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 This is based on two outdated studies from the 1990s and ignores current evidence on liquid biopsies, particularly circulating tumor DNA (ctDNA).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