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AI tool adjusts for ancestral bias in genetic data
Nature Biotechnology, Published online: 14 April 2025; doi:10.1038/s41587-025-02651-7
AI tool adjusts for ancestral bias in genetic data
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Circulating tumour DNA and circulating tumour cells in bladder cancer - from discovery to clinical implementation
Nat Rev Urol. 2025 Apr 15. doi: 10.1038/s41585-025-01023-9. Online ahead of print.
ABSTRACT
Liquid biopsies, indicating the sampling of body fluids rather than solid-tissue biopsies, have the potential to revolutionize cancer care through personalized, noninvasive disease detection and monitoring. Circulating tumour DNA (ctDNA) and circulating tumour cells (CTCs) are promising blood-based biomarkers in bladder cancer. Results from several studies have shown the clinical potential of ctDNA and CTCs in bladder cancer for prognostication, treatment-response monitoring, and early detection of minimal residual disease and disease recurrence. Following successful clinical trial evaluation, assessment of ctDNA and CTCs holds the potential to transform the therapeutic pathway for patients with bladder cancer - potentially in combination with the analysis of urinary tumour DNA - through tailored treatment guidance and optimized disease surveillance.
PMID:40234713 | DOI:10.1038/s41585-025-01023-9
AI Continent: European Commission Outlines Strategy for Scaling AI Development
The European Commission has presented the AI Continent Action Plan, a new strategy designed to strengthen the European Union’s capacity for AI development and deployment. The plan outlines coordinated investment in infrastructure, access to high-quality data, AI adoption in strategic sectors, and support for regulatory implementation.
By Robert Krzaczyński
MLG2Net: Molecular Global Graph Network for Drug Response Prediction in Lung Cancer Cell Lines
J Med Syst. 2025 Apr 10;49(1):47. doi: 10.1007/s10916-025-02182-3.
ABSTRACT
Drug response prediction (DRP) is a central task in the era of precision medicine. Over the past decade, the emergence of deep learning (DL) has greatly contributed to addressing DRP challenges. Notably, the prediction of DRP for cancer cell lines benefits significantly from data availability for model development. However, an effective predictive model is still challenging due to issues with data quality, high-dimensional data, and multi-omics data integration. In this study, we introduce MLG2Net, a deep-learning model inspired by graph neural networks designed to predict DRP in lung cancer cell lines based on pharmacogenomics data. Our model comprises two key components: drug SMILES described by local and global graph networks and cell line genomics are illustrated as a map. Our results show that MLG2Net outperforms three reference graph networks. MLG2Net performance reached a Pearson coefficient correlation ( ) of 0.8616 and a root mean square error (RMSE) of 2.94e-6 in predicting drug responses for Lung Adenocarcinoma (LUAD) cell lines. Subsequent testing on the Lung Squamous Cell Carcinoma (LUSC) dataset reveals lower performance ( : 0.7999, RMSE: 4.08e-6), attributed to the dataset's smaller size influencing model capacity. Moreover, we assessed the model's architecture by isolating its components, with results indicating that the global network is particularly effective in this task. In conclusion, MLG2Net exhibited promising applications in DRP for cancer cell lines, with potential advancements by incorporating larger datasets.
PMID:40208442 | DOI:10.1007/s10916-025-02182-3
Data centres will use twice as much energy by 2030 — driven by AI
Nature, Published online: 10 April 2025; doi:10.1038/d41586-025-01113-z
Data centres accounted for roughly 1.5% of global electricity consumption in 2024.
MLG2Net: Molecular Global Graph Network for Drug Response Prediction in Lung Cancer Cell Lines
J Med Syst. 2025 Apr 10;49(1):47. doi: 10.1007/s10916-025-02182-3.
ABSTRACT
Drug response prediction (DRP) is a central task in the era of precision medicine. Over the past decade, the emergence of deep learning (DL) has greatly contributed to addressing DRP challenges. Notably, the prediction of DRP for cancer cell lines benefits significantly from data availability for model development. However, an effective predictive model is still challenging due to issues with data quality, high-dimensional data, and multi-omics data integration. In this study, we introduce MLG2Net, a deep-learning model inspired by graph neural networks designed to predict DRP in lung cancer cell lines based on pharmacogenomics data. Our model comprises two key components: drug SMILES described by local and global graph networks and cell line genomics are illustrated as a map. Our results show that MLG2Net outperforms three reference graph networks. MLG2Net performance reached a Pearson coefficient correlation ( ) of 0.8616 and a root mean square error (RMSE) of 2.94e-6 in predicting drug responses for Lung Adenocarcinoma (LUAD) cell lines. Subsequent testing on the Lung Squamous Cell Carcinoma (LUSC) dataset reveals lower performance ( : 0.7999, RMSE: 4.08e-6), attributed to the dataset's smaller size influencing model capacity. Moreover, we assessed the model's architecture by isolating its components, with results indicating that the global network is particularly effective in this task. In conclusion, MLG2Net exhibited promising applications in DRP for cancer cell lines, with potential advancements by incorporating larger datasets.
PMID:40208442 | DOI:10.1007/s10916-025-02182-3
Early Screening and Subtype Identification of High-Risk Lung Nodules via Breathprint by Graphene eNose Platform: A Large Cohort Study
ACS Sens. 2025 Apr 25;10(4):3101-3111. doi: 10.1021/acssensors.5c00314. Epub 2025 Apr 7.
ABSTRACT
Early screening of individuals with high-risk lung nodules can significantly improve the prognosis of lung cancer patients, and accurate identification of lung nodule subtypes can provide guidance for medical treatment. Exhaled breath (EB) analysis via eNoses offers a quick and noninvasive approach, but current eNose technology lacks quality control and solid validation in large population studies. Herein, an eNose platform integrated with a metal ion-decorated graphene sensor array and a breath sampling accessory was established. EB samples from 427 healthy subjects and 2586 subjects with lung nodules, including various benign and malignant subtypes, were collected through the breath sampling accessory for quality control. The large-cohort clinical EB samples were analyzed by the eNose platform to acquire the cross-reactive resistance response. Breathprint analysis for high-risk lung nodules using SVM and age-matched training sets yielded strong and robust performance. Combined with baseline data, the model achieved an AUC of 0.93 (95% CI, 0.89-0.96) on the external test set, with 97% sensitivity and 73% specificity. Moreover, dimensionality reduction analysis of breathprints demonstrated separability across different lung nodule subtypes. This study demonstrates the reliability of the graphene eNose platform to identify high-risk lung nodules and classify lung nodule subtypes in a noninvasive and rapid method.
PMID:40193324 | DOI:10.1021/acssensors.5c00314
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...
Spatial multi-omics reveals cell-type-specific nuclear compartments
Nature, Published online: 09 April 2025; doi:10.1038/s41586-025-08838-x
A genomic barcoding scheme called two-layer DNA seqFISH+ enables the simultaneous mapping of more than 100,000 loci and has been used to identify cell-type-specific subnuclear compartments in the mouse brain.
Liquid Biopsy in Solid Tumours: An Overview
Cytopathology. 2025 Apr 11. doi: 10.1111/cyt.13485. Online ahead of print.
ABSTRACT
The advent of personalised and precision medicine has radically modified the management and the clinical outcome of cancer patients. However, the expanding number of predictive, prognostic, and diagnostic biomarkers has raised the need for simple, noninvasive, quicker, but equally efficient tests for molecular profiling. In this complex scenario, the adoption of liquid biopsy, particularly circulating tumour DNA (ctDNA), has been a real godsend for many cancer patients who would otherwise have been denied the benefits of targeted treatments. Undeniably, ctDNA analysis has several advantages over conventional tissue-based analysis. One advantage is that it can guide treatment decision making, especially when tissue samples are scarce or totally unavailable. Indeed, a simple blood test can inform clinicians on patients' response or resistance to targeted therapies, help them monitor minimal residual disease (MRD) after surgical resections, and facilitate them with early cancer detection and interception. Finally, an equally important advantage is that ctDNA analysis can help decipher temporal and spatial tumour heterogeneity, a mechanism highly responsible for therapeutic resistance. In this review, we gathered and analysed current evidence on the clinical usefulness of ctDNA analysis in solid tumours.
PMID:40219616 | DOI:10.1111/cyt.13485
Integration of Liquid Biopsy for Optimal Management of NSCLC
Tuberc Respir Dis (Seoul). 2025 Apr 8. doi: 10.4046/trd.2024.0146. Online ahead of print.
ABSTRACT
Molecular profiling of tumours from patients plays a crucial role in precision oncology. While tumour tissue-based genomic testing remains the gold standard in clinical management of patients with non-small cell lung cancer, advances in genomic technologies, the analysis of various bodily fluids, mainly blood but also saliva, pleural/ pericardial effusions, urine, and cerebrospinal fluid is now feasible and readily available. In this review, we will focus on the clinical application of circulating tumour DNA in patients with non-small cell lung cancer in the setting of early-stage disease, locally advanced disease with attention to the potential of ctDNA in prognostication, risk stratification, minimal residual disease, and in advanced disease, its role in the detection of genomic markers and mechanisms of acquired resistance. The role of ctDNA and liquid biopsies in lung cancer screening will also be discussed.
PMID:40195729 | DOI:10.4046/trd.2024.0146
A multiomics dataset of paired CT image and plasma cell-free DNA end motif for patients with pulmonary nodules
Sci Data. 2025 Apr 1;12(1):545. doi: 10.1038/s41597-025-04912-1.
ABSTRACT
Diagnosing lung cancer at a curable stage offers the opportunity for a favorable prognosis. The emerging epigenomics analysis on plasma cell-free DNA (cfDNA), including 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) modifications, has acted as a promising approach facilitating the identification of lung cancer. And, integrating 5mC biomarker with chest computed tomography (CT) image features could optimize the diagnosis of lung cancer, exceeding the performance of models built on single feature. However, the clinical applicability of integrated markers might be limited by the potential risk of overfitting due to small sample size. Hence, we prospectively collected peripheral blood sample and the paired chest CT images of 2032 patients with indeterminate pulmonary nodules across 5 centers, and constructed a large-scale, multi-institutional, multiomics database that encompass CT imaging data and plasma cfDNA fragmentomic in 5mC-, 5hmC-enriched regions. To our best knowledge, this dataset is the first radio-epigenomic dataset with the largest sample size, and provides multi-dimensional insights for early diagnosis of lung cancer, facilitating the individuated management for lung cancer.
PMID:40169596 | PMC:PMC11961589 | DOI:10.1038/s41597-025-04912-1
Translating the multifaceted use of liquid biopsy to management of early disease in pancreatic adenocarcinoma
Front Oncol. 2025 Mar 13;15:1520717. doi: 10.3389/fonc.2025.1520717. eCollection 2025.
ABSTRACT
Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer-related mortality, primarily due to late stage at diagnosis. This review examines the multifaceted applications of liquid biopsy and circulating tumor DNA (ctDNA) analysis in the diagnosis and management of PDAC. We review the current literature on the technological advancements in liquid biopsy analysis such as next generation sequencing (NGS) and digital droplet PCR (ddPCR) as well as multi-omics technologies, highlighting their potential for accurate molecular subtyping through ctDNA analysis. This review highlights the significant role of ctDNA in the assessment of tumor behavior, disease subtyping, prediction and monitoring of treatment response, and evaluation of minimal residual disease. We discuss the implications of integrating liquid biopsy techniques into clinical practice as well as its challenges and limitations. By drawing insights from recent studies, this review aims to provide a comprehensive overview of how liquid biopsy and ctDNA analysis can enhance early disease management strategies in PDAC. We underscore the need for additional prospective studies and clinical trials to validate its feasibility and accuracy in order to establish clinical utility, with the ultimate goal of routine incorporation into practice to improve patient outcomes and transform the treatment landscape for PDAC.
PMID:40182037 | PMC:PMC11966063 | DOI:10.3389/fonc.2025.1520717
Translating the multifaceted use of liquid biopsy to management of early disease in pancreatic adenocarcinoma
Front Oncol. 2025 Mar 13;15:1520717. doi: 10.3389/fonc.2025.1520717. eCollection 2025.
ABSTRACT
Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer-related mortality, primarily due to late stage at diagnosis. This review examines the multifaceted applications of liquid biopsy and circulating tumor DNA (ctDNA) analysis in the diagnosis and management of PDAC. We review the current literature on the technological advancements in liquid biopsy analysis such as next generation sequencing (NGS) and digital droplet PCR (ddPCR) as well as multi-omics technologies, highlighting their potential for accurate molecular subtyping through ctDNA analysis. This review highlights the significant role of ctDNA in the assessment of tumor behavior, disease subtyping, prediction and monitoring of treatment response, and evaluation of minimal residual disease. We discuss the implications of integrating liquid biopsy techniques into clinical practice as well as its challenges and limitations. By drawing insights from recent studies, this review aims to provide a comprehensive overview of how liquid biopsy and ctDNA analysis can enhance early disease management strategies in PDAC. We underscore the need for additional prospective studies and clinical trials to validate its feasibility and accuracy in order to establish clinical utility, with the ultimate goal of routine incorporation into practice to improve patient outcomes and transform the treatment landscape for PDAC.
PMID:40182037 | PMC:PMC11966063 | DOI:10.3389/fonc.2025.1520717
20 years of histone lysine demethylases: From discovery to the clinic and beyond
Histone lysine demethylases are conserved enzymes that remove methyl groups from histone proteins and play important roles in development and disease. On the 20th anniversary of their discovery, this Review provides an in-depth view of their functions and roles across various contexts as well as therapeutic options to be developed for diseases related to these enzymes.
Synonymous mutations promote tumorigenesis by disrupting m6A-dependent mRNA metabolism
The impact of synonymous mutations remains elusive. Here, the authors demonstrate that synonymous mutations can promote tumorigenesis by disrupting post-transcriptional m6A modification. The findings provide fresh insights into understanding the genotype-phenotype relationship in cancer and beyond.
Treatment of advanced-stage non-small cell lung cancer: Current progress and a glimpse into the future (Review)
Mol Clin Oncol. 2025 Mar 12;22(5):42. doi: 10.3892/mco.2025.2837. eCollection 2025 May.
ABSTRACT
Before the twentieth century, patients with advanced lung cancer had limited treatment options and chemotherapy was the primary form of treatment, with an overall survival often <0.5 years. However, with advances in society and medical technology, the treatment approaches for advanced non-small cell lung cancer (NSCLC) have markedly changed. Traditional chemotherapy has been gradually replaced by targeted therapy and immunotherapy, leading to the emergence of various new therapeutic options that offer patients more personalized and precise care. This raises the question of what the future holds for the treatment of NSCLC. This review provides a comprehensive analysis of the latest breakthroughs in targeted therapies, immunotherapies, and drugs for antibody-drug conjugates (ADCs), highlights advances in multimodal combination therapy strategies, and explores the causes of resistance and the challenges that exist in overcoming it. In particular, this review provides unique insights into key directions for future research in NSCLC, such as personalised treatment strategies and biomarker exploration based on multi-omics data, aiming to provide new inspiration for clinical decision-making and research.
PMID:40160297 | PMC:PMC11948471 | DOI:10.3892/mco.2025.2837
How a bankruptcy judge can stop a genetic privacy disaster
Stop me if you’ve heard this one before: A tech company accumulates a ton of user data, hoping to figure out a business model later. That business model never arrives, the company goes under, and the data is in the wind.
The latest version of that story emerged on March 24, when the onetime genetic testing darling 23andMe filed for bankruptcy. Now the fate of 15 million people’s genetic data rests in the hands of a bankruptcy judge. At a hearing on March 26, the judge gave 23andMe permission to seek offers for its users’ data. But, there’s still a small chance of writing a better ending for users.
After the bankruptcy filing, the immediate take from policymakers and privacy advocates was that 23andMe users should delete their accounts to prevent genetic data from falling into the wrong hands. That’s good advice for the individual user (and you can read how to do so here). But the reality is most people won’t do it. Maybe they won’t see the recommendations to do so. Maybe they don’t know why they should be worried. Maybe they have long since abandoned an account that they don’t even remember exists. Or maybe they’re just occupied with the chaos of everyday life.
This means the real value of this data comes from the fact that people have forgotten about it. Given 23andMe’s meager revenue—fewer than 4% of people who took tests pay for subscriptions—it seems inevitable that the new owner, whoever it is, will have to find some new way to monetize that data.
This is a terrible deal for users who just wanted to learn a little more about themselves or their ancestry. Because genetic data is forever. Contact information can go stale over time: you can always change your password, your email, your phone number, or even your address. But a bad actor who has your genetic data—whether a cybercriminal selling it to the highest bidder, a company building a profile of your future health risk, or a government trying to identify you—will have it tomorrow and the next day and all the days after that.
Users with exposed genetic data are not only vulnerable to harm today; they’re vulnerable to exploits that might be developed in the future.
While 23andMe promises that it will not voluntarily share data with insurance providers, employers, or public databases, its new owner could unwind those promises at any time with a simple change in terms.
In other words: If a bankruptcy court makes a mistake authorizing the sale of 23andMe’s user data, that mistake is likely permanent and irreparable.
All this is possible because American lawmakers have neglected to meaningfully engage with digital privacy for nearly a quarter-century. As a result, services are incentivized to make flimsy, deceptive promises that can be abandoned at a moment’s notice. And the burden falls on users to keep track of it all, or just give up.
Here, a simple fix would be to reverse that burden. A bankruptcy court could require that users individually opt in before their genetic data can be transferred to 23andMe’s new owners, regardless of who those new owners are. Anyone who didn’t respond or who opted out would have the data deleted.
Bankruptcy proceedings involving personal data don’t have to end badly. In 2000, the Federal Trade Commission settled with the bankrupt retailer ToySmart to ensure that its customer data could not be sold as a stand-alone asset, and that customers would have to affirmatively consent to unexpected new uses of their data. And in 2015, the FTC intervened in the bankruptcy of RadioShack to ensure that it would keep its promises never to sell the personal data of its customers. (RadioShack eventually agreed to destroy it.)
The ToySmart case also gave rise to the role of the consumer privacy ombudsman. Bankruptcy judges can appoint an ombuds to help the court consider how the sale of personal data might affect the bankruptcy estate, examining the potential harms or benefits to consumers and any alternatives that might mitigate those harms. The U.S. Trustee has requested the appointment of an ombuds in this case. While scholars have called for the role to have more teeth and for the FTC and states to intervene more often, a framework for protecting personal data in bankruptcy is available. And ultimately, the bankruptcy judge has broad power to make decisions about how (or whether) property in bankruptcy is sold.
Here, 23andMe has a more permissive privacy policy than ToySmart or RadioShack. But the risks incurred if genetic data falls into the wrong hands or is misused are severe and irreversible. And given 23andMe’s failure to build a viable business model from testing kits, it seems likely that a new business would use genetic data in ways that users wouldn’t expect or want.
An opt-in requirement for genetic data solves this problem. Genetic data (and other sensitive data) could be held by the bankruptcy trustee and released as individual users gave their consent. If users failed to opt in after a period of time, the remaining data would be deleted. This would incentivize 23andMe’s new owners to earn user trust and build a business that delivers value to users, instead of finding unexpected ways to exploit their data. And it would impose virtually no burden on the people whose genetic data is at risk: after all, they have plenty more DNA to spare.
Consider the alternative. Before 23andMe went into bankruptcy, its then-CEO made two failed attempts to buy it, at reported valuations of $74.7 million and $12.1 million. Using the higher offer, and with 15 million users, that works out to a little under $5 per user. Is it really worth it to permanently risk a person’s genetic privacy just to add a few dollars in value to the bankruptcy estate?
Of course, this raises a bigger question: Why should anyone be able to buy the genetic data of millions of Americans in a bankruptcy proceeding? The answer is simple: Lawmakers allow them to. Federal and state inaction allows companies to dissolve promises about protecting Americans’ most sensitive data at a moment’s notice. When 23andMe was founded, in 2006, the promise was that personalized health care was around the corner. Today, 18 years later, that era may really be almost here. But with privacy laws like ours, who would trust it?
Keith Porcaro is the Rueben Everett Senior Lecturing Fellow at Duke Law School.
Spatial immune remodeling of the liver metastases: discovering the path to antimetastatic therapy
J Immunother Cancer. 2025 Mar 18;13(3):e011002. doi: 10.1136/jitc-2024-011002.
ABSTRACT
The intrinsic characteristics of metastatic tumors are of great importance in terms of the development of antimetastatic treatment strategies. Elucidation from a spatial immune perspective has the potential to provide a more comprehensive understanding of the mechanisms underlying immune escape, effectively addressing the limitations of relying solely on the analysis of immune cell subpopulation transcriptional profiles. Advances in spatial omics technology enable researchers to precisely analyze precious liver metastasis samples in a high-throughput manner, revealing spatial alterations in immune cell distribution induced by metastasis and exploring the molecular basis of the remodeling process. The aggregation of specific cell subpopulations in distinct regions not only modifies local immune characteristics but also concurrently affects global biological behaviors of liver metastatic tumors. Identifying specific spatial immune characteristics in pretreatment or early-stage treatment tissue samples may achieve accurate clinical predictions. Moreover, developing strategies that target spatial immune remodeling is a promising avenue for future antimetastatic therapy.
PMID:40107672 | PMC:PMC11927485 | DOI:10.1136/jitc-2024-011002
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
