Adv Respir Med. 2025 Jun 12;93(3):17. doi: 10.3390/arm93030017.

ABSTRACT

Circulating free DNA (cfDNA) is genetic material released from various cells into bodily fluids. Among its fractions, circulating tumor DNA (ctDNA) originates from tumor cells and reflects their genetic material, including mutations and epigenetic changes. Methods commonly employed for detecting ctDNA in blood include next-generation sequencing (NGS) and various types of PCR. The presence of ctDNA can be utilized in liquid biopsies for many diagnostic purposes related to various cancers. It is a minimally invasive method of sampling molecular compounds from tumor cells. In this paper, we focus on current knowledge regarding the liquid biopsy of blood ctDNA in the context of lung cancer, one of the leading causes of cancer-related mortality. Currently, as a clinically approved method, liquid biopsy serves as a complementary technique in NSCLC diagnostic and genetic profiling. Other applications of liquid biopsy that are still being investigated include the detection of minimal residual disease (MRD) after curative treatment and response monitoring to systemic treatment. This review discusses current and future potential directions for the development and implementation of ctDNA for patients with NSCLC.

PMID:40558116 | PMC:PMC12189613 | DOI:10.3390/arm93030017

Trends Mol Med. 2025 Jun 23:S1471-4914(25)00118-2. doi: 10.1016/j.molmed.2025.05.007. Online ahead of print.

ABSTRACT

Gastric cancer (GC) remains a deadly disease because of late detection and limited treatment options at advanced stages. Treatment of patients with metastatic disease is based on chemotherapy, complemented by antibodies targeting HER2, VEGFR2, and more recently PD-1 or claudin 18.2. Further targets, such as FGFR2b, as well as novel drug classes including antibody-drug conjugates (ADCs) and bispecific antibodies, are promising developments in GC treatment. Despite the failure of several targeted agents, the landscape of GC therapy is evolving rapidly, facilitated by umbrella or platform precision medicine trials. The integration of next-generation sequencing and other omics techniques into molecular tumor boards, as well as functional drug testing on patient-derived models, might bring us closer to personalized oncology and ultimately improve patient survival.

PMID:40555635 | DOI:10.1016/j.molmed.2025.05.007

Cytokine Growth Factor Rev. 2025 Jun 6:S1359-6101(25)00071-1. doi: 10.1016/j.cytogfr.2025.06.001. Online ahead of print.

ABSTRACT

Lung cancer is a leading cause of cancer-related mortality worldwide, characterized by the uncontrolled growth and spread of abnormal cells. Despite constant progress in diagnosis and treatment of lung cancer remains highly challenging. Present review explores the molecular underpinnings of lung cancer, emphasizing genetic mutations, altered metabolism, and the role of biomarkers in diagnosis and treatment. Advances in diagnostic methods, particularly liquid biopsies, have enabled non-invasive detection and monitoring of lung cancer. Therapeutic approaches have evolved significantly, with molecular-targeted therapies focusing on pathways like EGFR, ALK, KRAS, and MET. Caner Immunotherapy, including immune checkpoint inhibitors, artificial intelligence, and multi-omics analysis, including genomics and image data to predict treatment response, has further enhanced precision medicine by enabling earlier detection, better prognostic models, and efficient drug discovery. AI has a significant impact on various aspects of oncology, including improving the earlier diagnosis of lung cancer. Despite these advancements, challenges such as drug resistance, tumor heterogeneity, and limited efficacy of certain therapies persist, necessitating continued research and innovation.

PMID:40544104 | DOI:10.1016/j.cytogfr.2025.06.001

Mikrochim Acta. 2025 Jun 16;192(7):428. doi: 10.1007/s00604-025-07286-8.

ABSTRACT

Cancer remains one of the most deadly diseases in the world, requiring constant growth and improvements in therapeutic strategies. Traditional cancer treatments, such as chemotherapy, radiotherapy, and surgery, have limitations like off-target release, toxicity, and inefficient drug delivery. This study explains the role of bioinformatics and AI in optimizing and analyzing liposomal formulations for innovative and better cancer therapy. Molecular docking (MD), molecular dynamics simulations, and machine learning models are the computational techniques that can help to design stable liposomal carriers for drugs, predict receptor-ligand interactions, and can improve drug release efficiency. Improved liposome nanoparticles (LNPs) surface functionalization, the discovery of tumor-specific biomarkers, and the improvement of receptor-ligand interactions for accurate drug targeting are all made possible by bioinformatics tools and methodologies. Moreover, AI-assisted predictions and in silico modeling can speed up drug discovery and processing while eliminating the experimental expenditures and time. In the present review, we conducted MD studies to complement the discussed literature. MD was performed between cyclic RGD peptides (liposomal ligands) and the GPR116 receptor in triple-negative breast cancer, and between folic acid (liposomal ligand) and the Axl tyrosine kinase receptor for lung cancer, revealing strong and stable interactions and highlighting the amino acid residues involved. Notwithstanding current obstacles, computational tools have shown notable progress in nanomedicine, exploring more options for more individualized and effective cancer therapies. The combination of AI, machine learning, and multi-omics techniques to improve therapeutic efficacy and reduce side effects is a substantial key to the future of LNP-based cancer treatment.

PMID:40523994 | DOI:10.1007/s00604-025-07286-8

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 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

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

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

Clin Transl Oncol. 2025 May 10. doi: 10.1007/s12094-025-03947-5. Online ahead of print.

ABSTRACT

Pancreatic cancer (PC) remains one of the most lethal malignancies, primarily due to its poor prognosis and late diagnosis. Biomarkers are essential in enhancing diagnostic accuracy, prognostic assessments, and therapeutic strategies, thereby addressing these challenges. Conventional biomarkers, such as CA 19-9, are widely used for monitoring disease progression but have limitations in early detection and specificity, necessitating complementary markers like CEA and MUC1. Emerging genetic biomarkers, including KRAS mutations and TP53 alterations, offer critical insights into tumorigenesis and serve as valuable diagnostic, prognostic, and therapeutic targets. Epigenetic biomarkers, such as DNA methylation and histone modifications, provide additional molecular layers, with aberrant methylation patterns and dysregulated histone modifications influencing tumor aggressiveness and therapy resistance. RNA-based biomarkers, particularly microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), play pivotal roles in regulating tumor biology and offer significant diagnostic and therapeutic potential. Protein-based biomarkers, including glycoproteins and cytokines, alongside liquid biopsy components like circulating tumor DNA (ctDNA), exosomes, and circulating tumor cells (CTCs), facilitate real-time disease monitoring and early detection. Personalized therapy is increasingly guided by these biomarkers, which predict responses to chemotherapy and immunotherapy. Despite challenges in biomarker validation and clinical implementation, advancements in multi-omics, artificial intelligence, and collaborative research hold promise for improving patient outcomes and survival rates in PC.

PMID:40348906 | DOI:10.1007/s12094-025-03947-5