Immune checkpoint inhibitors (ICIs) are potent and precise therapies for various cancer types, significantly improving survival rates in patients who respond positively to them. However, only a minority of patients benefit from ICI treatments.

Identifying ICI responders before treatment could greatly conserve medical resources, minimize potential drug side effects, and expedite the search for alternative therapies. Our goal is to introduce a novel deep-learning method to predict ICI treatment responses in cancer patients.

The proposed deep-learning framework leverages graph neural network and biological pathway knowledge. We trained and tested our method using ICI-treated patients' data from several clinical trials covering melanoma, gastric cancer, and bladder cancer.

Our results demonstrate that this predictive model outperforms current state-of-the-art methods and tumor microenvironment-based predictors. Additionally, the model quantifies the importance of pathways, pathway interactions, and genes in its predictions. A web server for IRnet has been developed and deployed, providing broad accessibility to users at https://irnet.missouri.edu.

IRnet is a competitive tool for predicting patient responses to immunotherapy, specifically ICIs. Its interpretability also offers valuable insights into the mechanisms underlying ICI treatments.

The tumor microenvironment (TME) plays a crucial role in the occurrence and progression of gastric cancer. Yet, we still don't understand how immune and stromal components of TMEs are modulated. In this study, we applied the ESTIMATE algorithm to calculate the number of immune and stromal components in 410 STAD cases in the Cancer Genome Atlas (TCGA) database. COX regression analysis and protein-protein interaction (PPI) network construction were used to analyze differentially expressed genes (DEGs). Then, P-selectin (SELP) was identified as a predictor by cross-analysis of univariate COX and PPI. After verifying the clinical significance of SELP for study, we performed an immune infiltration analysis and identified 54 immunomodulators associated with SELP through public data. Immunomodulation associated with gastric cancer prognosis was then confirmed by LASSO regression, and the previous results were further validated with single-cell data. Finally, we verified that SELP can promote EMT on gastric cancer cells. In conclusion, we validated that SELP may affect the biological phenotype of gastric cancer with the immune microenvironment alteration of gastric cancer.

Immune checkpoint inhibitors (ICIs) have demonstrated significant clinical benefits in cancer treatment, but only a minority of patients exhibit favorable response, highlighting the importance of determining patients who will benefit from immunotherapy. Currently, patient datasets regarding immunotherapy response are scarce, while ample experiments can be performed on syngeneic mouse tumor models to generate valuable data. Therefore, how to effectively utilize mouse data to identify predictors of immunotherapy response and subsequently transfer relevant knowledge to predict human response to ICIs is a question worth studying. In this study, we propose a novel methodology to address this issue. Firstly, we identify gene modules associated with immunotherapy response from mouse tumor profiles based on cancer gene panels. Subsequently, these identified modules are employed to build prediction models for immunotherapy response based on mouse data. Furthermore, we transfer these models to predict ICIs responses of human cancer patients. Experimental results demonstrate that the gene modules identified from mouse data are reliable predictors of immunotherapy response. The mouse-based models built on these modules could be transferred to humans, effectively predicting drug responses and survival outcomes for cancer patients. Compared to conventional cancer biomarkers and existing prediction models based on mouse data, our method exhibits superior performance. These findings provide a valuable reference for further in-depth research on immunotherapy response prediction model based on mouse tumor profiles, with the potential for transfer applications in human cancer therapy.

Immune checkpoint inhibitors have become standard care across many cancers, but most patients do not respond. Predicting response remains challenging due to complex tumor-immune interactions and the poor generalizability of current biomarkers and models. Predictors such as tumor mutational burden, PD-L1 expression, and transcriptomic signatures often fail across cancer types, therapies, and clinical settings. There is a clear need for a robust, interpretable model that captures shared immune response principles and adapts to diverse clinical contexts. We present Compass, a foundation model for predicting immunotherapy response from pan-cancer transcriptomic data using a concept bottleneck architecture. Compass encodes tumor gene expression through 44 biologically grounded immune concepts representing immune cell states, tumor-microenvironment interactions, and signaling pathways. Trained on 10,184 tumors across 33 cancer types, Compass outperforms 22 baseline methods in 16 independent clinical cohorts spanning seven cancers and six immune checkpoint inhibitors, increasing precision by 8.5%, Matthews correlation coefficient by 12.3%, and area under the precision-recall curve by 15.7%, with minimal or no additional training. The model generalizes to unseen cancer types and treatments, supporting indication selection and patient stratification in early-phase clinical trials. Survival analysis shows that Compass-stratified responders have significantly longer overall survival (hazard ratio = 4.7, p < 0.0001). Personalized response maps link gene expression to immune concepts, revealing distinct mechanisms of response and resistance. For example, among immune-inflamed non-responders, Compass identifies distinct resistance programs involving TGF- β signaling, endothelial exclusion, CD4+ T cell dysfunction, and B cell deficiency. By combining mechanistic interpretability with transfer learning, Compass provides mechanistic insights into treatment response variability, supports clinical decision-making, and informs trial design.

Predicting immune checkpoint inhibitor (ICI) response remains a significant challenge in cancer immunotherapy. Many existing approaches rely on differential gene expression analysis or predefined immune signatures, which may fail to capture the complex regulatory mechanisms underlying immune response. Network-based models attempt to integrate biological interactions, but they often lack a quantitative framework to assess how individual genes contribute within pathways, limiting the specificity and interpretability of biomarkers. Given these limitations, we developed PathNetDRP, a framework that integrates biological pathways, protein-protein interaction networks, and machine learning to identify functionally relevant biomarkers for ICI response prediction.

We introduce PathNetDRP, a novel biomarker discovery approach that applies the PageRank algorithm to prioritize ICI-associated genes, maps them to relevant biological pathways, and calculates PathNetGene scores to quantify their contribution to immune response. Unlike conventional methods that focus solely on gene expression differences, PathNetDRP systematically incorporates biological context to improve biomarker selection. Validation across multiple independent cancer cohorts showed that PathNetDRP achieved strong predictive performance, with cross-validation the area under the receiver operating characteristic curves increasing from 0.780 to 0.940. Interestingly, PathNetDRP did not merely improve predictive accuracy; it also provided insights into key immune-related pathways, reinforcing its potential for identifying clinically relevant biomarkers.

The biomarkers identified by PathNetDRP demonstrated robust predictive performance across cross-validation and independent validation datasets, suggesting their potential utility in clinical applications. Furthermore, enrichment analysis highlighted key immune-related pathways, providing a deeper understanding of their role in ICI response regulation. While these findings underscore the promise of PathNetDRP, future work will explore the integration of additional predictive features, such as tumor mutational burden and microsatellite instability, to further refine its applicability.

Immunotherapy emerges as a promising approach, marked by recent substantial progress in elucidating how the host immune response impacts tumor development and its sensitivity to various treatments. Immune checkpoint inhibitors have revolutionized cancer therapy by unleashing the power of the immune system to recognize and eradicate tumor cells. Among these, inhibitors targeting the programmed cell death protein 1 (PD-1) and its ligand (PD-L1) have garnered significant attention due to their remarkable clinical efficacy across various malignancies. This review delves into the mechanisms of action, clinical applications, and emerging therapeutic strategies surrounding PD-1/PD-L1 blockade. We explore the intricate interactions between PD-1/PD-L1 and other immune checkpoints, shedding light on combinatorial approaches to enhance treatment outcomes and overcome resistance mechanisms. Furthermore, we discuss the expanding landscape of immune checkpoint inhibitors beyond PD-1/PD-L1, including novel targets such as CTLA-4, LAG-3, TIM-3, and TIGIT. Through a comprehensive analysis of preclinical and clinical studies, we highlight the promise and challenges of immune checkpoint blockade in cancer immunotherapy, paving the way for future advancements in the field.

Gastric cancer (GC) remains one of the leading causes of cancer-related mortality worldwide, with most cases diagnosed at advanced stages. Traditional biomarkers provide only partial insights into GC's heterogeneity. Recent advances in machine learning (ML)-driven multiomics technologies, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, pathomics, and radiomics, have facilitated a deeper understanding of GC by integrating molecular and imaging data. In this review, we summarize the current landscape of ML-based multiomics integration for GC, highlighting its role in precision diagnosis, prognosis prediction, and biomarker discovery for achieving personalized medicine.

The immunotherapy landscape for gastrointestinal (GI) tumors is rapidly evolving. There is an urgent need for reliable biomarkers capable of predicting treatment outcomes to optimize therapeutic strategies and enhance patient prognosis. This review presents a comprehensive overview of biomarkers associated with the immunotherapy response of GI tumors, covering advances in molecular genetics, histopathological markers, and imaging. Key molecular biomarkers, such as microsatellite instability, tumor mutational burden, and programmed death-ligand 1 expression, remain critical for identifying patients likely to benefit from immune checkpoint inhibitors. The significance of tumor-infiltrating lymphocytes, notably the CD8+ T cell to regulatory T cell ratio, as a predictor of immunotherapy response is explored. In addition, advanced imaging techniques, including computed tomography (CT), magnetic resonance imaging, and positron emission tomography-CT, facilitate the noninvasive evaluation of tumor biology and therapeutic response. By bridging molecular and imaging data, this integrated strategy enhances precision in patient selection, treatment monitoring, and adaptive therapy design. Future studies should aim to validate these biomarkers in larger, multicenter cohorts and focus on clinical translation to advance precision medicine in GI oncology.

Patients with advanced metastatic colorectal cancer (mCRC) typically exhibit significant interindividual differences in treatment responses and face poor survival outcomes. To systematically analyze the heterogeneous tumor progression and recurrence observed in advanced mCRC patients, we developed a quantitative cancer-immunity cycle (QCIC) model. The QCIC model employs differential equations to capture the biological mechanisms underlying the cancer-immunity cycle and predicts tumor evolution dynamics under various treatment strategies through stochastic computational methods. We introduce the treatment response index (TRI) to quantify disease progression in virtual clinical trials and the death probability function (DPF) to estimate overall survival. Additionally, we investigate the impact of predictive biomarkers on survival prognosis in advanced mCRC patients, identifying tumor-infiltrating CD8+ cytotoxic T lymphocytes (CTLs) as key predictors of disease progression and the tumor-infiltrating CD4+ Th1/Treg ratio as a significant determinant of survival outcomes. This study presents an approach that bridges the gap between diverse clinical data sources and the generation of virtual patient cohorts, providing valuable insights into interindividual treatment variability and survival forecasting in mCRC patients.

Non-small cell lung cancer (NSCLC) represents the most common pathological type of lung cancer, and the combination of neoadjuvant immunotherapy with chemotherapy has emerged as the first-line treatment for NSCLC. Nevertheless, the efficacy of this therapeutic approach remains variable. The present study aims to examine the impact of chemoimmunotherapy in NSCLC patients, with a view to identifying key molecules, critical cell subpopulations, communication patterns and spatial distributions that potentially correlate with therapeutic sensitivity. A total of 16 lung cancer tissue samples were collected from a cohort of 12 NSCLC patients and subjected to single-cell RNA and spatial transcriptome sequencing. Our data demonstrated that the distribution of CD4 + Treg T cells and mCAFs indicated an immunosuppressive tumor microenvironment, while the accumulation of CD4 + Th17 T cells and iCAFs could act as a positive marker for the sensitivity to chemoimmunotherapy. Furthermore, a significant high level of SELENOP-macrophages was observed in tissues from positive responders, and a strong co-localization between SELENOP-macrophages and antigen-presenting cancer associated fibroblasts (CAFs) in the tumor boundaries was identified, indicating the cooperative roles of these two cell types in response to combined therapy. Moreover, SELENOP-macrophages were observed to be accumulated in tertiary lymphoid structures, which further suggested its critical role in recruiting lymphocytes. Furthermore, analysis of cell-cell communication, based on spatial transcriptomics, suggests that the interactions between SELENOP-macrophages, apCAFs, CD4 + and CD8 + T cells were significantly enhanced in responders. In addition, SELENOP-macrophages recruited CD4 + Naïve, Helper and CD8 + Naïve T cells through pathways such as the cholesterol, interleukin, chemokine and HLA when responding to combined therapy. The present study further unveils the dynamic spatial and transcriptional changes in the tumor microenvironment of non-small cell lung cancer in response to combination therapy.

This study conducted a novel systematic bibliometric and visualization analysis of global literature on immunotherapy for endometrial cancer (EC) to explore dynamic trends, research hotspots, and emerging topics, providing valuable references for future research.

Articles and reviews on EC immunotherapy published between 2012 and August 2024 were retrieved from the Web of Science Core Collection (WoSCC). Bibliometric tools, CiteSpace and VOSviewer, were used to analyze clustering patterns and research dynamics.

A total of 861 articles were contributed by 5,331 authors from 1,392 institutions across 58 countries or regions, involving 1,823 keywords. China demonstrated outstanding performance in this field, contributing over 40% of the total publications and ranking first in publication volume. However, the total citation counts for publications from China lags that of the United States, highlighting the latter's leading position and areas for further improvement in China's research efforts. The University of Texas Medical Anderson Cancer Center and Nanjing Medical University were the two institutions with the highest number of publications. In terms of authorship, research teams led by Bosse, Tjalling, and Creutzberg, Carien L made significant contributions to advancing the field. Among individual publications, the work by Talhouk et al. achieved the highest average annual citation count of 70.88, demonstrating its profound impact. In terms of journals, Gynecologic Oncology emerged as a pivotal academic platform, publishing numerous articles and achieving the highest co-citation frequency. Additionally, Frontiers in Oncology, Frontiers in Immunology, and Frontiers in Genetics have become some of the most active and rapidly developing journals in recent years. Research hotspots are concentrated on themes such as the "Tumor Immune Microenvironment", "Immune Checkpoint Inhibitors", and "Targeted Therapy". Recent trends and frontier research focus on the combined application of immune checkpoint inhibitors with other therapies, research on the application of nanotechnology in immunotherapy, and the integration of artificial intelligence to enhance precision medicine. Additionally, efforts are increasingly directed toward advancing various immunotherapy strategies from basic research to clinical applications.

This comprehensive analysis reveals rapid advancements and significant potential in EC immunotherapy. Strengthening international collaboration and addressing barriers in the translation of research to clinical practice will drive further progress in this promising field.

Gastric cancer (GC) is linked to high morbidity and mortality rates. Approximately two-thirds of GC patients are diagnosed at an advanced or metastatic stage. Conventional treatments for GC, including surgery, radiotherapy, and chemotherapy, offer limited prognostic improvement. Recently, immunotherapy has gained attention for its promising therapeutic effects in various tumors. Immunotherapy functions by activating and regulating the patient's immune cells to target and eliminate tumor cells, thereby reducing the tumor burden in the body. Among immunotherapies, immune checkpoint inhibitors (ICIs) are the most advanced. ICIs disrupt the inhibitory protein-small molecule (PD-L1, CTLA4, VISTA, TIM-3 and LAG3) interactions produced by immune cells, reactivating these cells to recognize and attack tumor cells. However, adverse reactions and resistance to ICIs hinder their further clinical and experimental development. Therefore, a comprehensive understanding of the advancements in ICIs for GC is crucial. This article discusses the latest developments in clinical trials of ICIs for GC and examines combination therapies involving ICIs (targeted therapy, chemotherapy, radiotherapy), alongside ongoing clinical trials. Additionally, the review investigates the tumor immune microenvironment and its role in non-responsiveness to ICIs, highlighting the function of tumor immune cells in ICI efficacy. Finally, the article explores the prospects and limitations of new immunotherapy-related technologies, such as tumor vaccines, nanotechnologies, and emerging therapeutic strategies, aiming to advance research into personalized and optimized immunotherapy for patients with locally advanced gastric cancer.

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, primarily due to challenges in early detection, suboptimal therapeutic efficacy, and severe adverse effects associated with conventional treatments. The convergence of nanotechnology and artificial intelligence (AI) offers transformative potential in precision oncology, enabling innovative solutions for lung cancer diagnosis and therapy. Intelligent nanomedicines facilitate targeted drug delivery, enhanced imaging, and theranostic applications, while AI-driven models harness big biomedical data to optimize nanomedicine design, functionality, and clinical application. This review explores the synergistic integration of AI and nanotechnology in lung cancer care, highlighting recent advancements, key challenges, and future directions for clinical translation. Ethical considerations, including data standardization and privacy concerns, are also addressed, providing a comprehensive roadmap to overcome current barriers and advance the adoption of AI-driven intelligent nanomedicines in precision oncology. This synthesis underscores the critical role of emerging technologies in revolutionizing lung cancer management.

Next-generation sequencing (NGS) is increasingly utilized in oncological practice; however, only a minority of patients benefit from targeted therapy. Developing drug response prediction (DRP) models is important for the "untargetable" majority. Prior DRP models typically use whole-transcriptome and whole-exome sequencing data, which are clinically unavailable. We aim to develop a DRP model toward the repurposing of chemotherapy, requiring only information from clinical-grade NGS (cNGS) panels of restricted gene sets. Data sparsity and limited patient drug response information make this challenging. We firstly show that existing DRPs perform equally with whole-exome versus cNGS (∼300 genes) data. Drug IDentifier (DruID) is then described, a DRP model for restricted gene sets using transfer learning, variant annotations, domain-invariant representation learning, and multi-task learning. DruID outperformed state-of-the-art DRP methods on pan-cancer data and showed robust response classification on two real-world clinical datasets, representing a step toward a clinically applicable DRP tool.

Bladder cancer (BCa) is a prevalent malignant neoplasm of the urinary tract with high incidence rate, frequent recurrence and rapid disease progression. Conventional approaches for diagnosing, prognosticating and monitoring BCa often rely on invasive procedures such as cystoscopy and tissue biopsy, which are associated with high costs and low patient compliance for follow‑up. Liquid biopsies have advantages, such as being non‑invasive, real‑time, and reproducible, in obtaining diverse biomarkers derived from cellular, molecular, proteomic and genetic signatures in urine or plasma samples. Although plasma‑based biomarkers have been clinically validated, urine provides greater specificity for directly assessing biological materials from urological sources. The present review summarizes advancements and current limitations in urinary protein, genetic and epigenetic biomarkers for disease progression and treatment response of BC, compares performance and application scenarios of urine and blood biomarkers and explores how urinary biomarkers may serve as an alternative or complementary tool to traditional diagnostic methods. The integration of urine‑based or plasma‑based biomarkers into existing diagnostic workflows offers promising avenues for improving accuracy and efficiency of diagnosis in the management of BCa. Notably, the emergence of synthetic biomarkers and urine metabolites, combined with artificial intelligence or bioinformatic technologies, has promise in the screening of potential targets. Continued research and validation efforts are needed to translate these findings into routine clinical practice, ultimately improving patient outcomes and decreasing the burden of BCa.

Metabolomics has emerged as a transformative tool in precision oncology, with substantial potential for advancing biomarker discovery, monitoring treatment responses, and aiding drug development. Integrating artificial intelligence (AI) into metabolomics optimizes data acquisition and analysis, facilitating the interpretation of complex metabolic networks and enabling more effective multiomics integration. In this opinion, we explore recent advances in the application of metabolomics within precision oncology, emphasizing the unique advantages that AI-driven metabolomics offers. We propose that AI not only complements but also amplifies the potential of current platforms, accelerating research progress and ultimately improving patient outcomes. Finally, we discuss the opportunities and challenges involved in translating AI-driven metabolomics into clinical practice for precision oncology.

The development and wider adoption of adoptive cell therapies is constrained by complex and costly manufacturing processes and by inconsistent efficacy across patients. Here we discuss how microfluidic and other fluidic devices can be implemented at each stage of cell manufacturing for adoptive cell therapies, from the harvesting and isolation of the cells to their editing, culturing and functional selection. We suggest that precise and controllable microfluidic systems can streamline the development of these therapies by offering scalability in cell production, bolstering the efficacy and predictability of the therapies and improving their cost-effectiveness and accessibility for broader populations of patients with cancer.

Breast cancer prognosis is complicated by tumor heterogeneity. Traditional methods focus on cancer-specific gene signatures, but cross-cancer strategies that provide deeper insights into tumor homogeneity are rarely used. Immunotherapy, particularly immune checkpoint inhibitors, results from variable responses across cancers, offering valuable prognostic insights. We introduced a network-based transfer (NBT) of pan-cancer immunotherapy responses to enhance breast cancer prognosis using node embedding and heat diffusion algorithms, identifying gene signatures netNE and netHD. Our results showed that netHD and netNE outperformed seven established breast cancer signatures in prognostic metrics, with netHD excelling. All nine gene signatures were grouped into three clusters, with netHD and netNE enriching the immune-related interferon-gamma pathway. Stratifying TCGA patients into two groups based on netHD revealed significant immunological differences and variations in 20 of 50 cancer hallmarks, emphasizing immune-related markers. This approach leverages pan-cancer insights to enhance breast cancer prognosis, facilitating insight transfer and improving tumor homogeneity understanding.Abstract graph of network-based insights translating pan-cancer immunotherapy responses to breast cancer prognosis. This abstract graph illustrates the conceptual framework for transferring immunotherapy response insights from pan-cancer studies to breast cancer prognosis. It highlights the integration of PPI networks to bridge genetic data and clinical phenotypes. The network-based method facilitates the identification of prognostic gene signatures in breast cancer by leveraging immunotherapy response information, providing a novel perspective on tumor homogeneity and its implications for clinical outcomes.

Molecular characterization of tumors is essential to identify predictive biomarkers that inform treatment decisions and improve precision immunotherapy development and administration. However, challenges such as the heterogeneity of tumors and patient responses, limited efficacy of current biomarkers, and the predominant reliance on single-omics data, have hindered advances in accurately predicting treatment outcomes. Standard therapy generally applies a "one size fits all" approach, which not only provides ineffective or limited responses, but also an increased risk of off-target toxicities and acceleration of resistance mechanisms or adverse effects. As the development of emerging multi- and spatial-omics platforms continues to evolve, an effective tumor assessment platform providing utility in a clinical setting should i) enable high-throughput and robust screening in a variety of biological matrices, ii) provide in-depth information resolved with single to subcellular precision, and iii) improve accessibility in economical point-of-care settings. In this perspective, we explore the application of label-free Raman spectroscopy as a tumor profiling tool for precision immunotherapy. We examine how Raman spectroscopy's non-invasive, label-free approach can deepen our understanding of intricate inter- and intra-cellular interactions within the tumor-immune microenvironment. Furthermore, we discuss the analytical advances in Raman spectroscopy, highlighting its evolution to be utilized as a single "Raman-omics" approach. Lastly, we highlight the translational potential of Raman for its integration in clinical practice for safe and precise patient-centric immunotherapy.

The rapid advancement of artificial intelligence (AI) has introduced transformative opportunities in oncology, enhancing the precision and efficiency of tumor diagnosis and treatment. This review examines recent advancements in AI applications across tumor imaging diagnostics, pathological analysis, and treatment optimization, with a particular focus on breast cancer, lung cancer, and liver cancer. By synthesizing findings from peer-reviewed studies published over the past decade, this paper analyzes the role of AI in enhancing diagnostic accuracy, streamlining therapeutic decision-making, and personalizing treatment strategies. Additionally, this paper addresses challenges related to AI integration into clinical workflows and regulatory compliance. As AI continues to evolve, its applications in oncology promise further improvements in patient outcomes, though additional research is needed to address its limitations and ensure ethical and effective deployment.

Immunotherapy is likely the most remarkable advancement in lung cancer treatment during the past decade. Although immunotherapy provides substantial benefits, their therapeutic responses differ from those of conventional chemotherapy and targeted therapy, and some patients present unique immunotherapy response patterns that cannot be judged under the current measurement standards. Therefore, the response monitoring of immunotherapy can be challenging, such as the differentiation between real response and pseudo-response. This review outlines the various tumor response patterns to immunotherapy and discusses methods for quantifying computed tomography (CT) and 18 F-fluorodeoxyglucose positron emission tomography (PET) in the field of lung cancer. Emerging technologies in magnetic resonance imaging (MRI) and non-FDG PET tracers are also explored. With immunotherapy responses, the role for imaging is essential in both anatomical radiological responses (CT/MRI) and molecular changes (PET imaging). Multiple aspects must be considered when assessing treatment responses using CT and PET. Finally, we introduce multimodal approaches that integrate imaging and nonimaging data, and we discuss future directions for the assessment and prediction of lung cancer responses to immunotherapy.

Gastric cancer is a malignant disease with a poor prognosis and few therapeutic options once it has advanced. Immunotherapy using ICIs has emerged as a viable therapeutic method; nevertheless, reliable immunological biomarkers are required to identify who may benefit from these therapies. It focuses on key immune biomarkers and predictive signatures in gastric cancer, such as PD-L1 expression, microsatellite instability (MSI), tumor mutational burden (TMB), and Epstein-Barr virus (EBV) status, to optimize gastric cancer patients' immunotherapy responses. PD-L1 expression is a popular biomarker for ICI effectiveness. Tumors with high MSI-H and TMB are the most susceptible to ICIs because they are highly immunogenic. EBV-positive stomach tumors are highly immunogenic, and immunotherapy has a high response rate. Combining composite biomarker panels with multi-omics-based techniques improved patient selection accuracy. In recent years, machine learning models have been integrated into next-generation sequencing. Dynamic, real-time-monitorable biomarkers for real-time immune response monitoring are also being considered. Thus, enhancing biomarker-driven immunotherapy is critical for improving clinical outcomes with gastric cancer. There is still more work to be done in this field, and verifying developing biomarkers will be an important component in the future of customized cancer therapy.

The emergence of effective immunotherapies has revolutionized therapies for many types of cancer. However, current immunotherapy has limited efficacy in certain patient populations and displays therapeutic resistance after a period of treatment. To address these challenges, a growing number of immunotherapy drugs have been investigated in clinical and preclinical applications. The diverse functionality of peptides has made them attractive as a therapeutic modality, and the global market for peptide-based therapeutics is witnessing significant growth. Peptides can act as immunotherapeutic agents for the treatment of many malignant cancers. However, a systematic understanding of the interactions between different peptides and the host's immune system remains unclear. This review describes in detail the roles of peptides in regulating the function of the immune system for cancer immunotherapy. Initially, we systematically elaborate on the relevant mechanisms of cancer immunotherapy. Subsequently, we categorize peptide-based nanomaterials into the following three categories: peptide-based vaccines, anti-cancer peptides, and peptide-based delivery systems. We carefully analyzed the roles of these peptides in overcoming the current barriers in immunotherapy, including multiple strategies to enhance the immunogenicity of peptide vaccines, the synergistic effect of anti-cancer peptides in combination with other immune agents, and peptide assemblies functioning as immune stimulators or vehicles to deliver immune agents. Furthermore, we introduce the current status of peptide-based immunotherapy in clinical applications and discuss the weaknesses and future prospects of peptide-based materials for cancer immunotherapy. Overall, this review aims to enhance comprehension of the potential applications of peptide-based materials in cancer immunotherapy and lay the groundwork for future research and clinical applications.

Exosomal microRNAs (exomiRs) play a critical role in intercellular communication, especially in cancer, where they regulate key cellular processes like proliferation, angiogenesis, and metastasis, highlighting their significance as potential diagnostic and therapeutic targets. Here, we aimed to characterize the role of exomiRs, derived from seven cancer types (four cell lines and three tumors), in influencing the pre-metastatic niche (PMN). In each cancer type we extracted high confidence exomiRs (LogFC >= 2 in exosomes relative to control), their experimentally validated targets, and the enriched pathways among those targets. We then selected the top100 high-confidence targets based on their frequency of appearance in the enriched pathways. We observed significantly higher GC content in exomiRs relative to genomic background. Gene Ontology analysis revealed both general cancer processes, such as wound healing and epithelial cell proliferation, as well as cancer-specific processes, such as "angiogenesis" in the kidney and "ossification" in the lung. ExomiR targets were enriched for cancer-specific tumor suppressor genes and downregulated in PMN formed in lungs compared to normal. Motif analysis showed high inter-cancer similarity among motifs enriched in exomiRs. Our analysis recapitulated exomiRs associated with M2 macrophage differentiation and chemoresistance, such as miR-21 and miR-222-3p, regulating signaling pathways like PTEN/PI3/Akt, NF-kB, etc. Additionally, Cox regression analysis in TCGA indicated that exomiR targets are significantly associated with better overall survival of patients. Lastly, support vector machine model using exomiR targets gene expression classified responders and non-responders to therapy with an AUROC ranging from 0.72 to 0.96, higher than previously reported gene signatures.

Liver cancer remains one of the most formidable challenges in modern medicine, characterized by its high incidence and mortality rate. Emerging evidence underscores the critical roles of the immune microenvironment in tumor initiation, development, prognosis, and therapeutic responsiveness. However, the composition of the immune microenvironment of liver cancer (LC-IME) and its association with clinicopathological significance remain unelucidated. In this review, we present the recent developments related to the use of artificial intelligence (AI) for studying the immune microenvironment of liver cancer, focusing on the deciphering of complex high-throughput data. Additionally, we discussed the current challenges of data harmonization and algorithm interpretability for studying LC-IME.

Distant metastasis in bladder cancer is linked to poor prognosis and significant mortality. Machine learning (ML), a key area of artificial intelligence, has shown promise in the diagnosis, staging, and treatment of bladder cancer. This study aimed to employ various ML techniques to predict distant metastasis in patients with bladder cancer.

Patients diagnosed with bladder cancer in the Surveillance, Epidemiology, and End Results (SEER) database from 2000 to 2021 were included in this study. After a rigorous screening process, a total of 4,108 patients were selected for further analysis, divided in a 7:3 ratio into a training cohort and an internal validation cohort. In addition, 118 patients treated at the Second Affiliated Hospital of Nanchang University were included as an external validation cohort. Features were filtered using the least absolute shrinkage and selection operator (LASSO) regression algorithm. Based on the significant features identified, three ML algorithms were utilized to develop prediction models: logistic regression, support vector machine (SVM), and linear discriminant analysis (LDA). The predictive performance of the three models was evaluated by obtaining the area under the receiver operating characteristic (ROC) curve (AUC), the precision, the accuracy, and the F1 score.

According to the statistical results, the final probability of distant metastasis in the population was 12.0% (n = 495). LASSO regression analysis revealed that age, chemotherapy, tumor size, the examination of non-regional lymph nodes, and regional lymph node evaluation were significantly associated with distant metastasis of bladder cancer. In the internal validation cohort, the prediction accuracy rates for logistic regression, SVM, and LDA were 0.874, 0.877, and 0.845, respectively. The precision rates were 0.805, 0.769, and 0.827, respectively, and the F1 scores were 0.821, 0.819, and 0.835, respectively. The ROC curve demonstrated that the AUC for all models was greater than 0.7. In the external validation cohort, the prediction accuracy rates for logistic regression, SVM, and LDA were 0.856, 0.848, and 0.797, respectively, with the ROC curve indicating that the AUC also exceeded 0.7. The precision rates were 0.877, 0.718, and 0.736, respectively, and the F1 scores were 0.797, 0.778, and 0.762, respectively. Among the algorithms used, logistic regression demonstrated better predictive efficiency than the other two methods. The top three variables with the highest importance scores in the logistic regression were non-regional lymph nodes, age, and chemotherapy.

The prediction model developed using three ML algorithms demonstrated strong accuracy and discriminative capability in predicting distant metastasis in patients with bladder cancer. This might help clinicians in understanding patient prognosis and in formulating personalized treatment strategies, ultimately improving the overall prognosis of patients with bladder cancer.

Gastric cancer (GC) is one of the most common malignant tumors and is associated with poor prognosis. To improve the prognosis of GC patients, an effective immune-related prognostic biomarker is urgent. Here, we aim to explore the correlation between the expression of procalcitonin gamma subfamily A, 10 (PCDHGA10) and clinicopathological characteristics, especially its relation with tumor-infiltrating immune cells (TILs) in GC.

The differential mRNA expression of PCDHGA10 between GC tissues and normal gastric mucosa and prognostic potential were assessed from The Cancer Genome Atlas (TCGA). Then, based on tissue microarrays (TMAs) with multiplex immunohistochemistry (mIHC) from GC patients, we statistically assess the correlation between PCDHGA10 protein expression and the clinical profiles and prognosis of the patients. Additionally, with IHC and mIHC, we applied the machine-learning algorithms to evaluate the localization and expression levels of TILs and immune checkpoints in the tumor microenvironment. We analyzed the relationship between PCDHGA10 protein expression and TILs and immune checkpoints.

Through the database and TMA analysis, the expression of PCDHGA10 was significantly higher in GC tissues compared with normal tissues. High PCDHGA10 expression independently predicted poor prognosis in GC. Additionally, elevated PCDHGA10 expression was positively associated with the number of CD8+ T cells, CD68+ macrophages, Foxp3+ T cells, and CD4+ T cells in GC tissues and the stromal region. Besides, the expression of PCDHGA10 was positively correlated with immune checkpoints, including CTLA-4, LAG3, and PD-L1.

PCDHGA10 might be a potential prognostic marker and an immunological therapeutic target for GC.

Integrating complementary diagnostic data sources promises enhanced robustness in the predictive performance of artificial intelligence (AI) models, a crucial requirement for future clinical validation/implementation. In this study, we investigate the potential value of integrating data from noninvasive diagnostic modalities, including chest computed tomography (CT) imaging, routine laboratory blood tests, and clinical parameters, to retrospectively predict 1-year survival in a cohort of patients with advanced non-small-cell lung cancer, melanoma, and urothelial cancer treated with immunotherapy.

The study included 475 patients, of whom 444 had longitudinal CT scans and 475 had longitudinal laboratory data. An ensemble of AI models was trained on data from each diagnostic modality, and subsequently, a model-agnostic integration approach was adopted for combining the prediction probabilities of each modality and producing an integrated decision.

Integrating different diagnostic data demonstrated a modest increase in predictive performance. The highest area under the curve (AUC) was achieved by CT and laboratory data integration (AUC of 0.83, 95% confidence interval 0.81-0.85, P < 0.001), whereas the performance of individual models trained on laboratory and CT data independently yielded AUCs of 0.81 and 0.73, respectively.

In our retrospective cohort, integrating different noninvasive data modalities improved performance.

Alzheimer's disease (AD) is a major neurodegenerative condition that affects a significant number of people around the world, making understanding the underlying molecular mechanisms fundamental for identifying predictive biomarkers and therapeutic targets for treating AD. Analysis of the gene expression profile GSE5281, consisting of 161 samples (87 AD and 74 control samples) revealed differentially expressed genes (DEGs) used for KEGG screening to connect dysregulated genes to metabolic pathways or other neurological diseases including Parkinson's, prion, and Huntington's and construction of a protein interaction network. Protein-protein interaction (PPI) network and module analysis uncovered the hub genes ACTB, ACTG1, ATP5A1, CCT2, CDC42, EGFR, FN1, GAPDH, GFAP, GRIA1, HSP90AB1, MAPK1, PSMA3, PSMD14, SNAP25, SNCA, SOD1, SOX2, TPI1, and YWHAZ. The analysis revealed a link between dysregulated genes and processes in AD pathology, including the promotion of osteoporosis, an altered nucleotide metabolism, microtubule stability, and the dysfunctionality of the blood-brain barrier (BBB). These targets might be used as predictive biomarkers or to develop curative and preventive therapeutic approaches for treating AD.

Recent studies suggest cGAS-STING pathway may play a crucial role in the genesis and development of hepatocellular carcinoma (HCC), closely associated with classical pathways and tumor immunity. We aimed to develop models predicting survival and anti-PD-1/PD-L1 outcomes centered on the cGAS-STING pathway in HCC. We identified classical pathways highly correlated with cGAS-STING pathway and constructed transformer survival model preserving raw structure of pathways. We also developed explainable XGBoost model for predicting anti-PD-1/PD-L1 outcomes using SHAP algorithm. We trained and validated transformer survival model on pan-cancer cohort and tested it on three independent HCC cohorts. Using 0.5 as threshold across cohorts, we divided each HCC cohort into two groups and calculated P values with log-rank test. TCGA-LIHC: C-index = 0.750, P = 1.52e-11; ICGC-LIRI-JP: C-index = 0.741, P = .00138; GSE144269: C-index = 0.647, P = .0233. We trained and validated [area under the receiver operating characteristic curve (AUC) = 0.777] XGBoost model on immunotherapy datasets and tested it on GSE78220 (AUC = 0.789); we also tested XGBoost model on HCC anti-PD-L1 cohort (AUC = 0.719). Our deep learning model and XGBoost model demonstrate potential in predicting survival risks and anti-PD-1/PD-L1 outcomes in HCC. We deployed these two prediction models to the GitHub repository and provided detailed instructions for their usage: deep learning survival model, https://github.com/mlwalker123/CSP_survival_model; XGBoost immunotherapy model, https://github.com/mlwalker123/CSP_immunotherapy_model.

To date, the public health system has been impacted by the increasing costs of many diagnostic and therapeutic pathways due to limited resources. At the same time, we are constantly seeking to improve these paths through approaches aimed at personalized medicine. To achieve the required levels of diagnostic and therapeutic precision, it is necessary to integrate data from different sources and simulation platforms. Today, artificial intelligence (AI), machine learning (ML), and predictive computer models are more efficient at guiding decisions regarding better therapies and medical procedures. The evolution of these multiparametric and multimodal systems has led to the creation of digital twins (DTs). The goal of our review is to summarize AI applications in discovering new immunotherapies and developing predictive models for more precise immunotherapeutic decision-making. The findings from this literature review highlight that DTs, particularly predictive mathematical models, will be pivotal in advancing healthcare outcomes. Over time, DTs will indeed bring the benefits of diagnostic precision and personalized treatment to a broader spectrum of patients.

The potential of precision medicine to transform complex autoimmune disease treatment is often challenged by limited data availability and inadequate sample size when compared with the number of molecular features found in high-throughput multi-omics data sets. To address this issue, the novel framework PRoBeNet (Predictive Response Biomarkers using Network medicine) was developed. PRoBeNet operates under the hypothesis that the therapeutic effect of a drug propagates through a protein-protein interaction network to reverse disease states. PRoBeNet prioritizes biomarkers by considering i) therapy-targeted proteins, ii) disease-specific molecular signatures, and iii) an underlying network of interactions among cellular components (the human interactome). PRoBeNet helped discover biomarkers predicting patient responses to both an established autoimmune therapy (infliximab) and an investigational compound (a mitogen-activated protein kinase 3/1 inhibitor). The predictive power of PRoBeNet biomarkers was validated with retrospective gene-expression data from patients with ulcerative colitis and rheumatoid arthritis and prospective data from tissues from patients with ulcerative colitis and Crohn disease. Machine-learning models using PRoBeNet biomarkers significantly outperformed models using either all genes or randomly selected genes, especially when data were limited. These results illustrate the value of PRoBeNet in reducing features and for constructing robust machine-learning models when data are limited. PRoBeNet may be used to develop companion and complementary diagnostic assays, which may help stratify suitable patient subgroups in clinical trials and improve patient outcomes.

Network biology is a powerful framework for studying the structure, function, and dynamics of biological systems, offering insights into the balance between health and disease states. The field is seeing rapid progress in all of its aspects: data availability, network synthesis, network analytics, and impactful applications in medicine and drug development. We review the most recent and significant results in network biomedicine, with a focus on the latest data, analytics, software resources, and applications in medicine. We also discuss what in our view are the likely directions of impactful development over the next few years.

While immune checkpoint inhibitors have revolutionized cancer therapy, many patients exhibit poor outcomes. Here, we show immunotherapy responses in bladder and non-small cell lung cancers are effectively predicted by factoring tumor mutation burden (TMB) into burdens on specific protein assemblies. This approach identifies 13 protein assemblies for which the assembly-level mutation burden (AMB) predicts treatment outcomes, which can be combined to powerfully separate responders from nonresponders in multiple cohorts (e.g., 76% versus 37% bladder cancer 1-year survival). These results are corroborated by (i) engineered disruptions in the predictive assemblies, which modulate immunotherapy response in mice, and (ii) histochemistry showing that predicted responders have elevated inflammation. The 13 assemblies have diverse roles in DNA damage checkpoints, oxidative stress, or Janus kinase/signal transducers and activators of transcription signaling and include unexpected genes (e.g., PIK3CG and FOXP1) for which mutation affects treatment response. This study provides a roadmap for using tumor cell biology to factor mutational effects on immune response.

Triple negative breast cancer (TNBC) is a malignant subtype of breast cancer characterized by the absence of ER, PR, and HER2. We aimed to explore target gene from the perspective of cancer-immunity cycle, providing insights into treatment of TNBC.

We obtained TNBC samples from METABRIC database and downloaded 4 datasets from GEO database, as well as an IMvigor210 dataset. WGCNA was applied to screen genes associated with cancer-immunity cycle in TNBC. GO, KEGG and GSEA analyses were performed to explore the target gene's potential functions and pathways. The binding motifs with transcription factors were predicted with FIMO. Immune infiltration analysis was conducted by CIBERSORT.

TUBB2A was screened out as our target gene which was negatively correlated with T cell recruitment in cancer-immunity cycle. TUBB2A expressed higher in TNBC samples than in normal samples. High expression of TUBB2A was associated with poor prognosis of TNBC. 12 transcription factors and 5 miRNAs might regulate TUBB2A's expression. The infiltration ratios of 7 types of immune cells such as CD8+ T cells, naive CD4+ T cells and activated memory CD4+ T cells were significantly lower in TUBB2A high expression group. TUBB2A was a potential drug target.

We screened a cancer-immunity cycle-related gene TUBB2A which was negatively correlated with T cell recruiting in TNBC. TUBB2A expressed higher in TNBC samples than in normal samples, associated with poor prognosis.

Melanoma is the most aggressive and deadly form of skin cancer, and its incidence has been steadily increasing over the past few decades, particularly in the Caucasian population. Immune checkpoint inhibitors (ICI), anti-PD-1 monotherapy or in combination with anti-CTLA-4, and more recently, anti-PD-1 plus anti-LAG-3 have changed the clinical evolution of this disease. However, a significant percentage of patients do not benefit from these therapies. Therefore, to improve patient selection, it is imperative to look for novel biomarkers. Immune subsets, particularly the quantification of lymphocyte T populations, could contribute to the identification of ICI responders. The main purpose of this review is to thoroughly examine significant published data on the potential role of lymphocyte T subset distribution in peripheral blood (PB) or intratumorally as prognostic and predictive of response biomarkers in advanced melanoma patients treated with ICI regardless of BRAFV600 mutational status.

Hepatocellular Carcinoma (HCC) remains a leading cause of cancer deaths. Despite the rise of immunotherapies, many HCC patients don't benefit. There's a clear need for biomarkers to guide treatment decisions. This research aims to identify such biomarkers by combining radiological data and machine learning. We analyzed clinical and CT imaging data of 54 HCC patients undergoing immunotherapy. Radiologic features were examined to develop a model predicting short-term immunotherapy effects. We utilized 9 machine learning and 2 ensemble learning techniques using RapidMiner for model construction. We conducted the validation of the above feature combination using 29 HCC patients who received immunotherapy from another hospital, and tested and validated it using XGBoost combined with a genetic algorithm. In 54 HCC patients, radiomics features varied significantly between those with partial response (PR) and stable disease (SD). Key features in Gray Level Run Length Matrix (GLRLM) and in adjacent tissues' Intensity Direct, Neighborhood Gray Tone Difference Matrix (NGTDM), and Shape correlated with short-term immunotherapy efficacy. Selected feature combinations of 15, 19, and 8/15 features yielded better outcomes. Deep learning, random forest, and naive bayes outperformed other methods, with Bagging being more reliable than Adaboost. In the validation set of 29 HCC patients, the mentioned feature combination also demonstrated favorable performance. Furthermore, we achieved improved results when employing XGBoost in conjunction with a genetic algorithm for testing and validation. The machine learning model built with CT image features derived from GLCM, GLRLM, IntensityDirect, NGTDM, and Shape can accurately forecast the short-term efficacy of immunotherapy for HCC.

Immune checkpoint inhibitors (ICIs) are used as salvage treatments for advanced gastric cancer (AGC) regardless of HER2 status. This study assessed the efficacy of ICIs based on HER2 expression in AGC patients who received pembrolizumab as salvage monotherapy at Samsung Medical Center from November 2017 to March 2023. HER2 status was determined via immunohistochemistry, and tumor response and survival outcomes were compared accordingly. Among the 113 patients analyzed, with a median age of 61 years and 64.6% being male, 12 patients (10.6%) were HER2-positive, and 101 patients (89.4%) were HER2-negative. Of 92 evaluable patients, none had a complete response. However, 50% of HER2-positive patients had a partial response, compared to 4.9% of HER2-negative patients (p < 0.001). The disease control rate was 70% in HER2-positive and 37.8% in HER2-negative patients (p = 0.086). Median progression-free survival was 5.53 months for HER2-positive patients versus 1.81 months for HER2-negative patients (p = 0.037). Pembrolizumab as a salvage chemotherapy for the treatment of AGC demonstrated superior effectiveness in HER2-positive patients compared with HER2-negative patients.

Exosomal microRNAs (exomiRs), transported via exosomes, play a pivotal role in intercellular communication. In cancer, exomiRs influence tumor progression by regulating key cellular processes such as proliferation, angiogenesis, and metastasis. Their role in mediating communication between cancer cells and the tumor microenvironment highlights their significance as potential diagnostic and therapeutic targets.

In this study, we aimed to characterize the role of exomiRs in influencing the pre-metastatic niche (PMN). Across 7 tumor types, including 4 cell lines and three tumors, we extracted high confidence exomiRs (Log FC >= 2 in exosomes relative to control) and their targets (experimentally identified and targeted by at least 2 exomiRs). Subsequently, we identified enriched pathways and selected the top 100 high-confidence exomiR targets based on the frequency of their appearance in the enriched pathways. These top 100 targets were consistently used throughout the analysis.

Cancer cell line and tumor derived ExomiRs have significantly higher GC content relative to genomic background. Pathway enriched among the top exomiR targets included general cancer-associated processes such as "wound healing" and "regulation of epithelial cell proliferation", as well as cancer-specific processes, such as "regulation of angiogenesis in kidney" (KIRC), "ossification" in lung (LUAD), and "positive regulation of cytokine production" in pancreatic cancer (PAAD). Similarly, 'Pathways in cancer' and 'MicroRNAs in cancer' ranked among the top 10 enriched KEGG pathways in all cancer types. ExomiR targets were not only enriched for cancer-specific tumor suppressor genes (TSG) but are also downregulated in pre-metastatic niche formed in lungs compared to normal lung. Motif analysis shows high similarity among motifs identified from exomiRs across cancer types. Our analysis recapitulates exomiRs associated with M2 macrophage differentiation and chemoresistance such as miR-21 and miR-222-3p, regulating signaling pathways such as PTEN/PI3/Akt, NF-κB, etc. Cox regression indicated that exomiR targets are significantly associated with overall survival of patients in TCGA. Lastly, a Support Vector Machine (SVM) model using exomiR target gene expression classified responders and non-responders to neoadjuvant chemotherapy with an AUROC of 0.96 (in LUAD), higher than other previously reported gene signatures.

Our study characterizes the pivotal role of exomiRs in shaping the PMN in diverse cancers, underscoring their diagnostic and therapeutic potential.

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

Not applicable.