Cambridge prisms, Precision medicine最新文献

{"title":"Biomarker understanding first lessons from drug development for IgA-driven autoimmune and fibrotic diseases.","authors":"Myrthe Alexandra Maria van Delft, Aleksandra Cegiel, Marjolein van Egmond, Louis Boon","doi":"10.1017/pcm.2025.10005","DOIUrl":"https://doi.org/10.1017/pcm.2025.10005","url":null,"abstract":"<p><p>The concept of personalized medicine and its significant benefits for patients and society was introduced over three decades ago. The Human Genome Project (initiated in 1990 and completed in 2003) greatly accelerated the development of precision medicine. In many cancers, defined biomarkers are used to select patients for therapy. For example, KRAS mutations are used to guide treatment with Sotorasib, while tumor expression of (wild type) human epidermal growth factor receptor 2 and 3 (HER2 and HER3) are used to select patients for trastuzumab and cetuximab, respectively. Nonetheless, the clinical adoption of companion diagnostics to facilitate a patient-centric approach in inflammatory diseases remains disappointing. One key reason why the development of companion diagnostics may be delayed autoimmune and fibrotic diseases can be the timing when clinical development teams inform R&D teams about relevant biomarkers or companion diagnostic to select patients, disease monitoring or treatment termination decisions. For clinical practicality, it is highly preferred to measure a biomarker in the systemic circulation, as blood samples can be obtained relatively easily in most diseases. However, discovering systemic biomarkers during clinical development has proven extremely challenging. Here, we describe an alternative approach, which we have used to select the most appropriate target for IgA driven autoimmune and fibrotic diseases. In this specific context, autoantigen-specific assays to determine autoantibody serum levels are widely available for a variety of indications. A detailed analysis of the biological pathways that affect the biomarker can uncover multiple potential therapeutic targets, allowing selection of the most optimal target from a clinical development perspective. Identification of a relevant biomarker before clinical development is initiated, enabling patient stratification in early clinical studies. Selection of the appropriate patient population based on biomarker presence reduces the number of patients needed and consequently, clinical development costs. Moreover, such a patient stratification approach minimizes the risk of including patients who are unlikely to respond, thereby avoiding unnecessary adverse events. This approach was applied during the selection of an anti-CD89 antagonist monoclonal antibody for IgA-mediated autoimmune and fibrotic diseases, serving as an illustrative example of this novel strategy.</p>","PeriodicalId":72491,"journal":{"name":"Cambridge prisms, Precision medicine","volume":"3 ","pages":"e8"},"PeriodicalIF":0.0,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12916248/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in breast cancer diagnosis: A systematic literature review.","authors":"Arslaan Javaeed, Anna Schuh","doi":"10.1017/pcm.2025.10006","DOIUrl":"10.1017/pcm.2025.10006","url":null,"abstract":"<p><p>Breast cancer is the second leading cause of cancer-related deaths among women globally and the most prevalent cancer in women. Artificial intelligence (AI)-based frameworks have shown great promise in correctly classifying breast carcinomas, particularly those that may have been difficult to discern through routine microscopy. Additionally, mitotic number quantification utilizing AI technology is more accurate than manual counting. With its many advantages, such as improved accuracy, efficiency and consistency as shown in this literature review, AI has promise for significantly enhancing breast cancer diagnosis in the clinical world despite the paramount obstacles that must be addressed. Ongoing research and innovation are essential for overcoming these challenges and effectively harnessing AI's transformative potential in breast cancer detection and assessment.</p>","PeriodicalId":72491,"journal":{"name":"Cambridge prisms, Precision medicine","volume":"3 ","pages":"e7"},"PeriodicalIF":0.0,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12708007/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145776556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerating eSource scale-up in oncology clinical trials: The i~HD Task Force initiative.","authors":"Christophe Maes, Dipak Kalra, Tracy Acito, Nadir Ammour, Paul Basset, Sarah Burge, Peter Castleyn, Ross Caldow, Camille Couvert, Amy Cramer, Chris Harrison, Joeri Holtzem, Pavitra Mariappan, Paul Jacobs, Lars Fransson, Veronique Berthou, Laurice Jackson, Nancy Wetzel, Christopher Thompson, Sharon Klein, Robert Green, Fakhry Kaoukdji, Michael Ward, Felix Nensa, Joe Lengfellner, Anna Patruno, Dawn Snow, Isabel Virchow, Angela Fritsche, Pascal Coorevits, Mats Sundgren","doi":"10.1017/pcm.2025.10004","DOIUrl":"https://doi.org/10.1017/pcm.2025.10004","url":null,"abstract":"<p><p>eSource - particularly EHR-to-EDC - is an emerging paradigm in clinical research that enables automated transfer of electronic health record (EHR) data into electronic data capture (EDC) systems, with the potential to reduce site burden, improve data quality and accelerate oncology clinical trial workflows. However, widespread implementation remains limited due to technical, regulatory and operational barriers. To address these challenges, the European Institute for Innovation through Health Data (i~HD) launched the eSource Scale-Up Task Force in 2024. This multi-stakeholder initiative brings together leading oncology centres and pharmaceutical sponsors to establish a consensus-driven roadmap for eSource adoption. Central to this effort are three foundational resources: readiness criteria for early adopters, a performance indicator framework for monitoring success and an operational playbook to guide implementation. This article provides a structured overview of the Task Force's objectives, collaborative model and outputs, with specific attention to its focus on interoperability, regulatory alignment and real-world validation. While initially developed for oncology, the Task Force's framework is applicable across therapeutic areas characterized by data-intensive workflows.</p>","PeriodicalId":72491,"journal":{"name":"Cambridge prisms, Precision medicine","volume":"3 ","pages":"e5"},"PeriodicalIF":0.0,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12644958/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145642865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precision oncology: Computational methods for multi-omics data integration to improve drug response prediction.","authors":"Guna Gouru","doi":"10.1017/pcm.2025.10003","DOIUrl":"https://doi.org/10.1017/pcm.2025.10003","url":null,"abstract":"<p><p>Cancer heterogeneity presents a major obstacle to effective drug treatment, emphasizing the need for personalized approaches that can accurately predict drug responses. Advances in high-throughput technologies have driven precision medicine initiatives toward integrating multi-omics data, enabling a more comprehensive understanding of tumor biology. However, integration of diverse omics layers poses challenges for computational modeling, as many traditional machine learning (ML) and statistical methods are not designed to capture complex, high-dimensional and multimodal data. This review examines the studies that integrate multi-omics datasets, aiming to enhance drug response prediction (DRP). Specifically, it outlines the most used omics types and computational approaches - classical ML models, as well as advanced deep learning and multimodal integration frameworks for improving DRP, detailing key methodologies and evaluation metrics, such as area under the dose-response curve, F1 score and mean square error, which assess model performance. By summarizing the integrated omics data, computational methods and challenges encountered, this review provides an in-depth overview of the existing landscape of precision medicine and future directions for advancing drug-response prediction.</p>","PeriodicalId":72491,"journal":{"name":"Cambridge prisms, Precision medicine","volume":"3 ","pages":"e6"},"PeriodicalIF":0.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12973241/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147438068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinically actionable pharmacogenomic landscape of antidepressants and antipsychotics in Qatar: a population-based cohort study.","authors":"Dinesh Velayutham, Kholoud Bastaki, Areeba Irfan, Mohammed Abuhaliqa, Aisha AlMulla, Suhaila Ghuloum, Muhammad Waqar Azeem, Munir Pirmohamed, Puthen Veettil Jithesh","doi":"10.1017/pcm.2025.2","DOIUrl":"10.1017/pcm.2025.2","url":null,"abstract":"<p><p>Consortia like the Clinical Pharmacogenetic Implementation Consortium (CPIC) and the Dutch Pharmacogenetic Working Group (DPWG) provide clinical guidelines but pharmacogenomics implementation depends on population prevalence of actionable genetic variants and response phenotypes. We analyzed the distribution of actionable genetic variants and clinical recommendations in 14,354 adult Qataris, focusing only genes with guidelines (<i>CYP2C19</i>, <i>CYP2D6</i>, <i>CYP2B6</i> and <i>CYP3A4</i>). Haplotypes and diplotypes were generated from 490 alleles using whole genome data and metabolizer phenotypes were predicted based on current knowledge. Qatari population predicted to have actionable metabolizer phenotypes of CYP2C19, CYP2B6 and CYP2D6 impacting response to antidepressants were in the range of 1%-58% and for antipsychotics 0.1%-33% based on <i>CYP3A4</i> and <i>CYP2D6.</i> Fine-grained analysis based on clinical guidelines also revealed that while the Qataris may need prescription of an alternate antidepressant not metabolized by CYP2C19, patients from other populations may just need altering the dosage of tricyclic antidepressants like amitriptyline. Further studies incorporating other factors such as diet, environment and cultural habits alongwith population-specific variants will help in the pharmacogenomics implementation in the Qatari population.</p>","PeriodicalId":72491,"journal":{"name":"Cambridge prisms, Precision medicine","volume":"3 ","pages":"e4"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277200/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144683718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harnessing the power of genomics in hypertension: tip of the iceberg?","authors":"Hafiz Naderi, Helen R Warren, Patricia B Munroe","doi":"10.1017/pcm.2025.1","DOIUrl":"10.1017/pcm.2025.1","url":null,"abstract":"<p><p>Despite the blaze of advancing knowledge on its complex genetic architecture, hypertension remains an elusive condition. Genetic studies of blood pressure have yielded bitter-sweet results thus far with the identification of more than 2,000 genetic loci, though the candidate causal genes and biological pathways remain largely unknown. The era of big data and sophisticated statistical tools has propelled insights into pathophysiology and causal inferences. However, new genetic risk tools for hypertension are the tip of the iceberg, and applications of genomic technology are likely to proliferate. We review the genomics of hypertension, exploring the significant milestones in our current understanding of this condition and the progress towards personalised treatment and management for hypertension.</p>","PeriodicalId":72491,"journal":{"name":"Cambridge prisms, Precision medicine","volume":"3 ","pages":"e2"},"PeriodicalIF":0.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11894416/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143607345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UK cancer vaccine advance - Recognising and realising opportunities.","authors":"Charles Craddock, Philip Earwaker, Matthew Fittall, Elisa Fontana, Divya Ganesh, Marco Gerlinger, Qamar Ghafoor, Robert P Jones, Victoria Kunene, Lennard Lee, Rebecca Lee, Siow-Ming Lee, Mark Linch, Martin Little, Justin Liu, Hayley McKenzie, Russell Petty, David J Pinato, Thomas Powles, Andrew Protheroe, Tim Robinson, Paul J Ross, Kai Keen Shiu, James Spicer, Stefan Symeonides, Michael Tilby, Dale Vimalachandran, Jenny Y Wang, Andrew Wardley, Helen Winter","doi":"10.1017/pcm.2024.5","DOIUrl":"10.1017/pcm.2024.5","url":null,"abstract":"<p><p>Vaccines have revolutionised the field of medicine, eradicating and controlling many diseases. Recent pandemic vaccine successes have highlighted the accelerated pace of vaccine development and deployment. Leveraging this momentum, attention has shifted to cancer vaccines and personalised cancer vaccines, aimed at targeting individual tumour-specific abnormalities. The UK, now regarded for its vaccine capabilities, is an ideal nation for pioneering cancer vaccine trials. This article convened experts to share insights and approaches to navigate the challenges of cancer vaccine development with personalised or precision cancer vaccines, as well as fixed vaccines. Emphasising partnership and proactive strategies, this article outlines the ambition to harness national and local system capabilities in the UK; to work in collaboration with potential pharmaceutic partners; and to seize the opportunity to deliver the pace for rapid advances in cancer vaccine technology.</p>","PeriodicalId":72491,"journal":{"name":"Cambridge prisms, Precision medicine","volume":"3 ","pages":"e1"},"PeriodicalIF":0.0,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11811843/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143411804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The challenges and opportunities of applying tumour mutational burden analysis to precision cancer medicine.","authors":"Attia M Elbehi","doi":"10.1017/pcm.2024.6","DOIUrl":"https://doi.org/10.1017/pcm.2024.6","url":null,"abstract":"<p><p>The discovery and development of immune checkpoint inhibitors (ICIs) has revolutionised the management of human cancers. However, only a subset of patients responds to ICI therapy, even though immune evasion is a hallmark of cancer. Initially, treatment was administered to patients on the basis of expression levels of one of the targets of ICI therapy, programmed cell death ligand 1. In clinical trials, the high response rate of melanoma and non-small cell lung cancer patients to ICI therapy supported the basic premise of cancer immunotherapy, that tumour-specific mutated proteins trigger an immune response. Tumour mutational burden subsequently emerged as a potential biomarker for response to ICI therapy. This review summarises the evidence supporting the scientific rationale for TMB as a biomarker for ICI therapy and focuses on some of the major challenges associated with incorporation of TMB into routine clinical practice.</p>","PeriodicalId":72491,"journal":{"name":"Cambridge prisms, Precision medicine","volume":"3 ","pages":"e3"},"PeriodicalIF":0.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12041339/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144059535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenges and solutions to system-wide use of precision oncology as the standard of care paradigm.","authors":"Nesrine Lajmi, Sofia Alves-Vasconcelos, Apostolos Tsiachristas, Andrew Haworth, Kerrie Woods, Charles Crichton, Theresa Noble, Hizni Salih, Kinga A Várnai, Harriet Branford-White, Liam Orrell, Andrew Osman, Kevin M Bradley, Lara Bonney, Daniel R McGowan, Jim Davies, Matthew S Prime, Andrew Bassim Hassan","doi":"10.1017/pcm.2024.1","DOIUrl":"https://doi.org/10.1017/pcm.2024.1","url":null,"abstract":"<p><p>The personalised oncology paradigm remains challenging to deliver despite technological advances in genomics-based identification of actionable variants combined with the increasing focus of drug development on these specific targets. To ensure we continue to build concerted momentum to improve outcomes across all cancer types, financial, technological and operational barriers need to be addressed. For example, complete integration and certification of the 'molecular tumour board' into 'standard of care' ensures a unified clinical decision pathway that both counteracts fragmentation and is the cornerstone of evidence-based delivery inside and outside of a research setting. Generally, integrated delivery has been restricted to specific (common) cancer types either within major cancer centres or small regional networks. Here, we focus on solutions in real-world integration of genomics, pathology, surgery, oncological treatments, data from clinical source systems and analysis of whole-body imaging as digital data that can facilitate cost-effectiveness analysis, clinical trial recruitment, and outcome assessment. This urgent imperative for cancer also extends across the early diagnosis and adjuvant treatment interventions, individualised cancer vaccines, immune cell therapies, personalised synthetic lethal therapeutics and cancer screening and prevention. Oncology care systems worldwide require proactive step-changes in solutions that include inter-operative digital working that can solve patient centred challenges to ensure inclusive, quality, sustainable, fair and cost-effective adoption and efficient delivery. Here we highlight workforce, technical, clinical, regulatory and economic challenges that prevent the implementation of precision oncology at scale, and offer a systematic roadmap of integrated solutions for standard of care based on minimal essential digital tools. These include unified decision support tools, quality control, data flows within an ethical and legal data framework, training and certification, monitoring and feedback. Bridging the technical, operational, regulatory and economic gaps demands the joint actions from public and industry stakeholders across national and global boundaries.</p>","PeriodicalId":72491,"journal":{"name":"Cambridge prisms, Precision medicine","volume":"2 ","pages":"e4"},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11062796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140861693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-population applications of genomics to understand the risk of multifactorial traits involving inflammation and immunity.","authors":"Bana Alamad, Kate Elliott, Julian C Knight","doi":"10.1017/pcm.2023.25","DOIUrl":"10.1017/pcm.2023.25","url":null,"abstract":"<p><p>The interplay between genetic and environmental factors plays a significant role in interindividual variation in immune and inflammatory responses. The availability of high-throughput low-cost genotyping and next-generation sequencing has revolutionized our ability to identify human genetic variation and understand how this varies within and between populations, and the relationship with disease. In this review, we explore the potential of genomics for patient benefit, specifically in the diagnosis, prognosis and treatment of inflammatory and immune-related diseases. We summarize the knowledge arising from genetic and functional genomic approaches, and the opportunity for personalized medicine. The review covers applications in infectious diseases, rare immunodeficiencies and autoimmune diseases, illustrating advances in diagnosis and understanding risk including use of polygenic risk scores. We further explore the application for patient stratification and drug target prioritization. The review highlights a key challenge to the field arising from the lack of sufficient representation of genetically diverse populations in genomic studies. This currently limits the clinical utility of genetic-based diagnostic and risk-based applications in non-Caucasian populations. We highlight current genome projects, initiatives and biobanks from diverse populations and how this is being used to improve healthcare globally by improving our understanding of genetic susceptibility to diseases and regional pathogens such as malaria and tuberculosis. Future directions and opportunities for personalized medicine and wider application of genomics in health care are described, for the benefit of individual patients and populations worldwide.</p>","PeriodicalId":72491,"journal":{"name":"Cambridge prisms, Precision medicine","volume":"2 ","pages":"e3"},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10953767/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140319991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}