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

Oncol Res Treat. 2025 Feb 25:1-11. doi: 10.1159/000544838. Online ahead of print.

ABSTRACT

BACKGROUND: Current breast cancer (BC) surveillance is limited to the detection of local, locoregional or contralateral recurrence. This is based on two outdated studies from the 1990s and ignores current evidence on liquid biopsies, particularly circulating tumor DNA (ctDNA).

SUMMARY: ctDNA has been shown to be a reliable prognostic biomarker in early BC surveillance. It can be detected using a tumor-informed or a tumor-agnostic approach. However, conclusive evidence for a survival benefit from ctDNA-guided follow-up, as needed for a paradigm shift in BC surveillance, is still lacking. According to current studies, the lead time, i.e. the time from biomarker detection to clinically overt relapse, can be up to several months. This stage of MRD (minimal or molecular residual disease) offers a new therapeutic window, and, currently, several studies are evaluating the efficacy of treatments initiated within this therapeutic window, based on a positive biomarker finding. Liquid biopsy might also open up the possibility of de-escalating therapy in patients with a negative biomarker result.

PMID:39999817 | DOI:10.1159/000544838

Cancer Cell. 2025 Feb 14:S1535-6108(25)00030-3. doi: 10.1016/j.ccell.2025.01.012. Online ahead of print.

ABSTRACT

Recent advances have shed light on the molecular heterogeneity of small cell lung cancer (SCLC), yet the spatial organizations and cellular interactions in tumor immune microenvironment remain to be elucidated. Here, we employ co-detection by indexing (CODEX) and multi-omics profiling to delineate the spatial landscape for 165 SCLC patients, generating 267 high-dimensional images encompassing over 9.3 million cells. Integrating CODEX and genomic data reveals a multi-positive tumor cell neighborhood within ASCL1⁺ (SCLC-A) subtype, characterized by high SLFN11 expression and associated with poor prognosis. We further develop a cell colony detection algorithm (ColonyMap) and reveal a spatially assembled immune niche consisting of antitumoral macrophages, CD8⁺ T cells and natural killer T cells (MT²) which highly correlates with superior survival and predicts improving immunotherapy response in an independent cohort. This study serves as a valuable resource to study SCLC spatial heterogeneity and offers insights into potential patient stratification and personalized treatments.

PMID:39983726 | DOI:10.1016/j.ccell.2025.01.012

Discov Oncol. 2025 Feb 25;16(1):238. doi: 10.1007/s12672-025-01981-x.

ABSTRACT

The fight against lung adenocarcinoma (LUAD) is challenged by tumor microenvironment (TME)-mediated drug resistance, which limits effective treatment. This study examines the LUAD TME and identifies four distinct subtypes through multi-omics profiling: immune-rich, immune-exhausted, stromal-dominant, and TME-desert. Each subtype has unique molecular features, tumor diversity, and links to clinical outcomes. Immune-rich subtypes respond better to immune checkpoint inhibitors, while stromal-dominant and TME-desert subtypes show resistance to treatment and poor prognosis. Molecular analysis uncovers subtype-specific mutations, chromosomal instability, and altered signaling pathways, pointing to potential therapeutic targets. In silico drug screening identifies promising treatments for resistant subtypes. These findings, validated in independent cohorts, highlight the critical role of the TME in drug resistance and treatment response, providing insights for personalized treatment strategies in LUAD.

PMID:40000527 | PMC:PMC11861463 | DOI:10.1007/s12672-025-01981-x

NPJ Precis Oncol. 2025 Feb 24;9(1):50. doi: 10.1038/s41698-025-00834-8.

ABSTRACT

Disulfidptosis, a newly discovered cell death mode distinct from other programmed cell death in lung and kidney cancer cells, is defined as extensive disulfide bonds to actin cytoskeleton proteins, leading to actin contraction and cytoskeletal disruption cell death. New cell death pattern discoveries often drive advances in tumor research. Therefore, the present study attempted to decipher the manifestation and importance of disulfidptosis in pan-cancer. Combining Clinical specimen immunofluorescence staining, single-cell analyses, and spatial transcriptome analyses, we demonstrated the manifestation of disulfidptosis in pan-cancer. Multi-omics analysis has revealed that genomic variants and DNA methylation in DRGs can affect the prognosis of patients with pan-cancer. The nomogram based on the DRGs Score model could accurately predict the prognosis of patients with pan-cancer. PF-562271, EHT-1864, and IPA-3 are potential therapeutic agents targeting disulfidptosis. Collectively, this study deciphered for the first time the importance of disulfidptosis for pan-cancer and developed the DRGs Score model that can assist clinicians in accurately predicting the prognosis and guiding individualized treatment of pan-cancer patients.

PMID:39994355 | DOI:10.1038/s41698-025-00834-8

Front Immunol. 2025 Feb 7;16:1479550. doi: 10.3389/fimmu.2025.1479550. eCollection 2025.

ABSTRACT

BACKGROUND: To construct a prediction model consisting of metabolites and proteins in peripheral blood plasma to predict whether patients with unresectable stage III and IV non-small cell lung cancer can benefit from immunotherapy before it is administered.

METHODS: Peripheral blood plasma was collected from unresectable stage III and IV non-small cell lung cancer patients who were negative for driver mutations before receiving immunotherapy. Then we classified samples according to the follow-up results after two courses of immunotherapy and non-targeted metabolomics and proteomics analyses were performed to select different metabolites and proteins. Finally, potential biomarkers were picked out by applying machine learning methods including random forest and stepwise regression and prediction models were constructed by logistic regression.

RESULTS: The presence of metabolites and proteins in peripheral blood plasma was causally associated with both non-small cell lung cancer and PD-L1/PD-1 expression levels. A total of 2 differential metabolites including 5-sulfooxymethylfurfural and Anthranilic acid and 2 differential proteins including Immunoglobulin heavy variable 1-45 and Microfibril-associated glycoprotein 4 were selected as reliable biomarkers. The area under the curve (AUC) of the prediction model built on clinical risks was merely 0.659. The AUC of metabolomics prediction model was 0.977 and the AUC of proteomics was 0.875 while the AUC of the integrative-omics prediction model was 0.955.

CONCLUSIONS: Metabolic and protein biomarkers in peripheral blood both have high efficacy and reliability in the prediction of immunotherapy sensitivity in unresectable stage III and IV non-small cell lung cancer, but validation in larger population-based cohorts is still needed.

PMID:39991162 | PMC:PMC11842339 | DOI:10.3389/fimmu.2025.1479550

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

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

Nat Comput Sci. 2025 Feb 7. doi: 10.1038/s43588-024-00764-8. Online ahead of print.

ABSTRACT

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally scalable analytical pipeline for functionally informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits in 61,838 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered and replicated new associations with lipid traits missed by single-trait analysis.

PMID:39920506 | DOI:10.1038/s43588-024-00764-8

Front Microbiol. 2025 Jan 22;15:1516667. doi: 10.3389/fmicb.2024.1516667. eCollection 2024.

ABSTRACT

Microbiome research, the study of microbial communities in diverse environments, has seen significant advances due to the integration of deep learning (DL) methods. These computational techniques have become essential for addressing the inherent complexity and high-dimensionality of microbiome data, which consist of different types of omics datasets. Deep learning algorithms have shown remarkable capabilities in pattern recognition, feature extraction, and predictive modeling, enabling researchers to uncover hidden relationships within microbial ecosystems. By automating the detection of functional genes, microbial interactions, and host-microbiome dynamics, DL methods offer unprecedented precision in understanding microbiome composition and its impact on health, disease, and the environment. However, despite their potential, deep learning approaches face significant challenges in microbiome research. Additionally, the biological variability in microbiome datasets requires tailored approaches to ensure robust and generalizable outcomes. As microbiome research continues to generate vast and complex datasets, addressing these challenges will be crucial for advancing microbiological insights and translating them into practical applications with DL. This review provides an overview of different deep learning models in microbiome research, discussing their strengths, practical uses, and implications for future studies. We examine how these models are being applied to solve key problems and highlight potential pathways to overcome current limitations, emphasizing the transformative impact DL could have on the field moving forward.

PMID:39911715 | PMC:PMC11794229 | DOI:10.3389/fmicb.2024.1516667

Curr Genomics. 2025;26(1):48-80. doi: 10.2174/0113892029308243240709073945. Epub 2024 Jul 18.

ABSTRACT

BACKGROUND: The damage in the liver and hepatocytes is where the primary liver cancer begins, and this is referred to as Hepatocellular Carcinoma (HCC). One of the best methods for detecting changes in gene expression of hepatocellular carcinoma is through bioinformatics approaches.

OBJECTIVE: This study aimed to identify potential drug target(s) hubs mediating HCC progression using computational approaches through gene expression and protein-protein interaction datasets.

METHODOLOGY: Four datasets related to HCC were acquired from the GEO database, and Differentially Expressed Genes (DEGs) were identified. Using Evenn, the common genes were chosen. Using the Fun Rich tool, functional associations among the genes were identified. Further, protein-protein interaction networks were predicted using STRING, and hub genes were identified using Cytoscape. The selected hub genes were subjected to GEPIA and Shiny GO analysis for survival analysis and functional enrichment studies for the identified hub genes. The up-regulating genes were further studied for immunohistopathological studies using HPA to identify gene/protein expression in normal vs HCC conditions. Drug Bank and Drug Gene Interaction Database were employed to find the reported drug status and targets. Finally, STITCH was performed to identify the functional association between the drugs and the identified hub genes.

RESULTS: The GEO2R analysis for the considered datasets identified 735 upregulating and 284 downregulating DEGs. Functional gene associations were identified through the Fun Rich tool. Further, PPIN network analysis was performed using STRING. A comparative study was carried out between the experimental evidence and the other seven data evidence in STRING, revealing that most proteins in the network were involved in protein-protein interactions. Further, through Cytoscape plugins, the ranking of the genes was analyzed, and densely connected regions were identified, resulting in the selection of the top 20 hub genes involved in HCC pathogenesis. The identified hub genes were: KIF2C, CDK1, TPX2, CEP55, MELK, TTK, BUB1, NCAPG, ASPM, KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A, NUSAP1, DLGAP5, PBK, and CCNB2. Further, GEPIA and Shiny GO analyses provided insights into survival ratios and functional enrichment studied for the hub genes. The HPA database studies further found that upregulating genes were involved in changes in protein expression in Normal vs HCC tissues. These findings indicated that hub genes were certainly involved in the progression of HCC. STITCH database studies uncovered that existing drug molecules, including sorafenib, regorafenib, cabozantinib, and lenvatinib, could be used as leads to identify novel drugs, and identified hub genes could also be considered as potential and promising drug targets as they are involved in the gene-chemical interaction networks.

CONCLUSION: The present study involved various integrated bioinformatics approaches, analyzing gene expression and protein-protein interaction datasets, resulting in the identification of 20 top-ranked hubs involved in the progression of HCC. They are KIF2C, CDK1, TPX2, CEP55, MELK, TTK, BUB1, NCAPG, ASPM, KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A, NUSAP1, DLGAP5, PBK, and CCNB2. Gene-chemical interaction network studies uncovered that existing drug molecules, including sorafenib, regorafenib, cabozantinib, and lenvatinib, can be used as leads to identify novel drugs, and the identified hub genes can be promising drug targets. The current study underscores the significance of targeting these hub genes and utilizing existing molecules to generate new molecules to combat liver cancer effectively and can be further explored in terms of drug discovery research to develop treatments for HCC.

PMID:39911278 | PMC:PMC11793067 | DOI:10.2174/0113892029308243240709073945