World J Hepatol. 2025 Feb 27;17(2):101201. doi: 10.4254/wjh.v17.i2.101201.

ABSTRACT

Liver cancer, particularly hepatocellular carcinoma (HCC), remains a significant global health challenge due to its high mortality rate and late-stage diagnosis. The discovery of reliable biomarkers is crucial for improving early detection and patient outcomes. This review provides a comprehensive overview of current and emerging biomarkers for HCC, including alpha-fetoprotein, des-gamma-carboxy prothrombin, glypican-3, Golgi protein 73, osteopontin, and microRNAs. Despite advancements, the diagnostic limitations of existing biomarkers underscore the urgent need for novel markers that can detect HCC in its early stages. The review emphasizes the importance of integrating multi-omics approaches, combining genomics, proteomics, and metabolomics, to develop more robust biomarker panels. Such integrative methods have the potential to capture the complex molecular landscape of HCC, offering insights into disease mechanisms and identifying targets for personalized therapies. The significance of large-scale validation studies, collaboration between research institutions and clinical settings, and consideration of regulatory pathways for clinical implementation is also discussed. In conclusion, while substantial progress has been made in biomarker discovery, continued research and innovation are essential to address the remaining challenges. The successful translation of these discoveries into clinical practice will require rigorous validation, standardization of protocols, and cross-disciplinary collaboration. By advancing the development and application of novel biomarkers, we can improve the early detection and management of HCC, ultimately enhancing patient survival and quality of life.

PMID:40027561 | PMC:PMC11866143 | DOI:10.4254/wjh.v17.i2.101201

J Liq Biopsy. 2024 Oct 18;6:100271. doi: 10.1016/j.jlb.2024.100271. eCollection 2024 Dec.

ABSTRACT

Bladder cancer (BC) is the sixth most prevalent cancer in the U.S. and the most expensive to treat. Integrating precision medicine into BC management promises improved outcomes and reduced costs. This review explores current treatment paradigms and the transformative potential of urine-based molecular diagnostics. Treatments for BC range from transurethral resection and intravesical therapy for non-muscle invasive bladder cancer (NMIBC) to neoadjuvant chemotherapy and radical cystectomy for muscle-invasive bladder cancer (MIBC). Recent breakthroughs include enfortumab vedotin with pembrolizumab for advanced urothelial carcinoma, PD-1 immunotherapy for minimal residual disease (MRD)-positive patients and erdafitinib for NMIBC. Traditional diagnostic methods like cystoscopy, urine cytology, and imaging have limitations; urine-based diagnostics, particularly urinary tumor DNA (utDNA) analysis, offer a non-invasive, sensitive, and cost-effective alternative. These diagnostics facilitate personalized treatment, monitor therapy response, detect MRD, and enable earlier cancer detection. Incorporating urine-based diagnostics into clinical practice can reduce healthcare costs and improve patient quality of life. This review highlights the need for these diagnostics in routine BC management and emphasizes the impact of recent therapeutic advances.

PMID:40027318 | PMC:PMC11863694 | DOI:10.1016/j.jlb.2024.100271

J Liq Biopsy. 2024 Feb 11;4:100146. doi: 10.1016/j.jlb.2024.100146. eCollection 2024 Jun.

ABSTRACT

As precision oncology has become a major part of the treatment landscape in oncology, liquid biopsies have developed as a particularly powerful tool as it surmounts several limitations of traditional tissue biopsies. These biopsies involve most commonly the isolation of circulating extracellular nucleic acids, including cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA), as well as circulating tumor cells (CTCs), typically from blood. The clinical applications of liquid biopsies are diverse, encompassing the initial diagnosis and cancer detection, the application as a tool for prognostication in early and advanced tumor settings, the identification of potentially actionable alterations, the monitoring of response and resistance under systemic therapy and the detection of resistance mechanisms, the differentiation of distinct immune checkpoint blockade response patterns through serial samples, the prediction of immune checkpoint blockade responses based on initial liquid biopsy characteristics and the assessment of tumor heterogeneity. Moreover, molecular relapse monitoring in early-stage cancers and the personalization of adjuvant or additive therapy via MRD have become a major field of research in recent years. Compared to tissue biopsies, liquid biopsies are less invasive and can be collected serially, offering real-time molecular insights. Furthermore, liquid biopsies may allow for a more holistic evaluation of a patient's disease, as they assess material from all tumor sites and can theoretically reflect tumor heterogeneity. Furthermore, quicker turnaround-time also constitutes an advantage of liquid biopsies. Disadvantages or hurdles include the challenge of detecting low amounts of tumor deposits in peripheral blood or other fluids and the potential of different amounts tumor-shedding from different metastatic sites, as well as potentially false-positive from clonal hematopoietic mutations of indeterminate potential (CHIP) mutations. The clinical utility of liquid biopsies still must be validated in most settings and further research has to be done. Clinal trials including alternate bodily fluids and leveraging AI-technology are expected to revolutionize the field of liquid biopsies.

PMID:40027149 | PMC:PMC11863819 | DOI:10.1016/j.jlb.2024.100146

J Liq Biopsy. 2024 Feb 10;4:100145. doi: 10.1016/j.jlb.2024.100145. eCollection 2024 Jun.

ABSTRACT

Blood-based biomarkers investigation does not require invasive tissue biopsies and may explore diverse tumoral components such as proteins, microRNAs, circulating tumor cells, ctDNA, and exosomes and may better reflect tumor molecular heterogeneity, either temporal or spatial. ctDNA is related to tumor burden and represents a more objective measure of the total body disease burden than imaging findings. ctDNA profiling can be therefore useful to determine minimal residual disease (MRD), which is defined as the remaining tumor cells or tumor-derived material after definitive treatment in patients with no clinical evidence of disease. The detection of MRD is highly predictive of future disease recurrence. Although detectable MRD is associated with a poor prognosis, it is not clear whether MRD detection can guide therapy escalation to improve patient outcomes. In this review, we present four cases of epidermal growth factor receptor (EGFR) mutant NSCLC patients who received standard of care curative treatment and periodic radiological assessment and liquid biopsy analyses were carried out as follow-up. A tumor-informed 52 genes Oncomine Pan-Cancer Cell-Free assay (Thermo Fisher Scientific, Waltham, MA, USA), was used to identify single-nucleotide variants (SNVs), indels, copy number variations (CNVs), and RNA fusions in blood based liquid biopsy ctDNA in three of four patients. In one patient the approach used was through Commercial Kit Cobas EGFR Mutation Test v2 CE-IVD (Roche Diagnostics SL) that identifies 42 mutations in the EGFR gene. In one patient, an actionable oncogene driver alteration was identified in the ctDNA analysis, four months after radical intent concurrent chemoradiotherapy and six weeks before radiological distant relapse was clearly confirmed. There is no evidence of ctDNA or radiological disease relapse in the other three patients. Finally, a review of the literature addressing the potential value of MRD detection in this clinical setting is presented and discussed as well.

PMID:40027142 | PMC:PMC11863883 | DOI:10.1016/j.jlb.2024.100145

bioRxiv [Preprint]. 2025 Feb 17:2025.02.13.637721. doi: 10.1101/2025.02.13.637721.

ABSTRACT

Cell type-specific regulatory programs that drive type 1 diabetes (T1D) in the pancreas are poorly understood. Here we performed single nucleus multiomics and spatial transcriptomics in up to 32 non-diabetic (ND), autoantibody-positive (AAB+), and T1D pancreas donors. Genomic profiles from 853,005 cells mapped to 12 pancreatic cell types, including multiple exocrine sub-types. Beta, acinar, and other cell types, and related cellular niches, had altered abundance and gene activity in T1D progression, including distinct pathways altered in AAB+ compared to T1D. We identified epigenomic drivers of gene activity in T1D and AAB+ which, combined with genetic association, revealed causal pathways of T1D risk including antigen presentation in beta cells. Finally, single cell and spatial profiles together revealed widespread changes in cell-cell signaling in T1D including signals affecting beta cell regulation. Overall, these results revealed drivers of T1D progression in the pancreas, which form the basis for therapeutic targets for disease prevention.

PMID:40027657 | PMC:PMC11870426 | DOI:10.1101/2025.02.13.637721

Cancer Innov. 2025 Feb 28;4(2):e160. doi: 10.1002/cai2.160. eCollection 2025 Apr.

ABSTRACT

BACKGROUND: Lung adenocarcinoma (LUAD) is a highly heterogeneous cancer type with a poor prognosis. Accurate subtype identification can help guide its treatment. The traditional subtype identification methods using a single-omics approach make it difficult to comprehensively characterize the molecular features of LUAD. Identification of subtypes through multi-omics association strategies can effectively supplement the shortcomings of single-omics information.

METHODS: In this study, we used the Generative Adversarial Network (GAN) to mine transcriptomic, proteomic, and epigenomic information and generate an integrated data set. The newly integrated data were then used to identify LUAD immune subtypes. In the improved GAN (MOGAN) method, we not only integrated multiple omics datasets but also included the interactions between proteins and genes and between methylation and genes. Thus, we achieved effective complementarity of multi-omics information.

RESULTS: Two subtypes, MOGANTPM_S1 and MOGANTPM_S2, were identified using immune cell infiltration analysis and the integrated multi-omics data. MOGANTPM_S1 patients displayed higher immune cell infiltration, better prognosis, and sensitivity to immune checkpoint inhibitors (ICIs), while MOGANTPM_S2 had lower immune cell infiltration, poorer prognosis, and were insensitive to ICIs. Therefore, immunotherapy was more suitable for MOGANTPM_S1 patients in clinical practice. In addition, this study developed a LUAD subtype diagnostic model using the transcriptomic and proteomic features of five genes, which can be used to guide clinical subtype diagnosis.

CONCLUSIONS: In summary, the MOGAN method was applied to integrate three omics data types and successfully identify two LUAD immune subtypes with significant survival differences. This classification method may be useful for LUAD treatment decisions.

PMID:40026873 | PMC:PMC11868734 | DOI:10.1002/cai2.160

Semin Cancer Biol. 2025 Jun;111:76-88. doi: 10.1016/j.semcancer.2025.02.009. Epub 2025 Feb 20.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in its late stages when treatment options are limited. Unlike other common cancers, there are no population-wide screening programmes for PDAC. Thus, early disease detection, although urgently needed, remains elusive. Individuals in certain high-risk groups are, however, offered screening or surveillance. Here we explore advances in understanding high-risk groups for PDAC and efforts to implement biomarker-driven detection of PDAC in these groups. We review current approaches to early detection biomarker development and the use of artificial intelligence as applied to electronic health records (EHRs) and social media. Finally, we address the cost-effectiveness of applying biomarker strategies for early detection of PDAC.

PMID:39986585 | DOI:10.1016/j.semcancer.2025.02.009