomimetic MPS for detailed mechanistic studies. It is projected that success in the development and maturation of patient-derived MPS with known genotypes and fully matured adult phenotypes will lead to advanced applications in precision medicine. In this Review, we examine human biomimetic liver MPS that are designed to recapitulate the liver acinus structure and functions to enhance our knowledge of the mechanisms of disease progression and of the absorption, distribution, metabolism, excretion and toxicity of therapeutic candidates and drugs as well as to evaluate their mechanisms of action and their application in precision medicine and preclinical trials.

Background: The integration of artificial intelligence (AI) with precision medicine represents a promising frontier in urological cancer management. This systematic review evaluates the current landscape of AI applications in precision medicine for urological cancers, analyzing methodological approaches, clinical applications, and implementation challenges.Methods: A comprehensive literature search was conducted across PubMed/MEDLINE, Embase, Web of Science, and IEEE Xplore databases from January 2015 to December 2024. The review followed PRISMA guidelines, focusing on original research articles exploring AI applications in precision medicine for urological cancers. Quality assessment was performed using QUADAS-2 and ROBINS-I tools, with AI-specific evaluation using AI-RADS criteria.Results: Among 2,847 identified articles, 89 studies met the inclusion criteria. Prostate cancer studies dominated the literature (47.2%), followed by bladder (28.1%), kidney (20.2%), and testicular cancer (4.5%). Deep learning approaches were most prevalent (42.7%), achieving the highest performance metrics in prostate cancer applications (accuracy 88.5%, AUC-ROC 0.91). External validation was reported in 50.6% of studies, with multi-institutional validation in 31.5%. Implementation challenges were identified in 75.3% of studies, primarily concerning data quality (77.6%) and workflow integration (71.6%).Conclusion: AI applications in precision medicine for urological cancers demonstrate promising performance metrics and potential for clinical impact. However, the field faces significant challenges in data standardization, external validation, and clinical integration. Future developments should focus on multi-institutional collaboration, standardized validation protocols, and improved implementation strategies to enhance the clinical utility of AI-driven precision medicine approaches in urological oncology

Green carbon dots (GCDs) have emerged as a revolutionary tool in precision medicine, offering transformative capabilities for personalized diagnostics and therapeutic strategies. Their unique optical and biocompatible properties make them ideal for non-invasive imaging, real-time monitoring, and integration with genomics, proteomics, and bioinformatics, enabling accurate diagnosis and tailored treatments based on patients' genetic and molecular profiles. This study explores the potential of GCDs in advancing individualized patient care by examining their applications in precision medicine. It evaluates their utility in non-invasive diagnostic imaging, targeted therapy delivery, and the formulation of personalized treatment plans, emphasizing their interaction with advanced genomic, proteomic, and bioinformatics platforms. GCDs demonstrated exceptional versatility in enabling precise diagnostics and delivering targeted therapies. Their integration with cutting-edge technologies showed significant promise in crafting personalized treatment strategies, enhancing their functionality and effectiveness in real-time monitoring and patient-specific applications. The findings underscore the pivotal role of GCDs in reshaping healthcare by advancing precision medicine and improving patient outcomes. The ongoing development and integration of GCDs with emerging technologies promise to further enhance their capabilities, paving the way for more effective, individualized medical care.

Precision medicine, which customizes healthcare based on individual genetic, environmental, and lifestyle factors, has significantly advanced various medical fields. However, its adoption in emergency medicine remains limited despite the potential to enhance patient outcomes through more accurate diagnostics and personalized treatments. This systematic review examined current evidence on the application of precision medicine in emergency care by analyzing studies published between 2010 and 2024. Out of 218 records, 10 studies met the inclusion criteria, highlighting areas such as genomic applications, machine learning, point-of-care diagnostics, and biomarkers. The findings indicate that precision medicine can improve diagnostic accuracy and personalize patient care in emergency settings, although challenges such as time constraints, technological limitations, and the need for enhanced clinician training remain. Overcoming these barriers through interdisciplinary collaboration, investment in rapid diagnostic technologies, and comprehensive education programs is essential for effectively integrating precision medicine into emergency care. Ultimately, advancing precision emergency medicine holds promise for transforming emergency care into a more personalized and effective practice, thereby improving patient outcomes in acute situations.

Precision medicine is growing due to technological advancements including next generation sequencing techniques and artificial intelligence. However, with the application of precision medicine many ethical and potential risks may emerge. Although, its benefits and potential harms are relevantly known to professional societies and practitioners, patients' attitudes toward these potential ethical risks are not well-known. The aim of this systematic review was to focus on patients' perspective on ethics and risks that may rise with the application of precision medicine. A systematic search was conducted on 4/1/2023 in the database of PubMed, for the period 1/1/2012 to 4/1/2023 identifying 914 articles. After initial screening, only 50 articles were found to be relevant. From these 50 articles, 24 articles were included in this systematic review, 2 articles were excluded as not in English language, 1 was a review, and 23 articles did not include enough relevant qualitative data regarding our research question to be included. All full texts were evaluated following PRISMA guidelines for reporting systematic reviews following the Joanna Briggs Institute criteria. There were eight main themes emerging from the point of view of the patients regarding ethical concerns and risks of precision medicine: privacy and security of patient data, economic impact on the patients, possible harms of precision medicine including psychosocial harms, risk for discrimination of certain groups, risks in the process of acquiring informed consent, mistrust in the provider and in medical research, issues with the diagnostic accuracy of precision medicine and changes in the doctor-patient relationship. Ethical issues and potential risks are important for patients in relation to the applications of precision medicine and need to be addressed with patient education, dedicated research and official policies. Further research is needed for validation of the results and awareness of these findings can guide clinicians to understand and address patients concerns in clinical praxis.

As a useful model in cancer research, zebrafish (Danio rerio) have made substantial contributions to precision medicine, drug testing, and discovery. They are the perfect system for in vivo research of cancer progression and therapy responses because of their fast growth, genetic resemblance to humans, and transparent embryos. This article examines the use of zebrafish in pharmacogenomics, personalised medicine development, and high-throughput cancer medication screening. We also go over the benefits and drawbacks of using zebrafish models in oncological research as well as its possible applications in precision medicine in the future.

Artificial Intelligence in Medicine: Revolutionizing Healthcare for Improved Patient Outcomes

P245 Immune response genetics and the 1000 genomes samples: toward application in precision medicine

The primary purpose of 'omics' technologies is to understand the intricacy of genomics, proteomics, metabolomics and other molecular mechanisms to reveal the complex traits of human diseases. The significant use of omics technologies and their applications in medicine gear up the study of the pathogenesis of several disorders. The detection of biomarkers in the early onset of diseases is challenging; still, omics can discover novel molecular mechanisms and biomarkers. In this review, the different types of omics and their technologies are explicated and aimed to provide their emerging applications in cardiovascular precision medicine. These technologies significantly impact optimizing medical treatment for individuals to reach a higher level in precision medicine.

Precision Medicine in Inflammatory Bowel Diseases: Challenges and Considerations for the Path Forward