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

BMC Cancer. 2025 May 31;25(1):972. doi: 10.1186/s12885-025-14396-2.

ABSTRACT

BACKGROUND: Gastric cancer (GC) is a leading cause of cancer-related deaths worldwide, with early diagnosis remaining a significant challenge. Available serum biomarkers lack specificity, making it difficult to accurately identify early non-metastatic GC cases. Reliable diagnostic biomarkers that can detect early GC are critical to improve prognosis.

METHODS: We employed serum proteomics combined with bioinformatics to identify genes differentially expressed in the serum of non-metastatic GC patients. Single-cell RNA sequencing (ScRNA-seq) and immune infiltration analysis were performed to evaluate the relationship between gene expression and immune cell function. Then we evaluated 107 machine learning models for biomarker-based early GC diagnosis and develops a nomogram validated for accuracy and clinical utility, subsequently comparing the performance of potential biomarkers with traditional tumor markers in diagnosing early gastric cancer. Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) and immunohistochemical staining using the Human Protein Atlas (HPA) database were used to validate the differential expression of candidate genes in GC tissues and adjacent non-cancerous tissues.

RESULTS: The proteomic analysis identified several genes upregulated in the serum of GC patients compared to healthy controls. Single-cell RNA sequencing analysis further revealed that these upregulated genes were associated with altered immune cell infiltration in the tumor microenvironment. The glmBoost + XGBoost model incorporating B2M, CFL1, CTSD, and HSP90AB1 demonstrated strong diagnostic performance (mean AUC = 0.792), with 101 algorithm combinations achieving an average AUC > 0.7. A nomogram integrating gene expression and clinical data was developed, validated through calibration and decision curve analyses, highlighting its potential for early GC diagnosis. Additionally, four genes—TAGLN2, HSP90AB1, SH3BGRL3, and CFL1—were found to be highly expressed in non-metastatic GC tissues and were significantly correlated with immune infiltration, including CD8 + T cells, monocytes, and myeloid-derived suppressor cells. These findings were validated by qRT-PCR and immunohistochemical analyses, confirming their elevated expression in GC tissues.

CONCLUSIONS: TAGLN2, HSP90AB1, SH3BGRL3 and CFL1 are potential diagnostic biomarkers for early-stage GC, with strong associations with immune cell infiltration. Machine learning model shows excellent diagnostic performance. These results provide a foundation for future studies to improve early diagnosis and individualized treatment strategies for GC.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-025-14396-2.

PMID:40450287 | PMC:PMC12126892 | DOI:10.1186/s12885-025-14396-2

Front Pharmacol. 2025 May 15;16:1534974. doi: 10.3389/fphar.2025.1534974. eCollection 2025.

ABSTRACT

BACKGROUND: Frizzled class receptor 2 (FZD2), is a critical protein in the Wnt signaling pathway, which plays significant roles in various cancers. However, its role in cancer progression, prognosis, and diagnosis remains largely unexplored. This study investigates the correlation between FZD2 expression and clinical outcomes, as well as its underlying molecular mechanisms in pan-cancer.

METHODS: A comprehensive bioinformatic analysis was performed using pan-cancer data from The Cancer Genome Atlas (TCGA), which included 33 cancer types. Gene set enrichment analysis (GSEA) was conducted to explore functional pathways, while a protein-protein interaction (PPI) network was constructed to further elucidate the role of FZD2 in tumor biology. The relationship between FZD2 expression and immune cell infiltration across 22 categories was assessed using CIBERSORT. Additionally, single-cell analysis was employed to examine FZD2 expression levels across different cell types. To investigate the functional impact of FZD2, loss-of-function experiments were carried out in gastric cancer cell lines using siRNA-mediated knockdown. Subsequent assays, including Polymerase Chain Reaction (PCR), Western blotting (WB), Cell Counting Kit-8 (CCK8), Flow Cytometry, wound healing, and transwell migration and invasion assays, were performed to assess cellular responses. A subcutaneous gastric cancer xenograft model was established in nude mice to investigate the effect of FZD2 knockdown on tumor growth in vivo.

RESULTS: Our analysis revealed significant upregulation of FZD2 in multiple malignancies, including stomach adenocarcinoma (STAD), bladder cancer (BLCA), and cholangiocarcinoma (CHOL). FZD2 expression was correlated with various cancer characteristics, including stemness score, matrix score, immune score, tumor mutational burden (TMB), microsatellite instability (MSI), RNA modification genes, and drug sensitivity. Notably, FZD2 was associated with altered sensitivity to several anticancer agents, suggesting its role in modulating treatment responses. FZD2 knockdown was demonstrated by both in vitro and in vivo experiments to suppress tumor cell proliferation, migration, and invasion in gastric cancer cell lines, indicating its critical role in tumor progression. Furthermore, FZD2 exhibited significant correlations with other Wnt pathway genes (e.g., Wnt2, Wnt4, Wnt5B), indicating a complex interaction network contributing to tumorigenesis.

CONCLUSION: FZD2 is widely upregulated in various tumor types, with its expression closely associated with key clinical outcomes, including overall survival, disease-specific survival, disease-free interval, as well as tumor mutations, drug sensitivity, immune cell infiltration, and immunotherapy-related biomarkers such as TMB and MSI. These findings highlight the pivotal role of FZD2 in cancer prognosis and treatment, offering potential for novel therapeutic approaches and the development of personalized medicine strategies in oncology.

PMID:40444048 | PMC:PMC12120476 | DOI:10.3389/fphar.2025.1534974

J Pers Med. 2025 Apr 24;15(5):166. doi: 10.3390/jpm15050166.

ABSTRACT

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.

PMID:40423038 | PMC:PMC12113022 | DOI:10.3390/jpm15050166

MedComm (2020). 2025 May 15;6(6):e70193. doi: 10.1002/mco2.70193. eCollection 2025 Jun.

ABSTRACT

Minimal residual disease (MRD) serves as a pivotal biomarker for the clinical diagnosis and subsequent treatment of cancer patients. In hematological malignancies, MRD pose an increasingly serious threat to the health of Chinese people. Accurate MRD detection is essential for assessing relapse risk and optimizing therapeutic strategies, yet current methods such as flow cytometry, polymerase chain reaction (PCR), and next-generation sequencing (NGS) each have distinct limitations, and significant gaps remain in achieving optimal sensitivity and specificity of these technologies. This review provides a comprehensive analysis of MRD detection methods, high-lighting their clinical implications, including their roles in treatment decision-making, risk stratification, and patient outcomes. It discusses the strengths and weaknesses of existing techniques and explores emerging technologies that promise enhanced diagnostic precision. Key advancements such as integrating NGS with other methodologies and novel approaches like liquid biopsy and PCR are examined. The review underscores the academic and practical value of early and accurate MRD detection, emphasizing its impact on improving patient management and treatment outcomes. By addressing the limitations of current technologies and exploring future directions, this review aims to advance the field and support personalized medicine approaches to cancer treatment.

PMID:40384986 | PMC:PMC12079024 | DOI:10.1002/mco2.70193

Cancer Res. 2025 May 16. doi: 10.1158/0008-5472.CAN-25-2070. Online ahead of print.

ABSTRACT

Emerging spatial profiling technologies have revolutionized our understanding of how tissue architecture shapes disease progression, yet the contribution of cellular diversity remains underexplored. Here, Ding and colleagues introduce multiomics and ecological spatial analysis (MESA), an ecology-inspired framework that integrates spatial and single-cell expression data to quantify tissue diversity across multiple scales. MESA both identifies distinct cellular neighborhoods and computes a variety of diversity metrics alongside the identification of diversity "hotspots". Applied to human tonsil tissue, MESA revealed previously undetected germinal center organization, while in spleen tissue of a murine lupus model, MESA highlights increasing cellular diversity with disease progression. Importantly, diversity hotspots do not correspond to conventional compartments identified by existing methods, presenting an orthogonal metric of spatial organization. In colorectal cancer, MESA's diversity metrics outperformed established subtypes at predicting patient survival, while in hepatocellular carcinoma, multi-omic integration identified significantly more ligand-receptor interactions between immune cells compared to single-modality analysis. This work establishes cellular diversity within tissues as a critical correlate of disease progression and underscores the value of multi-omic integration in spatial biology.

PMID:40378285 | DOI:10.1158/0008-5472.CAN-25-2070

Front Digit Health. 2025 May 1;7:1583490. doi: 10.3389/fdgth.2025.1583490. eCollection 2025.

ABSTRACT

Lung transplantation (LTx) is an effective method for treating end-stage lung disease. The management of lung transplant recipients is a complex, multi-stage process that involves preoperative, intraoperative, and postoperative phases, integrating multidimensional data such as demographics, clinical data, pathology, imaging, and omics. Artificial intelligence (AI) and machine learning (ML) excel in handling such complex data and contribute to preoperative assessment and postoperative management of LTx, including the optimization of organ allocation, assessment of donor suitability, prediction of patient and graft survival, evaluation of quality of life, and early identification of complications, thereby enhancing the personalization of clinical decision-making. However, these technologies face numerous challenges in real-world clinical applications, such as the quality and reliability of datasets, model interpretability, physicians' trust in the technology, and legal and ethical issues. These problems require further research and resolution so that AI and ML can more effectively enhance the success rate of LTx and improve patients' quality of life.

PMID:40376618 | PMC:PMC12078212 | DOI:10.3389/fdgth.2025.1583490

Cell Death Dis. 2025 May 15;16(1):382. doi: 10.1038/s41419-025-07720-y.

ABSTRACT

M2-polarized tumor-associated macrophages (TAMs) are a key factor contributing to the poor prognosis of pancreatic ductal adenocarcinoma (PDAC). While various factors within the tumor microenvironment (TME) drive their formation, the role of PDAC-derived exosomes in this process remains unclear. We aim to clarify the regulatory impacts of tumor-derived exosomes to TAMs. After the intratumoral injection to subcutaneous tumor of C57BL/6 mice, we demonstrated PDAC-derived exosomes exacerbate PDAC progression, accompanied with upregulated M2 phenotype of TAMs and unaffected proliferation signatures. Through intratumoral injection model and multi-Omics analyses, we identified CCT6A as a novel tumor-derived exosomal protein, bridging TAMs M2 polarization and PDAC prognosis. Co-culture with exosomes derived from CCT6A^high PDAC leads to greater M2 phenotype of TAMs via PI3K-AKT signaling. According to proteomics data, chemokines' abundance reduces over tenfold once exosomal CCT6A absence, including CXCL1, CXCL3, CCL20 and CCL5, whose interaction with CCT6A in PDAC cells was confirmed by interactomics data. Moreover, we found silencing CCT6A abrogated the antagonism effects of CD47 antibody immunotherapy. Our findings implied that the subunit of the T-complex protein Ring Complex (TRiC) CCT6A serves as a matchmaker during exosome-mediated chemokines transfer from PDAC to TAMs. Silencing CCT6A effectively sensitized PDAC to CD47 antibody immunotherapy in vivo.

PMID:40374617 | PMC:PMC12081750 | DOI:10.1038/s41419-025-07720-y

Dermatologie (Heidelb). 2025 Jun;76(6):361-364. doi: 10.1007/s00105-025-05506-z. Epub 2025 May 7.

ABSTRACT

BACKGROUND: Personalized treatment of melanoma is becoming increasingly more important. Biomarkers offer the possibility of controlling treatment more precisely and reducing side effects.

OBJECTIVE: The aim of this text is to provide an overview of current tissue-based, blood-based and radiological biomarkers and their clinical application in melanomas.

MATERIAL AND METHODS: A literature research and analysis of current studies on biomarkers in adjuvant and neoadjuvant treatment of melanomas were carried out and relevant congress contributions were additionally included.

RESULTS: Tissue-based programmed cell death 1 ligand 1 (PD-L1) expression, interferon gamma (IFNγ) signature, gene expression profiles (GEP) and tumor mutational burden (TMB) are of prognostic and predictive relevance. Blood-based circulating tumor DNA (ctDNA) in the sense of a liquid biopsy should be emphasized as a personalized biomarker for longitudinal tracking during treatment or aftercare. Positron emission tomography computed tomography (PET-CT) and body composition enable an improved assessment of treatment efficiency. There are currently no data from prospective validation studies on these biomarkers; initial data from the NivoMela study are awaited.

CONCLUSION: The combination of tissue-based, blood-based and radiological biomarkers in terms of multiparametric approaches is promising but further prospective validation is needed for broad clinical use. These are currently not comprehensively implemented in the clinical routine in centers or in remuneration procedures.

PMID:40335648 | DOI:10.1007/s00105-025-05506-z