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Obesity influences the biological response to injury: a multi-omics analysis

Eur J Trauma Emerg Surg. 2025 Jun 27;51(1):238. doi: 10.1007/s00068-025-02922-7.

ABSTRACT

PURPOSE: Obesity is a prevalent disease, but its influence on post-injury biology remains unclear. In this study, we aimed to characterize the independent effect of obesity on the proteomic and metabolomic signatures of trauma.

METHODS: Plasma was obtained on arrival from injured patients at a Level 1 Trauma Center and analyzed with modern mass spectrometry-based proteomics and metabolomics. Samples obtained after start of transfusion were excluded. Patients were stratified by "obesity" (body mass index [BMI]≥30 kg/m2) vs. "no obesity" (BMI < 30 kg/m2). In sub-group analyses, patients were sub-stratified by Low Injury/Low Shock (ISS < 15, base excess [BE]≥-6mEq/L) and High Injury/High Shock (ISS≥15, BE<-6). Multiple regression was used to adjust the omics data for significant covariates prior to performing ome-wide analyses.

RESULTS: There were 183 patients included (48 [26%] with obesity and 135 [74%] without). After covariate-adjustment, multiple proteins and metabolites were correlated with ISS and/or BE and were significantly different from Low Injury/Low Shock to High Injury/High Shock only in patients with obesity. This obesity-specific omics response to injury was characterized by increased inflammation, hypercoagulability, altered nitrogen metabolism, and mitochondrial dysfunction. Patients with obesity also exhibited excessive injury-provoked tissue destruction and organ damage compared to patients without obesity. In injury severity-adjusted analyses, the obesity signature consistently displayed markers of hemolysis, likely reflecting a pre-injury hemolytic propensity.

CONCLUSION: Obesity is independently associated with altered post-injury biology, which likely underlies unique pathology in trauma patients with obesity. Identifying this aberrant response to injury is the first step in developing personalized therapies for this patient population.

PMID:40576654 | DOI:10.1007/s00068-025-02922-7

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Biomarkers in adjuvant and neoadjuvant treatment of melanoma

Dermatologie (Heidelb). 2025 Jun;76(6):361-364. doi: 10.1007/s00105-025-05506-z. Epub 2025 May 7.

ABSTRACT

BACKGROUND: Personalized treatment of melanoma is becoming increasingly more important. Biomarkers offer the possibility of controlling treatment more precisely and reducing side effects.

OBJECTIVE: The aim of this text is to provide an overview of current tissue-based, blood-based and radiological biomarkers and their clinical application in melanomas.

MATERIAL AND METHODS: A literature research and analysis of current studies on biomarkers in adjuvant and neoadjuvant treatment of melanomas were carried out and relevant congress contributions were additionally included.

RESULTS: Tissue-based programmed cell death 1 ligand 1 (PD-L1) expression, interferon gamma (IFNγ) signature, gene expression profiles (GEP) and tumor mutational burden (TMB) are of prognostic and predictive relevance. Blood-based circulating tumor DNA (ctDNA) in the sense of a liquid biopsy should be emphasized as a personalized biomarker for longitudinal tracking during treatment or aftercare. Positron emission tomography computed tomography (PET-CT) and body composition enable an improved assessment of treatment efficiency. There are currently no data from prospective validation studies on these biomarkers; initial data from the NivoMela study are awaited.

CONCLUSION: The combination of tissue-based, blood-based and radiological biomarkers in terms of multiparametric approaches is promising but further prospective validation is needed for broad clinical use. These are currently not comprehensively implemented in the clinical routine in centers or in remuneration procedures.

PMID:40335648 | DOI:10.1007/s00105-025-05506-z

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Cross-sectional and longitudinal association of seven DNAm-based predictors with metabolic syndrome and type 2 diabetes

To date, various epigenetic clocks have been constructed to estimate biological age, most commonly using DNA methylation (DNAm). These include “first-generation” clocks such as DNAmAgeHorvath and “second-gener...
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Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine: Bringing next-generation precision oncology to patients

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

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Train clinical AI to reason like a team of doctors

Nature, Published online: 04 March 2025; doi:10.1038/d41586-025-00618-x

As the European Union’s Artificial Intelligence Act takes effect, AI systems that mimic how human teams collaborate can improve trust in high-risk situations, such as clinical medicine.
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Protocol for the creation and utilization of 3D pancreatic cancer models from circulating tumor cells

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

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Presentation: Modernizing DevOps with AI, Boosting Productivity, and Redefining Developer Experience

The panelists discuss how generative AI is boosting productivity, redefining the developer experience, and affecting software development in 2025.

By Christian Bonzelet, Jessica Andersson, Garima Bajpai, Shobhit Verma, Renato Losio
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A commonly inherited human PCSK9 germline variant drives breast cancer metastasis via LRP1 receptor

Characterization of a common germline variant in PCSK9 unveils a hereditary basis underlying breast cancer metastasis and suggests that PCSK9 inhibition therapy could be a promising strategy for breast cancer metastasis prevention.
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Heritable polygenic editing: the next frontier in genomic medicine?

Nature, Published online: 08 January 2025; doi:10.1038/s41586-024-08300-4

We discuss the potential consequences and ethical concerns of polygenic genome editing of human embryos to alter specific variants associated with polygenic diseases, highlighting the possibility of reducing disease susceptibility while exacerbating health inequalities.
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Complex rearrangements fuel ER<sup>+</sup> and HER2<sup>+</sup> breast tumours

Nature, Published online: 08 January 2025; doi:10.1038/s41586-024-08377-x

A study identifies three dominant genomic archetypes of breast cancer induced by discrete mutational processes, describing a continuum of genomic profiles and detailing the mechanisms underlying the progression of breast cancer.
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High-resolution genomic history of early medieval Europe

Nature, Published online: 01 January 2025; doi:10.1038/s41586-024-08275-2

Twigstats, a method for ancestry reconstruction, provides high-resolution genetic histories and movement patterns of people living in Europe during the first millennium ce.
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How to build the virtual cell with artificial intelligence: Priorities and opportunities

Advances in AI and omics enable the creation of AI virtual cells (AIVCs)—multi-scale, multimodal neural network models that simulate molecules, cells, and tissues across diverse states. This vision outlines their design and collaborative development, promising to transform biological research through high-fidelity simulations, accelerating discoveries, and fostering interdisciplinary open science collaborations.
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Evolutionary trajectories of small cell lung cancer under therapy

Nature, Published online: 13 March 2024; doi:10.1038/s41586-024-07177-7

We uncover key processes of the genomic evolution of small cell lung cancer under therapy, identify the common ancestor as the source of clonal diversity at relapse and show central genomic patterns associated with drug response.
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