Biomol Biomed. 2025 Jun 10. doi: 10.17305/bb.2025.12324. Online ahead of print.

ABSTRACT

Immune checkpoint inhibitors (ICIs) demonstrate substantial interpatient variability in clinical efficacy for unresectable non-small cell lung cancer (NSCLC), underscoring the unmet need for noninvasive biomarkers to predict early therapeutic responses and improve survival outcomes. To address this, we developed a CT-based deep learning model integrated with the systemic immune-inflammatory-nutritional index (SIINI) for early prediction of ICI response. In a retrospective multicenter study of 265 patients treated with ICIs (incorporating chest CT and laboratory data), the cohort was divided into training (70%), internal validation (30%), and external validation sets. The combined model-leveraging DenseNet121-derived deep radiomic features alongside SIINI-achieved strong predictive performance, with AUCs of 0.865 (95% CI: 0.7709-0.9595) in the internal validation cohort and 0.823 (95% CI: 0.6627-0.9827) in the external validation cohort. Gradient-weighted class activation mapping (Grad-CAM) highlighted key CT regions contributing to model predictions, enhancing interpretability for clinical application. These findings highlight the potential of integrating deep learning with inflammatory biomarkers to support personalized ICI therapy in unresectable NSCLC. Future directions include incorporating multi-omics biomarkers, expanding multicenter validation, and increasing sample sizes to further improve predictive accuracy and facilitate clinical translation.

PMID:40525631 | DOI:10.17305/bb.2025.12324

Infect Agent Cancer. 2025 Jun 15;20(1):37. doi: 10.1186/s13027-025-00672-0.

ABSTRACT

Mendelian Randomization (MR) is increasingly used in cancer research to infer causal relationships by leveraging genetic variants as instrumental variables. While the growth of genome-wide association studies and biobank data has expanded the utility of MR, this surge-particularly pronounced in China-raises concerns about methodological rigor. The widespread adoption may be partly driven by the Chinese translation of key MR literature. Recent advances such as multivariable MR, mediation analysis, and integration with AI and omics data have enhanced the robustness and biological interpretability of MR studies. However, challenges persist, including horizontal pleiotropy, weak instrument bias, and misinterpretation of biomarkers as causal exposures. To improve MR study credibility, frameworks like STROBE-MR and MR-GRADE are being adopted. This article reviews methodological improvements and persistent pitfalls in MR, especially within cancer epidemiology, and highlights strategies for ensuring validity in this rapidly evolving field.

PMID:40518507 | PMC:PMC12168377 | DOI:10.1186/s13027-025-00672-0

Comput Biol Med. 2025 Aug;194:110559. doi: 10.1016/j.compbiomed.2025.110559. Epub 2025 Jun 14.

ABSTRACT

BACKGROUND: The in silico analyses provide evidence supporting the potential of methylation-driven differentially expressed genes as therapeutic targets across cancer types. This leads us to identify novel targets and their associated drug compounds for further progress towards pancreatic cancer treatment.

OBJECTIVE: To identify targeted drugs based on methylation driven genes identified using bulk multi-omics data and single-cell level data to pinpoint important disease markers.

METHODS: The workflow involves screening using the TCGA and ICGC databases, followed by validation with GEO datasets. The study employs supervised learning algorithms like kNN and random forests, and constructs a prediction model using adaptive LASSO-Cox regression. The process also includes pathway analysis, evaluation of survival status, and immune profile deconvolution, as well as multistage evaluation of the methylation driven genes. We conducted drug targeting and molecular dynamic simulations, taking into account genes of interest.Lastly, molecular docking and dynamics simulations were used to find out if the key MEDEGs could be utilized as drug targets.

RESULTS: CD36, UGT1A1, TFF1, S100P, MUC13, CALHM3 and ANKRD44 were found to be top 7 methylation driven genes. The mutational profile was also documented along with pathway analysis, which showed concordance with our observation based on their significant enriched terms namely "Maintenance of Gastrointestinal Epithelium", and "Digestive System Homeostasis". CD36 had prognostic capabilities and was seen to significant in terms of survival and also showed significant immune dysregulation. Our novel findings suggest TFF1, S100P, and MUC13 were found to be associated with cell type specific expression as seen in single cell data and UGT1A1 was found to be suitable for probable drug targeting. CD36, UGT1A1, TFF1, S100P, and MUC13 showed concordance when observed at proteomics level and across other datasets. Apigenin-7-O-glucuronide emerged as the top binder for UDP-glucuronosyltransferase 1A1 (also known as UDP 1A1), forming stable complexes with favourable interactions. Catechin and epicatechin were identified as the best ligands for TFF1 and S100P, while rutin showed high-affinity binding to MUC13.

CONCLUSION: The study successfully identified and validated a panel of biomarkers specific to pancreatic cancer, with potential applications in early diagnosis and treatment. The findings highlight the importance of multi-omics data integration in cancer research and the potential of personalized medicine in improving patient outcomes. The in-silico drug targeting analysis provides a foundation for the development of novel drugs for PanCa treatment. Hence TFF1, S100P, MUC13, and UGT1A1 showcased themselves as most promising biomarkers and novel drug targets.

PMID:40517592 | DOI:10.1016/j.compbiomed.2025.110559

Signal Transduct Target Ther. 2025 Jun 15;10(1):186. doi: 10.1038/s41392-025-02243-6.

ABSTRACT

Over the past two decades, non-small cell lung cancer (NSCLC) has witnessed encouraging advancements in basic and clinical research. However, substantial unmet needs remain for patients worldwide, as drug resistance persists as an inevitable reality. Meanwhile, the journey towards amplifying the breadth and depth of the therapeutic effect requires comprehending and integrating diverse and profound progress. In this review, therefore, we aim to comprehensively present such progress that spans the various aspects of molecular pathology, encompassing elucidations of metastatic mechanisms, identification of therapeutic targets, and dissection of spatial omics. Additionally, we also highlight the numerous small molecule and antibody drugs, encompassing their application alone or in combination, across later-line, frontline, neoadjuvant or adjuvant settings. Then, we elaborate on drug resistance mechanisms, mainly involving targeted therapies and immunotherapies, revealed by our proposed theoretical models to clarify interactions between cancer cells and a variety of non-malignant cells, as well as almost all the biological regulatory pathways. Finally, we outline mechanistic perspectives to pursue innovative treatments of NSCLC, through leveraging artificial intelligence to incorporate the latest insights into the design of finely-tuned, biomarker-driven combination strategies. This review not only provides an overview of the various strategies of how to reshape available armamentarium, but also illustrates an example of clinical translation of how to develop novel targeted drugs, to revolutionize therapeutic landscape for NSCLC.

PMID:40517166 | PMC:PMC12167388 | DOI:10.1038/s41392-025-02243-6

Front Immunol. 2025 May 29;16:1585505. doi: 10.3389/fimmu.2025.1585505. eCollection 2025.

ABSTRACT

BACKGROUND: Lung adenocarcinoma (LUAD) is a common and aggressive subtype of lung cancer associated with poor clinical outcomes. The role of mitochondrial dynamics (MD)-related genes in tumor progression and immune regulation remains poorly understood.

METHODS: Data from public databases were integrated, and subtypes were classified based on 23 MD-related genes. A five-gene prognostic model was constructed. Associations between the model and immune infiltration, tumor mutational burden (TMB), tumor stemness, and drug sensitivity were analyzed. The function of the key gene MTCH2 was validated through in vitro experiments.

RESULTS: Two distinct MD molecular subtypes were identified, exhibiting significant differences in prognosis and immune characteristics. A corresponding risk score model was established. Patients in the low-risk group showed better prognosis and enhanced immune activity, whereas the high-risk group displayed higher TMB and stemness scores. Drug sensitivity analysis revealed distinct responses to chemotherapeutic agents such as cisplatin and docetaxel between risk groups. Functional assays demonstrated that MTCH2 knockout significantly inhibited LUAD cell proliferation, migration, and invasion, and induced G0/G1 phase arrest, suggesting that MTCH2 may act as a potential adverse prognostic marker.

CONCLUSION: MD-related genes exhibit strong prognostic and immune subtyping value. The proposed risk model holds clinical potential, and MTCH2 may serve as a promising target for precision therapy in LUAD.

PMID:40510359 | PMC:PMC12159055 | DOI:10.3389/fimmu.2025.1585505

Genome Biol. 2025 Jun 13;26(1):165. doi: 10.1186/s13059-025-03557-y.

ABSTRACT

Existing approaches to identifying cancer genes rely overwhelmingly on DNA sequencing data. Here, we introduce RVdriver, a computational tool that leverages paired bulk genomic and transcriptomic data to classify RNA variant allele frequencies (VAFs) of non-synonymous mutations relative to a synonymous mutation background. We analyze 7882 paired exomes and transcriptomes from 31 cancer types and identify novel, as well as known, cancer genes, complementing other DNA-based approaches. Furthermore, RNA VAFs of individual mutations are able to distinguish "driver" from "passenger" mutations within established cancer genes. This approach highlights the value of multi-omic approaches for cancer gene discovery.

PMID:40514689 | PMC:PMC12164115 | DOI:10.1186/s13059-025-03557-y

J Adv Res. 2025 Jun 11:S2090-1232(25)00427-8. doi: 10.1016/j.jare.2025.06.017. Online ahead of print.

ABSTRACT

INTRODUCTION: Lung neuroendocrine carcinomas (Lu-NECs) are rare, highly aggressive lung tumors with poor prognosis and limited therapeutic options. Understanding the tumor immune microenvironment (TIME) is crucial towards personalized therapeutic strategies.

OBJECTIVES: This study aims to systematically characterize the heterogeneity and complexity of the TIME in Lu-NECs by integrating proteomic, transcriptomic, and genomic data.

METHODS: We performed comprehensive immune-proteomic profiling of 76 Lu-NECs across diverse histopathological subtypes to elucidate intra-tumoral TIME heterogeneity at the proteomic level. Validation was conducted in multiple independent cohorts, including 112 Lu-NECs using immunohistochemistry, 147 Lu-NECs, and 17 small cell lung carcinoma samples using transcriptomics. We integrated proteomic, transcriptomic, genomic, and clinical data to assess molecular, immunological, and clinical features, as well as therapeutic vulnerabilities across different immune subtypes.

RESULTS: We delineated the immuno-proteomic landscape of Lu-NECs and identified two major immuno-proteomic clusters with distinct immunological, molecular, and clinical characteristics. IPC1 was characterized by high immune cell infiltration, while IPC2 exhibited sparse immune cell presence. Genomic analysis revealed distinct mutational patterns, with IPC1 showing a higher incidence of APOBEC-associated mutation signatures and IPC2 being enriched for mutations associated with defective DNA mismatch repair and tobacco-related mutagens. Functional analyses indicated that IPC1 was related to immune and oncogenic signaling activity, whereas IPC2 was associated with cancer stemness and proliferation-related features. Furthermore, IPC1 and IPC2 demonstrated histological subtype-specific clinical benefits from postoperative chemotherapy. Finally, we developed a machine learning model (iPROM) to predict Lu-NECs immune classification and improve risk stratification, which was validated across multiple independent cohorts.

CONCLUSIONS: This study advances the understanding of the tumor immune microenvironment in Lu-NECs through multi-omics characterization and highlights potential personalized therapeutic vulnerabilities tailored to the specific immune landscapes of Lu-NECs.

PMID:40513660 | DOI:10.1016/j.jare.2025.06.017

Cell Stem Cell. 2025 Jun 6:S1934-5909(25)00191-2. doi: 10.1016/j.stem.2025.05.011. Online ahead of print.

ABSTRACT

Deciphering interactions between tumor micro- and systemic immune macroenvironments is essential for developing more effective cancer diagnosis and therapeutic strategies. Here, we established a gel-liquid interface (GLI) co-culture model of lung cancer organoids (LCOs) and paired peripheral-blood mononuclear cells (PBMCs), featuring enhanced interactions between immune cells and tumor organoids for optimized simulation of in vivo systemic anti-tumor immunity. By constructing a cohort of lung cancer patients, we demonstrated that the responses of GLI models under αPD1 treatment reflected the immunotherapy outcomes of the corresponding patients precisely. Furthermore, we dissected the various tumor immune processes mediated by PBMC-derived T cells within GLI models through functional multi-omics analyses, along with the characterization of circulating tumor-reactive T cells (GNLY⁺CD44⁺CD9⁺) with effector memory-like phenotypes as a potential indicator of immunotherapy efficacy. Our findings indicate that the GLI co-culture model can be used to develop diagnostic strategies for precision immunotherapies, as well as understanding the underlying mechanisms.

PMID:40513558 | DOI:10.1016/j.stem.2025.05.011

J Exp Clin Cancer Res. 2025 Jun 12;44(1):174. doi: 10.1186/s13046-025-03433-4.

ABSTRACT

BACKGROUND: Epithelial ovarian cancer (EOC) is a leading cause of cancer mortality in women, often diagnosed at advanced stages. While first-line treatments improve survival, relapses remain common, with 5-year survival rates below 40%. Circulating tumor DNA (ctDNA) is a promising biomarker for non-invasive EOC detection and monitoring. It may help assess treatment response, notably microscopic residual disease. Our objective was to compare two ctDNA characterization strategies in EOC for assessing tumor burden during first-line treatment: a tumor-informed approach based on somatic mutations and a tumor-type informed approach utilizing DNA methylation patterns.

METHODS: In the tumor-informed approach, whole exome sequencing (WES) was performed on EOC tumor DNA and matched PBMCs from 22 patients to identify tumor-specific mutations. Personalized panels were then designed to track these mutations in plasma cfDNA. In the tumor-type informed approach, differentially methylated loci (DMLs) were identified by comparing EOC samples, healthy ovarian tissues, and PBMCs. A unique custom methylation panel was designed, and a support vector machine classifier was trained to distinguish between methylation profiles in plasma cfDNA from healthy donors and from EOC patients. Plasma samples from 47 advanced-stage EOC patients receiving chemotherapy and 54 healthy subjects were analyzed.

RESULTS: For the tumor-informed approach, WES identified an average of 72 somatic mutations per patient. For the tumor-type informed approach, 52,173 DMLs were identified as tumor-specific markers. In 47 plasma samples tested by both approaches, ctDNA levels were significantly correlated (R = 0.56, p = 4.3 × 10^-5), with 70.2% concordance in detection. At baseline, ctDNA was detected in 21/22 patients with the tumor-informed approach, and in 11/12 non-training baseline samples with the tumor-type-informed classifier. At end-of-treatment, the latter detected ctDNA in 16/22 samples, outperforming the former. Detection using this more sensitive approach was significantly associated with relapse (log-rank p = 0.009; hazard ratio = 9.44; 95% CI 1.22-73.26) and poorer overall survival (log-rank p = 0.041).

CONCLUSION: The tumor-type informed classifier demonstrated sensitivity and specificity for ctDNA detection, outperforming the tumor-informed approach in monitoring EOC progression. Requiring fewer sequencing data, it offers a practical, efficient solution for clinical management of EOC.

PMID:40506726 | PMC:PMC12160408 | DOI:10.1186/s13046-025-03433-4

Crit Rev Oncol Hematol. 2025 May 28;213:104776. doi: 10.1016/j.critrevonc.2025.104776. Online ahead of print.

ABSTRACT

Over the past decade, liquid biopsy (LB) has emerged as a key tool in oncology. Its utility in non-invasive sampling and real-time monitoring has made it a cornerstone in precision medicine. Since 2020, publications on LB in solid tumors have doubled, underscoring its pivotal role in advancing cancer care. Notably, 2024 marked a peak in scientific papers on this topic. Blood remained the most studied biofluid, with circulating tumor DNA (ctDNA) as the most frequently analyzed analyte, followed by circulating tumor cells, extracellular vesicles, and microRNAs. Among tumor types, gastrointestinal, lung, breast, and genitourinary cancers were the most investigated, collectively accounting for more than half of the studies. Early cancer and minimal residual disease detection are critical areas of interest, emphasizing the expanding potential of fragmentomics and methylation profiling, as well as the prognostic significance of ctDNA across various cancer types. Moreover, serial ctDNA monitoring demonstrated the ability to predict relapse and guide treatment (de)-escalation strategies. In metastatic setting, ctDNA profiling plays a crucial role in capturing tumor heterogeneity, detecting resistance mechanisms, and informing treatment selection. Non-blood biofluids gained interest for their potential to enhance the detection of clinically relevant alterations in different cancer types such as central nervous system and head and neck cancers. Other than biomarkers selection, the technological advancements and artificial intelligence significantly improved the sensitivity and specificity of LB assays. This evidence in combination with the rapid advancement of machine learning and other computational approaches, are paving the way for a new chapter of LB research.

PMID:40447209 | DOI:10.1016/j.critrevonc.2025.104776

Front Oncol. 2025 May 15;15:1520733. doi: 10.3389/fonc.2025.1520733. eCollection 2025.

ABSTRACT

Circulating tumor DNA (ctDNA), a subset of cell-free DNA (cfDNA), originates from primary tumors and metastatic lesions in cancer patients, often carrying genomic variations identical to those of the primary tumor. ctDNA analysis via liquid biopsy has proven to be a valuable biomarker for early cancer detection, minimal residual disease (MRD) assessment, monitoring tumor recurrence, and evaluating treatment efficacy. However, despite advancements in ctDNA analysis technologies, standardized protocols for its extraction and detection have yet to be established. Each step of the process-from pre-analytical variables to detection techniques-significantly impacts the accuracy and reliability of ctDNA analysis. This review examines recent developments in ctDNA detection methods, focusing on pre-analytical factors such as specimen types, collection tubes, centrifugation protocols, and storage conditions, alongside high-throughput and ultra-sensitive detection technologies. It also briefly discusses the clinical potential of liquid biopsy in nasopharyngeal carcinoma (NPC).

PMID:40444084 | PMC:PMC12119280 | DOI:10.3389/fonc.2025.1520733