Molecular and functional landscape of malignant serous effusions for precision oncology.

Personalized treatment for patients with advanced solid tumors critically depends on the deep characterization of tumor cells from patient biopsies. Here, we comprehensively characterize a pan-cancer cohort of 150 malignant serous effusion (MSE) samples at the cellular, molecular, and functional level. We find that MSE-derived cancer cells retain the genomic and transcriptomic profiles of their corresponding primary tumors, validating their use as a patient-relevant model system for solid tumor biology. Integrative analyses reveal that baseline gene expression patterns relate to global ex vivo drug sensitivity, while high-throughput drug-induced transcriptional changes in MSE samples are indicative of drug mode of action and acquired treatment resistance. A case study exemplifies the added value of multi-modal MSE profiling for patients who lack genetically stratified treatment options. In summary, our study provides a functional multi-omics view on a pan-cancer solid tumor cohort and underlines the feasibility and utility of MSE-based precision oncology.

Personalized prediction of immunotherapy response in lung cancer patients using advanced radiomics and deep learning.

BACKGROUND: Lung cancer (LC) is a leading cause of cancer-related mortality, and immunotherapy (IO) has shown promise in treating advanced-stage LC. However, identifying patients likely to benefit from IO and monitoring treatment response remains challenging. This study aims to develop a predictive model for progression-free survival (PFS) in LC patients with IO based on clinical features and advanced imaging biomarkers. MATERIALS AND METHODS: A retrospective analysis was conducted on a cohort of 206 LC patients receiving IO treatment. Pre-treatment computed tomography images were used to extract advanced imaging biomarkers, including intratumoral and peritumoral-vasculature radiomics. Clinical features, including age, gene status, hematology, and staging, were also collected. Key radiomic and clinical features for predicting IO outcomes were identified using a two-step feature selection process, including univariate Cox regression and chi-squared test, followed by sequential forward selection. The DeepSurv model was constructed to predict PFS based on clinical and radiomic features. Model performance was evaluated using the area under the time-dependent receiver operating characteristic curve (AUC) and concordance index (C-index). RESULTS: Combining radiomics of intratumoral heterogeneity and peritumoral-vasculature with clinical features demonstrated a significant enhancement (p < 0.001) in predicting IO response. The proposed DeepSurv model exhibited a prediction performance with AUCs ranging from 0.76 to 0.80 and a C-index of 0.83. Furthermore, the predicted personalized PFS curves revealed a significant difference (p < 0.05) between patients with favorable and unfavorable prognoses. CONCLUSIONS: Integrating intratumoral and peritumoral-vasculature radiomics with clinical features enabled the development of a predictive model for PFS in LC patients with IO. The proposed model's capability to estimate individualized PFS probability and differentiate the prognosis status held promise to facilitate personalized medicine and improve patient outcomes in LC.

A deep-learning model for characterizing tumor heterogeneity using patient-derived organoids.

Genotypic and phenotypic diversity, which generates heterogeneity during disease evolution, is common in cancer. The identification of features specific to each patient and tumor is central to the development of precision medicine and preclinical studies for cancer treatment. However, the complexity of the disease due to inter- and intratumor heterogeneity increases the difficulty of effective analysis. Here, we introduce a sequential deep learning model, preprocessing to organize the complexity due to heterogeneity, which contrasts with general approaches that apply a single model directly. We characterized morphological heterogeneity using microscopy images of patient-derived organoids (PDOs) and identified gene subsets relevant to distinguishing differences among original tumors. PDOs, which reflect the features of their origins, can be reproduced in large quantities and varieties, contributing to increasing the variation by enhancing their common characteristics, in contrast to those from different origins. This resulted in increased efficiency in the extraction of organoid morphological features sharing the same origin. Linking these tumor-specific morphological features to PDO gene expression data enables the extraction of genes strongly correlated with intertumor differences. The relevance of the selected genes was assessed, and the results suggest potential applications in preclinical studies and personalized clinical care.

Synergistic Potential of Nanomedicine in Prostate Cancer Immunotherapy: Breakthroughs and Prospects.

Given the global prevalence of prostate cancer in men, it is crucial to explore more effective treatment strategies. Recently, immunotherapy has emerged as a promising cancer treatment due to its unique mechanism of action and potential long-term effectiveness. However, its limited efficacy in prostate cancer has prompted renewed interest in developing strategies to improve immunotherapy outcomes. Nanomedicine offers a novel perspective on cancer treatment with its unique size effects and surface properties. By employing targeted delivery, controlled release, and enhanced immunogenicity, nanoparticles can be synergized with nanomedicine platforms to amplify the effectiveness of immunotherapy in treating prostate cancer. Simultaneously, nanotechnology can address the limitations of immunotherapy and the challenges of immune escape and tumor microenvironment regulation. Additionally, the synergistic effects of combining nanomedicine with other therapies offer promising clinical outcomes. Innovative applications of nanomedicine include smart nanocarriers, stimulus-responsive systems, and precision medicine approaches to overcome translational obstacles in prostate cancer immunotherapy. This review highlights the transformative potential of nanomedicine in enhancing prostate cancer immunotherapy and emphasizes the need for interdisciplinary collaboration to drive research and clinical applications forward.

Exploring the design of clinical research studies on the efficacy mechanisms in type 2 diabetes mellitus.

This review examines the complexities of Type 2 Diabetes Mellitus (T2DM), focusing on the critical role of integrating omics technologies with traditional experimental methods. It underscores the advancements in understanding the genetic diversity of T2DM and emphasizes the evolution towards personalized treatment modalities. The paper analyzes a variety of omics approaches, including genomics, methylation, transcriptomics, proteomics, metabolomics, and intestinal microbiomics, delineating their substantial contributions to deciphering the multifaceted mechanisms underlying T2DM. Furthermore, the review highlights the indispensable role of non-omics experimental techniques in comprehending and managing T2DM, advocating for their integration in the development of tailored medicine and precision treatment strategies. By identifying existing research gaps and suggesting future research trajectories, the review underscores the necessity for a comprehensive, multidisciplinary approach. This approach synergistically combines clinical insights with cutting-edge biotechnologies, aiming to refine the management and therapeutic interventions of T2DM, and ultimately enhancing patient outcomes. This synthesis of knowledge and methodologies paves the way for innovative advancements in T2DM research, fostering a deeper understanding and more effective treatment of this complex condition.

Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones.

Intratumoral cellular heterogeneity necessitates multi-targeting therapies for improved clinical benefits in advanced malignancies. However, systematic identification of patient-specific treatments that selectively co-inhibit cancerous cell populations poses a combinatorial challenge, since the number of possible drug-dose combinations vastly exceeds what could be tested in patient cells. Here, we describe a machine learning approach, scTherapy, which leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors. Patient-specific treatments reveal a wide spectrum of co-inhibitors of multiple biological pathways predicted for primary cells from heterogenous cohorts of patients with acute myeloid leukemia and high-grade serous ovarian carcinoma, each with unique resistance patterns and synergy mechanisms. Experimental validations confirm that 96% of the multi-targeting treatments exhibit selective efficacy or synergy, and 83% demonstrate low toxicity to normal cells, highlighting their potential for therapeutic efficacy and safety. In a pan-cancer analysis across five cancer types, 25% of the predicted treatments are shared among the patients of the same tumor type, while 19% of the treatments are patient-specific. Our approach provides a widely-applicable strategy to identify personalized treatment regimens that selectively co-inhibit malignant cells and avoid inhibition of non-cancerous cells, thereby increasing their likelihood for clinical success.

Personalized mRNA vaccines in glioblastoma therapy: from rational design to clinical trials.

Glioblastomas (GBMs) are the most common and aggressive malignant brain tumors, presenting significant challenges for treatment due to their invasive nature and localization in critical brain regions. Standard treatment includes surgical resection followed by radiation and adjuvant chemotherapy with temozolomide (TMZ). Recent advances in immunotherapy, including the use of mRNA vaccines, offer promising alternatives. This review focuses on the emerging use of mRNA vaccines for GBM treatment. We summarize recent advancements, evaluate current obstacles, and discuss notable successes in this field. Our analysis highlights that while mRNA vaccines have shown potential, their use in GBM treatment is still experimental. Ongoing research and clinical trials are essential to fully understand their therapeutic potential. Future developments in mRNA vaccine technology and insights into GBM-specific immune responses may lead to more targeted and effective treatments. Despite the promise, further research is crucial to validate and optimize the effectiveness of mRNA vaccines in combating GBM.

Personalised transcranial magnetic stimulation for treatment-resistant depression, depression with comorbid anxiety and negative symptoms of schizophrenia: a narrative review.

ABSTRACT: Transcranial magnetic stimulation (TMS) is a promising intervention for treatment-resistant psychiatric disorders. However, conventional TMS typically utilises a one-size-fits-all approach when determining stimulation targets. Recent retrospective brain circuit-based analyses using lesion network mapping have suggested that a left dorsal lateral prefrontal cortex target has a higher efficacy for alleviating depression symptoms, a dorsomedial prefrontal cortex target is more effective for anxiety symptoms, and a rostromedial prefrontal cortex target is effective for schizophrenia-associated psychiatric symptoms. Nonetheless, symptom-specific brain circuit targeting has not been tested prospectively. We conducted a narrative review of selected literature to investigate individualised targeting for TMS and discuss potential future directions to elucidate the efficacy of this approach.

Molecular targets and strategies in the development of nucleic acid cancer vaccines: from shared to personalized antigens.

Recent breakthroughs in cancer immunotherapies have emphasized the importance of harnessing the immune system for treating cancer. Vaccines, which have traditionally been used to promote protective immunity against pathogens, are now being explored as a method to target cancer neoantigens. Over the past few years, extensive preclinical research and more than a hundred clinical trials have been dedicated to investigating various approaches to neoantigen discovery and vaccine formulations, encouraging development of personalized medicine. Nucleic acids (DNA and mRNA) have become particularly promising platform for the development of these cancer immunotherapies. This shift towards nucleic acid-based personalized vaccines has been facilitated by advancements in molecular techniques for identifying neoantigens, antigen prediction methodologies, and the development of new vaccine platforms. Generating these personalized vaccines involves a comprehensive pipeline that includes sequencing of patient tumor samples, data analysis for antigen prediction, and tailored vaccine manufacturing. In this review, we will discuss the various shared and personalized antigens used for cancer vaccine development and introduce strategies for identifying neoantigens through the characterization of gene mutation, transcription, translation and post translational modifications associated with oncogenesis. In addition, we will focus on the most up-to-date nucleic acid vaccine platforms, discuss the limitations of cancer vaccines as well as provide potential solutions, and raise key clinical and technical considerations in vaccine development.

Integrating deep phenotyping with genetic analysis: a comprehensive workflow for diagnosis and management of rare bone diseases.

Phenotypes play a fundamental role in medical genetics, serving as external manifestations of underlying genotypes. Deep phenotyping, a cornerstone of precision medicine, involves precise multi-system phenotype assessments, facilitating disease subtyping and genetic understanding. Despite their significance, the field lacks standardized protocols for accurate phenotype evaluation, hindering clinical comprehension and research comparability. We present a comprehensive workflow of deep phenotyping for rare bone diseases from the Genetics Clinic of Skeletal Deformity at Peking Union Medical College Hospital. Our workflow integrates referral, informed consent, and detailed phenotype evaluation through HPO standards, capturing nuanced phenotypic characteristics using clinical examinations, questionnaires, and multimedia documentation. Genetic testing and counseling follow, based on deep phenotyping results, ensuring personalized interventions. Multidisciplinary team consultations facilitate comprehensive patient care and clinical guideline development. Regular follow-up visits emphasize dynamic phenotype reassessment, ensuring treatment strategies remain responsive to evolving patient needs. In conclusion, this study highlights the importance of deep phenotyping in rare bone diseases, offering a standardized framework for phenotype evaluation, genetic analysis, and multidisciplinary intervention. By enhancing clinical care and research outcomes, this approach contributes to the advancement of precision medicine in the field of medical genetics.

Comprehensive insights into UTIs: from pathophysiology to precision diagnosis and management.

Urinary tract infections (UTIs) are the second most common infectious disease, predominantly impacting women with 150 million individuals affected globally. It increases the socio-economic burden of society and is mainly caused by Escherichia coli, Proteus mirabilis, Klebsiella pneumoniae, Enterobacter spp., and Staphylococcus spp. The severity of the infection correlates with the host factors varying from acute to chronic infections. Even with a high incidence rate, the diagnosis is mainly based on the symptoms, dipstick analysis, and culture analysis, which are time-consuming, labour-intensive, and lacking sensitivity and specificity. During this period, medical professionals prescribe empirical antibiotics, which may increase the antimicrobial resistance rate. Timely and precise UTI diagnosis is essential for addressing antibiotic resistance and improving overall quality of life. In response to these challenges, new techniques are emerging. The review provides a comprehensive overview of the global burden of UTIs, associated risk factors, implicated organisms, traditional and innovative diagnostic methods, and approaches to UTI treatment and prevention.

Exploring adverse effects of deep brain stimulation: the need for personalized care and long-term monitoring.

Deep Brain Stimulation (DBS), an FDA-approved treatment for movement disorders such as Parkinson's Disease (PD), is increasingly used for various neurological and neuropsychiatric conditions. A recent systematic review and meta-analysis by Bahadori et al. highlighted a significant increase in Body Mass Index (BMI) among patients post-DBS, with most participants having PD. The study, however, noted moderate heterogeneity (I² = 67.566%) without thoroughly addressing its potential causes or proposing strategies to mitigate it. The review's limited patient diversity and short follow-up period also challenge its generalizability and long-term implications. In addition to BMI changes, DBS has been linked to motor, cognitive, and psychiatric side effects. Patients undergoing subthalamic nucleus (STN) stimulation, for example, face risks of motor complications, including speech and gait issues, while cognitive declines, particularly in verbal fluency and executive function, are also concerning. Psychiatric side effects such as depression, anxiety, and psychosis further complicate treatment outcomes. These findings underscore the importance of personalized treatment strategies, preoperative assessments, and ongoing patient education to minimize adverse effects and optimize the therapeutic potential of DBS.

Monoclonal antibodies: From magic bullet to precision weapon.

Monoclonal antibodies (mAbs) are used to prevent, detect, and treat a broad spectrum of non-communicable and communicable diseases. Over the past few years, the market for mAbs has grown exponentially with an expected compound annual growth rate (CAGR) of 11.07% from 2024 (237.64 billion USD estimated at the end of 2023) to 2033 (679.03 billion USD expected by the end of 2033). Ever since the advent of hybridoma technology introduced in 1975, antibody-based therapeutics were realized using murine antibodies which further progressed into humanized and fully human antibodies, reducing the risk of immunogenicity. Some benefits of using mAbs over conventional drugs include a drastic reduction in the chances of adverse reactions, interactions between drugs, and targeting specific proteins. While antibodies are very efficient, their higher production costs impede the process of commercialization. However, their cost factor has been improved by developing biosimilar antibodies as affordable versions of therapeutic antibodies. Along with the recent advancements and innovations in antibody engineering have helped and will furtherly help to design bio-better antibodies with improved efficacy than the conventional ones. These novel mAb-based therapeutics are set to revolutionize existing drug therapies targeting a wide spectrum of diseases, thereby meeting several unmet medical needs. This review provides comprehensive insights into the current fundamental landscape of mAbs development and applications and the key factors influencing the future projections, advancement, and incorporation of such promising immunotherapeutic candidates as a confrontation approach against a wide list of diseases, with a rationalistic mentioning of any limitations facing this field.

Clinical Implications of the Molecular and Genomic Landscape of Upper Tract Urothelial Carcinoma.

PURPOSE OF REVIEW: Upper tract urothelial carcinoma (UTUC) is an aggressive entity with treatment strategies mirroring bladder cancer. Genomic and molecular profiling allows for a better characterization of this disease and allows for patient-tailored approaches. We aim to describe the genomic and molecular implications of this disease. RECENT FINDINGS: Technological advances have the potential for early diagnosis and precise molecular analysis in patients with UTUC. Genomic profile clustering, specific mRNA signatures, and pathway-specific protein abundance tools have oncologic and clinical implications. We describe their utility in the context of this disease. In the era of precision medicine, designing clinical trials that explore the diagnostic and prognostic implications of biomolecular signatures in the context of UTUC is of utmost importance. Promising advances in this arena provide tools for physicians to avoid overtreatment in this patient population.

Cross-omics strategies and personalised options for lung cancer immunotherapy.

Lung cancer is one of the most common malignant tumours worldwide and its high mortality rate makes it a leading cause of cancer-related deaths. To address this daunting challenge, we need a comprehensive understanding of the pathogenesis and progression of lung cancer in order to adopt more effective therapeutic strategies. In this regard, integrating multi-omics data of the lung provides a highly promising avenue. Multi-omics approaches such as genomics, transcriptomics, proteomics, and metabolomics have become key tools in the study of lung cancer. The application of these methods not only helps to resolve the immunotherapeutic mechanisms of lung cancer, but also provides a theoretical basis for the development of personalised treatment plans. By integrating multi-omics, we have gained a more comprehensive understanding of the process of lung cancer development and progression, and discovered potential immunotherapy targets. This review summarises the studies on multi-omics and immunology in lung cancer, and explores the application of these studies in early diagnosis, treatment selection and prognostic assessment of lung cancer, with the aim of providing more personalised and effective treatment options for lung cancer patients.

Relationship between the expressions of DLL3, ASC1, TTF-1 and Ki-67: First steps of precision medicine at SCLC.

This study presents an observational, cross-sectional analysis of 64 patients diagnosed with small cell lung cancer (SCLC) at a reference laboratory for thoracic pathology between 2022 and 2024. The primary objective was to evaluate the expression of Delta-like ligand 3 (DLL3) and other neuroendocrine markers such as Chromogranin, and Synaptophysin, utilizing both traditional immunohistochemistry and digital pathology tools. Patients were primarily older adults, with a median age of over 71, and most biopsies were obtained from lung parenchyma. Immunohistochemistry (IHC) was performed using specific monoclonal antibodies, with DLL3 showing variable expression across the samples. Notably, DLL3 was expressed in 72.3% of the cases, with varied intensities and a semi-quantitative H-score applied for more nuanced analysis. ASCL1 was expressed in 97% of cases, with the majority considered low-expressors. Only 11% had high expression. TTF-1, traditionally not a conventional marker for the diagnosis of SCLC, was positive in half of the cases, suggesting its potential as a biomarker. The study underscores the significant variability in the expression of neuroendocrine markers in SCLC, with implications for both diagnosis and potential therapeutic targeting. DLL3, particularly, shows promise as a therapeutic target due to its high expression rate in the cohort. The use of digital pathology software QuPath enhanced the accuracy and depth of analysis, allowing for detailed morphometric analysis and potentially informing more personalized treatment approaches. The findings emphasize the need for further research into the role of these markers in the management and treatment of SCLC, considering the poor prognosis and high mortality rate observed in the cohort.

Atomically dispersed recognition unit for selective in vivo photoelectrochemical medicine detection.

Continuous and long-term therapeutic monitoring of medicine molecules in biological systems will revolutionize healthcare by offering personalized pharmacokinetic reports. However, the extremely complex biological environment brings great challenges for in vivo molecule detection in living organisms. Here we introduce an in vivo photoelectrochemical biosensor following a reverse design strategy with single atoms as molecular recognition units. Atomic dispersion of Cu single atoms on TiO2-x substrate create synergistic anchoring triple-site for efficiently and selectively capturing of dual-carbonyl group and neighboring dual-hydroxyl group of tetracycline molecules. The photoelectrode is encapsulated with antibiofouling layer and implanted into the vein of living mouse to enable long-term in vivo monitoring of tetracycline in real biological environments. It is important to note that our approach was exclusively tested in male mice, and therefore, the findings may not be generalizable to female mice or other species without further research. The rationally designed biological-components-free in vivo biosensor with excellent selectivity, robustness, and stability endows possibility for enabling personalized medicine guidance through real-time feedbacking information and providing direct and authentic medicine molecular analysis.