Nat Commun. 2025 Jan 8;16(1):430. doi: 10.1038/s41467-024-55428-y.

ABSTRACT

The analysis of circulating tumour DNA (ctDNA) through minimally invasive liquid biopsies is promising for early multi-cancer detection and monitoring minimal residual disease. Most existing methods focus on targeted deep sequencing, but few integrate multiple data modalities. Here, we develop a methodology for ctDNA detection using deep (80x) whole-genome TET-Assisted Pyridine Borane Sequencing (TAPS), a less destructive approach than bisulphite sequencing, which permits the simultaneous analysis of genomic and methylomic data. We conduct a diagnostic accuracy study across multiple cancer types in symptomatic patients, achieving 94.9% sensitivity and 88.8% specificity. Matched tumour biopsies are used for validation, not for guiding the analysis, imitating an early detection scenario. Furthermore, in silico validation demonstrates strong discrimination (86% AUC) at ctDNA fractions as low as 0.7%. Additionally, we successfully track tumour burden and ctDNA shedding from precancerous lesions post-treatment without requiring matched tumour biopsies. This pipeline is ready for further clinical evaluation to extend cancer screening and improve patient triage and monitoring.

PMID:39779727 | PMC:PMC11711490 | DOI:10.1038/s41467-024-55428-y

Int J Mol Sci. 2024 Dec 20;25(24):13669. doi: 10.3390/ijms252413669.

ABSTRACT

Liquid biopsy (LB) involves the analysis of circulating tumour-derived DNA (ctDNA), providing a minimally invasive method for gathering both quantitative and qualitative information. Genomic analysis of ctDNA through next-generation sequencing (NGS) enables comprehensive genetic profiling of tumours, including non-driver alterations that offer prognostic insights. LB can be applied in both early-stage disease settings, for the diagnosis and monitoring of minimal residual disease (MRD), and advanced disease settings, for monitoring treatment response and understanding the mechanisms behind disease progression and tumour heterogeneity. Currently, LB has limited use in clinical practice, primarily due to its significant costs, limited diagnostic yield, and uncertain prognostic role. The application of artificial intelligence (AI) in the medical field is a promising approach to processing extensive information and applying it to individual cases to enhance therapeutic decision-making and refine risk assessment.

PMID:39769431 | PMC:PMC11727717 | DOI:10.3390/ijms252413669

Exp Mol Med. 2025 Jan 8. doi: 10.1038/s12276-024-01382-0. Online ahead of print.

ABSTRACT

Most cancer mutation profiling studies are laboratory-based and lack direct clinical application. For clinical use, it is necessary to focus on key genes and integrate them with relevant clinical variables. We aimed to evaluate the prognostic value of the dosage of the KRAS G12 mutation, a key pancreatic ductal adenocarcinoma (PDAC) variant and to investigate the biological mechanism of the prognosis associated with the dosage of the KRAS G12 mutation. In this retrospective cohort study, we analyzed 193 surgically treated patients with PDAC between 2009 and 2016. RNA, whole-exome, and KRAS-targeted sequencing data were used to estimate the dosage of the KRAS G12 mutant. Our prognostic scoring system included the mutation dosage from targeted sequencing ( > 0.195, 1 point), maximal tumor diameter at preoperative imaging ( > 20 mm, 1 point), and carbohydrate antigen 19-9 levels ( > 150 U/mL, 1 point). The KRAS mutation dosage exhibited comparable performance with clinical variables for survival prediction. High KRAS mutation dosages activated the cell cycle, leading to high mutation rates and poor prognosis. According to prognostic scoring systems that integrate mutation dosage with clinical factors, patients with 0 points had superior median overall survival of 97.0 months and 1-year, 3-year, and 5-year overall survival rates of 95.8%, 70.8%, and 66.4%, respectively. In contrast, patients with 3 points had worse median overall survival of only 16.0 months and 1-year, 3-year, and 5-year overall survival rates of 65.2%, 8.7%, and 8.7%, respectively. The incorporation of the KRAS G12 mutation dosage variable into prognostic scoring systems can improve clinical variable-based survival prediction, highlighting the feasibility of an integrated scoring system with clinical significance.

PMID:39779977 | DOI:10.1038/s12276-024-01382-0

Sci Rep. 2025 Jan 2;15(1):129. doi: 10.1038/s41598-024-83742-4.

ABSTRACT

Integration of multi-omic data for the purposes of biomarker discovery can provide novel and robust panels across multiple biological compartments. Appropriate analytical methods are key to ensuring accurate and meaningful outputs in the multi-omic setting. Here, we extensively profile the proteome and transcriptome of patient pancreatic cyst fluid (PCF) (n = 32) and serum (n = 68), before integrating matched omic and biofluid data, to identify biomarkers of pancreatic cancer risk. Differential expression analysis, feature reduction, multi-omic data integration, unsupervised hierarchical clustering, principal component analysis, spearman correlations and leave-one-out cross-validation were performed using RStudio and CombiROC software. An 11-feature multi-omic panel in PCF [PIGR, S100A8, REG1A, LGALS3, TCN1, LCN2, PRSS8, MUC6, SNORA66, miR-216a-5p, miR-216b-5p] generated an AUC = 0.806. A 13-feature multi-omic panel in serum [SHROOM3, IGHV3-72, IGJ, IGHA1, PPBP, APOD, SFN, IGHG1, miR-197-5p, miR-6741-5p, miR-3180, miR-3180-3p, miR-6782-5p] produced an AUC = 0.824. Integration of the strongest performing biomarkers generated a 10-feature cross-biofluid multi-omic panel [S100A8, LGALS3, SNORA66, miR-216b-5p, IGHV3-72, IGJ, IGHA1, PPBP, miR-3180, miR-3180-3p] with an AUC = 0.970. Multi-omic profiling provides an abundance of potential biomarkers. Integration of data from different omic compartments, and across biofluids, produced a biomarker panel that performs with high accuracy, showing promise for the risk stratification of patients with pancreatic cystic lesions.

PMID:39747972 | PMC:PMC11696309 | DOI:10.1038/s41598-024-83742-4

Semin Cancer Biol. 2024 Dec 27;109:25-43. doi: 10.1016/j.semcancer.2024.12.005. Online ahead of print.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) is the most lethal and common form of pancreatic cancer, it has no specific symptoms, and most of the patients are diagnosed when the disease is already at an advanced stage. Chemotherapy typically has only a modest effect, making surgery the most effective treatment option. However, only a small percentage of patients are amenable to surgery. One viable strategy to reduce PDAC death burden associated with the disease is to focus on precursor lesions and identify markers able to predict who will evolve into PDAC. While most PDACs are believed to be preceded by pancreatic intraepithelial neoplasms (PanINs), 5-10 % arise from Intraductal papillary mucinous neoplasms (IPMNs), which are mass-forming cystic lesions that are very common in the general population. IPMNs offer an invaluable model of pancreatic carcinogenesis for researchers to analyse, as well as a target population for PDAC early detection by clinicians. The evolution of IPMN into cancer is a complex and multistep process, therefore the identification of individual markers will not be the solution. In recent years, multiple omics technologies have been instrumental to identify possible biomarkers of IPMN progression and carcinogenesis. The only foreseeable strategy will be to integrate multi-omics data, alongside clinical and morphological features, into a progression score or signature using either standard epidemiologic tools or artificial intelligence. The aim of this manuscript is to review the current knowledge on genetic biomarkers and to briefly mention also additional omics, such as metabolomics, the exposome, the miRNome and epigenomics of IPMNs.

PMID:39733817 | DOI:10.1016/j.semcancer.2024.12.005

Front Immunol. 2024 Dec 6;15:1517312. doi: 10.3389/fimmu.2024.1517312. eCollection 2024.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) is a highly heterogeneous tumor, and the development of accurate predictive models for prognosis and drug sensitivity remains challenging.

METHODS: We integrated laboratory data and public cohorts to conduct a multi-omics analysis of HCC, which included bulk RNA sequencing, proteomic analysis, single-cell RNA sequencing (scRNA-seq), spatial transcriptomics sequencing (ST-seq), and genome sequencing. We constructed a tumor purity (TP) and tumor microenvironment (TME) prognostic risk model. Proteomic analysis validated the TP-TME-related signatures. Joint analysis of scRNA-seq and ST-seq revealed characteristic clusters associated with TP high-risk subtypes, and immunohistochemistry confirmed the expression of key genes. We conducted functional enrichment analysis, transcription factor activity inference, cell-cell interaction, drug efficacy analysis, and mutation information analysis to identify a novel subtype of HCC.

RESULTS: Our analyses constructed a robust HCC prognostic risk prediction model. The patients with TP-TME high-risk subtypes predominantly exhibit hypoxia and activation of the Wnt/beta-catenin, Notch, and TGF-beta signaling pathways. Furthermore, we identified a novel subtype, XPO1+Epithelial. This subtype expresses signatures of the TP risk subtype and aligns with the biological behavior of high-risk patients. Additional analyses revealed that XPO1+Epithelial is influenced primarily by fibroblasts via ligand-receptor interactions, such as FN1-(ITGAV+ITGB1), and constitute a significant component of the TP-TME subtype. Moreover, XPO1+Epithelial interact with monocytes/macrophages, T/NK cells, and endothelial cells through ligand-receptor pairs, including MIF-(CD74+CXCR4), MIF-(CD74+CD44), and VEGFA-VEGFR1R2, respectively, thereby promoting the recruitment of immune-suppressive cells and angiogenesis. The ST-seq cohort treated with Tyrosine Kinase Inhibitors (TKIs) and Programmed Cell Death Protein 1 (PD-1) presented elevated levels of TP and TME risk subtype signature genes, as well as XPO1+Epithelial, T-cell, and endothelial cell infiltration in the treatment response group. Drug sensitivity analyses indicated that TP-TME high-risk subtypes, including sorafenib and pembrolizumab, were associated with sensitivity to multiple drugs. Further exploratory analyses revealed that CTLA4, PDCD1, and the cancer antigens MSLN, MUC1, EPCAM, and PROM1 presented significantly increase expression levels in the high-risk subtype group.

CONCLUSIONS: This study constructed a robust prognostic model for HCC and identified novel subgroups at the single-cell level, potentially assisting in the assessment of prognostic risk for HCC patients and facilitating personalized drug therapy.

PMID:39712016 | PMC:PMC11659151 | DOI:10.3389/fimmu.2024.1517312

MedComm (2020). 2024 Dec 20;6(1):e70020. doi: 10.1002/mco2.70020. eCollection 2025 Jan.

ABSTRACT

Metastatic brain tumors, also called brain metastasis (BM), represent a challenging complication of advanced tumors. Tumors that commonly metastasize to the brain include lung cancer and breast cancer. In recent years, the prognosis for BM patients has improved, and significant advancements have been made in both clinical and preclinical research. This review focuses on BM originating from lung cancer and breast cancer. We briefly overview the history and epidemiology of BM, as well as the current diagnostic and treatment paradigms. Additionally, we summarize multiomics evidence on the mechanisms of tumor occurrence and development in the era of artificial intelligence and discuss the role of the tumor microenvironment. Preclinically, we introduce the establishment of BM models, detailed molecular mechanisms, and cutting-edge treatment methods. BM is primarily treated with a comprehensive approach, including local treatments such as surgery and radiotherapy. For lung cancer, targeted therapy and immunotherapy have shown efficacy, while in breast cancer, monoclonal antibodies, tyrosine kinase inhibitors, and antibody-drug conjugates are effective in BM. Multiomics approaches assist in clinical diagnosis and treatment, revealing the complex mechanisms of BM. Moreover, preclinical agents often need to cross the blood-brain barrier to achieve high intracranial concentrations, including small-molecule inhibitors, nanoparticles, and peptide drugs. Addressing BM is imperative.

PMID:39712454 | PMC:PMC11661909 | DOI:10.1002/mco2.70020