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

J Med Syst. 2025 Apr 10;49(1):47. doi: 10.1007/s10916-025-02182-3.

ABSTRACT

Drug response prediction (DRP) is a central task in the era of precision medicine. Over the past decade, the emergence of deep learning (DL) has greatly contributed to addressing DRP challenges. Notably, the prediction of DRP for cancer cell lines benefits significantly from data availability for model development. However, an effective predictive model is still challenging due to issues with data quality, high-dimensional data, and multi-omics data integration. In this study, we introduce MLG2Net, a deep-learning model inspired by graph neural networks designed to predict DRP in lung cancer cell lines based on pharmacogenomics data. Our model comprises two key components: drug SMILES described by local and global graph networks and cell line genomics are illustrated as a map. Our results show that MLG2Net outperforms three reference graph networks. MLG2Net performance reached a Pearson coefficient correlation ( $C{C}_p$ ) of 0.8616 and a root mean square error (RMSE) of 2.94e-6 in predicting drug responses for Lung Adenocarcinoma (LUAD) cell lines. Subsequent testing on the Lung Squamous Cell Carcinoma (LUSC) dataset reveals lower performance ( $C{C}_p$ : 0.7999, RMSE: 4.08e-6), attributed to the dataset's smaller size influencing model capacity. Moreover, we assessed the model's architecture by isolating its components, with results indicating that the global network is particularly effective in this task. In conclusion, MLG2Net exhibited promising applications in DRP for cancer cell lines, with potential advancements by incorporating larger datasets.

PMID:40208442 | DOI:10.1007/s10916-025-02182-3

J Med Syst. 2025 Apr 10;49(1):47. doi: 10.1007/s10916-025-02182-3.

ABSTRACT

Drug response prediction (DRP) is a central task in the era of precision medicine. Over the past decade, the emergence of deep learning (DL) has greatly contributed to addressing DRP challenges. Notably, the prediction of DRP for cancer cell lines benefits significantly from data availability for model development. However, an effective predictive model is still challenging due to issues with data quality, high-dimensional data, and multi-omics data integration. In this study, we introduce MLG2Net, a deep-learning model inspired by graph neural networks designed to predict DRP in lung cancer cell lines based on pharmacogenomics data. Our model comprises two key components: drug SMILES described by local and global graph networks and cell line genomics are illustrated as a map. Our results show that MLG2Net outperforms three reference graph networks. MLG2Net performance reached a Pearson coefficient correlation ( $C{C}_p$ ) of 0.8616 and a root mean square error (RMSE) of 2.94e-6 in predicting drug responses for Lung Adenocarcinoma (LUAD) cell lines. Subsequent testing on the Lung Squamous Cell Carcinoma (LUSC) dataset reveals lower performance ( $C{C}_p$ : 0.7999, RMSE: 4.08e-6), attributed to the dataset's smaller size influencing model capacity. Moreover, we assessed the model's architecture by isolating its components, with results indicating that the global network is particularly effective in this task. In conclusion, MLG2Net exhibited promising applications in DRP for cancer cell lines, with potential advancements by incorporating larger datasets.

PMID:40208442 | DOI:10.1007/s10916-025-02182-3

ACS Sens. 2025 Apr 25;10(4):3101-3111. doi: 10.1021/acssensors.5c00314. Epub 2025 Apr 7.

ABSTRACT

Early screening of individuals with high-risk lung nodules can significantly improve the prognosis of lung cancer patients, and accurate identification of lung nodule subtypes can provide guidance for medical treatment. Exhaled breath (EB) analysis via eNoses offers a quick and noninvasive approach, but current eNose technology lacks quality control and solid validation in large population studies. Herein, an eNose platform integrated with a metal ion-decorated graphene sensor array and a breath sampling accessory was established. EB samples from 427 healthy subjects and 2586 subjects with lung nodules, including various benign and malignant subtypes, were collected through the breath sampling accessory for quality control. The large-cohort clinical EB samples were analyzed by the eNose platform to acquire the cross-reactive resistance response. Breathprint analysis for high-risk lung nodules using SVM and age-matched training sets yielded strong and robust performance. Combined with baseline data, the model achieved an AUC of 0.93 (95% CI, 0.89-0.96) on the external test set, with 97% sensitivity and 73% specificity. Moreover, dimensionality reduction analysis of breathprints demonstrated separability across different lung nodule subtypes. This study demonstrates the reliability of the graphene eNose platform to identify high-risk lung nodules and classify lung nodule subtypes in a noninvasive and rapid method.

PMID:40193324 | DOI:10.1021/acssensors.5c00314

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

Tuberc Respir Dis (Seoul). 2025 Apr 8. doi: 10.4046/trd.2024.0146. Online ahead of print.

ABSTRACT

Molecular profiling of tumours from patients plays a crucial role in precision oncology. While tumour tissue-based genomic testing remains the gold standard in clinical management of patients with non-small cell lung cancer, advances in genomic technologies, the analysis of various bodily fluids, mainly blood but also saliva, pleural/ pericardial effusions, urine, and cerebrospinal fluid is now feasible and readily available. In this review, we will focus on the clinical application of circulating tumour DNA in patients with non-small cell lung cancer in the setting of early-stage disease, locally advanced disease with attention to the potential of ctDNA in prognostication, risk stratification, minimal residual disease, and in advanced disease, its role in the detection of genomic markers and mechanisms of acquired resistance. The role of ctDNA and liquid biopsies in lung cancer screening will also be discussed.

PMID:40195729 | DOI:10.4046/trd.2024.0146

Sci Data. 2025 Apr 1;12(1):545. doi: 10.1038/s41597-025-04912-1.

ABSTRACT

Diagnosing lung cancer at a curable stage offers the opportunity for a favorable prognosis. The emerging epigenomics analysis on plasma cell-free DNA (cfDNA), including 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) modifications, has acted as a promising approach facilitating the identification of lung cancer. And, integrating 5mC biomarker with chest computed tomography (CT) image features could optimize the diagnosis of lung cancer, exceeding the performance of models built on single feature. However, the clinical applicability of integrated markers might be limited by the potential risk of overfitting due to small sample size. Hence, we prospectively collected peripheral blood sample and the paired chest CT images of 2032 patients with indeterminate pulmonary nodules across 5 centers, and constructed a large-scale, multi-institutional, multiomics database that encompass CT imaging data and plasma cfDNA fragmentomic in 5mC-, 5hmC-enriched regions. To our best knowledge, this dataset is the first radio-epigenomic dataset with the largest sample size, and provides multi-dimensional insights for early diagnosis of lung cancer, facilitating the individuated management for lung cancer.

PMID:40169596 | PMC:PMC11961589 | DOI:10.1038/s41597-025-04912-1

Front Oncol. 2025 Mar 13;15:1520717. doi: 10.3389/fonc.2025.1520717. eCollection 2025.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer-related mortality, primarily due to late stage at diagnosis. This review examines the multifaceted applications of liquid biopsy and circulating tumor DNA (ctDNA) analysis in the diagnosis and management of PDAC. We review the current literature on the technological advancements in liquid biopsy analysis such as next generation sequencing (NGS) and digital droplet PCR (ddPCR) as well as multi-omics technologies, highlighting their potential for accurate molecular subtyping through ctDNA analysis. This review highlights the significant role of ctDNA in the assessment of tumor behavior, disease subtyping, prediction and monitoring of treatment response, and evaluation of minimal residual disease. We discuss the implications of integrating liquid biopsy techniques into clinical practice as well as its challenges and limitations. By drawing insights from recent studies, this review aims to provide a comprehensive overview of how liquid biopsy and ctDNA analysis can enhance early disease management strategies in PDAC. We underscore the need for additional prospective studies and clinical trials to validate its feasibility and accuracy in order to establish clinical utility, with the ultimate goal of routine incorporation into practice to improve patient outcomes and transform the treatment landscape for PDAC.

PMID:40182037 | PMC:PMC11966063 | DOI:10.3389/fonc.2025.1520717

Front Oncol. 2025 Mar 13;15:1520717. doi: 10.3389/fonc.2025.1520717. eCollection 2025.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer-related mortality, primarily due to late stage at diagnosis. This review examines the multifaceted applications of liquid biopsy and circulating tumor DNA (ctDNA) analysis in the diagnosis and management of PDAC. We review the current literature on the technological advancements in liquid biopsy analysis such as next generation sequencing (NGS) and digital droplet PCR (ddPCR) as well as multi-omics technologies, highlighting their potential for accurate molecular subtyping through ctDNA analysis. This review highlights the significant role of ctDNA in the assessment of tumor behavior, disease subtyping, prediction and monitoring of treatment response, and evaluation of minimal residual disease. We discuss the implications of integrating liquid biopsy techniques into clinical practice as well as its challenges and limitations. By drawing insights from recent studies, this review aims to provide a comprehensive overview of how liquid biopsy and ctDNA analysis can enhance early disease management strategies in PDAC. We underscore the need for additional prospective studies and clinical trials to validate its feasibility and accuracy in order to establish clinical utility, with the ultimate goal of routine incorporation into practice to improve patient outcomes and transform the treatment landscape for PDAC.

PMID:40182037 | PMC:PMC11966063 | DOI:10.3389/fonc.2025.1520717

Mol Clin Oncol. 2025 Mar 12;22(5):42. doi: 10.3892/mco.2025.2837. eCollection 2025 May.

ABSTRACT

Before the twentieth century, patients with advanced lung cancer had limited treatment options and chemotherapy was the primary form of treatment, with an overall survival often <0.5 years. However, with advances in society and medical technology, the treatment approaches for advanced non-small cell lung cancer (NSCLC) have markedly changed. Traditional chemotherapy has been gradually replaced by targeted therapy and immunotherapy, leading to the emergence of various new therapeutic options that offer patients more personalized and precise care. This raises the question of what the future holds for the treatment of NSCLC. This review provides a comprehensive analysis of the latest breakthroughs in targeted therapies, immunotherapies, and drugs for antibody-drug conjugates (ADCs), highlights advances in multimodal combination therapy strategies, and explores the causes of resistance and the challenges that exist in overcoming it. In particular, this review provides unique insights into key directions for future research in NSCLC, such as personalised treatment strategies and biomarker exploration based on multi-omics data, aiming to provide new inspiration for clinical decision-making and research.

PMID:40160297 | PMC:PMC11948471 | DOI:10.3892/mco.2025.2837