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

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

Bioinformatics. 2024 Nov 1;40(11):btae623. doi: 10.1093/bioinformatics/btae623.

ABSTRACT

MOTIVATION: Enzymatic reaction play a pivotal role in regulating cellular processes with a high degree of specificity to biological functions. When enzymatic reactions are disrupted by gene, protein, or metabolite dysfunctions in diseases, it becomes crucial to visualize the resulting perturbed enzymatic reaction-induced multi-omics network. Multi-omics network visualization aids in gaining a comprehensive understanding of the functionality and regulatory mechanisms within biological systems.

RESULTS: In this study, we designed PhenoMultiOmics, an enzymatic reaction-based multi-omics web server designed to explore the scope of the multi-omics network across various cancer types. We first curated the PhenoMultiOmics database, which enables the retrieval of cancer-gene-protein-metabolite relationships based on the enzymatic reactions. We then developed the MultiOmics network visualization module to depict the interplay between genes, proteins, and metabolites in response to specific cancer-related enzymatic reactions. The biomarker discovery module facilitates functional analysis through differential omic feature expression and pathway enrichment analysis. PhenoMultiOmics has been applied to analyze the transcriptomics data of gastric cancer and the metabolomics data of lung cancer, providing mechanistic insights into interrupted enzymatic reactions and the associated multi-omics network.

AVAILABILITY AND IMPLEMENTATION: PhenoMultiOmics is freely accessed at https://phenomultiomics.shinyapps.io/cancer/ with a user-friendly and interactive web interface.

PMID:39418180 | PMC:PMC11549024 | DOI:10.1093/bioinformatics/btae623

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

Eur J Radiol. 2024 Dec;181:111796. doi: 10.1016/j.ejrad.2024.111796. Epub 2024 Oct 21.

ABSTRACT

Liquid biopsy with sequencing of circulating tumor DNA (ctDNA) is a minimally invasive method for sampling body fluids and offers a promising alternative to tissue biopsies that involve greater risks, costs, and time. ctDNA not only identifies actionable targets by revealing unique molecular signatures in cancer, but also may assess treatment response, treatment resistance and progression, and recurrence. Imaging correlates of these applications are already being identified and utilized for various solid tumors. Radiologists have new challenges in interpreting oncologic imaging. Given their integral role in cancer surveillance, they must become familiar with the importance of ctDNA in detecting recurrence and minimal residual disease, measuring treatment response, predicting survival and metastatic patterns, and identifying new molecular therapeutic targets. In this review, we provide an overview of ctDNA testing, and a snapshot of current clinical guidelines from the National Comprehensive Cancer Network and the European Society of Molecular Oncology on the use of ctDNA in lung, breast, colorectal, pancreatic, and hepatobiliary cancers. For each cancer type, we also highlight current research applications of ctDNA that are relevant to the field of diagnostic radiology.

PMID:39461058 | DOI:10.1016/j.ejrad.2024.111796

Cancer Med. 2024 Oct;13(20):e70346. doi: 10.1002/cam4.70346.

ABSTRACT

BACKGROUND: For early detection and postoperative monitoring of upper tract urothelial carcinoma (UTUC), the traditional detection method was limited to its invasiveness and insufficient sensitivity. We aim to use urine tumour DNA (utDNA) for detecting minimal residual disease (MRD), early diagnosis and perioperative monitoring in UTUC.

METHOD: We previously established a utDNA multidimensional bioinformatic valuation model, named utLIFE, using low-coverage whole-genome sequencing and targeted deep sequencing. This prospective cohort enrolled 93 patients diagnosed with UTUC without metastasis. We collected morning urine samples on the day of surgery and the discharge day after the operation for utLIFE testing. In addition, we also enrolled 80 healthy controls to further validate the specificity of the utLIFE model in the study.

RESULTS: The utLIFE of preoperative samples could discriminate UTUC with high specificity (96.25%, 77/80), and high sensitivity (96.77%, 90/93) regardless of stage and grade. The sensitivity of utLIFE was significantly higher than urine cytology (p < 0.001) and fluorescence in situ hybridisation (FISH) (p < 0.001) (N = 19), especially in early-stage and low-grade UTUC. Postoperative utLIFE scores were significantly decreased compared with those of preoperative samples (79 vs. 36, p < 0.001), indicating its association with tumour burden. For special pathology types, utLIFE performed less well in sensitivity and perioperative alteration.

CONCLUSION: In conclusion, we established a bioinformatic utDNA valuation model, utLIFE, which was validated to be a rapid and noninvasive approach with high sensitivity for early detection and MRD monitoring for UTUC.

PMID:39440792 | PMC:PMC11497171 | DOI:10.1002/cam4.70346