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Clinical applications of cell-free DNA-based liquid biopsy analysis

7 September 2025 at 18:00

Transl Oncol. 2025 Nov;61:102519. doi: 10.1016/j.tranon.2025.102519. Epub 2025 Sep 6.

ABSTRACT

Liquid biopsies, particularly those involving circulating tumor DNA (ctDNA) from patient blood, have emerged as crucial and minimally invasive adjuncts to standard tissue-based testing. ctDNA testing enables the identification of actionable mutations for targeted therapy and can be routinely used when tissue samples are unavailable for genotyping. Compared to tissue-based testing, ctDNA testing has the advantages of capturing spatial or temporal genomic heterogeneity and facilitating repeated assessments. The utility of liquid biopsies extends to multiple clinical applications, including cancer diagnosis, treatment monitoring, and minimal residual disease (MRD) detection. Numerous clinical trials are currently evaluating treatment strategies using ctDNA testing. In particular, the implementation of adjuvant treatment escalation or de-escalation based on MRD detection could dramatically transform future approaches to solid tumor treatment. Various ctDNA assays have been developed, and it is important to understand their strengths and weaknesses for effective clinical applications. Furthermore, ctDNA testing faces several technical challenges, including low sensitivity in detecting copy number alterations and fusions, as well as the possibility of detecting mutations associated with clonal hematopoiesis of indeterminate potential. In this review, we comprehensively discuss the methodologies and recent advancements in cfDNA-based liquid biopsies for cancer patients, covering diagnosis, genomic profiling, and treatment monitoring. Furthermore, we explore clinical trial designs employing ctDNA testing and anticipate forthcoming changes in patient care.

PMID:40915174 | PMC:PMC12450568 | DOI:10.1016/j.tranon.2025.102519

DNA methylation subtypes dictate metastatic heterogeneity of osteosarcoma via distinct tumor-stromal interactions: Multi-omics profiling and decitabine validation

7 September 2025 at 18:00

Int J Biol Macromol. 2025 Sep 5;327(Pt 2):147473. doi: 10.1016/j.ijbiomac.2025.147473. Online ahead of print.

ABSTRACT

Osteosarcoma (OS), the most prevalent primary bone malignancy in adolescents, is characterized by aggressive progression and early metastasis. However, the epigenetic drivers of its metastatic heterogeneity remain poorly understood. Herein, we integrated bulk DNA methylation profiling and single-cell RNA sequencing (scRNA-seq) to elucidate the epigenetic mechanisms driving OS metastatic heterogeneity. Consensus clustering identified two methylation subtypes (K = 2) with distinct survival outcomes, where hypermethylated (MSO-high) tumors exhibited poor prognosis. Weighted gene co-expression network analysis (WGCNA) revealed methylation-associated modules enriched in metabolic and immune pathways, pinpointing key genes such as CAMK1G and SLC11A1. Single-cell profiling uncovered MSO-high myeloid cells associated with inflammatory and oxidative phosphorylation pathways, while MSO-high OS cells displayed transdifferentiation toward fibroblasts via pseudotime trajectories, remodeling the extracellular matrix (ECM) to facilitate lung metastasis. Conversely, MSO-low tumors activated HLA-B-mediated neutrophil-CD8+ T cell interactions, promoting lymphatic metastasis via CXCR4/CXCL12 signaling. Furthermore, functional validation using the DNA demethylating agent decitabine demonstrated reduced fibroblastic transdifferentiation and suppressed invasive capacity in MSO-high osteosarcoma cells, supporting the therapeutic potential of targeting methylation dysregulation. These findings establish a model where DNA methylation dictates metastatic phenotypes through differential tumor-stromal crosstalk, providing novel targets for epigenetic therapy to disrupt fibrotic-immune networks and metastatic colonization.

PMID:40915448 | DOI:10.1016/j.ijbiomac.2025.147473

GASPS: A Multi-Omics Framework for Defining Genomic Aberration-Driven Signatures and Predicting Patient Outcomes in Lung Cancer

bioRxiv [Preprint]. 2025 Aug 25:2025.08.21.671519. doi: 10.1101/2025.08.21.671519.

ABSTRACT

Lung cancer is the most common cause of cancer-related death worldwide. Recent advancements in targeted therapies and immunotherapies have achieved remarkable success. However, patient responses to treatments with lung cancer vary substantially. The mutation status of driver genes can direct personalized treatment, but their prognostic value and treatment efficacy are limited. In this study, we developed a statistical framework named Genomic Aberration-Derived Signature for Patient Stratification (GASPS) to characterize the transcriptomic deregulation of driver genomic aberrations and stratify patients. By applying GASPS to The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) data, we developed gene signatures for 38 driver genomic aberrations, including gene mutations, amplifications, and deletions. These signatures were applied to independent lung cancer transcriptomic datasets containing a total of 2,226 patient samples. Our results indicated that these driver gene signatures are much more prognostic than their corresponding genomic mutations. Interestingly, the two EGFR-related signatures characterizing EGFR mutation and amplification, respectively, exhibited contrasting associations with prognosis, treatment response, and immune infiltration in the tumor microenvironment. Moreover, the STK11 mutation signature, rather than the mutation status, was found to be predictive of the response and long-term benefit of patients treated with immune checkpoint blockade therapy in lung cancer. This framework is readily applicable to most cancer types using existing data to improve prognostic risk assessment and treatment efficacy by guiding personalized therapies.

PMID:40909579 | PMC:PMC12407784 | DOI:10.1101/2025.08.21.671519

GASPS: A Multi-Omics Framework for Defining Genomic Aberration-Driven Signatures and Predicting Patient Outcomes in Lung Cancer

bioRxiv [Preprint]. 2025 Aug 25:2025.08.21.671519. doi: 10.1101/2025.08.21.671519.

ABSTRACT

Lung cancer is the most common cause of cancer-related death worldwide. Recent advancements in targeted therapies and immunotherapies have achieved remarkable success. However, patient responses to treatments with lung cancer vary substantially. The mutation status of driver genes can direct personalized treatment, but their prognostic value and treatment efficacy are limited. In this study, we developed a statistical framework named Genomic Aberration-Derived Signature for Patient Stratification (GASPS) to characterize the transcriptomic deregulation of driver genomic aberrations and stratify patients. By applying GASPS to The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) data, we developed gene signatures for 38 driver genomic aberrations, including gene mutations, amplifications, and deletions. These signatures were applied to independent lung cancer transcriptomic datasets containing a total of 2,226 patient samples. Our results indicated that these driver gene signatures are much more prognostic than their corresponding genomic mutations. Interestingly, the two EGFR-related signatures characterizing EGFR mutation and amplification, respectively, exhibited contrasting associations with prognosis, treatment response, and immune infiltration in the tumor microenvironment. Moreover, the STK11 mutation signature, rather than the mutation status, was found to be predictive of the response and long-term benefit of patients treated with immune checkpoint blockade therapy in lung cancer. This framework is readily applicable to most cancer types using existing data to improve prognostic risk assessment and treatment efficacy by guiding personalized therapies.

PMID:40909579 | PMC:PMC12407784 | DOI:10.1101/2025.08.21.671519

Extracting Clinical Guideline Information Using Two Large Language Models: Evaluation Study

Background: The effective implementation of personalized pharmacogenomics (PGx) requires the integration of released clinical guidelines into decision support systems (CDSS) to facilitate clinical applications. Large language models (LLMs) can be valuable tools for automating information extraction and updates. Objective: To assess the effectiveness of repeated cross-comparisons and an agreement-threshold strategy in two advanced LLMs as supportive tools for updating information. Methods: The study evaluated the performance of two LLMs, GPT-4o and Gemini-1.5-Pro, in extracting PGx clinical guidelines and comparing their outputs with expert-annotated evaluations. The two LLMs classified 385 PGx clinical guidelines, with each recommendation tested 20 times per model. Accuracy was assessed by comparing the results with manually labeled data. Two prospectively defined strategies were employed to identify inconsistent predictions. The first involved repeated cross-comparison, flagging discrepancies between the most frequent classifications from each model. The second employed a consistency threshold strategy, which designated predictions appearing in less than 60% of the 40 combined outputs as unstable. Cases flagged by either strategy were subjected to manual review. This study also estimated the overall cost of model usage and was conducted between October 1 and November 30, 2024. Results: GPT-4o and Gemini-1.5-Pro yielded reproducibility rates of 97.8% (7,534/7,700) and 98.9% (7,612/7,700), respectively, based on the most frequent classification for each query. Compared with expert labels, GPT-4o achieved 93.5% accuracy (Cohen’s Kappa=0.90; P<.001 and gemini-1.5-pro accuracy kappa="0.89;" p both models demonstrated high overall performance with comparable weighted average f1 scores gemini: the generated consistent predictions for of guideline items reducing need manual review by among these agreed-upon cases only one diverged from expert labels. applying a predefined agreement-threshold strategy further reduced number priority to although error rate slightly increased inconsistencies identified through methods prompted prioritization minimize errors enhance clinical applicability. total combined cost using llms was conclusions: findings suggest that two can effectively streamline pgx integration into cdss while maintaining minimal cost. selective remains necessary this approach offers practical scalable solution classification in workflows.>
  • ✇Nature Medicine
  • Digital twins for the personal touch Paul Webster
    Nature Medicine, Published online: 05 September 2025; doi:10.1038/s41591-025-03938-7A digital twin, or virtual organ, can help clinicians and patients make better decisions, and can even help in the design of more-efficient clinical trials.
     

Article: Virtual Panel: How Software Engineers and Team Leaders Can Excel with Artificial Intelligence

5 September 2025 at 17:00

Artificial intelligence is impacting the individual work of software developers, how professionals work together in teams, and how software teams are being managed. In this panel, we'll discuss how artificial intelligence is reshaping software development, and what mindset and skills are required for software developers and engineering leaders to become adaptable and resilient in the age of AI.

By Ben Linders, Courtney Nash, Mandy Gu, Hien Luu

Scalable generation and functional classification of genetic variants in inborn errors of immunity to accelerate clinical diagnosis and treatment

In lieu of traditional genetic variant testing approaches, an approach using scalable variant classification in primary human T cells with a clinically relevant readout can inform rapid diagnosis and treatment of inborn errors of immunity.

STAMP: Single-cell transcriptomics analysis and multimodal profiling through imaging

Single-cell transcriptomics analysis and multimodal profiling (STAMP) by imaging enables single-cell analysis of cells in suspension without the need for sequencing. The markedly reduced costs and flexible experimental designs support the profiling of millions of cells or the large-scale multiplexing of conditions, perturbations, and sample types.

Spatial joint profiling of DNA methylome and transcriptome in tissues

Nature, Published online: 03 September 2025; doi:10.1038/s41586-025-09478-x

DNA-methylation and gene-expression profiling of tissue sections at near single-cell resolution can be used to create detailed spatial maps showing how methylation and transcription interact to shape cell identity and tissue development.

Artificial intelligence for early diagnosis and risk prediction of periodontal-systemic interactions: Clinical utility and future directions

World J Methodol. 2025 Dec 20;15(4):105516. doi: 10.5662/wjm.v15.i4.105516. eCollection 2025 Dec 20.

ABSTRACT

BACKGROUND: Artificial intelligence (AI) is transforming healthcare by improving diagnostic accuracy and predictive analytics. Periodontal diseases are recognized as risk factors for systemic conditions, including type 2 diabetes mellitus, cardiovascular disease, Alzheimer's disease, polycystic ovary syndrome, thyroid dysfunction, and post-coronavirus disease 2019 complications. These conditions exhibit complex bidirectional interactions, underscoring the importance of early detection and risk stratification. Current diagnostic tools often fail to capture these interactions at an early stage, limiting timely intervention. This study hypothesizes that AI-driven approaches can significantly improve early diagnosis and risk prediction of periodontal-systemic interactions, enhancing clinical outcomes.

AIM: To evaluate AI's role in diagnosing and predicting periodontal-systemic interactions in studies from 2010 to 2024.

METHODS: This systematic review followed PRISMA guidelines (2009) and included peer-reviewed articles from PubMed, Scopus, and Embase. Studies with large sample sizes (≥ 500 participants) were selected, focusing on AI models integrating multi-omics data and advanced imaging techniques such as cone beam computed tomography and magnetic resonance imaging. Machine learning models processed structured clinical data, deep learning models combined imaging and clinical data, and natural language processing models extracted insights from clinical notes.

RESULTS: AI applications significantly enhanced diagnostic and predictive accuracy, reducing diagnostic time by 40% and improving predictive accuracy by 25% in periodontal patients with type 2 diabetes mellitus. Studies with sample sizes of 1000-1500 participants reported diagnostic accuracy improvements up to 92%, with specificity and sensitivity rates of 94% and 90%, respectively. Increasing sample sizes over the years reflected advancements in AI, data collection, and model training, reinforcing model reliability.

CONCLUSION: AI's integration of multi-omics and imaging data has transformed early diagnosis and risk prediction in periodontal-systemic interactions, improving clinical outcomes and decision-making.

PMID:40900876 | PMC:PMC12400324 | DOI:10.5662/wjm.v15.i4.105516

  • ✇AI News
  • Meta revises AI chatbot policies amid child safety concerns Muhammad Zulhusni
    Meta is revising how its AI chatbots interact with users after a series of reports exposed troubling behaviour, including interactions with minors. The company told TechCrunch it is now training its bots not to engage with teenagers on topics like self-harm, suicide, or eating disorders, and to avoid romantic banter. These are temporary steps while it develops longer-term rules. The changes follow a Reuters investigation that found Meta’s systems could generate sexualised content, including shir
     

Meta revises AI chatbot policies amid child safety concerns

3 September 2025 at 16:39

Meta is revising how its AI chatbots interact with users after a series of reports exposed troubling behaviour, including interactions with minors. The company told TechCrunch it is now training its bots not to engage with teenagers on topics like self-harm, suicide, or eating disorders, and to avoid romantic banter. These are temporary steps while it develops longer-term rules.

The changes follow a Reuters investigation that found Meta’s systems could generate sexualised content, including shirtless images of underage celebrities, and engage children in conversations that were romantic or suggestive. One case reported by the news agency described a man dying after rushing to an address provided by a chatbot in New York.

Meta spokesperson Stephanie Otway admitted the company had made mistakes. She said Meta is “training our AIs not to engage with teens on these topics, but to guide them to expert resources,” and confirmed that certain AI characters, like highly sexualised ones like “Russian Girl,” will be restricted.

Child safety advocates argue the company should have acted earlier. Andy Burrows of the Molly Rose Foundation called it “astounding” that bots were allowed to operate in ways that put young people at risk. He added: “While further safety measures are welcome, robust safety testing should take place before products are put on the market – not retrospectively when harm has taken place.”

Wider problems with AI misuse

The scrutiny of Meta’s AI chatbots comes amid broader worries about how AI chatbots may affect vulnerable users. A California couple recently filed a lawsuit against OpenAI, claiming ChatGPT encouraged their teenage son to take his own life. OpenAI has since said it is working on tools to promote healthier use of its technology, noting in a blog post that “AI can feel more responsive and personal than prior technologies, especially for vulnerable individuals experiencing mental or emotional distress.”

The incidents highlight a growing debate about whether AI firms are releasing products too quickly without proper safeguards. Lawmakers in several countries have already warned that chatbots, while useful, may amplify harmful content or give misleading advice to people who are not equipped to question it.

Meta’s AI Studio and chatbot impersonation issues

Meanwhile, Reuters reported that Meta’s AI Studio had been used to create flirtatious “parody” chatbots of celebrities like Taylor Swift and Scarlett Johansson. Testers found the bots often claimed to be the real people, engaged in sexual advances, and in some cases generated inappropriate images, including of minors. Although Meta removed several of the bots after being contacted by reporters, many were left active.

Some of the AI chatbots were created by outside users, but others came from inside Meta. One chatbot made by a product lead in its generative AI division impersonated Taylor Swift and invited a Reuters reporter to meet for a “romantic fling” on her tour bus. This was despite Meta’s policies explicitly banning sexually suggestive imagery and the direct impersonation of public figures.

The issue of AI chatbot impersonation is particularly sensitive. Celebrities face reputational risks when their likeness is misused, but experts point out that ordinary users can also be deceived. A chatbot pretending to be a friend, mentor, or romantic partner may encourage someone to share private information or even meet in unsafe situations.

Real-world risks

The problems are not confined to entertainment. AI chatbots posing as real people have offered fake addresses and invitations, raising questions about how Meta’s AI tools are being monitored. One example involved a 76-year-old man in New Jersey who died after falling while rushing to meet a chatbot that claimed to have feelings for him.

Cases like this illustrate why regulators are watching AI closely. The Senate and 44 state attorneys general have already begun probing Meta’s practices, adding political pressure to the company’s internal reforms. Their concern is not only about minors, but also about how AI could manipulate older or vulnerable users.

Meta says it is still working on improvements. Its platforms place users aged 13 to 18 into “teen accounts” with stricter content and privacy settings, but the company has not yet explained how it plans to address the full list of problems raised by Reuters. That includes bots offering false medical advice and generating racist content.

Ongoing pressure on Meta’s AI chatbot policies

For years, Meta has faced criticism over the safety of its social media platforms, particularly regarding children and teenagers. Now Meta’s AI chatbot experiments are drawing similar scrutiny. While the company is taking steps to restrict harmful chatbot behaviour, the gap between its stated policies and the way its tools have been used raises ongoing questions about whether it can enforce those rules.

Until stronger safeguards are in place, regulators, researchers, and parents will likely continue to press Meta on whether its AI is ready for public use.

(Photo by Maxim Tolchinskiy)

See also: Agentic AI: Promise, scepticism, and its meaning for Southeast Asia

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The post Meta revises AI chatbot policies amid child safety concerns appeared first on AI News.

Liquid Biopsy in CRC Management: Early Detection, Minimal Residual Disease, and Therapy Optimization-Clinical Evidence and Challenges

4 September 2025 at 18:00

Diagn Cytopathol. 2025 Nov;53(11):580-591. doi: 10.1002/dc.70009. Epub 2025 Sep 4.

ABSTRACT

Colorectal cancer (CRC) is a major global health burden, ranking among the leading causes of cancer-related deaths. Despite improvements in screening and treatment, challenges such as late-stage diagnosis, high recurrence rates, and therapy resistance continue to impede optimal outcomes. Liquid biopsy, a minimally invasive technique that analyzes tumor-derived components in bodily fluids-including circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and extracellular vesicles (EVs)-is emerging as a powerful tool to transform CRC management across the disease continuum. This review provides a comprehensive overview of liquid biopsy's current and emerging applications in CRC. We examine its role in early detection, where sensitive ctDNA-based assays and epigenetic biomarkers have demonstrated the ability to identify CRC at asymptomatic or early stages, potentially improving screening uptake and compliance. Furthermore, we explore how liquid biopsy enables dynamic monitoring of treatment response and clonal evolution, facilitating the timely identification of resistance mutations and supporting personalized therapy adjustments. Innovations in multi-omics integration, artificial intelligence, and ultra-sensitive sequencing technologies are also discussed as pivotal advancements that enhance the clinical utility of liquid biopsy. Despite significant progress, the widespread adoption of liquid biopsy faces several hurdles, including assay standardization, sensitivity for low-shedding tumors, regulatory approval, and cost-effectiveness. Continued research, validation in large prospective trials, and harmonization of testing protocols are essential to overcome these challenges. Ultimately, liquid biopsy holds the potential to become a cornerstone of precision oncology in CRC, enabling earlier intervention, more tailored treatment strategies, and improved patient outcomes.

PMID:40905096 | DOI:10.1002/dc.70009

  • ✇Omics In Lung
  • Challenges in diagnosis of sarcoidosis Karol Bączek · Wojciech J Piotrowski · Francesco Bonella
    Curr Opin Immunol. 2025 Sep 1;97:102652. doi: 10.1016/j.coi.2025.102652. Online ahead of print.ABSTRACTPURPOSE OF REVIEW: Diagnosing sarcoidosis remains challenging. Histology findings and a variable clinical presentation can mimic other infectious, malignant, and autoimmune diseases. This review synthesizes current evidence on histopathology, sampling techniques, imaging modalities, and biomarkers and explores how emerging 'omics' and artificial intelligence tools may sharpen diagnostic accurac
     

Challenges in diagnosis of sarcoidosis

Curr Opin Immunol. 2025 Sep 1;97:102652. doi: 10.1016/j.coi.2025.102652. Online ahead of print.

ABSTRACT

PURPOSE OF REVIEW: Diagnosing sarcoidosis remains challenging. Histology findings and a variable clinical presentation can mimic other infectious, malignant, and autoimmune diseases. This review synthesizes current evidence on histopathology, sampling techniques, imaging modalities, and biomarkers and explores how emerging 'omics' and artificial intelligence tools may sharpen diagnostic accuracy.

RECENT FINDINGS: Within the typical granulomatous lesions, limited or 'burned-out' necrosis is an ancillary finding, which can be present in up to one-third of sarcoid biopsies, and demands a careful differential diagnostic work-up. Endobronchial ultrasound-guided transbronchial needle aspiration of lymph nodes has replaced mediastinoscopy as first-line sampling tool, while cryobiopsy is still under validation. Volumetric PET metrics such as total lung glycolysis and somatostatin-receptor tracers refine activity assessment; combined FDG PET/MRI improves detection of occult cardiac disease. Advanced bronchoalveolar lavage (BAL) immunophenotyping via flow cytometry and serum, BAL, and genetic biomarkers show to correlate with inflammatory burden but have low diagnostic value. Multi-omics signatures and Positron Emission Tomography with Computer Tomography radiomics, supported by deep-learning algorithms, show promising results for noninvasive diagnostic confirmation, phenotyping, and disease monitoring.

SUMMARY: No single test is conclusive for diagnosing sarcoidosis. An integrated, multidisciplinary strategy is needed. Large, multicenter, and multiethnic studies are essential to translate and validate data from emerging AI tools and -omics research into clinical routine.

PMID:40902264 | DOI:10.1016/j.coi.2025.102652

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