Int Rev Cell Mol Biol. 2025;391:43-93. doi: 10.1016/bs.ircmb.2024.08.006. Epub 2024 Sep 7.

ABSTRACT

The sampling of circulating biomarkers provides an opportunity for non-invasive evaluation and monitoring of cancer activity. In modern day practice, this has typically been in the form of circulating tumor DNA (ctDNA) detected in plasma. The field of ctDNA has been a burgeoning technology, with prominent applications for blood-based cancer screening and in disease status assessment, especially after curative-intent surgery to evaluate for minimal residual disease (MRD). Clinical applications for the latter show an incredibly high sensitivity in certain cancer types with a need for additional studies to determine how much clinical decision-making should be adapted based on ctDNA results and which cancer types, stages, and treatments are best informed by ctDNA results. This chapter provides an overview of ctDNA detection as tool for cancer screening, detecting MRD, and/or molecularly characterizing a cancer, highlighting the rapidly amassing research as a prognostic biomarker and emerging data on ctDNA as a predictive biomarker.

PMID:39939078 | DOI:10.1016/bs.ircmb.2024.08.006

STAR Protoc. 2025 Feb 11;6(1):103635. doi: 10.1016/j.xpro.2025.103635. Online ahead of print.

ABSTRACT

We introduce a protocol for generating 3D organoids from circulating tumor cells (CTCs), enabling longitudinal functional and molecular analyses in pancreatic cancer patients, including those with unresectable disease, which constitutes the majority of cases. We outline the process for isolating and characterizing CTCs from the blood of pancreatic cancer patients and provide detailed instructions for initiating, passaging, and phenotyping CTC-derived organoids. Additionally, we describe techniques for utilizing these organoids in drug screening with a focus on stemness-related pathways. For complete details on the use and execution of this protocol, please refer to Tang et al.¹.

PMID:39946239 | PMC:PMC11870243 | DOI:10.1016/j.xpro.2025.103635

J Cancer. 2025 Jan 13;16(4):1243-1263. doi: 10.7150/jca.104424. eCollection 2025.

ABSTRACT

Background: This study aimed to investigate glycogen metabolism in gastric cancer (GC) and develop a glycogen-based riskScore model for predicting GC prognosis. Methods: Patients' expression profiles for 33 tumor types were retrieved from TCGA. Four GC bulk and one single-cell sequencing datasets were obtained from GEO database. This study also enrolled a bladder urothelial carcinoma immunotherapeutic IMvigor210 cohort. The ssGSEA method was conducted to assess glycogen biosynthesis and degradation level. Consensus clustering analysis was conducted to identify different clusters. A glycogen riskScore signature was developed to evaluate prognostic value across different cohorts. Besides, in vitro experiments were conducted to further evaluate the role of glycogen metabolism related genes in GC. Results: Both glycogen biosynthesis and degradation were significantly associated with worse overall survival and were also related with malignant phenotype in GC at both bulk and single-cell levels. Differential outcomes and immune functions were verified in the three identified clusters. The constructed glycogen riskScore model accurately classified GC patients with different outcomes, genomic and immune landscape, and performed well in predicting prognosis through external validation, immunotherapy and pan-cancer cohorts. Furthermore, the riskScore could predict response to chemotherapy and immunotherapy. Functional analyses revealed the signature's connection to pro-tumor and immunosuppression related pathways across pan-cancer. Additionally, glycogen metabolism related genes were found to regulate the malignant phenotypes of GC cells. Conclusion: This study revealed important roles of glycogen metabolism in promoting progression of GC and presented a glycogen riskScore model as a novel tool for predicting prognosis and treatment response.

PMID:39895799 | PMC:PMC11786036 | DOI:10.7150/jca.104424

iScience. 2024 Dec 22;28(2):111662. doi: 10.1016/j.isci.2024.111662. eCollection 2025 Feb 21.

ABSTRACT

KRAS mutations are linked to some of the deadliest forms of cancer. Pharmacological studies suggest that co-targeting KRAS with feedback/bypass pathways could lead to enhanced anti-tumor activity. The underlying premise is that cancers display a deep-rooted hypersensitivity to KRAS inactivation. Here, we investigate the role of intratumor heterogeneity in pancreatic ductal adenocarcinoma, focusing on oncogenic KRAS addiction and treatment resistance. Integrated analysis of single-cell and bulk RNA sequencing data reveals that most tumors display a mixture of cells with vastly different degrees of KRAS dependency. We identify distinct cell populations that vary in their gene expression patterns pertaining to the predicted level of KRAS signaling activity, cell growth, and differentiation commitment within each tumor. Selective targeting of mutant KRAS suppresses the growth of tumor cells with high RAS/mitogen-activated protein kinase (MAPK) activity while sparing pre-existing subsets with low RAS signaling activity, necessitating alternative treatments. Combination immunotherapy leads to durable tumor regression in preclinical models.

PMID:39898020 | PMC:PMC11787500 | DOI:10.1016/j.isci.2024.111662

Clin Transl Med. 2025 Feb;15(2):e70225. doi: 10.1002/ctm2.70225.

ABSTRACT

BACKGROUND: Lung cancer is a leading cause of cancer mortality, highlighting the need for innovative non-invasive early detection methods. Although cell-free DNA (cfDNA) analysis shows promise, its sensitivity in early-stage lung cancer patients remains a challenge. This study aimed to integrate insights from epigenetic modifications and fragmentomic features of cfDNA using machine learning to develop a more accurate lung cancer detection model.

METHODS: To address this issue, a multi-centre prospective cohort study was conducted, with participants harbouring suspicious malignant lung nodules and healthy volunteers recruited from two clinical centres. Plasma cfDNA was analysed for its epigenetic and fragmentomic profiles using chromatin immunoprecipitation sequencing, reduced representation bisulphite sequencing and low-pass whole-genome sequencing. Machine learning algorithms were then employed to integrate the multi-omics data, aiding in the development of a precise lung cancer detection model.

RESULTS: Cancer-related changes in cfDNA fragmentomics were significantly enriched in specific genes marked by cell-free epigenomes. A total of 609 genes were identified, and the corresponding cfDNA fragmentomic features were utilised to construct the ensemble model. This model achieved a sensitivity of 90.4% and a specificity of 83.1%, with an AUC of 0.94 in the independent validation set. Notably, the model demonstrated exceptional sensitivity for stage I lung cancer cases, achieving 95.1%. It also showed remarkable performance in detecting minimally invasive adenocarcinoma, with a sensitivity of 96.2%, highlighting its potential for early detection in clinical settings.

CONCLUSIONS: With feature selection guided by multiple epigenetic sequencing approaches, the cfDNA fragmentomics-based machine learning model demonstrated outstanding performance in the independent validation cohort. These findings highlight its potential as an effective non-invasive strategy for the early detection of lung cancer.

KEYPOINTS: Our study elucidated the regulatory relationships between epigenetic modifications and their effects on fragmentomic features. Identifying epigenetically regulated genes provided a critical foundation for developing the cfDNA fragmentomics-based machine learning model. The model demonstrated exceptional clinical performance, highlighting its substantial potential for translational application in clinical practice.

PMID:39909829 | PMC:PMC11798665 | DOI:10.1002/ctm2.70225