J Immunother Cancer. 2025 Mar 18;13(3):e011002. doi: 10.1136/jitc-2024-011002.

ABSTRACT

The intrinsic characteristics of metastatic tumors are of great importance in terms of the development of antimetastatic treatment strategies. Elucidation from a spatial immune perspective has the potential to provide a more comprehensive understanding of the mechanisms underlying immune escape, effectively addressing the limitations of relying solely on the analysis of immune cell subpopulation transcriptional profiles. Advances in spatial omics technology enable researchers to precisely analyze precious liver metastasis samples in a high-throughput manner, revealing spatial alterations in immune cell distribution induced by metastasis and exploring the molecular basis of the remodeling process. The aggregation of specific cell subpopulations in distinct regions not only modifies local immune characteristics but also concurrently affects global biological behaviors of liver metastatic tumors. Identifying specific spatial immune characteristics in pretreatment or early-stage treatment tissue samples may achieve accurate clinical predictions. Moreover, developing strategies that target spatial immune remodeling is a promising avenue for future antimetastatic therapy.

PMID:40107672 | PMC:PMC11927485 | DOI:10.1136/jitc-2024-011002

Oncotarget. 2025 Mar 12;16:140-162. doi: 10.18632/oncotarget.28703.

ABSTRACT

The human genome project ushered in a genomic medicine era that was largely unimaginable three decades ago. Discoveries of druggable cancer drivers enabled biomarker-driven gene- and immune-targeted therapy and transformed cancer treatment. Minimizing treatment not expected to benefit, and toxicity-including financial and time-are important goals of modern oncology. The Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine founded by Drs. John Mendelsohn and Thomas Tursz provided a vision for innovation, collaboration and global impact in precision oncology. Through pursuit of transcriptomic signatures, artificial intelligence (AI) algorithms, global precision cancer medicine clinical trials and input from an international Molecular Tumor Board (MTB), WIN has led the way in demonstrating patient benefit from precision-therapeutics through N-of-1 molecularly-driven studies. WIN Next-Generation Precision Oncology (WINGPO) trials are being developed in the neoadjuvant, adjuvant or metastatic settings, incorporate real-world data, digital pathology, and advanced algorithms to guide MTB prioritization of therapy combinations for a diverse global population. WIN has pursued combinations that target multiple drivers/hallmarks of cancer in individual patients. WIN continues to be impactful through collaboration with industry, government, sponsors, funders, academic and community centers, patient advocates, and other stakeholders to tackle challenges including drug access, costs, regulatory barriers, and patient support. WIN's collaborative next generation of precision oncology trials will guide treatment selection for patients with advanced cancers through MTB and AI algorithms based on serial liquid and tissue biopsies and exploratory omics including transcriptomics, proteomics, metabolomics and functional precision medicine. Our vision is to accelerate the future of precision oncology care.

PMID:40073368 | PMC:PMC11907938 | DOI:10.18632/oncotarget.28703

J Clin Endocrinol Metab. 2025 Mar 8:dgaf159. doi: 10.1210/clinem/dgaf159. Online ahead of print.

ABSTRACT

The prevalence of diabetes, particularly type 2 diabetes, has reached epidemic proportions globally. The number of patients with type 1 diabetes (T1D) is also increasing rapidly. Despite advancements in understanding the pathogenesis of diabetes, the lack of circulating pancreatic biomarkers and reliable clinical-grade assays remains a major gap in diabetes research, often hindering the ability to adequately assess disease progression and therapeutic responses. This mini-review discusses emerging pancreatic biomarkers with an emphasis on T1D, the limitations of current immunoassays, and the expanding role of mass spectrometry-based assays. Highlights include the recent work within the NIDDK-funded "Targeted Mass Spectrometry Assays for Diabetes and Obesity Research (TaMADOR)" consortium, which aims to develop robust, quantitative, and transferable assays for translational research. The review also emphasizes the importance of proteoform-specific assays for monitoring pancreatic function, including prohormone processing during disease progression or in responses to therapy.

PMID:40056450 | DOI:10.1210/clinem/dgaf159

bioRxiv [Preprint]. 2025 Feb 17:2025.02.13.637721. doi: 10.1101/2025.02.13.637721.

ABSTRACT

Cell type-specific regulatory programs that drive type 1 diabetes (T1D) in the pancreas are poorly understood. Here we performed single nucleus multiomics and spatial transcriptomics in up to 32 non-diabetic (ND), autoantibody-positive (AAB+), and T1D pancreas donors. Genomic profiles from 853,005 cells mapped to 12 pancreatic cell types, including multiple exocrine sub-types. Beta, acinar, and other cell types, and related cellular niches, had altered abundance and gene activity in T1D progression, including distinct pathways altered in AAB+ compared to T1D. We identified epigenomic drivers of gene activity in T1D and AAB+ which, combined with genetic association, revealed causal pathways of T1D risk including antigen presentation in beta cells. Finally, single cell and spatial profiles together revealed widespread changes in cell-cell signaling in T1D including signals affecting beta cell regulation. Overall, these results revealed drivers of T1D progression in the pancreas, which form the basis for therapeutic targets for disease prevention.

PMID:40027657 | PMC:PMC11870426 | DOI:10.1101/2025.02.13.637721

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