The CAR macrophage cells, a novel generation of chimeric antigen-based approach against solid tumors

Biomarker Research, Год журнала: 2023, Номер 11(1)

Опубликована: Ноя. 28, 2023

Abstract Today, adoptive cell therapy has many successes in cancer therapy, and this subject is brilliant using chimeric antigen receptor T cells. The CAR with its FDA-approved drugs, could treat several types of hematological malignancies thus be very attractive for treating solid cancer. Unfortunately, the cannot functional cancers due to unique features. This treatment method harmful adverse effects that limit their applications, so novel treatments must use new cells like NK cells, NKT macrophage Among these innate features, are more tumor seem a better candidate prior methods. have vital roles microenvironment and, direct effect, can eliminate efficiently. In addition, being part immune system, attended sites. With high infiltration, modulations effective. review investigates last achievements CAR-macrophage future immunotherapy method.

Язык: Английский

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Therapeutic Antibodies in Medicine

Molecules, Год журнала: 2023, Номер 28(18), С. 6438 - 6438

Опубликована: Сен. 5, 2023

Antibody engineering has developed into a wide-reaching field, impacting multitude of industries, most notably healthcare and diagnostics. The seminal work on developing the first monoclonal antibody four decades ago witnessed exponential growth in last 10–15 years, where regulators have approved antibodies as therapeutics for several diagnostic applications, including remarkable attention it garnered during pandemic. In recent become fastest-growing class biological drugs treatment wide range diseases, from cancer to autoimmune conditions. This review discusses field therapeutic stands today. It summarizes outlines clinical relevance application treating landscape diseases different disciplines medicine. nomenclature, various approaches therapies, evolution therapeutics. also risk profile adverse immune reactions associated with sheds light future applications perspectives drug discovery.

Язык: Английский

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T cell exhaustion initiates tertiary lymphoid structures and turbocharges cancer-immunity cycle

EBioMedicine, Год журнала: 2024, Номер 104, С. 105154 - 105154

Опубликована: Май 14, 2024

Язык: Английский

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Towards the Prediction of Responses to Cancer Immunotherapy: A Multi-Omics Review

Life, Год журнала: 2025, Номер 15(2), С. 283 - 283

Опубликована: Фев. 12, 2025

Tumor treatment has undergone revolutionary changes with the development of immunotherapy, especially immune checkpoint inhibitors. Because not all patients respond positively to therapeutic agents, and severe immune-related adverse events (irAEs) are frequently observed, biomarkers evaluating response a patient is key for application immunotherapy in wider range. Recently, various multi-omics features measured by high-throughput technologies, such as tumor mutation burden (TMB), gene expression profiles, DNA methylation have been proved be sensitive accurate predictors immunotherapy. A large number predictive models based on these features, utilizing traditional machine learning or deep frameworks, also proposed. In this review, we aim cover recent advances predicting using features. These include new measurements, research cohorts, data sources, models. Key findings emphasize importance TMB, neoantigens, MSI, mutational signatures ICI responses. The integration bulk single-cell RNA sequencing enhanced our understanding microenvironment enabled identification like PD-L1 IFN-γ signatures. Public datasets improved tools. However, challenges remain, need diverse clinical datasets, standardization data, model interpretability. Future will require collaboration among researchers, clinicians, scientists address issues enhance cancer precision.

Язык: Английский

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An exosome-derived lncRNA signature identified by machine learning associated with prognosis and biomarkers for immunotherapy in ovarian cancer

Frontiers in Immunology, Год журнала: 2024, Номер 15

Опубликована: Фев. 9, 2024

Background Ovarian cancer (OC) has the highest mortality rate among gynecological malignancies. Current treatment options are limited and ineffective, prompting discovery of reliable biomarkers. Exosome lncRNAs, carrying genetic information, promising new markers. Previous studies only focused on exosome-related genes employed Lasso algorithm to construct prediction models, which not robust. Methods 420 OC patients from TCGA datasets were divided into training validation datasets. The GSE102037 dataset was used for external validation. LncRNAs associated with selected using Pearson analysis. Univariate COX regression analysis filter prognosis-related lncRNAs. overlapping lncRNAs identified as candidate machine learning. Based 10 learning algorithms 117 combinations, optimal predictor combinations according C index. LncRNA Signature (ERLS) model constructed multivariate regression. median risk score datasets, high- low-risk groups. Kaplan-Meier survival analysis, time-dependent ROC, immune cell infiltration, immunotherapy response, checkpoints analyzed. Results 64 subjected a machine-learning process. stepCox (forward) combined Ridge algorithm, 20 lncRNA ERLS model. showed that high-risk group had lower rate. area under curve (AUC) in predicting OS at 1, 3, 5 years 0.758, 0.816, 0.827 entire cohort. xCell ssGSEA higher may contribute activation cytolytic activity, inflammation promotion, T-cell co-stimulation pathways. expression levels PDL1, CTLA4, TMB. can predict response anti-PD1 anti-CTLA4 therapy. Patients low PDL1 or high CTLA4 exhibited significantly better prospects, whereas poorest outcomes. Conclusion Our study an prognostic optimizing clinical management patients.

Язык: Английский

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Integrating AI into Cancer Immunotherapy—A Narrative Review of Current Applications and Future Directions

Diseases, Год журнала: 2025, Номер 13(1), С. 24 - 24

Опубликована: Янв. 20, 2025

Background: Cancer remains a leading cause of morbidity and mortality worldwide. Traditional treatments like chemotherapy radiation often result in significant side effects varied patient outcomes. Immunotherapy has emerged as promising alternative, harnessing the immune system to target cancer cells. However, complexity responses tumor heterogeneity challenges its effectiveness. Objective: This mini-narrative review explores role artificial intelligence [AI] enhancing efficacy immunotherapy, predicting responses, discovering novel therapeutic targets. Methods: A comprehensive literature was conducted, focusing on studies published between 2010 2024 that examined application AI immunotherapy. Databases such PubMed, Google Scholar, Web Science were utilized, articles selected based relevance topic. Results: significantly contributed identifying biomarkers predict immunotherapy by analyzing genomic, transcriptomic, proteomic data. It also optimizes combination therapies most effective treatment protocols. AI-driven predictive models help assess response guiding clinical decision-making minimizing effects. Additionally, facilitates discovery targets, neoantigens, enabling development personalized immunotherapies. Conclusions: holds immense potential transforming related data privacy, algorithm transparency, integration must be addressed. Overcoming these hurdles will likely make central component future offering more treatments.

Язык: Английский

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Investigation of Aframomum melegueta compounds as ERK5 inhibitor related to breast cancer via molecular docking and dynamic simulation

In Silico Pharmacology, Год журнала: 2025, Номер 13(1)

Опубликована: Янв. 25, 2025

Язык: Английский

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Advances in Antibody-Based Immune-Stimulating Drugs: Driving Innovation in Cancer Therapy

International Journal of Molecular Sciences, Год журнала: 2025, Номер 26(4), С. 1440 - 1440

Опубликована: Фев. 8, 2025

Antibody-based immune-stimulating drugs (ABIs) represent a transformative frontier in cancer immunotherapy, designed to reshape the tumor microenvironment and overcome immune suppression. This study highlighted recent advances ABIs, including antibody conjugates (ISACs), bispecific antibodies (BsAbs), checkpoint blockade enhancers, with focus on their mechanisms of action, clinical advancements, challenges. Preclinical findings revealed that ISACs effectively boost overall anti-cancer immunity by reprogramming tumor-associated macrophages, enhancing T cell activation, engaging other pathways. Similarly, BsAbs redirect cells tumors, achieving significant regression. Additionally, artificial intelligence (AI) is revolutionizing development ABIs optimizing drug design, identifying novel targets, accelerating preclinical validation, enabling personalized therapeutic strategies. Despite these challenges remain, resistance off-target effects. Future research should prioritize next-generation multifunctional antibodies, AI-driven innovations, combination therapies enhance efficacy expand applications. Connecting gaps could unlock full potential upgrading treatment improving outcomes for patients refractory or resistant tumors.

Язык: Английский

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Artificial Intelligence and Breast Cancer Management: From Data to the Clinic

Cancer Innovation, Год журнала: 2025, Номер 4(2)

Опубликована: Фев. 20, 2025

Breast cancer (BC) remains a significant threat to women's health worldwide. The oncology field had an exponential growth in the abundance of medical images, clinical information, and genomic data. With its continuous advancement refinement, artificial intelligence (AI) has demonstrated exceptional capabilities processing intricate multidimensional BC-related AI proven advantageous various facets BC management, encompassing efficient screening diagnosis, precise prognosis assessment, personalized treatment planning. However, implementation into precision medicine practice presents ongoing challenges that necessitate enhanced regulation, transparency, fairness, integration multiple pathways. In this review, we provide comprehensive overview current research related BC, highlighting extensive applications throughout whole cycle management potential for innovative impact. Furthermore, article emphasizes significance constructing patient-oriented algorithms. Additionally, explore opportunities directions within burgeoning field.

Язык: Английский

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Deciphering the tumor immune microenvironment: Microscale measurements for precision detection and multidimensional analysis

View, Год журнала: 2025, Номер unknown

Опубликована: Март 20, 2025

Abstract The precision of cancer immunotherapy is critically dependent on accurately characterizing the tumor immune microenvironment (TIME), which represents a complex interplay cellular components, cytokines, and metabolic factors. Traditional diagnostic methods lack resolution to capture dynamic molecular interactions within TIME at microscale level. This review focuses recent advancements in measurements for identifying novel immune‐oncology biomarkers therapeutic targets TIME, emphasizing importance high‐fidelity data infiltrates significance longitudinal high‐dimensional analysis predicting treatment responses. Furthermore, discusses impact reprogramming potential new responses immunotherapy. role nanotechnology enhancing detection checkpoints development AI‐based sensors real‐time predictive modeling also explored, highlighting these advanced technologies revolutionize field

Язык: Английский

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