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Toward an AI Reasoning-Enabled System for Patient-Clinical Trial Matching

✇cs.AI, q-bio.NC updates on arXiv.org
By: Caroline N. Leach · Mitchell A. Klusty · Samuel E. Armstrong · Justine C. Pickarski · Kristen L. Hankins · Emily B. Collier · Maya Shah · Aaron D. Mullen · V. K. Cody Bumgardner
arXiv:2512.08026v1 Announce Type: new Abstract: Screening patients for clinical trial eligibility remains a manual, time-consuming, and resource-intensive process. We present a secure, scalable proof-of-concept system for Artificial Intelligence (AI)-augmented patient-trial matching that addresses key implementation challenges: integrating heterogeneous electronic health record (EHR) data, facilitating expert review, and maintaining rigorous security standards. Leveraging open-source, reasoning-enabled large language models (LLMs), the system moves beyond binary classification to generate structured eligibility assessments with interpretable reasoning chains that support human-in-the-loop review. This decision support tool represents eligibility as a dynamic state rather than a fixed determination, identifying matches when available and offering actionable recommendations that could render a patient eligible in the future. The system aims to reduce coordinator burden, intelligently broaden the set of trials considered for each patient and guarantee comprehensive auditability of all AI-generated outputs.
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Beyond Traditional Diagnostics: Transforming Patient-Side Information into Predictive Insights with Knowledge Graphs and Prototypes

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yibowen Zhao · Yinan Zhang · Zhixiang Su · Lizhen Cui · Chunyan Miao
arXiv:2512.08261v1 Announce Type: new Abstract: Predicting diseases solely from patient-side information, such as demographics and self-reported symptoms, has attracted significant research attention due to its potential to enhance patient awareness, facilitate early healthcare engagement, and improve healthcare system efficiency. However, existing approaches encounter critical challenges, including imbalanced disease distributions and a lack of interpretability, resulting in biased or unreliable predictions. To address these issues, we propose the Knowledge graph-enhanced, Prototype-aware, and Interpretable (KPI) framework. KPI systematically integrates structured and trusted medical knowledge into a unified disease knowledge graph, constructs clinically meaningful disease prototypes, and employs contrastive learning to enhance predictive accuracy, which is particularly important for long-tailed diseases. Additionally, KPI utilizes large language models (LLMs) to generate patient-specific, medically relevant explanations, thereby improving interpretability and reliability. Extensive experiments on real-world datasets demonstrate that KPI outperforms state-of-the-art methods in predictive accuracy and provides clinically valid explanations that closely align with patient narratives, highlighting its practical value for patient-centered healthcare delivery.
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Principles2Plan: LLM-Guided System for Operationalising Ethical Principles into Plans

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tammy Zhong · Yang Song · Maurice Pagnucco
arXiv:2512.08536v1 Announce Type: new Abstract: Ethical awareness is critical for robots operating in human environments, yet existing automated planning tools provide little support. Manually specifying ethical rules is labour-intensive and highly context-specific. We present Principles2Plan, an interactive research prototype demonstrating how a human and a Large Language Model (LLM) can collaborate to produce context-sensitive ethical rules and guide automated planning. A domain expert provides the planning domain, problem details, and relevant high-level principles such as beneficence and privacy. The system generates operationalisable ethical rules consistent with these principles, which the user can review, prioritise, and supply to a planner to produce ethically-informed plans. To our knowledge, no prior system supports users in generating principle-grounded rules for classical planning contexts. Principles2Plan showcases the potential of human-LLM collaboration for making ethical automated planning more practical and feasible.
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Multi-Agent Intelligence for Multidisciplinary Decision-Making in Gastrointestinal Oncology

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rongzhao Zhang · Junqiao Wang · Shuyun Yang · Mouxiao Bian · Chao Ding · Yuwei Bai · Chihao Zhang · Yuguang Shen · Lei Wang · Lei Zheng · Qiujuan Yan · Yun Zhong · Meiling Liu · Jiwei Yu · Zheng Wang · Jie Xu · Meng Luo
arXiv:2512.08674v1 Announce Type: new Abstract: Multimodal clinical reasoning in the field of gastrointestinal (GI) oncology necessitates the integrated interpretation of endoscopic imagery, radiological data, and biochemical markers. Despite the evident potential exhibited by Multimodal Large Language Models (MLLMs), they frequently encounter challenges such as context dilution and hallucination when confronted with intricate, heterogeneous medical histories. In order to address these limitations, a hierarchical Multi-Agent Framework is proposed, which emulates the collaborative workflow of a human Multidisciplinary Team (MDT). The system attained a composite expert evaluation score of 4.60/5.00, thereby demonstrating a substantial improvement over the monolithic baseline. It is noteworthy that the agent-based architecture yielded the most substantial enhancements in reasoning logic and medical accuracy. The findings indicate that mimetic, agent-based collaboration provides a scalable, interpretable, and clinically robust paradigm for automated decision support in oncology.
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Towards Foundation Models with Native Multi-Agent Intelligence

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shuyue Hu · Haoyang Yan · Yiqun Zhang · Yang Chen · Dongzhan Zhou · Lei Bai
arXiv:2512.08743v1 Announce Type: new Abstract: Foundation models (FMs) are increasingly assuming the role of the "brain" of AI agents. While recent efforts have begun to equip FMs with native single-agent abilities -- such as GUI interaction or integrated tool use -- we argue that the next frontier is endowing FMs with native multi-agent intelligence. We identify four core capabilities of FMs in multi-agent contexts: understanding, planning, efficient communication, and adaptation. Contrary to assumptions about the spontaneous emergence of such abilities, we provide extensive empirical evidence across 41 large language models showing that strong single-agent performance alone does not automatically yield robust multi-agent intelligence. To address this gap, we outline key research directions -- spanning dataset construction, evaluation, training paradigms, and safety considerations -- for building FMs with native multi-agent intelligence.
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Biothreat Benchmark Generation Framework for Evaluating Frontier AI Models I: The Task-Query Architecture

✇cs.AI, q-bio.NC updates on arXiv.org
By: Gary Ackerman · Brandon Behlendorf · Zachary Kallenborn · Sheriff Almakki · Doug Clifford · Jenna LaTourette · Hayley Peterson · Noah Sheinbaum · Olivia Shoemaker · Anna Wetzel
arXiv:2512.08130v1 Announce Type: cross Abstract: Both model developers and policymakers seek to quantify and mitigate the risk of rapidly-evolving frontier artificial intelligence (AI) models, especially large language models (LLMs), to facilitate bioterrorism or access to biological weapons. An important element of such efforts is the development of model benchmarks that can assess the biosecurity risk posed by a particular model. This paper describes the first component of a novel Biothreat Benchmark Generation (BBG) Framework. The BBG approach is designed to help model developers and evaluators reliably measure and assess the biosecurity risk uplift and general harm potential of existing and future AI models, while accounting for key aspects of the threat itself that are often overlooked in other benchmarking efforts, including different actor capability levels, and operational (in addition to purely technical) risk factors. As a pilot, the BBG is first being developed to address bacterial biological threats only. The BBG is built upon a hierarchical structure of biothreat categories, elements and tasks, which then serves as the basis for the development of task-aligned queries. This paper outlines the development of this biothreat task-query architecture, which we have named the Bacterial Biothreat Schema, while future papers will describe follow-on efforts to turn queries into model prompts, as well as how the resulting benchmarks can be implemented for model evaluation. Overall, the BBG Framework, including the Bacterial Biothreat Schema, seeks to offer a robust, re-usable structure for evaluating bacterial biological risks arising from LLMs across multiple levels of aggregation, which captures the full scope of technical and operational requirements for biological adversaries, and which accounts for a wide spectrum of biological adversary capabilities.
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A Practical Framework for Evaluating Medical AI Security: Reproducible Assessment of Jailbreaking and Privacy Vulnerabilities Across Clinical Specialties

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jinghao Wang · Ping Zhang · Carter Yagemann
arXiv:2512.08185v1 Announce Type: cross Abstract: Medical Large Language Models (LLMs) are increasingly deployed for clinical decision support across diverse specialties, yet systematic evaluation of their robustness to adversarial misuse and privacy leakage remains inaccessible to most researchers. Existing security benchmarks require GPU clusters, commercial API access, or protected health data -- barriers that limit community participation in this critical research area. We propose a practical, fully reproducible framework for evaluating medical AI security under realistic resource constraints. Our framework design covers multiple medical specialties stratified by clinical risk -- from high-risk domains such as emergency medicine and psychiatry to general practice -- addressing jailbreaking attacks (role-playing, authority impersonation, multi-turn manipulation) and privacy extraction attacks. All evaluation utilizes synthetic patient records requiring no IRB approval. The framework is designed to run entirely on consumer CPU hardware using freely available models, eliminating cost barriers. We present the framework specification including threat models, data generation methodology, evaluation protocols, and scoring rubrics. This proposal establishes a foundation for comparative security assessment of medical-specialist models and defense mechanisms, advancing the broader goal of ensuring safe and trustworthy medical AI systems.
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ClinicalTrialsHub: Bridging Registries and Literature for Comprehensive Clinical Trial Access

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiwoo Park · Ruoqi Liu · Avani Jagdale · Andrew Srisuwananukorn · Jing Zhao · Lang Li · Ping Zhang · Sachin Kumar
arXiv:2512.08193v1 Announce Type: cross Abstract: We present ClinicalTrialsHub, an interactive search-focused platform that consolidates all data from ClinicalTrials.gov and augments it by automatically extracting and structuring trial-relevant information from PubMed research articles. Our system effectively increases access to structured clinical trial data by 83.8% compared to relying on ClinicalTrials.gov alone, with potential to make access easier for patients, clinicians, researchers, and policymakers, advancing evidence-based medicine. ClinicalTrialsHub uses large language models such as GPT-5.1 and Gemini-3-Pro to enhance accessibility. The platform automatically parses full-text research articles to extract structured trial information, translates user queries into structured database searches, and provides an attributed question-answering system that generates evidence-grounded answers linked to specific source sentences. We demonstrate its utility through a user study involving clinicians, clinical researchers, and PhD students of pharmaceutical sciences and nursing, and a systematic automatic evaluation of its information extraction and question answering capabilities.
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Biothreat Benchmark Generation Framework for Evaluating Frontier AI Models III: Implementing the Bacterial Biothreat Benchmark (B3) Dataset

✇cs.AI, q-bio.NC updates on arXiv.org
By: Gary Ackerman · Theodore Wilson · Zachary Kallenborn · Olivia Shoemaker · Anna Wetzel · Hayley Peterson · Abigail Danfora · Jenna LaTourette · Brandon Behlendorf · Douglas Clifford
arXiv:2512.08459v1 Announce Type: cross Abstract: The potential for rapidly-evolving frontier artificial intelligence (AI) models, especially large language models (LLMs), to facilitate bioterrorism or access to biological weapons has generated significant policy, academic, and public concern. Both model developers and policymakers seek to quantify and mitigate any risk, with an important element of such efforts being the development of model benchmarks that can assess the biosecurity risk posed by a particular model. This paper discusses the pilot implementation of the Bacterial Biothreat Benchmark (B3) dataset. It is the third in a series of three papers describing an overall Biothreat Benchmark Generation (BBG) framework, with previous papers detailing the development of the B3 dataset. The pilot involved running the benchmarks through a sample frontier AI model, followed by human evaluation of model responses, and an applied risk analysis of the results along several dimensions. Overall, the pilot demonstrated that the B3 dataset offers a viable, nuanced method for rapidly assessing the biosecurity risk posed by a LLM, identifying the key sources of that risk and providing guidance for priority areas of mitigation priority.
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Multi-domain performance analysis with scores tailored to user preferences

✇cs.AI, q-bio.NC updates on arXiv.org
By: S\'ebastien Pi\'erard · Adrien Deli\`ege · Marc Van Droogenbroeck
arXiv:2512.08715v1 Announce Type: cross Abstract: The performance of algorithms, methods, and models tends to depend heavily on the distribution of cases on which they are applied, this distribution being specific to the applicative domain. After performing an evaluation in several domains, it is highly informative to compute a (weighted) mean performance and, as shown in this paper, to scrutinize what happens during this averaging. To achieve this goal, we adopt a probabilistic framework and consider a performance as a probability measure (e.g., a normalized confusion matrix for a classification task). It appears that the corresponding weighted mean is known to be the summarization, and that only some remarkable scores assign to the summarized performance a value equal to a weighted arithmetic mean of the values assigned to the domain-specific performances. These scores include the family of ranking scores, a continuum parameterized by user preferences, and that the weights to consider in the arithmetic mean depend on the user preferences. Based on this, we rigorously define four domains, named easiest, most difficult, preponderant, and bottleneck domains, as functions of user preferences. After establishing the theory in a general setting, regardless of the task, we develop new visual tools for two-class classification.
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AI-powered virtual tissues from spatial proteomics for clinical diagnostics and biomedical discovery

✇cs.AI, q-bio.NC updates on arXiv.org
By: Johann Wenckstern · Eeshaan Jain · Yexiang Cheng · Benedikt von Querfurth · Kiril Vasilev · Matteo Pariset · Phil F. Cheng · Petros Liakopoulos · Olivier Michielin · Andreas Wicki · Gabriele Gut · Charlotte Bunne
arXiv:2501.06039v2 Announce Type: replace-cross Abstract: Spatial proteomics technologies have transformed our understanding of complex tissue architecture in cancer but present unique challenges for computational analysis. Each study uses a different marker panel and protocol, and most methods are tailored to single cohorts, which limits knowledge transfer and robust biomarker discovery. Here we present Virtual Tissues (VirTues), a general-purpose foundation model for spatial proteomics that learns marker-aware, multi-scale representations of proteins, cells, niches and tissues directly from multiplex imaging data. From a single pretrained backbone, VirTues supports marker reconstruction, cell typing and niche annotation, spatial biomarker discovery, and patient stratification, including zero-shot annotation across heterogeneous panels and datasets. In triple-negative breast cancer, VirTues-derived biomarkers predict anti-PD-L1 chemo-immunotherapy response and stratify disease-free survival in an independent cohort, outperforming state-of-the-art biomarkers derived from the same datasets and current clinical stratification schemes.
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OMNIGUARD: An Efficient Approach for AI Safety Moderation Across Languages and Modalities

✇cs.AI, q-bio.NC updates on arXiv.org
By: Sahil Verma · Keegan Hines · Jeff Bilmes · Charlotte Siska · Luke Zettlemoyer · Hila Gonen · Chandan Singh
arXiv:2505.23856v2 Announce Type: replace-cross Abstract: The emerging capabilities of large language models (LLMs) have sparked concerns about their immediate potential for harmful misuse. The core approach to mitigate these concerns is the detection of harmful queries to the model. Current detection approaches are fallible, and are particularly susceptible to attacks that exploit mismatched generalization of model capabilities (e.g., prompts in low-resource languages or prompts provided in non-text modalities such as image and audio). To tackle this challenge, we propose Omniguard, an approach for detecting harmful prompts across languages and modalities. Our approach (i) identifies internal representations of an LLM/MLLM that are aligned across languages or modalities and then (ii) uses them to build a language-agnostic or modality-agnostic classifier for detecting harmful prompts. Omniguard improves harmful prompt classification accuracy by 11.57\% over the strongest baseline in a multilingual setting, by 20.44\% for image-based prompts, and sets a new SOTA for audio-based prompts. By repurposing embeddings computed during generation, Omniguard is also very efficient ($\approx\!120 \times$ faster than the next fastest baseline). Code and data are available at: https://github.com/vsahil/OmniGuard.
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Development of a Hospital-at-Home Digital Twin for Patients With Frailty: Scoping Review

✇Journal of Medical Internet Research
By: Faiza Yahya · Matthew Cooper · Wahib Saif · Mohamad Kassem · Hamde Nazar
Background: Increasing demand on healthcare systems requires innovative and transformative solutions to deliver efficient, high-quality care. One promising approach is Digital Twin (DT) technology, which leverages real time data to create dynamic virtual representations of a physical entity (individuals or space) to anticipate future scenarios and support care decisions. While DTs have been explored in various sectors, their application in Hospital at Home (HaH), which delivers acute level care in home environments, remains unexplored. Objective: This review bridges a critical knowledge gap and examines the existing evidence on DT-enabling tools for managing patients with frailty in home settings. This will identify the underpinning architectural components required to inform a HaH-DT system which can support clinical decision-making. Methods: Six electronic databases (Embase, MEDLINE, Cochrane CENTRAL, CINAHL, Web of Science and Scopus) were searched, along with grey literature, to identifying primary studies published in English, between January 2019 and September 2025. Included studies had to report on the monitoring or management of patients with frailty within their own home, and information was charted on a pre-defined data collection form to answer the research objectives. Review articles, protocols, and conference abstracts were excluded. Results: Sixty-nine reports were included, of which 54% (n=37) used quantitative approaches, and 36% (n=25) were pilot or feasibility studies. Reports were analysed for DT-enabling tools and systematically mapped across the proposed five-layered DT architecture: sensing, communication, storage, analytics, and visualisation. Taxonomies of DT layers, their interconnections, and the classifications of the types of data collected (e.g., about the patient, the home environment, the use of medical equipment) are presented. This evidence identifies DT-enabling tools used for a variety of functions and a range of sensing technologies that exist (e.g., passive sensing via wearables, active physiological sensors, ambient sensors to detect motion/environmental changes). The most prevalent modes of communication were wireless and network-based (n=36), with the majority using Bluetooth (n=12). This review highlights better understanding of data management, in particular secure storage, is required within local healthcare systems. The emerging potential of predictive and prescriptive analytics, which can enable clinicians to predict risk, support clinical decision-making, or activate alert-triggered health interventions were mapped. Existing evidence suggests analytics methods are currently largely descriptive with a lack of advanced methods such as prescriptive analytics to enable recommendations of an optimal course of action, and the absence of diagnostic analytics which can highlight why a situation has occurred. Reported DT-enabling tools demonstrate patient-centered benefits, including enhanced motivation, reassurance, and personalised care. However, concerns persist regarding device accuracy, user acceptability, and implications for carers and organisational workflows. Conclusions: This review is among the first to systematically map DT-enabling tools to inform a potential HaH-DT in patients with frailty and organised by a 5-layered conceptual model. Understanding these architectural layers provides the foundations to enable stakeholders advance research and development in areas where there are knowledge gaps and consider how a HaH DT can effectively operate within current healthcare systems. By leveraging technology-enabled care in complex home-based settings, there is great potential to deliver safer, personalised and timely care.
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Somatic evolution following cancer treatment in normal tissue

✇Nature - Issue - nature.com science feeds
By: Oriol Pich · Sophia Ward · Andrew Rowan · Cristina Naceur-Lombardelli · Oliver Shutkever · Carlos Martinez-Ruiz · Siân Harries · Sonya Hessey · Babu Naidu · James D. Brenton · John Le Quesne · Anne Thomas · Cathy Richards · Matthew G. Krebs · Samra Turajlic · Sanjay Jogai · Simone Zaccaria · David Moore · Crispin T. Hiley · Kai-Keen Shiu · John Bridgewater · Daniel Hochhauser · Martin Forster · Siow Ming Lee · Tanya Ahmad · Dionysis Papadatos-Pastos · Sam Janes · Peter Van Loo · Katey Enfield · Ariana Huebner · Sergio Quezada · Stephan Beck · Tariq Enver · David R. Pearce · Mary Falzon · Ron Sinclair · Zoe Rhodes · Teresa Marafioti · Miriam Mitchison · Mark Linch · Sebastian Brandner · Selvaraju Veeriah · Heather Shaw · Gerhardt Attard · Faye Gishen · Maise Al-Bakir · Nnenna Kanu · Francisco Gimeno-Valiente · Emilia L. Lim · James Reading · Benny Chain · Adrienne Flanagan · Emma Colliver · Mihaela Angelova · James Black · Olivia Lucas · William Hill · Wing Kin Liu · Alexander Frankell · Roberto Salgado · Kristiana Grigoriadis · Takahiro Karasaki · Abigail Bunkum · Sara

Nature, Published online: 10 December 2025; doi:10.1038/s41586-025-09792-4

High-depth sequencing of non-cancerous tissue from patients with metastatic cancer reveals single-base mutational signatures of alcohol, smoking and cancer treatments, and reveals how exogenous factors, including cancer therapies, affect somatic cell evolution.
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Liquid biopsies across the cancer care continuum

✇Nature Medicine
By: Blair V. Landon · Akshaya V. Annapragada · Noushin Niknafs · Victor E. Velculescu · Valsamo Anagnostou

Nature Medicine, Published online: 10 December 2025; doi:10.1038/s41591-025-04093-9

This Review summarizes current and emerging liquid biopsy methods, together with their clinical validity and utility, and highlights opportunities to support their implementation throughout the cancer care continuum.
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Lung Cancer Diagnosis and Prognostic Monitoring Through Cell-Free RNA via Liquid Biopsy

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Yuanming Pan · Chongbo Jiang · Mengchan Ye · Dongmei Li · Jinghui Wang

Ther Clin Risk Manag. 2025 Dec 2;21:1615-1636. doi: 10.2147/TCRM.S542338. eCollection 2025.

ABSTRACT

Lung cancer remains a leading cause of cancer-related mortality worldwide, largely due to challenges in its early detection and effective management. Despite advances in treatment modalities, the complex nature of lung cancer, characterized by its molecular heterogeneity and resistance mechanisms, underscores the need for innovative approaches. Cell-free RNA (cfRNA) has emerged as a promising biomarker with significant clinical applications in lung cancer diagnosis, monitoring, and precision medicine. We explore key themes including the utility of cfRNA in early detection, differentiation between benign and malignant lung nodules, molecular subtyping, and real-time therapeutic monitoring. Advances in liquid biopsy technologies, particularly non-invasive cfRNA analysis, provide dynamic means of tracking tumor evolution. cfRNA biomarkers such as miRNA, long non-coding RNAs, and circular RNAs offer unique insights into tumor biology, paving the way for personalized treatment strategies. Further, we discuss the application of cutting-edge technologies such as AI-driven analytics, next-generation sequencing, and multi-omics integration, which are enhancing the clinical utility of cfRNA in identifying treatment resistance and improving outcomes in immunotherapy, targeted therapy, and chemotherapy. The review addresses significant challenges facing cfRNA applications, including pre-analytical variability, technical limitations in detection methods, economic constraints, and the lack of standardization in clinical protocols. Through multidisciplinary collaborations and standardized methodologies, significant progress can be made toward integrating cfRNA into routine clinical practice. Emphasis is placed on future research directions, which include validating cfRNA biomarkers across diverse populations, streamlining workflows, and addressing scalability issues for real-world applications. This comprehensive exploration positions cfRNA at the forefront of innovations in lung cancer management, offering a pathway for improved diagnostic accuracy and individualized care.

PMID:41367889 | PMC:PMC12682701 | DOI:10.2147/TCRM.S542338

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Decoding the enigma of multiple primary lung cancers: from mechanism to bedside-a narrative review

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Pan Yang · Siyu Liu · Yuansi Chen · Yihan Guo · Yi Li · Zhen Wang · Weimin Li · Zhoufeng Wang

Transl Lung Cancer Res. 2025 Nov 30;14(11):5181-5197. doi: 10.21037/tlcr-2025-957. Epub 2025 Nov 27.

ABSTRACT

BACKGROUND AND OBJECTIVE: Lung cancer is the leading cause of global cancer mortality. Multiple primary lung cancer (MPLC) represents a clinically challenging subtype characterized by independent tumor foci. Distinguishing MPLC from intrapulmonary metastases is crucial for prognosis and treatment. This review integrates current evidence on MPLC's etiology, molecular mechanisms, diagnosis, and management, aiming to provide a clinical reference and highlight future precision medicine directions.

METHODS: We searched PubMed/MEDLINE, Web of Science, and Google Scholar for articles published between January 2000 and September 2024. Search terms included "multiple primary lung cancer", "diagnosis", "molecular characteristics", and "treatment". The selection focused on English-language research and reviews addressing MPLC pathogenesis, diagnosis, or management.

KEY CONTENT AND FINDINGS: The review delineates the multifactorial pathogenesis of MPLC, encompassing genetic susceptibility, somatic heterogeneity, clonal evolution, and epigenetic dysregulation. It frames these mechanisms against a backdrop of "field cancerization" and dynamic tumor microenvironment interactions. The evolution of diagnosis from histology to integrated molecular-artificial intelligence (AI) models is detailed, alongside treatment strategies that must overcome the challenge of inter-lesional heterogeneity.

CONCLUSIONS: MPLC is a distinct entity arising from genetic, epigenetic, and microenvironmental interplay. Advancing its management requires multi-omics integration to decipher pathology and identify biomarkers. Future work should develop AI-enhanced diagnostics and lesion-specific treatment strategies. This review synthesizes current evidence to inform and direct future research and clinical innovation in MPLC.

PMID:41367572 | PMC:PMC12683420 | DOI:10.21037/tlcr-2025-957

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Causal relationship of immune cell characteristics in hepatocellular carcinoma: A multi-omics analysis based on Mendelian randomization

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: XiaoLing Tian · BaoChun Wang · XinYi Zhang · Ge Song · YuBo Liu · YuQian Gao · JinYan Wang · YunQi Hua

Medicine (Baltimore). 2025 Dec 5;104(49):e45942. doi: 10.1097/MD.0000000000045942.

ABSTRACT

The tumor immune microenvironment of hepatocellular carcinoma (HCC) is complex, yet the causal relationship between immune cell subpopulations and HCC risk remains incompletely elucidated. This study aims to systematically evaluate the causal association between immune cell subpopulations and HCC using Mendelian randomization (MR) analysis, and to validate the biological mechanisms underlying these associations through multi-omics data. Bidirectional two-sample MR analysis was performed to examine causal relationships between 731 immune cell subpopulations and HCC. Inverse-variance weighting (IVW) served as the primary analysis method, with robustness validation through Bayesian weighted MR (BWMR) and machine learning algorithms. Therefore, for significantly associated immune subpopulations, independent analyses of gene expression, prognosis, and tumor immune microenvironment were conducted using HCC data from the Cancer Genome Atlas (TCGA) LIHC cohort. MR analysis and validation identified 21 immune cell subpopulations with significant causal associations to HCC risk. Among these, 12 were identified as risk factors, and 9 as protective factors. Validation in the TCGA cohort revealed that risk-associated immune subpopulations were predominantly enriched for markers of T cell exhaustion and immunosuppressive microenvironments, whereas protective subpopulations likely represented a distinct regulatory B cell subset whose function was associated with the anti-inflammatory factor interleukin-10. This study genetically confirms that specific immune cell functional subpopulations constitute causal risk factors for HCC. These subpopulations exert their effects by shaping distinct tumor immune microenvironments. These findings provide novel mechanisms for understanding the immunopathogenesis of HCC and identify potential targets for developing novel immune intervention strategies.

PMID:41366997 | DOI:10.1097/MD.0000000000045942

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