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

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

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

Per Med. 2025 Apr 21:1-10. doi: 10.1080/17410541.2025.2494982. Online ahead of print.

ABSTRACT

Lung cancer (LC) remains the leading cause of cancer-related mortality worldwide, with most cases diagnosed at advanced stages, resulting in poor survival rates. Early detection significantly improves outcomes, yet current screening methods, such as low-dose computed tomography (LDCT), are limited by high false-positive rates, radiation exposure, and restricted eligibility criteria. This review highlights the transformative potential of genomic and molecular technologies in advancing the early detection of LC. Key innovations include liquid biopsy tools, such as circulating tumor DNA (ctDNA) and cell-free DNA (cfDNA) analysis, which offer minimally invasive approaches to detect tumor-specific genetic and epigenetic alterations. Emerging biomarkers, including methylation signatures, cfDNA fragmentomics, and multi-omics profiles, demonstrate improved sensitivity and specificity in identifying early-stage tumors. Advanced platforms like next-generation sequencing (NGS) and machine-learning algorithms further enhance diagnostic accuracy. Integrated approaches that combine genomic data with LDCT imaging and artificial intelligence (AI) show promise in addressing current limitations by improving risk stratification and nodule characterization. The review also explores multi-cancer early detection assays and precision diagnostic strategies tailored for diverse at-risk populations. By leveraging these advancements, clinicians can achieve earlier diagnoses, reduce unnecessary procedures, and ultimately decrease LC mortality.

PMID:40255184 | DOI:10.1080/17410541.2025.2494982

J Liq Biopsy. 2025 Mar 27;8:100293. doi: 10.1016/j.jlb.2025.100293. eCollection 2025 Jun.

ABSTRACT

Liquid biopsy (LB) has emerged as a transformative tool in oncology, providing a minimally invasive approach for tumor detection, molecular characterization, and real-time treatment monitoring. By analyzing circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), extracellular vesicles (EVs), and microRNA (miRNA), LB enables comprehensive tumor profiling without the need for traditional tissue biopsies. Over the past decade, research in this field has expanded exponentially, leading to the integration of LB into clinical practice for specific cancer types, including lung and breast cancer. In 2024, the Journal of Liquid Biopsy (JLB) published innovative studies exploring the latest advancements in LB technologies, biomarkers, and their applications for cancer detection, minimal residual disease (MRD) monitoring, and therapy response assessment. This review synthesizes recent findings on the role of LB in cancer treatment and monitoring across different biomarkers, with a particular focus on newly published studies and their context within translational research. Additionally, it highlights emerging techniques such as fragmentomics, artificial intelligence, and multiomics, paving the way for more precise, personalized treatment decisions. Despite these advancements, challenges remain in standardizing methodologies, optimizing clinical validation, and integrating LB into routine oncological workflows. This mini-review highlights the evolving landscape of LB research and its potential to revolutionize cancer diagnosis, treatment monitoring, and therapeutic decision-making, ushering in a new era of precision oncology.

PMID:40255897 | PMC:PMC12008596 | DOI:10.1016/j.jlb.2025.100293

Nat Rev Urol. 2025 Apr 15. doi: 10.1038/s41585-025-01023-9. Online ahead of print.

ABSTRACT

Liquid biopsies, indicating the sampling of body fluids rather than solid-tissue biopsies, have the potential to revolutionize cancer care through personalized, noninvasive disease detection and monitoring. Circulating tumour DNA (ctDNA) and circulating tumour cells (CTCs) are promising blood-based biomarkers in bladder cancer. Results from several studies have shown the clinical potential of ctDNA and CTCs in bladder cancer for prognostication, treatment-response monitoring, and early detection of minimal residual disease and disease recurrence. Following successful clinical trial evaluation, assessment of ctDNA and CTCs holds the potential to transform the therapeutic pathway for patients with bladder cancer - potentially in combination with the analysis of urinary tumour DNA - through tailored treatment guidance and optimized disease surveillance.

PMID:40234713 | DOI:10.1038/s41585-025-01023-9

Cytopathology. 2025 Apr 11. doi: 10.1111/cyt.13485. Online ahead of print.

ABSTRACT

The advent of personalised and precision medicine has radically modified the management and the clinical outcome of cancer patients. However, the expanding number of predictive, prognostic, and diagnostic biomarkers has raised the need for simple, noninvasive, quicker, but equally efficient tests for molecular profiling. In this complex scenario, the adoption of liquid biopsy, particularly circulating tumour DNA (ctDNA), has been a real godsend for many cancer patients who would otherwise have been denied the benefits of targeted treatments. Undeniably, ctDNA analysis has several advantages over conventional tissue-based analysis. One advantage is that it can guide treatment decision making, especially when tissue samples are scarce or totally unavailable. Indeed, a simple blood test can inform clinicians on patients' response or resistance to targeted therapies, help them monitor minimal residual disease (MRD) after surgical resections, and facilitate them with early cancer detection and interception. Finally, an equally important advantage is that ctDNA analysis can help decipher temporal and spatial tumour heterogeneity, a mechanism highly responsible for therapeutic resistance. In this review, we gathered and analysed current evidence on the clinical usefulness of ctDNA analysis in solid tumours.

PMID:40219616 | DOI:10.1111/cyt.13485