Nature, Published online: 25 June 2025; doi:10.1038/d41586-025-01925-z
If companies do step in to support research, will they publish the results, or keep them close to their chest?
Nature, Published online: 25 June 2025; doi:10.1038/d41586-025-01929-9
Here’s where academic researchers are flocking to — and the topics they’re pursuing.
Nature, Published online: 25 June 2025; doi:10.1038/s41586-025-09202-9
Disruption of dendritic cell (DC) interstitial motility in the tumour microenvironment promotes immune evasion, and enhancement of DC interstitial motility offers a route for DC-centric immunotherapy.
Nature, Published online: 25 June 2025; doi:10.1038/d41586-025-01998-w
Tool aims to solve the mystery of non-coding sequences — but is still in its infancy.
Adv Respir Med. 2025 Jun 12;93(3):17. doi: 10.3390/arm93030017.
ABSTRACT
Circulating free DNA (cfDNA) is genetic material released from various cells into bodily fluids. Among its fractions, circulating tumor DNA (ctDNA) originates from tumor cells and reflects their genetic material, including mutations and epigenetic changes. Methods commonly employed for detecting ctDNA in blood include next-generation sequencing (NGS) and various types of PCR. The presence of ctDNA can be utilized in liquid biopsies for many diagnostic purposes related to various cancers. It is a minimally invasive method of sampling molecular compounds from tumor cells. In this paper, we focus on current knowledge regarding the liquid biopsy of blood ctDNA in the context of lung cancer, one of the leading causes of cancer-related mortality. Currently, as a clinically approved method, liquid biopsy serves as a complementary technique in NSCLC diagnostic and genetic profiling. Other applications of liquid biopsy that are still being investigated include the detection of minimal residual disease (MRD) after curative treatment and response monitoring to systemic treatment. This review discusses current and future potential directions for the development and implementation of ctDNA for patients with NSCLC.
PMID:40558116 | PMC:PMC12189613 | DOI:10.3390/arm93030017
MiniMax has introduced MiniMax-M1, a new open-weight reasoning model built to handle extended contexts and complex problem-solving with high efficiency. Built on top of the earlier MiniMax-Text-01, M1 features a hybrid Mixture-of-Experts (MoE) architecture and a novel “lightning attention” mechanism.
By Robert Krzaczyński
Trends Mol Med. 2025 Jun 23:S1471-4914(25)00118-2. doi: 10.1016/j.molmed.2025.05.007. Online ahead of print.
ABSTRACT
Gastric cancer (GC) remains a deadly disease because of late detection and limited treatment options at advanced stages. Treatment of patients with metastatic disease is based on chemotherapy, complemented by antibodies targeting HER2, VEGFR2, and more recently PD-1 or claudin 18.2. Further targets, such as FGFR2b, as well as novel drug classes including antibody-drug conjugates (ADCs) and bispecific antibodies, are promising developments in GC treatment. Despite the failure of several targeted agents, the landscape of GC therapy is evolving rapidly, facilitated by umbrella or platform precision medicine trials. The integration of next-generation sequencing and other omics techniques into molecular tumor boards, as well as functional drug testing on patient-derived models, might bring us closer to personalized oncology and ultimately improve patient survival.
PMID:40555635 | DOI:10.1016/j.molmed.2025.05.007
Front Immunol. 2025 Jun 6;16:1606125. doi: 10.3389/fimmu.2025.1606125. eCollection 2025.
ABSTRACT
BACKGROUND: Lung adenocarcinoma (LUAD) is characterized by metabolic and immune heterogeneity, driving tumor progression and therapy resistance. While G protein-coupled receptors (GPCR) signaling is known to regulate metabolism and immunity in cancers, its role in LUAD remains poorly defined. This study explores the influence of GPCR signaling on LUAD metabolism and immune landscape.
METHODS: We performed non-negative matrix factorization (NMF) clustering of GPCR signaling genes in TCGA-LUAD cohort to identify distinct molecular subgroups. A prognostic model was developed based on GPCR signaling genes using least absolute shrinkage and selection operator (LASSO) analysis and Cox regression. Differentially expressed genes were analyzed for metabolic pathway enrichment and immune infiltration. In addition, key genes within GPCR signaling were identified and validated through functional assays.
RESULTS: NMF clustering based on GPCR signaling identified three subgroups in LUAD, with cluster 3 exhibiting poorer overall survival and significant enrichment in multiple prognostic associated metabolism pathways including purine, pyrimidine, glyoxylate and dicarboxylate metabolism. Then, we developed a GPCRscore prognostic model and validated across multiple cohorts, which effectively stratified LUAD patients into distinct risk groups. High-risk LUAD patients had an immunosuppressive microenvironment and activated metabolic reprogramming. ADM was identified as a key gene in the high-risk group, correlating with tumor stage, immune suppression, and resistance to immunotherapy. Clinically, ADM was highly expressed in tumor tissues and shows elevated concentrations in the peripheral blood of patients with advanced-stage LUAD. Subsequently, we demonstrated that knock-down of ADM in LUAD cells impaired their proliferation, migration, and invasion, while also reducing the angiogenic potential of endothelial cells in vitro. Adrenomedullin promoted LUAD progression in a murine metastasis model. Further, adrenomedullin inhibited CD8+ T cells proliferation, induced exhaustion, and impaired cytotoxic function. Finally, drug sensitivity and cell viability analysis showed LUAD patients with high levels of ADM exhibited sensitivity to the treatment of Staurosporine and Dasatinib.
CONCLUSIONS: In summary, this study reveals the pivotal role of GPCR signaling particularly mediated by ADM in orchestrating metabolic reprogramming and immune modulation in LUAD. ADM emerges as a potential predictive biomarker and therapeutic target, offering valuable implications for optimizing strategies.
PMID:40547013 | PMC:PMC12179119 | DOI:10.3389/fimmu.2025.1606125
When testing an AI model, it’s hard to tell if it is reasoning or just regurgitating answers from its training data. Xbench, a new benchmark developed by the Chinese venture capital firm HSG, or HongShan Capital Group, might help to sidestep that issue. That’s thanks to the way it evaluates models not only on the ability to pass arbitrary tests, like most other benchmarks, but also on the ability to execute real-world tasks, which is more unusual. It will be updated on a regular basis to try to keep it evergreen.
This week the company is making part of its question set open-source and letting anyone use for free. The team has also released a leaderboard comparing how mainstream AI models stack up when tested on Xbench. (ChatGPT o3 ranked first across all categories, though ByteDance’s Doubao, Gemini 2.5 Pro, and Grok all still did pretty well, as did Claude Sonnet.)
Development of the benchmark at HongShan began in 2022, following ChatGPT’s breakout success, as an internal tool for assessing which models are worth investing in. Since then, led by partner Gong Yuan, the team has steadily expanded the system, bringing in outside researchers and professionals to help refine it. As the project grew more sophisticated, they decided to release it to the public.
Xbench approached the problem with two different systems. One is similar to traditional benchmarking: an academic test that gauges a model’s aptitude on various subjects. The other is more like a technical interview round for a job, assessing how much real-world economic value a model might deliver.
Xbench’s methods for assessing raw intelligence currently include two components: Xbench-ScienceQA and Xbench-DeepResearch. ScienceQA isn’t a radical departure from existing postgraduate-level STEM benchmarks like GPQA and SuperGPQA. It includes questions spanning fields from biochemistry to orbital mechanics, drafted by graduate students and double-checked by professors. Scoring rewards not only the right answer but also the reasoning chain that leads to it.
DeepResearch, by contrast, focuses on a model’s ability to navigate the Chinese-language web. Ten subject-matter experts created 100 questions in music, history, finance, and literature—questions that can’t just be googled but require significant research to answer. Scoring favors breadth of sources, factual consistency, and a model’s willingness to admit when there isn’t enough data. A question in the publicized collection is “How many Chinese cities in the three northwestern provinces border a foreign country?” (It’s 12, and only 33% of models tested got it right, if you are wondering.)
On the company’s website, the researchers said they want to add more dimensions to the test—for example, aspects like how creative a model is in its problem solving, how collaborative it is when working with other models, and how reliable it is.
The team has committed to updating the test questions once a quarter and to maintain a half-public, half-private data set.
To assess models’ real-world readiness, the team worked with experts to develop tasks modeled on actual workflows, initially in recruitment and marketing. For example, one task asks a model to source five qualified battery engineer candidates and justify each pick. Another asks it to match advertisers with appropriate short-video creators from a pool of over 800 influencers.
The website also teases upcoming categories, including finance, legal, accounting, and design. The question sets for these categories have not yet been open-sourced.
ChatGPT-o3 again ranks first in both of the current professional categories. For recruiting, Perplexity Search and Claude 3.5 Sonnet take second and third place, respectively. For marketing, Claude, Grok, and Gemini all perform well.
“It is really difficult for benchmarks to include things that are so hard to quantify,” says Zihan Zheng, the lead researcher on a new benchmark called LiveCodeBench Pro and a student at NYU. “But Xbench represents a promising start.”
Around the world, countries are facing the challenges of aging populations, growing rates of chronic disease, and workforce shortages, leading to a growing burden on health care systems. From diagnosis to treatment, AI and other digital solutions can enhance the efficiency and effectiveness of health care, easing the burden on straining systems. According to the World Health Organization (WHO), spending an additional $0.24 per patient per year on digital health interventions could save more than two million lives from non-communicable diseases over the next decade.
To work most effectively, digital solutions need to be scaled and embedded in an ecosystem that ensures a high degree of interoperability, data security, and governance. If not, the proliferation of point solutions— where specialized software or tools focus on just one specific area or function—could lead to silos and digital canyons, complicating rather than easing the workloads of health care professionals, and potentially impacting patient treatment. Importantly, technologies that enhance workforce productivity should keep humans in the loop, aiming to augment their capabilities, rather than replace them.
Through a survey of 300 health care executives and a program of interviews with industry experts, startup leaders, and academic researchers, this report explores the best practices for success when implementing integrated digital solutions into health care, and how these can support decision-makers in a range of settings, including laboratories and hospitals.
Key findings include:
Health care is primed for digital adoption. The global pandemic underscored the benefits of value-based care and accelerated the adoption of digital and AI-powered technologies in health care. Overwhelmingly, 96% of the survey respondents say they are “ready and resourced” to use digital health, while one in four say they are “very ready.” However, 91% of executives agree interoperability is a challenge, with a majority (59%) saying it will be “tough” to solve. Two in five leaders say balancing security with usability is the biggest challenge for digital health. With the adoption of cloud solutions, organizations can enjoy the benefits of modernized IT infrastructure: 36% of the survey respondents believe scalability is the main benefit, followed by improved security (28%).
Digital health care can help health care institutions transform patient outcomes—if built on the right foundations. Solutions like AI-powered diagnostics, telemedicine, and remote monitoring can offer measurable impact across the patient journey, from improving early disease detection to reducing hospital readmission rates. However, these technologies can only support fully connected health care when scaled up and embedded in ecosystems with robust data governance, interoperability, and security.
Health care data has immense potential—but fragmentation and poor interoperability hinder impact. Health care systems generate vast quantities of data, yet much of it remains siloed or unusable due to inconsistent formats and incompatible IT systems, limiting scalability.
Digital tools must augment, not overload, the workforce. With global health care workforce shortages worsening, digital solutions like clinical decision support tools, patient prediction, and remote monitoring can be seen as essential aids rather than threats to the workforce. Successful deployment depends on usability, clinician engagement, and training.
Regulatory evolution, open data policies, and economic sustainability are key to scaling digital health. Even the best digital tools struggle to scale without reimbursement frameworks, regulatory support, and viable business models. Open data ecosystems are needed to unleash the clinical and economic value of innovation. Regulatory and reimbursement innovation is also critical to transitioning from pilot projects to high-impact, system-wide adoption.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.
This content was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
iScience. 2025 May 15;28(6):112681. doi: 10.1016/j.isci.2025.112681. eCollection 2025 Jun 20.
ABSTRACT
Metabolic reprogramming is a defining feature of pancreatic cancer, influencing tumor progression and the tumor microenvironment. By integrating single-cell transcriptomics, spatial transcriptomics, and spatial metabolomics, this study visualized the spatial co-localization of metabolites and gene expression within tumor samples, uncovering metabolic heterogeneity and intercellular interactions. Spatial transcriptomics identified distinct pathological regions, which were further characterized using single-cell transcriptomic data and pathologist annotations. Pseudotime trajectory analysis revealed metabolic shifts along the malignant progression, while single-cell Metabolism (scMetabolism) delineated metabolic differences between pathological regions, classifying them as hypermetabolic or hypometabolic. Notably, aberrant cell communication between cancer cells, macrophages, and fibroblasts was observed, with key receptor-ligand pairs significantly co-expressed in malignant regions and correlated with poor prognosis. Spatial metabolomics imaging identified signature metabolites, highlighting metabolic alterations in amino acid metabolism, polyamine metabolism, fatty acid synthesis, and phospholipid metabolism. This integrated analysis provides critical insights into pancreatic cancer metabolism, offering potential avenues for targeted therapeutic interventions.
PMID:40538442 | PMC:PMC12177182 | DOI:10.1016/j.isci.2025.112681
Cancer Treat Rev. 2025 Jun 14;139:102979. doi: 10.1016/j.ctrv.2025.102979. Online ahead of print.
ABSTRACT
Breast cancer is characterized by significant molecular heterogeneity; therefore, there are distinct clinical features, treatment modalities, and prognostic outcomes across its various molecular subtypes. In the era of precision medicine, liquid biopsy has emerged as a convenient and minimally invasive technique capable of dynamically representing the comprehensive tumor gene spectrum. This review systematically elaborates the clinical value of liquid biopsy as a breakthrough tool for precision diagnosis and treatment in breast cancer through dynamic detection of key biomarkers, including circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), exosomes, and non-coding RNA (ncRNA). Specific genetic mutations and methylation signatures in ctDNA can be applied to early breast cancer screening, minimal residual disease monitoring, and tracking drug resistance mechanisms. CTCs enumeration (≥1/7.5 mL in early-stage cancer or ≥ 5/7.5 mL in metastatic cancer) and PD-L1 expression levels demonstrate direct correlations with prognostic stratification and the efficacy of immunotherapy. As the specificity and sensitivity of liquid biopsy continue to improve, personalized treatment strategies, informed by biomarker analysis and targeted precision therapies, have unveiled new avenues of hope for patients with breast cancer. However, several challenges persist in the practical application of liquid biopsy. Despite persistent challenges, such as insufficient standardization and difficulties in resolving low-abundance variants, future advancements should focus on multi-omics integration and AI-driven technological breakthroughs to overcome bottlenecks in clinical translation. This review summarizes cutting-edge liquid biopsy technologies for identifying clinically significant molecular biomarkers, focusing on discussing critical challenges in the strategies to advance precision oncology applications for optimized treatment guidance and disease surveillance in breast cancer.
PMID:40540857 | DOI:10.1016/j.ctrv.2025.102979
Cell Signal. 2025 Jun 18;134:111950. doi: 10.1016/j.cellsig.2025.111950. Online ahead of print.
ABSTRACT
FAAP100 plays an essential role in DNA damage repair, with dysregulation associated with elevated cancer susceptibility. Nevertheless, comprehensive pan-cancer analyses examining FAAP100 prognostic significance, immune correlations, and epigenetic regulation remains unexplored. This study systematically characterized FAAP100 across 33 cancer types utilizing multi-omics data from TCGA, UALCAN, cBioPortal, TIMER2.0, and CPTAC. Analytical assessments included expression profiles, prognostic significance, and diagnostic utility, alongside associations with DNA methylation, immune cell infiltration, immune checkpoint gene expression, tumor mutational load (TMB), microsatellite instability (MSI), and drug resistance. Findings revealed significant FAAP100 upregulation across multiple cancer types, exhibiting inverse correlations to patient survival. Genomic characterization identified associations between FAAP100 overexpression and both copy number amplification and promoter hypomethylation. Immune profiling demonstrated robust correlations with immune cell infiltration levels and checkpoint molecule activity. Functional assays utilizing PC9 and H1299 cells indicated that FAAP100 enhances cellular proliferation and migration while inhibiting apoptosis processes. In vivo studies confirmed tumor growth suppression upon FAAP100 knockdown. Collectively, this multi-omics investigation identifies FAAP100 as a pan-cancer oncogene driver, highlighting its potential as both a prognostic biomarker and therapeutic target. The integrated analysis of expression patterns, epigenetic modifications, immune characteristics, and genomic alterations elucidates the mechanistic involvement of FAAP100 in tumor progression, providing a foundation for clinical application in precision oncology approaches..
PMID:40541815 | DOI:10.1016/j.cellsig.2025.111950
J Gastrointest Cancer. 2025 Jun 19;56(1):139. doi: 10.1007/s12029-025-01249-1.
ABSTRACT
BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary malignant liver tumor, with rising incidence and mortality rates posing a significant threat to global public health. Accurate prediction of liver cancer occurrence and progression is essential for improving patient prognosis. This study uses bibliometric methods to analyze the current state and future trends in liver cancer prediction research.
METHODS: A search was conducted in the Web of Science (WOS) database on October 22, 2023, identifying 1092 articles on liver cancer prediction. These articles were quantitatively analyzed using CiteSpace 6.2 software, with a focus on research hotspots, authors, countries, and keywords.
RESULTS: The study involved 114 countries, 4254 institutions, and 280 journals, with 48,788 citations. China (826 papers) and the USA (96 papers) dominate the field. Leading institutions include Sun Yat-sen University, Fudan University, Zhejiang University, and Yonsei University. The most cited journals were Hepatology (2209 citations) and Journal of Hepatology (946 citations). Frontiers in Oncology had the highest H-index (14). Key authors include Kim Seung Up (23 papers) and Ahn Sang Hoon (H-index = 14). Early research focused on risk factors and staging, while recent studies emphasize DNA methylation, immune microenvironments, and tumor metastasis. Future research will focus on multi-omics data integration and AI-driven predictive model optimization.
CONCLUSION: This study provides a comprehensive overview of liver cancer prediction research, highlighting key trends and the potential of multi-omics data and machine learning to enhance predictive models and clinical outcomes.
PMID:40537718 | DOI:10.1007/s12029-025-01249-1
AlphaWrite is a new framework designed to enhance creative writing with structure and measurable improvements. Developed by Toby Simonds, it employs an evolutionary process to iteratively boost storytelling quality during inference.
By Robert Krzaczyński
The Void IDE was recently released in beta, positioning itself as a privacy-focused and free alternative to popular closed-source AI editors like Cursor and GitHub Copilot. Void IDE is a fork of Visual Studio Code. While Microsoft recently announced plans to open Source its GitHub Copilot Chat Extension possibly in a few months, the beta release is available now for the community to fiddle with.
By Bruno Couriol
Support Care Cancer. 2025 Jun 19;33(7):596. doi: 10.1007/s00520-025-09670-9.
ABSTRACT
PURPOSE: Anorexia is a frequent and serious symptom in patients with lung cancer, often leading to malnutrition and cachexia, and negatively affecting quality of life and survival. This scoping review systematically synthesizes current evidence on biomarkers associated with cancer-related anorexia (CRA) in lung cancer, aiming to clarify biological mechanisms and inform targeted interventions.
METHODS: We performed a comprehensive literature search of studies evaluating the associations between CRA and various biomarkers in patients with lung cancer. Data were extracted and analyzed for pathway, genomic, transcriptomic, epigenetic, proteomic, metabolic, and composite biomarkers.
RESULTS: A total of 33 studies were included, identifying more than 100 biomarkers closely associated with CRA in lung cancer. These include inflammatory cytokines, energy metabolism markers, epigenetic and transcriptomic alterations, and disruptions in multiple cellular signaling pathways. Our analysis demonstrates that CRA is not the result of a single factor but reflects widespread dysregulation across metabolic, immune, and signaling networks. Some studies suggest that nutritional and anti-inflammatory interventions, such as n-3 fatty acid and antioxidant supplementation, can modulate biomarker profiles and potentially improve clinical outcomes.
CONCLUSION: CRA in lung cancer is a multifactorial syndrome involving complex interactions among inflammatory, metabolic, and signaling pathways. Multi-omics biomarker integration holds promise for early detection and individualized treatment, but larger, multi-center studies are needed to confirm clinical utility and optimize management strategies. Precision interventions based on biomarker profiles should be further explored in future research and practice.
PMID:40536584 | DOI:10.1007/s00520-025-09670-9
Front Genet. 2025 Jun 4;16:1610284. doi: 10.3389/fgene.2025.1610284. eCollection 2025.
ABSTRACT
INTRODUCTION: Lung cancer continues to pose significant global health burdens due to its high morbidity and mortality. This study aimed to systematically integrate biomedical datasets, particularly incorporating traditional Chinese medicine (TCM)-associated multi-omics data, employing advanced deep-learning methods enhanced by graph attention mechanisms. We sought to investigate molecular mechanisms underlying stage-wise lung cancer progression and identify pivotal stage-specific biomarkers to support precise cancer staging classification.
METHODS: We developed a novel multi-omics integrative model, named the Multi-Omics Lung Cancer Graph Network (MOLUNGN), based on Graph Attention Networks (GAT). Clinical datasets of non-small cell lung cancer (NSCLC), including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), were analyzed to create omics-specific feature matrices comprising mRNA expression, miRNA mutation profiles, and DNA methylation data. MOLUNGN incorporated omics-specific GAT modules (OSGAT) combined with a Multi-Omics View Correlation Discovery Network (MOVCDN), effectively capturing intra- and inter-omics correlations. This framework enabled comprehensive classification of clinical cases into precise cancer stages, alongside the extraction of stage-specific biomarkers.
RESULTS: Evaluations utilizing publicly available datasets confirmed MOLUNGN's superior performance over existing methodologies. On the LUAD dataset, MOLUNGN achieved accuracy (ACC) of 0.84, Recall_weighted of 0.84, F1_weighted of 0.83, and F1_macro of 0.82. On the LUSC dataset, the model further improved, achieving ACC of 0.86, Recall_weighted of 0.86, F1_weighted of 0.85, and F1_macro of 0.84. Notably, critical stage-specific biomarkers with significant biological relevance to lung cancer progression were identified, facilitating robust gene-disease associations.
DISCUSSION: Our findings underscore the efficacy of MOLUNGN as an integrative framework in accurately classifying lung cancer stages and uncovering essential biomarkers. These biomarkers provide deep insights into lung cancer progression mechanisms and represent promising targets for future clinical validation. Integrating these biomarkers into the TCM-target-disease network enriches the understanding of TCM therapeutic potentials, laying a robust foundation for future precision medicine applications.
PMID:40534839 | PMC:PMC12174459 | DOI:10.3389/fgene.2025.1610284
Nature, Published online: 18 June 2025; doi:10.1038/s41586-025-09180-y
During visual learning, neural plasticity is driven by unsupervised learning in mice.
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