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

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

J Gastroenterol. 2024 Dec 12. doi: 10.1007/s00535-024-02197-6. Online ahead of print.

ABSTRACT

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) remains a formidable health challenge due to its detection at a late stage and a lack of reliable biomarkers for early detection. Although levels of carbohydrate antigen 19-9 are often used in conjunction with imaging-based tests to aid in the diagnosis of PDAC, there is still a need for more sensitive and specific biomarkers for early detection of PDAC.

METHODS: We obtained serum samples from 88 subjects (patients with PDAC (n = 58) and controls (n = 30)). We carried out a multi-omics analysis to measure cytokines and related proteins using proximity extension technology and lipidomics and metabolomics using tandem mass spectrometry. Statistical analysis was carried out to find molecular alterations in patients with PDAC and a machine learning model was used to derive a molecular signature of PDAC.

RESULTS: We quantified 1,462 circulatory proteins along with 873 lipids and 1,001 metabolites. A total of 505 proteins, 186 metabolites and 33 lipids including bone marrow stromal antigen 2 (BST2), keratin 18 (KRT18), and cholesteryl ester(20:5) were found to be significantly altered in patients. We identified different levels of sphingosine, sphinganine, urobilinogen and lactose indicating that glycosphingolipid and galactose metabolisms were significantly altered in patients compared to controls. In addition, elevated levels of diacylglycerols and decreased cholesteryl esters were observed in patients. Using a machine learning model, we identified a signature of 38 biomarkers for PDAC, composed of 21 proteins, 4 lipids, and 13 metabolites.

CONCLUSIONS: Overall, this study identified several proteins, metabolites and lipids involved in various pathways including cholesterol and lipid metabolism to be changing in patients. In addition, we discovered a multi-analyte signature that could be further tested for detection of PDAC.

PMID:39666045 | DOI:10.1007/s00535-024-02197-6

Mol Cancer. 2024 Nov 19;23(1):259. doi: 10.1186/s12943-024-02174-w.

ABSTRACT

BACKGROUND: Biomarkers have revolutionized the management of colorectal cancer (CRC), facilitating early detection, prevention, personalized treatment, and minimal residual disease (MRD) monitoring. This review explores current CRC screening strategies and emerging biomarker applications.

MAIN BODY: We summarize the landscape of non-invasive CRC screening and MRD detection strategies, discuss the limitations of the current approaches, and highlight the promising potential of novel biomarker solutions. The fecal immunochemical test remained the cornerstone of CRC screening, but its sensitivity has been improved by assays that combined its performance with other stool analytes. However, their sensitivity for advanced adenomas and the patient compliance both remain suboptimal. Blood-based tests promise to increase compliance but require further refinement to compete with stool-based biomarker tests. The ideal scenario involves leveraging blood tests to increase screening participation, and simultaneously promote stool- and endoscopy-based screening among those who are compliant. Once solely reliant on upfront surgery followed by stage and pathology-driven adjuvant chemotherapy, the treatment of stage II and III colon cancer has undergone a revolutionary transformation with the advent of MRD testing after surgery. A decade ago, the concept of using a post-surgical test instead of stage and pathology to determine the need for adjuvant chemotherapy was disruptive. Today, a blood test may be more informative of the need for chemotherapy than the stage at diagnosis.

CONCLUSION: Biomarker research is not just improving, but bringing a transformative change to CRC clinical management. Early detection is not just getting better, but improving thanks to a multi-modality approach, and personalized treatment plans are not just becoming a reality, but a promising future with MRD testing.

PMID:39558327 | PMC:PMC11575410 | DOI:10.1186/s12943-024-02174-w

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

J Thorac Oncol. 2024 Sep;19(9):1272-1283. doi: 10.1016/j.jtho.2024.05.006. Epub 2024 May 16.

ABSTRACT

INTRODUCTION: Electronic nose (E-nose) technology has reported excellent sensitivity and specificity in the setting of lung cancer screening. However, the performance of E-nose specifically for early-stage tumors remains unclear. Therefore, the aim of our study was to assess the diagnostic performance of E-nose technology in clinical stage I lung cancer.

METHODS: This phase IIc trial (NCT04734145) included patients diagnosed with a single greater than or equal to 50% solid stage I nodule. Exhalates were prospectively collected from January 2020 to August 2023. Blinded bioengineers analyzed the exhalates, using E-nose technology to determine the probability of malignancy. Patients were stratified into three risk groups (low-risk, [<0.2]; moderate-risk, [≥0.2-0.7]; high-risk, [≥0.7]). The primary outcome was the diagnostic performance of E-nose versus histopathology (accuracy and F1 score). The secondary outcome was the clinical performance of the E-nose versus clinicoradiological prediction models.

RESULTS: Based on the predefined cutoff (<0.20), E-nose agreed with histopathologic results in 86% of cases, achieving an F1 score of 92.5%, based on 86 true positives, two false negatives, and 12 false positives (n = 100). E-nose would refer fewer patients with malignant nodules to observation (low-risk: 2 versus 9 and 11, respectively; p = 0.028 and p = 0.011) than would the Swensen and Brock models and more patients with malignant nodules to treatment without biopsy (high-risk: 27 versus 19 and 6, respectively; p = 0.057 and p < 0.001).

CONCLUSIONS: In the setting of clinical stage I lung cancer, E-nose agrees well with histopathology. Accordingly, E-nose technology can be used in addition to imaging or as part of a "multiomics" platform.

PMID:38762120 | PMC:PMC11380592 | DOI:10.1016/j.jtho.2024.05.006