Search Results (184)

Background/Objectives: Diffuse large B-cell lymphoma (DLBCL) is a molecularly and pathogenically heterogenous disease with varying clinical outcomes, as reflected by the significant number of patients who develop relapse/refractory disease (rrDLBCL) following standard treatment with the combined R-CHOP regimen. The molecular background of rrDLBCL is not yet fully understood, and prognostic and/or companion diagnostic biomarkers for identification and treatment stratification of these patients are in high demand. Methods: This exploratory study used comprehensive proteomic data to identify proteins associated with treatment response. Proteome profiles of DLBCL cells were analyzed through groupwise comparison between cell lines with a resistant or sensitive response to rituximab, cyclophosphamide, doxorubicin, and vincristine. Their responses were determined using subsequent drug response screens, mimicking the conditions of diagnostic samples prior to treatment. Results: A total of 98 differentially abundant proteins, including NSFL1C, GET4, PCNA, and SMC5, were found between resistant and sensitive cells. These same 98 proteins were examined in two cohorts of DLBCL patients, leading to the identification of 16 proteins whose expression was consistently associated with treatment response both in vitro and in patient tissue samples. Among these, GET4 and NSFL1C showed the highest enrichment in R-CHOP resistant patients compared to sensitive responders. In the cell line study, GET4 was enriched in cyclophosphamide-resistant cell lines and NSFL1C enriched in vincristine-resistant cell lines, associating GET4 and NSFL1C enrichment in patient samples to responsiveness to cyclophosphamide and vincristine, respectively. Enrichment of DNA damage repair proteins was observed within the differential proteins, highlighting the need to investigate DNA damage repair involvement in treatment responses. Conclusions: This study identifies 16 proteins with concordant treatment response specificity in DLBCL cell lines and lymphoma tissue patient samples, suggesting their potential as prognostic markers for DLBCL. Full article

Cancer therapy is rapidly evolving from a one-size-fits-all paradigm toward highly personalized approaches. Traditional chemotherapies and radiotherapies, while broadly applied, often yield suboptimal outcomes due to tumor heterogeneity and are limited by significant toxicities. In contrast, precision oncology tailors prevention, diagnosis, and treatment to the individual patient’s genetic and molecular profile. Key advancements underscore this shift: molecularly targeted drugs (e.g., trastuzumab for HER2-positive breast cancer, EGFR and ALK inhibitors for lung cancer) have improved efficacy and reduced toxicity compared to conventional therapy. Pharmacokinetic (PK) and pharmacodynamic (PD) considerations are central to personalizing treatment, explaining variability in drug exposure and response among patients and guiding dose optimization. Modern strategies like therapeutic drug monitoring and model-informed precision dosing seek to maintain drug levels in the therapeutic range, improving outcomes. Immunotherapies, including checkpoint inhibitors and CAR-T cells, have transformed oncology, though patient selection via biomarkers (such as PD-L1 expression or tumor mutational burden) is critical to identify likely responders. Innovative drug delivery systems, notably nanomedicine, address PK challenges by enhancing tumor-specific drug accumulation and enabling novel therapeutics. Furthermore, rational combination regimens (informed by PK/PD and tumor biology) are being designed to achieve synergistic efficacy and overcome resistance. Key barriers include the high cost of biomarker testing, insufficient laboratory infrastructure, and inconsistent reimbursement policies. Operational inefficiencies such as long turnaround times or lack of clinician awareness further limit the use of precision diagnostics. Regulatory processes also remain complex, particularly around the co-development of targeted drugs and companion diagnostics, and the evidentiary requirements for rare subgroups. Addressing these barriers will require harmonized policies, investment in infrastructure, and educational initiatives to ensure that the promise of personalized medicine becomes accessible to all patients. Ensuring that advances are implemented responsibly—guided by pharmacological insights, supported by real-world evidence, and evaluated within ethical and economic frameworks—will be critical to realizing the full potential of personalized cancer medicine. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly applied in breast imaging, with potential to improve diagnostic accuracy and reduce workload in mammographic breast cancer screening. However, real-world integration of AI into national screening programs remains limited, and little is known about radiologists’ perspectives in Asian settings. This study aimed to explore radiologists’ perceptions of AI adoption in Singapore’s breast screening program, focusing on perceived benefits, barriers, and requirements for safe integration. Methods: We conducted a mixed methods study involving a cross-sectional survey of 17 radiologists with prior experience using AI-assisted mammography, followed by semi-structured interviews with 10 radiologists across all three public healthcare clusters. The survey measured confidence in AI, attitudes toward its diagnostic role, and integration preferences. Interviews were analyzed thematically, guided by the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Results: Among survey respondents, 64.7% recommended AI as a companion reader, though only 29.4% rated its performance as comparable to humans. Confidence was highest when AI was validated on local datasets (mean 9.3/10). Interviews highlighted AI’s strengths in routine, fatigue-prone tasks, but skepticism for complex cases. Concerns included false positives, workflow inefficiencies, medico-legal accountability, and long-term costs. Radiologists emphasized the importance of national guidelines, local validation, and clear role definition to build trust. Conclusions: Radiologists support AI as an adjunct to, but not a replacement for, human readers in breast cancer screening. Adoption will require robust regulatory frameworks, seamless workflow integration, transparent validation on local data, and structured user training to ensure safe and effective implementation. Full article

Ovarian remnant syndrome (ORS) is a recognized postoperative complication in spayed dogs and cats, resulting from incomplete excision or inadvertent revascularisation of ovarian tissue during gonadectomy. Affected animals typically exhibit recurrent oestrous behaviour and may develop serious sequelae, including stump pyometra, mammary neoplasia, and granulosa cell tumours. This retrospective study evaluated 93 cases (70 dogs, 23 cats) diagnosed with ORS referred to the University Veterinary Hospital of Bologna, Italy, focusing on signalment, clinical presentation, diagnostic protocols, and treatment outcomes. Diagnosis relied on a multimodal approach combining clinical history, vaginal cytology, serum progesterone assays, ultrasonography, and histopathological examination of excised tissue. Surgical excision of residual ovarian tissue was the only curative treatment, with improved outcomes when performed during hormonally active phases of the oestrous cycle to optimize remnant localisation. Histopathology confirmed ovarian tissue in the majority of cases, with neoplastic transformation identified in 10% of dogs. Bilateral ovarian remnants were more prevalent than previously reported. Surgical revision was complicated by adhesions involving vital abdominal structures, emphasizing the need for meticulous technique. These findings highlight the critical importance of precise surgical technique during initial gonadectomy, early recognition of ORS, and comprehensive surgical management to prevent severe complications and promote companion animal welfare. Full article

Background/Objectives: Glypican-3 (GPC3) is a cell surface oncofetal protein that is highly expressed in hepatocellular carcinoma (HCC) but absent in normal liver tissue, making it an attractive target for molecularly targeted diagnosis and therapy. To support GPC3-targeted treatment strategies, there is a need for a non-invasive imaging tool capable of detecting GPC3-positive tumors. Methods: We conjugated a commercially available murine anti-GPC3 antibody (1G12), or a proprietary chimeric anti-GPC3 antibody (ET58) to the standard magnetic resonance imaging (MRI) contrast agent, gadolinium, via a DOTA chelator. The resulting probes, 1G12-DOTA-Gd or ET58-DOTA-Gd, respectively, were assessed for in vitro relaxivity and binding specificity to GPC3-positive HCC cells, as well as for in vivo imaging performance in mouse xenograft models bearing GPC3-positive or GPC3-negative HCC tumors. Conclusions: ET58-DOTA-Gd shows high specificity, imaging efficacy, and a favorable immunogenicity profile, thereby making it a promising candidate for clinical translation as a GPC3-targeted MRI probe. It holds potential as a non-invasive companion diagnostic for identifying GPC3-positive HCC patients who may benefit from GPC3-targeted therapies. Full article

Background/Objectives: Cervical Pap smears are routinely used to detect cellular abnormalities as a cervical cancer screening tool and to assess the presence of HPV for risk stratification of the disease. Here, we aimed to extend the applications of this sampling procedure by combining it with multicolor flow cytometry to characterize cell populations across cervical cancer disease stages. Methods: Cervical Pap smears from 30 patients with various disease stages ranging from normal to intraepithelial neoplasia up to treated cancers were analyzed as biofluids using multicolor flow cytometry. Individual samples were evaluated, and statistical analyses were performed over all sample stages. Cancer cell lines (CaSki, SiHa, HeLa, A549, U2OS) were examined as tumor cell controls. Results: Cervical biofluids were subdivided into cell populations according to their scattering properties and the expression of specific biomarkers: EpCAM and cytokeratin 8 for epithelial cells from tumors as well as healthy ectocervical and endocervical regions, and CD45 for immune cells. Discrimination of tumor cells was facilitated with cancer cell lines. Statistical analysis revealed that the composition of cell populations differs among disease stages, whereas treated cancer samples were consistently associated with a reduction in squamous epithelial cells and an increase in immune cells compared to normal samples. Conclusions: Herein, we identified the major cell populations in cervical biofluid samples and demonstrated that this method can detect changes in the cellular composition across different disease stages. This approach could be further exploited in cancer research and potentially serve as a companion diagnostic tool in tumor development, progression and during treatment. Full article

Inflammatory bowel disease (IBD) demonstrates chronic relapsing inflammation extending beyond adaptive immunity dysfunction. “Trained immunity”—the reprogramming of innate immune memory in myeloid cells and hematopoietic progenitors—maintains intestinal inflammation; however, the mechanism by which gut microbiome orchestration determines protective versus pathological outcomes remains unclear. Microbial metabolites demonstrate context-dependent dual effects along the gut–bone marrow axis. Short-chain fatty acids typically induce tolerogenic immune memory, whereas metabolites like succinate and polyamines exhibit dual roles: promoting inflammation in certain contexts while enhancing barrier integrity in others, influenced by cell-specific receptors and microenvironmental factors. Interventions include precision probiotics and postbiotics delivering specific metabolites, fecal microbiota transplantation addressing dysbiotic trained immunity, targeted metabolite supplementation, and pharmacologic reprogramming of pathological myeloid training states. Patient stratification based on microbiome composition and host genetics enhances therapeutic precision. Future research requires integration of non-coding RNAs regulating trained immunity, microbiome–immune–neuronal axis interactions, and host genetic variants modulating microbiome–immunity crosstalk. Priorities include developing companion diagnostics, establishing regulatory frameworks for microbiome therapeutics, and defining mechanistic switches for personalized interventions. Full article

The use of the HOMA (Homeostatic Model Assessment) index in veterinary medicine is emerging as a promising and valuable method for evaluating insulin resistance and beta-cell function in companion animals, particularly in dogs and cats. Originally developed for use in human medicine, HOMA enables a minimally invasive assessment of glucose and insulin homeostasis, offering clinicians a practical tool for diagnosing and monitoring diabetes mellitus in animals. Its application in veterinary practice brings several advantages, including cost-effectiveness, ease of use, and the potential for early detection of metabolic disturbances before clinical symptoms appear. Nonetheless, important limitations persist, such as inter-individual variability, the effects of stress and comorbidities on glucose and insulin values, and the absence of standardized, species-specific reference ranges. These factors highlight the need for methodological refinement and the establishment of validated protocols tailored to the unique physiological characteristics of dogs and cats. Despite these challenges, HOMA represents a promising avenue for advancing the understanding of diabetes pathophysiology in veterinary patients. Future longitudinal studies and controlled trials are essential to confirm its reliability and enhance its clinical relevance. With further development, the HOMA index could become an essential tool in improving diagnostic accuracy and optimizing the management of diabetes in companion animal practice. Full article

Antimicrobial resistance (AMR) is a significant public health concern in companion animals, yet systematic surveillance in North Macedonia is lacking. This study investigated the prevalence of resistance in Escherichia coli isolated from 112 fecal samples from dogs in six shelters in North Macedonia and evaluated the associated risk factors, providing the first baseline dataset for this population. High resistance was observed for sulfamethoxazole (68.75%), ampicillin (52.68%), and ciprofloxacin (41.07%). Multidrug resistance was present in 50% of the isolates, with 17 (15.17%) confirmed as ESBL producers. Additionally, 18 isolates (16.1%) were identified as AmpC producers, 16 of which carried the blaCMY-2 gene. Notably, 72.2% of ESBL/AmpC isolates were resistant to ertapenem despite the absence of carbapenemase genes, a finding that warrants further investigation. Risk factors such as shared housing, longer shelter stays, and frequent empirical antimicrobial use were identified as probable contributors to the carriage of ESBL-/AmpC-producing E. coli. None of the shelters had antimicrobial stewardship protocols or routine diagnostic testing, revealing critical gaps in infection control and antimicrobial practices. These findings underscore the urgent need for antimicrobial stewardship and surveillance in North Macedonia’s companion animal populations within the One Health framework. Full article

This perspective provides a short overview of cancer biomarkers, balancing the technical details with the broad implications for biomarker discovery and innovation; early detection and screening; personalized treatment and monitoring; and emerging technologies. It also briefly discusses challenges in their clinical translation while exploring recent advancements and future implications for clinical practice. Finally, we offer thoughts on the role of artificial intelligence (AI) in biomarker development. AI is accelerating the discovery and validation of biomarkers by mining complex datasets, identifying hidden patterns, and improving the predictive accuracy. AI-powered tools enhance image-based diagnostics, automate genomic interpretation, and facilitate real-time monitoring of treatment responses. By integrating multi-omics data, AI offers new avenues for precision medicine and scalable cancer diagnostics, pushing biomarker development into a new era of intelligent, data-driven oncology. This editorial is a reflection on the state of biomarkers based on the contributions to the Special Issue “Cancer Biomarkers: Recent Progress, Innovations, and Future Clinical Implications”. Full article

Cushing’s disease (CD) is a rare neuroendocrine disorder caused by ACTH-secreting pituitary adenomas, presenting significant diagnostic and therapeutic challenges. Given the evolutionary conservation of the hypothalamic–pituitary–adrenal axis, this review explores the translational value of spontaneous CD forms in dogs, horses, cats, small mammals, and rats, as well as of experimental models in mice, rats, and zebrafish. Dogs are the most studied, showing strong molecular and clinical similarities with human CD, making them valuable for preclinical drug and diagnostic research. While equine and feline CD are less characterized, they may provide insights into dopaminergic therapies and glucocorticoid resistance. Nevertheless, practical and ethical challenges limit the experimental use of companion animals. In preclinical research, mouse models are widely used to study hypercortisolism and test therapeutic agents via transgenic and xenograft strategies. Conversely, few studies are available on a zebrafish transgenic model for CD, displaying pituitary corticotroph expansion and partial resistance to glucocorticoid-negative feedback at the larval stage, while adults exhibit hypercortisolism resembling the human phenotype. Future transplantable systems in zebrafish may overcome several limitations observed in mice, supporting CD research. Collectively, these animal models, each offering unique advantages and limitations, provide a diverse toolkit for advancing CD research and improving human clinical outcomes. Full article

Osteoarthritis (OA) is a multifactorial chronic musculoskeletal disorder characterised by cartilage degradation, synovial inflammation, and subchondral bone remodelling. Conventional diagnostic modalities, including radiographic imaging and symptom-based assessments, primarily detect disease in its later stages, limiting the potential for timely intervention. Inflammatory biomarkers, particularly Interleukin-6 (IL-6), Tumour Necrosis Factor-alpha (TNF-α), and Myeloperoxidase (MPO), have emerged as biologically relevant indicators of disease activity, with potential applications as companion diagnostics in precision medicine. This review examines the diagnostic and prognostic relevance of IL-6, TNF-α, and MPO in OA, focusing on their mechanistic roles in inflammation and joint degeneration, particularly through the activity of fibroblast-like synoviocytes (FLSs). The influence of sample type (serum, plasma, synovial fluid) and analytical performance, including enzyme-linked immunosorbent assay (ELISA), is discussed in the context of biomarker detectability. Advanced statistical and computational methodologies, including rank-based analysis of covariance (ANCOVA), discriminant function analysis (DFA), and Cox proportional hazards modelling, are explored for their capacity to validate biomarker associations, adjust for demographic variability, and stratify patient risk. Further, the utility of synthetic data generation, hierarchical clustering, and dimensionality reduction techniques (e.g., t-distributed stochastic neighbour embedding) in addressing inter-individual variability and enhancing model generalisability is also examined. Collectively, this synthesis supports the integration of biomarker profiling with advanced analytical modelling to improve early OA detection, enable patient-specific classification, and inform the development of targeted therapeutic strategies. Full article

Extracellular vesicles (EVs) are mediators of intercellular communication and serve as promising tools for drug discovery and targeted therapies. These lipid bilayer-bound nanovesicles facilitate the transfer of functional proteins, RNAs, lipids, and other biomolecules between cells, thereby influencing various physiological and pathological processes. This review outlines the molecular mechanisms governing EV biogenesis and cargo sorting, emphasizing the role of key regulatory proteins in modulating selective protein packaging. We explore the critical involvement of EVs in various disease microenvironments, including cancer progression, neurodegeneration, and immunological modulation. Their ability to cross biological barriers and deliver bioactive cargo makes them desirable candidates for precise drug delivery systems, especially in neurological and oncological disorders. Moreover, this review highlights advances in engineering EVs for the delivery of RNA therapeutics, CRISPR-Cas systems, and targeted small molecules. The utility of EVs as diagnostic tools in liquid biopsies and their integration into personalized medicine and companion diagnostics are also discussed. Patient-derived EVs offer dynamic insights into disease states and enable real-time treatment stratification. Despite their potential, challenges such as scalable isolation, cargo heterogeneity, and regulatory ambiguity remain significant hurdles. Recent studies have reported novel pharmacological approaches targeting EV biogenesis, secretion, and uptake pathways, with emerging regulators showing promise as drug targets for modulating EV cargo. Future directions include the standardization of EV analytics, scalable biomanufacturing, and the classification of EV-based therapeutics under evolving regulatory frameworks. This review emphasizes the multifaceted roles of EVs and their transformative potential as therapeutic platforms and biomarker reservoirs in next-generation precision medicine. Full article

Progress in clinical practice, research, and teaching needs a common language. Agreement among veterinary endocrinologists on definitions of concepts related to Cushing’s syndrome (CS) and hypoadrenocorticism is lacking. After a successful inaugural cycle on diabetes mellitus terminology, project Agreeing Language in Veterinary Endocrinology (ALIVE) held a second cycle, with simplified methodology, and brought together 10 experts of the European Society of Veterinary Endocrinology (ESVE) and the Society of Comparative Endocrinology (SCE). It employed a four-round modified Delphi Method to generate draft definitions and try and achieve consensus. A final round used an endorsement survey of the expert-generated definitions distributed to the ESVE and SCE memberships, seeking a simple majority endorsement. A minimum of 20% membership participation was sought. The 10 experts achieved 100% consensus on the definition of 35 adrenal disease-associated concepts, including disease definitions, diagnostic criteria, and test definitions, a disease classification system for CS and hypoadrenocorticism, and a clinical scoring system for CS. Definitions were subsequently assessed by 78 ESVE and SCE members (26% of combined memberships). All definitions achieved a simple majority, ranging from 83.1 to 100%. ALIVE proved effective in creating a body of terminology for adrenal disease in companion animals, which met the overall approval of a majority of those participating in the endorsement phase. The prospective use of these definitions could help improve comparability and standards for adrenal disease research, education, and clinics. Full article

Precision oncology, also known as personalized oncology or precision medicine, is the tailoring of cancer treatment to individual patients based on the specific genetic, molecular, and other unique characteristics of their tumor. The goal of precision oncology is to optimize the effectiveness of cancer treatment while minimizing toxicities and improving patient outcomes. Precision oncology recognizes that cancer is a highly heterogeneous disease and that each patient’s tumor has a distinct genetic diversity. Precision medicine individualizes therapy by using information from a patient’s tumor in the context of clinical history to determine optimal therapeutic approaches and increasing numbers of drugs target specific tumor alterations. Several targeted therapies with approved companion diagnostics are commercially available, the haves of precision oncology, where predictive biomarkers guide clinical decision-making and improve outcomes. However, many therapies still lack clear biomarkers, the have nots, posing a challenge to fully realizing the promise of precision oncology. Herein, we describe the current state of the art for breast cancer precision oncology and highlight the therapeutic agents that require a more robust biomarker. Full article