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PubMed2025

Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine: Bringing next-generation precision oncology to patients.

The human genome project ushered in a genomic medicine era that was largely unimaginable three decades ago. Discoveries of druggable cancer drivers enabled biomarker-driven gene- and immune-targeted therapy and transformed cancer treatment. Minimizing treatment not expected to benefit, and toxicity-including financial and time-are important goals of modern oncology. The Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine founded by Drs. John Mendelsohn and Thomas Tursz provided a vision for innovation, collaboration and global impact in precision oncology. Through pursuit of transcriptomic signatures, artificial intelligence (AI) algorithms, global precision cancer medicine clinical trials and input from an international Molecular Tumor Board (MTB), WIN has led the way in demonstrating patient benefit from precision-therapeutics through N-of-1 molecularly-driven studies. WIN Next-Generation Precision Oncology (WINGPO) trials are being developed in the neoadjuvant, adjuvant or metastatic settings, incorporate real-world data, digital pathology, and advanced algorithms to guide MTB prioritization of therapy combinations for a diverse global population. WIN has pursued combinations that target multiple drivers/hallmarks of cancer in individual patients. WIN continues to be impactful through collaboration with industry, government, sponsors, funders, academic and community centers, patient advocates, and other stakeholders to tackle challenges including drug access, costs, regulatory barriers, and patient support. WIN's collaborative next generation of precision oncology trials will guide treatment selection for patients with advanced cancers through MTB and AI algorithms based on serial liquid and tissue biopsies and exploratory omics including transcriptomics, proteomics, metabolomics and functional precision medicine. Our vision is to accelerate the future of precision oncology care.

Humans↗

PubMed1992

Possibilities of software phantoms for quality control of KBS in nuclear medicine.

The assessment of the results of a "knowledge-based system" (KBS) for quality control is a basic requirement for clinical application: Large numbers of test studies are necessary in order to cover as widely as possible the spectrum of cases to be analyzed by the KBS. The use of original patient data as test data is one possibility, but real data are provided unevenly. This is due to the set of characteristics which are relevant to the analysis. Data are available in a limited quantity only. This implies a remaining set of unvalidated cases which are not represented in the data pool. The software phantom is an approach towards systematically guided validation. It permits the generation of test data adjusted to the demands of the validation.

Artificial Intelligence↗

PubMed1991

Automatic generation of plans for biomedical image interpretation.

This paper presents a new object-centered, goal-driven planning approach to biomedical image interpretation. We describe here a prototype system which takes advantage of spatial and detectability constraints from an expert-derived model of expected anatomical structures to automatically generate plans for the interpretation of multimodality images.

Artificial Intelligence↗

PubMed2026

Artificial intelligence-derived myocardial fibrosis on cardiac magnetic resonance for prognosis in cardiomyopathy: A systematic review of a sparse evidence base.

BACKGROUND: Myocardial fibrosis on cardiovascular magnetic resonance (CMR), assessed by late gadolinium enhancement (LGE) and parametric mapping, is an established predictor of adverse events in cardiomyopathy. We assessed whether artificial intelligence (AI) quantification of fibrosis adds independent prognostic value. METHODS: We searched six databases, a clinical-trials register, and a preprint server from inception to 13 June 2026. Eligible studies used AI to generate a fibrosis marker in adults with ischemic or nonischemic cardiomyopathy, with covariate-adjusted outcomes over ≥12 months. Risk of bias was assessed using PROBAST, PROBAST+AI, and QUIPS. Fewer than three comparable studies precluded meta-analysis; certainty was rated using GRADE. RESULTS: Of 448 records (381 after de-duplication), 18 full texts were reviewed and two included, one peer-reviewed and one preprint. In an ischemic-cardiomyopathy registry (Ghanbari et al.; n = 216 analytic, 26 events), AI-derived dense LGE scar predicted arrhythmic events (univariable hazard ratio [HR] 2.35, 95% CI 1.33-4.15), and AI-derived but not manual scar improved discrimination beyond guideline criteria (area under the curve 0.63 to 0.68; p = 0.02). In a nonischemic dilated-cardiomyopathy preprint (Kim et al.; n = 347, 119 events), automated extracellular volume ≥30% predicted cardiovascular death or heart-failure hospitalization (adjusted HR 2.00, 95% CI 1.32-3.03). Both were at high risk of bias, with data-derived thresholds and no external validation. CONCLUSIONS: Across only two studies, AI-derived fibrosis was independently associated with adverse cardiovascular events, but its added value over manual quantification remains unproven. Certainty was very low. The evidence base is sparse and not yet ready for clinical use.

Humans↗

PubMed1992

Neural computing in discovering RNA interactions.

High-order RNA structures are involved in regulating many biological processes; various algorithms have been designed to predict them. Experimental methods to probe such structures and to decipher the results are tedious. Artificial intelligence and the neural network approach can support the process of discovering RNA structures. Secondary structures of RNA molecules are probed by autoradiographing gels, separating end-labeled fragments generated by base-specific RNases. This process is performed in both conditions, denaturing (for sequencing purposes) and native. The resultant autoradiograms are scanned using line-detection techniques to identify the fragments by comparing the lines with those obtained by 'alkaline ladders'. The identified paired bases are treated by either one of two methods to find the foldings which are consistent with the RNases' 'cutting' rules. One exploits the maximum independent set algorithm; the other, the planarization algorithm. They require, respectively, n and n2 processing elements, where n is the number of base pairs. The state of the system usually converges to the near-optimum solution within about 500 iteration steps, where each processing element implements the McCulloch-Pitts binary neuron. Our simulator, based on the proposed algorithm, discovered a new structure in a sequence of 38 bases, which is more stable than that formerly proposed.

Algorithms↗

PubMed2026

Engineering extracellular vesicles for targeted siRNA delivery: Advances, therapeutic applications, and clinical translation.

Small interfering RNA (siRNA) therapeutics have emerged as a transformative approach for sequence-specific gene silencing, offering the potential to treat a broad spectrum of diseases by selectively suppressing disease-associated genes. However, the clinical translation of siRNA remains limited by rapid enzymatic degradation, poor cellular uptake, inadequate endosomal escape, and off-target effects, necessitating the development of efficient delivery systems. Extracellular vesicles (EVs) have gained considerable attention as natural nanocarriers owing to their excellent biocompatibility, low immunogenicity, intrinsic targeting capability, and ability to protect therapeutic cargo while traversing complex biological barriers. This review comprehensively discusses the biological characteristics of EVs, the molecular basis of RNA interference, and the major challenges associated with siRNA delivery [Fig. 1]. Recent advances in EV engineering, including cargo-loading strategies such as electroporation, sonication, extrusion, parent-cell engineering, and microfluidic approaches, together with surface functionalization using peptides, antibodies, aptamers, and hybrid nanoplatforms, are critically evaluated for improving targeting specificity and intracellular delivery. Furthermore, the therapeutic applications of engineered EV-mediated siRNA delivery in cancer, neurological disorders, liver diseases, cardiovascular diseases, inflammatory disorders, and infectious diseases are systematically summarized, highlighting their potential to enhance gene silencing while minimizing systemic toxicity. Current challenges related to large-scale manufacturing, cargo-loading efficiency, standardization, quality control, regulatory approval, and clinical translation are also discussed, together with emerging technologies involving synthetic biology, genome engineering, artificial intelligence, and multifunctional hybrid vesicles. Overall, engineered extracellular vesicles represent a highly versatile and biologically inspired platform for targeted siRNA delivery, providing a promising foundation for the development of next-generation precision RNA therapeutics and accelerating the clinical translation of gene-silencing strategies.

Extracellular vesicle engineering↗

PubMed2026

Biomarker-guided selection of intravesical therapy in high-risk non-muscle invasive bladder cancer: A contemporary review.

High-risk non-muscle invasive bladder cancer poses therapeutic challenges, with significant rates of recurrence and progression with standard intravesical bacillus Calmette-Guérin (BCG) therapy. Current surveillance strategies lack accurate risk stratification models to predict individual treatment response and personalized treatment options. Simultaneously, there are no well-validated alternatives to replace the current gold-standard approach based on clinical and pathologic features. This review examines emerging biomarkers and advanced technologies with the potential to enhance patient selection and personalize intravesical therapy in HR-NMIBC. Artificial intelligence(AI)-driven histopathologic tools, such as the computer histological AI biomarker, have demonstrated the ability to identify non-responders to standard therapy using whole-slide digital pathology images. In parallel, radiomics-enhanced imaging has shown promise in assessing tumor biology and immune microenvironment features predictive of BCG responsiveness. Liquid biopsy, especially urine tumor DNA analysis, is now available in the arsenal to detect minimal residual disease, stratify recurrence risk, and predict treatment response even before clinical or radiographic evidence of recurrence. Tissue-based genomic profiling has also revealed molecular alterations associated with treatment resistance, though additional validation is needed. Together, these next-generation biomarkers may represent a pivotal shift toward precision oncology in bladder cancer and their incorporation into NMIBC future clinical guidelines is both anticipated and necessary.

BCG-unresponsive disease↗

PubMed1991

Using comparative clinical information to understand practice patterns and affect organizational change.

The University Hospital Consortium is collecting clinical, administrative and financial data from its members to develop a Clinical Information Network. The value of this collective data lies in how comparative information about peer hospitals and physicians in the same specialty can be used to influence practice. The raw data from each hospital is analyzed, classified, normalized and stored in a data repository which is easily accessible. This data becomes information when it is presented in a variety of ways, and is supported by a knowledge-base of health care rules. The "drilling down" technique to progressive levels of detail serves the needs of all levels in the organization--executives, managers, and analysts. The system combines the power of a mainframe for the data repository with the ease of use of a PC-based workstation. With an open-ended approach, the users can ask a variety of questions of the data, as well as perform statistical analysis, create graphical presentations and generate explanations of the analysis techniques.

Artificial Intelligence↗

PubMed2026

Immunopeptidomics-guided cancer vaccine design: Advances, challenges, and emerging opportunities.

Selecting clinically relevant tumor antigens remains a major challenge in the development of therapeutic cancer vaccines. Although computational approaches have considerably improved neoantigen prediction, many candidate epitopes identified in silico are not ultimately presented on the tumor cell surface. The emergence of immunopeptidomics has provided direct access to naturally processed HLA-associated peptides and has offered new opportunities for antigen discovery. Increasing evidence has shown that information derived from the immunopeptidome becomes considerably more informative when interpreted alongside genomic, transcriptomic, and proteomic data. This integrative view has broadened the spectrum of targetable antigens and has also revealed important limitations related to peptide abundance, HLA diversity, tumor heterogeneity, and the imperfect relationship between antigen presentation and immunogenicity. These issues have renewed interest in multi-antigen vaccine strategies designed to better reflect the complexity of tumor antigen landscapes. Advances in bioinformatics and artificial intelligence are facilitating the interpretation of increasingly complex datasets and are beginning to support more systematic approaches to antigen prioritization. In this review, we discuss how immunopeptidomics is contributing to next-generation cancer vaccine development, summarize the major translational challenges, and highlight emerging concepts that may improve the clinical applicability of immunopeptidomics-guided immunotherapy.

Cancer immunotherapy↗

PubMed2026

Adenoviral Vectors in Gene Therapy: A Detailed Overview.

Adenoviral vectors (AdVs) represent one of the most extensively researched platforms in the realm of gene therapy, providing advantages such as high transduction efficiency, large transgene capacity, and broad tropism. This review provides a detailed and structured overview of AdVs, highlighting their biology, gene delivery mechanisms, clinical applications, and challenges limiting their broader therapeutic applicability. The study also explores recent progress in vector engineering, such as rare serotypes, capsid modifications, third-generation vectors, as well as strategies for immune modulation and toxicity reduction. AdVs are used in therapies for genetic disorders, oncology, and vaccinology, alongside innovations such as CRISPR-Cas9, nanotechnology, and artificial intelligence design. Nevertheless, persistent hurdles, including vector immunogenicity, hepatotoxicity, scalability, and the lack of durable expression, prevent widespread clinical use. This review consolidates current knowledge and presents a future perspective on how AdVs may evolve as powerful, adaptable, and precise tools in modern gene therapy. By contextualizing strengths and unresolved challenges, this work aims to give researchers and clinicians a balanced foundation for evaluating their future roles in translational medicine.

Humans↗

PubMed1992

Hemodynamic and oxygen transport patterns for outcome prediction, therapeutic goals, and clinical algorithms to improve outcome. Feasibility of artificial intelligence to customize algorithms.

A generalized decision tree or clinical algorithm for treatment of high-risk elective surgical patients was developed from a physiologic model based on empirical data. First, a large data bank was used to do the following: (1) describe temporal hemodynamic and oxygen transport patterns that interrelate cardiac, pulmonary, and tissue perfusion functions in survivors and nonsurvivors; (2) define optimal therapeutic goals based on the supranormal oxygen transport values of high-risk postoperative survivors; (3) compare the relative effectiveness of alternative therapies in a wide variety of clinical and physiologic conditions; and (4) to develop criteria for titration of therapy to the endpoints of the supranormal optimal goals using cardiac index (CI), oxygen delivery (DO2), and oxygen consumption (VO2) as proxy outcome measures. Second, a general purpose algorithm was generated from these data and tested in preoperatively randomized clinical trials of high-risk surgical patients. Improved outcome was demonstrated with this generalized algorithm. The concept that the supranormal values represent compensations that have survival value has been corroborated by several other groups. We now propose a unique approach to refine the generalized algorithm to develop customized algorithms and individualized decision analysis for each patient's unique problems. The present article describes a preliminary evaluation of the feasibility of artificial intelligence techniques to accomplish individualized algorithms that may further improve patient care and outcome.

Algorithms↗

PubMed1991

A machine learning approach to computer-aided molecular design.

Preliminary results of a machine learning application concerning computer-aided molecular design applied to drug discovery are presented. The artificial intelligence techniques of machine learning use a sample of active and inactive compounds, which is viewed as a set of positive and negative examples, to allow the induction of a molecular model characterizing the interaction between the compounds and a target molecule. The algorithm is based on a twofold phase. In the first one--the specialization step--the program identifies a number of active/inactive pairs of compounds which appear to be the most useful in order to make the learning process as effective as possible and generates a dictionary of molecular fragments, deemed to be responsible for the activity of the compounds. In the second phase--the generalization step--the fragments thus generated are combined and generalized in order to select the most plausible hypothesis with respect to the sample of compounds. A knowledge base concerning physical and chemical properties is utilized during the inductive process.

Amino Acid Sequence↗

PubMed2025

Challenges and Opportunities in Analyzing Cancer-Associated Microbiomes.

The study of cancer-associated microbiomes has gained significant attention in recent years, spurred by advances in high-throughput sequencing and metagenomic analysis. Microbiome research holds promise for identifying noninvasive biomarkers and possibly new paradigms for cancer treatment. In this review, we explore the key computational challenges and opportunities in analyzing cancer-associated microbiomes (in tumor/normal tissues and other body sites, e.g., gut, oral, and skin), focusing on sequencing-driven strategies and associated considerations for taxonomic and functional characterization. The discussion covers the strengths and limitations of current analysis tools for identifying contamination, determining compositional bias, and resolving species and strains, as well as the statistical, metabolic, and network inferences that are essential to uncover host-microbiome interactions. Several key considerations are required to guide the choice of databases used for metagenomic analysis in such studies. Recent advances in spatial and single-cell technologies have provided insights into cancer-associated microbiomes, and Artificial Intelligence-driven protein function prediction might enable rapid advances in this field. Finally, we provide a perspective on how the field can evolve to manage the ever-growing size of datasets and generate robust and testable hypotheses. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .

Humans↗

PubMed2025

Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detection.

Artificial intelligence models using digital histopathology slides stained with hematoxylin and eosin offer promising, tissue-preserving diagnostic tools for patients with cancer. Despite their advantages, their clinical utility in real-world settings remains unproven. Assessing EGFR mutations in lung adenocarcinoma demands rapid, accurate and cost-effective tests that preserve tissue for genomic sequencing. PCR-based assays provide rapid results but with reduced accuracy compared with next-generation sequencing and require additional tissue. Computational biomarkers leveraging modern foundation models can address these limitations. Here we assembled a large international clinical dataset of digital lung adenocarcinoma slides (N = 8,461) to develop a computational EGFR biomarker. Our model fine-tunes an open-source foundation model, improving task-specific performance with out-of-center generalization and clinical-grade accuracy on primary and metastatic specimens (mean area under the curve: internal 0.847, external 0.870). To evaluate real-world clinical translation, we conducted a prospective silent trial of the biomarker on primary samples, achieving an area under the curve of 0.890. The artificial-intelligence-assisted workflow reduced the number of rapid molecular tests needed by up to 43% while maintaining the current clinical standard performance. Our retrospective and prospective analyses demonstrate the real-world clinical utility of a computational pathology biomarker.

Humans↗

PubMed2026

PubMind: literature-based genetic variant extraction and functional annotation using large language models.

Biomedical literature contains extensive functional knowledge on genetic variants, but much remains inaccessible in unstructured text. Existing resources such as ClinVar and HGMD remain limited by coverage, submission bias, update frequency, and sparse annotation. We develop PubMind, an artificial intelligence (AI) framework that uses large language models (LLMs) to triage and extract variant-function-disease associations and supporting evidence from biomedical text. PubMind captures single-nucleotide, copy-number, structural, and gene-fusion variants, and normalizes records to genomic and transcriptomic coordinates. Benchmarking shows >90% accuracy for variant recognition and 99% precision for disease extraction. Applied to >41 million PubMed abstracts and >5 million full-text articles, PubMind generates PubMind-DB, a database of ~1.3 million unique variants with contextual annotations, accessible via web interface and API. Only ~10% of PubMind variants overlap with ClinVar, and >80% of them show concordant pathogenicity labels. PubMind transforms unstructured biomedical text into structured genomic knowledge, advancing variant interpretation for precision medicine.

Large Language Models↗

PubMed2026

The Computational Revolution in Natural Product Research: A Data-Driven Roadmap for Next-Generation Drug Development.

Natural products (NPs) have historically provided the foundational scaffolds for drug development, yet traditional bioprospecting faces critical limitations: high rediscovery rates, laborious isolation workflows, and substantial attrition during clinical translation. The emergence of big data technologies is fundamentally transforming this landscape, enabling a shift from serendipity-based discovery toward systematic, data-driven approaches. This review examines how the integration of artificial intelligence (AI), machine learning (ML), and multi-omics datasets is accelerating natural product research across three key domains: (1) genome mining for biosynthetic gene cluster identification using platforms such as antiSMASH, (2) cheminformatics-driven prediction of structure-activity relationships and ADMET properties, and (3) metabolomics-guided dereplication to prioritize novel bioactive scaffolds. We evaluate the convergence of genomics, metabolomics, and computational chemistry in enabling in silico lead optimization and the discovery of cryptic metabolites from previously inaccessible microbial taxa. While challenges in data standardization and scalability persist, the synergy between big data and NP research is accelerating clinical translation. Despite persistent challenges in data standardization, scalability, and equitable benefit-sharing, the convergence of big data and NP research is poised to redefine drug development. These advances position computational NP research as a cornerstone of next-generation drug development.

big data analytics↗

PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗

PubMed2025

Cost-Effectiveness Analysis of 3D Total-Body Photography for People at High Risk of Melanoma.

IMPORTANCE: Greater use of novel digital technologies could be associated with improved health outcomes and save health care costs by detecting smaller melanomas earlier (needing less treatment) or benign tumors (needing no treatment). OBJECTIVE: To compare costs and health effects of 3-dimensional (3D) total-body photography (TBP) and sequential digital dermoscopy imaging (SDDI) vs usual care for early detection of melanoma. DESIGN, SETTING, AND PARTICIPANTS: This prespecified cost-effectiveness analysis using randomized clinical trial (n = 309) data with 2 years of follow-up was conducted at a research hospital in Brisbane, Australia, and took a health system perspective. It included adults 18 years or older at high risk of developing a primary or subsequent melanoma. INTERVENTION: The intervention group received usual care plus clinical skin examinations by junior clinicians at baseline and 6, 12, 18, and 24 months with 3D TBP-SDDI reviewed by a teledermatologist. The control group continued to receive usual care and completed online surveys every 6 months. MAIN OUTCOMES AND MEASURES: Government health care costs, patient out-of-pocket costs, numbers of benign and malignant skin tumor excisions, and quality-adjusted life-years. Skin biopsy, excisions, pathology, and their costs were collected using administrative claims data. Quality of life was collected using the EuroQol-5D-5L. RESULTS: The trial included 314 participants (mean [SD] age, 51.6 [12.8] years; 194 female individuals [62%]) who completed all of the study procedures (158 in the intervention and 156 in the control groups). Compared with controls, intervention group participants had fewer melanoma excisions, more keratinocyte carcinomas and benign excisions, and more biopsy specimens. Over 24 months, mean per-person costs (analyzed in Australian dollars and converted to US$) for the intervention group were $1708 (95% CI, $1455-$1961) vs $763 (95% CI, $655-$870) for controls, an incremental cost of $945 (95% CI, $738-$1157) to provide the intervention. Total quality-adjusted life-years per person were similar for the intervention (1.84; 95% CI, 1.82-1.86) and control groups (1.84; 95% CI, 1.83-1.86). The incremental cost per additional malignant skin tumor excised was $40 (95% CI, $34-$48). CONCLUSIONS AND RELEVANCE: Over 2 years of the trial, the 3D TBP-SDDI model by junior clinicians and teledermatologist review generated higher costs and detected similar numbers of malignant tumors than usual care in a high-risk melanoma cohort. Cost-effectiveness is a necessary but not sufficient consideration for implementation. Other benefits of 3D TBP-SDDI may arise once artificial intelligence clinician support systems are integrated, and more research is needed to understand factors associated with costs and whether there are other benefits of 3D TBP-SDDI.

Adult↗