arXiv:2602.15019v1 Announce Type: new
Abstract: Bio-pharmaceutical innovation has shifted: many new drug assets now originate outside the United States and are disclosed primarily via regional, non-English channels. Recent data suggests >85% of patent filings originate outside the U.S., with China accounting for nearly half of the global total; a growing share of scholarly output is also non-U.S. Industry estimates put China at ~30% of global drug development, spanning 1,200+ novel candidates. In this high-stakes environment, failing to surface "under-the-radar" assets creates multi-billion-dollar risk for investors and business development teams, making asset scouting a coverage-critical competition where speed and completeness drive value. Yet today's Deep Research AI agents still lag human experts in achieving high-recall discovery across heterogeneous, multilingual sources without hallucinations.
We propose a benchmarking methodology for drug asset scouting and a tuned, tree-based self-learning Bioptic Agent aimed at complete, non-hallucinated scouting. We construct a challenging completeness benchmark using a multilingual multi-agent pipeline: complex user queries paired with ground-truth assets that are largely outside U.S.-centric radar. To reflect real deal complexity, we collected screening queries from expert investors, BD, and VC professionals and used them as priors to conditionally generate benchmark queries. For grading, we use LLM-as-judge evaluation calibrated to expert opinions. We compare Bioptic Agent against Claude Opus 4.6, OpenAI GPT-5.2 Pro, Perplexity Deep Research, Gemini 3 Pro + Deep Research, and Exa Websets. Bioptic Agent achieves 79.7% F1 versus 56.2% (Claude Opus 4.6), 50.6% (Gemini 3 Pro + Deep Research), 46.6% (GPT-5.2 Pro), 44.2% (Perplexity Deep Research), and 26.9% (Exa Websets). Performance improves steeply with additional compute, supporting the view that more compute yields better results.
arXiv:2602.13332v1 Announce Type: cross
Abstract: Long-form clinical videos are central to visual evidence-based decision-making, with growing importance for applications such as surgical robotics and related settings. However, current multimodal large language models typically process videos with passive sampling or weakly grounded inspection, which limits their ability to iteratively locate, verify, and justify predictions with temporally targeted evidence. To close this gap, we propose MedScope, a tool-using clinical video reasoning model that performs coarse-to-fine evidence seeking over long-form procedures. By interleaving intermediate reasoning with targeted tool calls and verification on retrieved observations, MedScope produces more accurate and trustworthy predictions that are explicitly grounded in temporally localized visual evidence. To address the lack of high-fidelity supervision, we build ClinVideoSuite, an evidence-centric, fine-grained clinical video suite. We then optimize MedScope with Grounding-Aware Group Relative Policy Optimization (GA-GRPO), which directly reinforces tool use with grounding-aligned rewards and evidence-weighted advantages. On full and fine-grained video understanding benchmarks, MedScope achieves state-of-the-art performance in both in-domain and out-of-domain evaluations. Our approach illuminates a path toward medical AI agents that can genuinely "think with videos" through tool-integrated reasoning. We will release our code, models, and data.
An operational AI forecasting model developed by Hertfordshire University researchers aims to improve resource efficiency within healthcare.
Public sector organisations often hold large archives of historical data that do not inform forward-looking decisions. A partnership between the University of Hertfordshire and regional NHS health bodies addresses this issue by applying machine learning to operational planning. The project analyses healthcare demand to assist managers with decisions regarding staffing, patient care, and resources.
Most AI initiatives in healthcare focus on individual diagnostics or patient-level interventions. The project team notes that this tool targets system-wide operational management instead. This distinction matters for leaders evaluating where to deploy automated analysis within their own infrastructure.
The model uses five years of historical data to build its projections. It integrates metrics such as admissions, treatments, re-admissions, bed capacity, and infrastructure pressures. The system also accounts for workforce availability and local demographic factors including age, gender, ethnicity, and deprivation.
Iosif Mporas, Professor of Signal Processing and Machine Learning at the University of Hertfordshire, leads the project. The team includes two full-time postdoctoral researchers and will continue development through 2026.
“By working together with the NHS, we are creating tools that can forecast what will happen if no action is taken and quantify the impact of a changing regional demographic on NHS resources,” said Professor Mporas.
Using AI for forecasting in healthcare operations
The model produces forecasts showing how healthcare demand is likely to change. It models the impact of these changes in the short-, medium-, and long-term. This capability allows leadership to move beyond reactive management.
Charlotte Mullins, Strategic Programme Manager for NHS Herts and West Essex, commented: “The strategic modelling of demand can affect everything from patient outcomes including the increased number of patients living with chronic conditions.
“Used properly, this tool could enable NHS leaders to take more proactive decisions and enable delivery of the 10-year plan articulated within the Central East Integrated Care Board as our strategy document.”
The University of Hertfordshire Integrated Care System partnership funds the work, which began last year. Testing of the AI model tailored for healthcare operations is currently underway in hospital settings. The project roadmap includes extending the model to community services and care homes.
This expansion aligns with structural changes in the region. The Hertfordshire and West Essex Integrated Care Board serves 1.6 million residents and is preparing to merge with two neighbouring boards. This merger will create the Central East Integrated Care Board. The next phase of development will incorporate data from this wider population to improve the predictive accuracy of the model.
The initiative demonstrates how legacy data can drive cost efficiencies and shows that predictive models can inform “do nothing” assessments and resource allocation in complex service environments like the NHS. The project highlights the necessity of integrating varied data sources – from workforce numbers to population health trends – to create a unified view for decision-making.
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Int J Mol Sci. 2026 Feb 6;27(3):1597. doi: 10.3390/ijms27031597.
ABSTRACT
Ampullary carcinoma (AC) is a rare gastrointestinal malignancy with dual intestinal and pancreatobiliary differentiation, complicating diagnosis, staging, and treatment. This review synthesizes current epidemiology, pathology, and multi-omic data to outline a pragmatic care pathway: lineage-first at presentation, mutation-fast at progression. Histology remains the primary classifier: the intestinal subtype generally aligns with colorectal regimens, whereas pancreatobiliary and mixed subtypes favor pancreaticobiliary therapy. In selected fit patients, modified FOLFIRINOX may address mixed phenotypes. Next-generation sequencing adds precision by identifying therapeutically relevant alterations, including ERBB2/HER2 amplifications, MSI-high/dMMR, BRAF V600E, and rare NTRK or RET fusions, while KRAS mutations are enriched in pancreatobiliary tumors. We recommend early application of a rapid-core panel (KRAS/BRAF, MSI/dMMR, ERBB2/HER2, RNA-based fusions) to capture high-impact targets, followed by comprehensive profiling at first progression. Liquid biopsy, plasma circulating tumor DNA (ctDNA), or bile-derived DNA may complement tissue and help identify the dominant lineage. Research priorities include ampulla-enriched umbrella trials, explicit AC subcohorts in tissue-agnostic studies, and ctDNA-informed endpoints. This lineage-first, mutation-fast paradigm supports precision care and evidence generation in AC.
The Food and Drug Administration’s refusal to review Moderna’s flu vaccine this month has renewed fears that Trump administration policies could paralyze the vaccine industry, dissuading companies from developing new shots in the U.S. and leaving the country flat-footed in the event of future pandemics.
“I consider it an unprecedented action that really violates the basic principles of a data-driven regulatory agency and the fundamentals of public health, and it’s that simple,” said Gary Nabel, former head of the National Institutes of Health’s Vaccine Research Center and chief scientist at Sanofi, who now runs a vaccine and cancer startup. “It’s a destructive precedent that will undermine the future of vaccine development and the preeminence of American research.”
OpenAI described how it scaled PostgreSQL to support ChatGPT and its API platform, handling millions of queries per second for hundreds of millions of users. By running a single-primary PostgreSQL deployment on Azure with nearly 50 read replicas, optimizing query patterns, and offloading write-heavy workloads to sharded systems, OpenAI maintained low-latency reads while managing write pressure.
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Good morning health tech readers!
Today, we’ve got a ton of updates including news about venture capital funding, telehealth policy, the government’s progress on information blocking, and research into the accuracy of Apple’s new hypertension feature.
Transl Cancer Res. 2026 Jan 31;15(1):43. doi: 10.21037/tcr-2025-1499. Epub 2026 Jan 27.
ABSTRACT
BACKGROUND: Osteoglycin (OGN), an extracellular matrix protein, has emerging but poorly characterized roles in cancer. This study presents the first pan-cancer investigation of OGN's expression patterns, clinical significance, immune interactions, and functional mechanisms.
METHODS: Multi-omics data from Genotype Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE), The Cancer Genome Atlas (TCGA), and Human Protein Atlas (HPA) databases were integrated. Differential expression was analyzed in normal tissues and tumor samples. Diagnostic utility was evaluated using area under the curve (AUC) of receiver operating characteristic (ROC) curve. Prognostic value was assessed via Kaplan-Meier [overall survival (OS); disease-specific survival (DSS); disease free interval (DFI); progression-free interval (PFI)] and Cox regression analyses. Immune microenvironment correlations were quantified using ESTIMATE, CIBERSORT, and gene set enrichment. Functional pathways were explored through gene set enrichment analysis (GSEA) and correlation with hallmark cancer signatures.
RESULTS: OGN was broadly expressed in normal tissues (brain, liver, kidney) but significantly downregulated in most tumor types (P<0.05, TCGA; validated at protein level, HPA). OGN demonstrated high diagnostic accuracy in pan-cancer (AUC: 0.703-0.990), achieving near-perfect performance in colon adenocarcinoma (COAD) (AUC: 0.966) and thyroid cancer (THCA) (AUC: 0.920). High OGN expression correlated with improved survival outcomes in thymoma (THYM) (OS/DSS) and cholangiocarcinoma (CHOL) (PFI/DFI), but worse prognosis in lung adenocarcinoma/liver hepatocellular carcinoma (LUAD/LIHC), indicating cancer-type specificity. OGN expression strongly associated with immune cell infiltration (macrophages, natural killer cells, T cells), chemokine signaling, programmed death-ligand 1 (PD-L1) levels, microsatellite instability (MSI), and tumor mutation burden (TMB). GSEA revealed enrichment of OGN-linked genes in epithelial-mesenchymal transition (EMT), angiogenesis, JAK-STAT, and PI3K pathways across cancers.
CONCLUSIONS: Our pan-cancer analysis highlights OGN as a context-dependent regulator linking extracellular matrix (ECM) remodeling with immune and angiogenic signaling. Its pan-cancer dysregulation, diagnostic/prognostic value, and crosstalk with immune evasion mechanisms nominate OGN as a promising multi-functional biomarker and therapeutic target.
Oncogene. 2026 Feb 11. doi: 10.1038/s41388-026-03679-y. Online ahead of print.
ABSTRACT
Non-small cell lung cancer (NSCLC) with spinal metastasis represents a clinical challenge due to its aggressive nature, limited treatment options, and profound impact on patient quality of life. Here, we report the development of an innovative upconversion mesoporous silica nanoparticle (UCMS) platform co-loaded with celecoxib and rose bengal (UCMS@CXB/RB), engineered to synergistically combine photodynamic therapy (PDT) and cyclooxygenase-2 (COX-2) inhibition. Upon near-infrared (NIR) irradiation, UCMS@CXB/RB generated abundant reactive oxygen species, triggered immunogenic cell death, and significantly suppressed prostaglandin E2 signaling, leading to reduced angiogenesis and improved antitumor immunity. In vitro and in vivo studies confirmed that this nanoplatform effectively remodeled the tumor microenvironment, inhibited tumor growth, and alleviated cancer-induced spinal dysfunction. Single-cell multi-omics analysis further revealed dynamic crosstalk among immune cells, tumor cells, and endothelial populations, providing mechanistic insights into the multifaceted therapeutic effects of UCMS@CXB/RB. Our results underscore the clinical potential of integrating PDT with targeted COX-2 blockade to address the complex pathophysiology of NSCLC spinal metastasis. This study presents a promising minimally invasive therapeutic strategy with strong translational relevance for managing metastatic NSCLC and improving patient outcomes.
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So: Moderna says it was blindsided by the FDA, and Vinay Prasad, on its mRNA flu vaccine. Meanwhile, regulators are moving aggressively against Hims & Hers, and midsized biotechs have come together in solidarity — saying they could be crushed by President Trump’s drug pricing policy. Needless to say, it is not business as usual in Washington.
Out west, STAT’s Jonathan Wosen spoke with Novartis’ chief of biomedical research, who was in San Diego for the groundbreaking on a $1.1 billion research hub. She explained why company is pruning its pipeline and how it’s harnessing AI.
Hello, everyone, and welcome to the middle of the week. Congratulations on making it this far. It is an accomplishment, after all. The next step is to… keep going. And why not? Just consider the alternatives. On that optimistic note, please join us for a needed cup or three of stimulation. Our choice today is coconut rum. Meanwhile, here are some items of interest to get you going. Have a wonderful day and do drop us a line when you hear something juicy …
The U.S. Food and Drug Administration refused to review Moderna’s application for a new influenza vaccine, a surprise decision that could raise concerns about the agency’s posture toward drug companies and the Trump administration’s policies on vaccines, STAT writes. Moderna, revealing the rejection, took the unusual step of releasing the letter it had received from Vinay Prasad, who heads the FDA’s biologics division. They also issued a strongly worded statement from its chief executive officer Stephane Bancel, who said the decision “does not further our shared goal of enhancing America’s leadership in developing innovative medicines.” At the heart of the dispute is what existing influenza vaccine Moderna should have used as a control when testing the efficacy of its new shot, which utilizes the same mRNA technology the company used in its Covid-19 vaccine.
The recent moves by the Trump administration against Hims & Hers might only be the start of a crackdown on compounding, STAT explains. In recent days, the Food and Drug Administration issued a warning, the Department of Health & Human Services asked the Department of Justice to open an investigation and, meanwhile, Novo Nordisk filed a patent infringement lawsuit against the company. But while compounded weight-loss drugs proliferated during recent shortages and continued to remain available, the flurry of developments underscores growing unease among regulators with mass-marketed compounded drugs sold by national, vertically integrated telehealth platforms. The FDA has so far focused publicly on misleading marketing, but signs that it may scrutinize compounding practices themselves have the industry on edge, given how many telehealth companies rely on compounded versions of everything from acne treatments to libido drugs.
Agentic AI in healthcare is graduating from answering prompts to autonomously executing complex marketing tasks – and life sciences companies are betting their commercial strategies on it.
According to a recent report cited by Capgemini Invent, AI agents could generate up to $450 billion in economic value through revenue uplift and cost savings globally by 2028, with 69% of executives planning to deploy agents in marketing processes by year’s end.
The stakes are particularly high in pharmaceutical marketing, where sales representatives have increasingly limited face-time with healthcare professionals (HCPs) – a trend accelerated by Covid-19. The challenge isn’t just access; it’s making those rare interactions count with intelligence that’s currently trapped in data silos.
The fragmented intelligence problem
Briggs Davidson, senior director of digital, data & marketing strategy for life Sciences at Capgemini Invent, outlines a scenario that will sound familiar to anyone in pharma marketing: An HCP attends a conference where a competitor showcases promising drug results, publishes research, and shifts their prescriptions to a rival product – in a single quarter.
“In most companies, legacy IT infrastructure and data silos keep this information in disparate systems in CRM, events databases and claims data,” Davidson writes. “Chances are, none of that information was accessible to sales reps before they met with the HCP.”
The solution, according to Davidson, isn’t to connect these systems, it’s deploying agentic AI in healthcare marketing to autonomously query, synthesising and acting on unified data. Unlike conversational AI that responds to queries, agentic systems can independently execute multi-step tasks.
Instead of a data engineer building a new pipeline, an AI agent could autonomously query the CRM and claims database to answer business questions like: “Identify oncologists in the Northwest who have a 20% lower prescription volume but attended our last medical congress.”
From orchestration to autonomous execution
Davidson frames the change as moving from an “omnichannel view” – coordinating experiences in channels – to true orchestration powered by agentic AI.
In practice, this means a sales representative could have an agent assist with call and visit planning by asking: “What messages has my HCP responded to most recently?” or “Can you create a detailed intelligence brief on my HCP?”
The agentic system would compile:
Their most recent conversation with the HCP,
The HCP’s prescribing behaviour,
Thought-leaders the HCP follows,
Relevant content to share,
The HCP’s preferred outreach channels (in-person visits, emails, webinars).
More significantly, the AI agent would then create a custom call plan for each HCP based on their unified profile and recommend follow-up steps based on engagement outcomes. “Agentic AI systems are about driving action, graduating from ‘answer my prompt,’ to ‘autonomously execute my task,'” Davidson explains.
“That means evolving the sales representative mindset from asking questions to coordinating small teams of specialised agents that work together: one plans, another retrieves and checks content, a third schedules and measures, and a fourth enforces compliance guardrails – all under human oversight.”
The AI-ready data prerequisite
The operational promise hinges on what Davidson calls “AI-ready data” – standardised, accessible, complete, and trustworthy information that enables three abilities:
Faster decision making: Predictive analytics that provide near real-time alerts on what’s about to happen, letting sales representatives act proactively.
Personalisation at scale: Delivering customised experiences to thousands of HCPs simultaneously with small human teams enabled by specialised agent networks.
True marketing ROI: Moving beyond monthly historical reports to understanding which marketing activities are actively driving prescriptions.
Davidson emphasises that successful deployment starts with marketing and IT alignment on initial use cases, with stakeholders identifying KPIs that demonstrate tangible outcomes – like specific percentage increases in HCP engagement or sales representative productivity.
Critical implementation questions
The article frames agentic AI in healthcare as “not simply another technology-led ability; it’s a new operating layer for commercial teams.” But it acknowledges that “agentic AI’s full value only materialises with AI-ready data, trustworthy deployment and workflow redesign.”
What remains unaddressed is the regulatory and compliance complexity of autonomous systems querying claims databases containing prescriber behaviour, particularly under HIPAA’s minimum necessary standard. The piece also doesn’t detail actual client implementations or metrics beyond the aspirational $450B economic value projection.
For global organisations, Davidson says use cases “can and should be tailored to fit each market’s maturity for maximum ROI,” suggesting that deployment will vary in regulatory environments. The fundamental value proposition, according to Davidson, centres on bidirectional benefit: “The HCP receives directly relevant content, and the marketing teams can drive increased HCP engagement and conversion.”
Whether that vision of autonomous marketing agents coordinating in CRM, events, and claims systems becomes standard practice by 2028 – or remains constrained by data governance realities – will likely determine if life sciences achieves anything close to that $450 billion opportunity.
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.
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Chem Biol Interact. 2026 Feb 7:111952. doi: 10.1016/j.cbi.2026.111952. Online ahead of print.
ABSTRACT
The scarcity of reliable biomarkers and predictive models for platinum resistance in lung adenocarcinoma (LUAD) poses a significant clinical challenge. This study endeavors to identify molecular subtypes related to platinum resistance and construct a robust predictive model through multi-omics techniques. We performed integrative analysis of public datasets using advanced bioinformatics strategies, including spatial transcriptome deconvolution and consensus clustering. Bulk RNA deconvolution analysis was conducted to characterize tumor microenvironment heterogeneity. Feature selection was performed using the Supervised Principal Component (SuperPC) algorithm, followed by diagnostic model construction validated through receiver operating characteristic (ROC) analysis. Functional validation was performed through cytological experiments measuring cisplatin IC50 alterations following gene manipulation in LUAD cell lines. Consensus clustering revealed distinct LUAD subtypes, with Cluster1 demonstrating significant platinum resistance. We first subtyped the patients in the bulk transcriptome data based on consistency clustering, and then analyzed the differences between different platinum-resistant subtypes (Cluster 1 and Cluster 2), so as to screen 333 isotype-specific differentially expressed genes and 15 platinum resistance-related (PRR) genes were selected through machine learning. A refined 5-gene signature (ANKRD29/CACNA2D2/DSP/HSD17B6/SPP1) achieved exceptional predictive performance (AUC=0.9639). Spatial transcriptomics demonstrated compartmentalized expression patterns: SPP1/DSP localized to tumor niches, HSD17B6/CACNA2D2 to epithelial regions, and ANKRD29 depletion in stromal areas. Cellular colocalization analysis revealed malignant epithelial PH proximity to myeloid and mast cells. Functional validation confirmed that ANKRD29/CACNA2D2 overexpression sensitized A549/DDP cells to cisplatin, while DSP/SPP1/HSD17B6 overexpression induced resistance. Experiments in nude mice have shown that these genes are closely related to cisplatin resistance in LUAD. This study identifies the Cluster1 subtype and malignant epithelial PH as crucial determinants of platinum resistance in LUAD. Our innovative 5-gene predictive model exhibits clinical-grade diagnostic accuracy, and spatial transcriptomic characterization offers mechanistic insights into the dynamics of the tumor microenvironment.
Background: Since their introduction with the Digital Care Act in 2019, DiGA are a part of the German statutory healthcare system. In order to become a DiGA, mHealth apps have to complete a certification process covering both technical and evidence related aspects. After completion, DiGA are added to the DiGA-directory, containing a list of all reimbursable DiGA within German statutory health insurance (SHI). The first apps were added at the end of 2020 with the number steadily increasing. The novelty of the introduction leads to problems and barriers to optimal use along the way, which is studied from different stakeholder perspectives in this research article. Objective: The aim of the survey was to identify problems and barriers in the context of certification, financing and use of DiGA in Germany. Methods: We used semi-structured expert interviews to evaluate the perspective of stakeholders of the German healthcare system on DiGA. The interview guide was developed according to Helfferich, the interviews were transcribed and analyzed using the qualitative content approach by Mayring and Kuckartz. Results: We identified problems from stakeholder perspectives regarding the certification/admission, financing and service distribution regarding DiGA. The interviewed stakeholders reported problems with authorization of DiGA and the corresponding process. DiGA prices and the different negotiation positions were criticized, as well as financial challenges for smaller DiGA-manufacturers. Within service provision, technical problems, e. g., with activation codes or software surrounding DiGA-prescription were mentioned. Problems were also seen in insufficient knowledge and skills on the side of the patients as well as the medical providers. Conclusions: mHealth applications provide potentially disruptive innovations within the healthcare sector. Nevertheless, since the evidence-based and regulated use of this technology is relatively new there are still problems and barriers limiting the optimized, patient-centered use. This study provides an overview of problems in the context of DiGA in Germany from the stakeholder perspective. Since other countries showed interest in potentially adopting the German system, valuable implications can be drawn from this survey.
Phenomics. 2025 Dec 15;5(5):469-486. doi: 10.1007/s43657-024-00188-4. eCollection 2025 Oct.
ABSTRACT
Quantifying individual health status from increasingly accumulated omics data is essential for both early prevention and intervention of diseases, which attracts great attention from communities of biology and medicine. Most of the existing approaches mainly classify individuals into different catalogues or classes based on phenotypes and biomarkers. However, an individual's health status from a dynamical systems viewpoint can be viewed as a non-equilibrium steady state, which can generally be characterized by two key features, i.e. (1) homeostatic potential that represents the ability of homeostatic resilience to withstand perturbations or maintain functions at the current state/phenotype of this individual and (2) phenotypic potential that represents the state/phenotype of the individual on the whole process from health to disease. Here, we proposed a health state manifold (HSM) method derived from dynamic network biomarker method and diffusion map theory to quantify individual health status with the characterization of such two features in a robust and accurate manner based on multi-omics data. To verify our method, HSM method was applied to the quantification of diabetes mellitus (rat subjects) and the Roux-en-Y Gastric Bypass (human subjects) for both disease progression process and recovery process, which demonstrated its effectiveness and potential for personalized medicine and preventive medicine.
SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s43657-024-00188-4.
Cancer Res. 2026 Feb 9. doi: 10.1158/0008-5472.CAN-25-2854. Online ahead of print.
ABSTRACT
Pancreatic ductal adenocarcinoma (PDAC) patients with diabetes mellitus (DM) exhibit poor clinical outcomes. Metabolic reprogramming of both cancer cells and immune compartments plays a crucial role in shaping the anti-tumor immune response in PDAC. DM-induced metabolic alteration may disrupt the intricate crosstalk between immune cells and tumor-associated immune factors, profoundly influencing PDAC progression. Here, we performed an integrated, spatially resolved multi-omics study to investigate DM-associated, cell-specific metabolic remodeling within the PDAC tumor microenvironment. DM influenced interactions between tumor cells and immune cells, which accelerated PDAC growth in both humans and mice. PDAC patients with DM exhibited higher tumor-stage, poorer differentiation, and worse outcomes. Spatial metabolic and transcriptional profiling revealed that SREBP2-dependent cholesterol biosynthesis exacerbated PDAC progression. Increased cholesterol biosynthesis promoted neutrophil recruitment and accelerated formation of neutrophil extracellular traps (NETs) by stimulating the CXCL1-CXCR1/CXCR2 signaling axis, ultimately promoting PDAC growth. Inhibition of SREBP2, pharmacological blockade of CXCL1, or perturbation of NETs markedly reduced PDAC growth in diabetic mouse models. Together, these multi-omics analyses and follow-up mechanistic studies constitute an integrated approach that elucidates a metabolic mechanism by which diabetes promotes PDAC development by remodeling the tumor immune microenvironment and highlights a potential therapeutic strategy for PDAC with DM.
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