J Liq Biopsy. 2025 Aug 9;9:100323. doi: 10.1016/j.jlb.2025.100323. eCollection 2025 Sep.ABSTRACTLiquid biopsy, specifically circulating tumor DNA (ctDNA) analysis, has emerged as a transformative tool in precision oncology, providing real-time, minimally invasive characterizations of the tumor and tumor dynamics. While tissue biopsy is a critical tool for baseline diagnosis of malignancy, it is often limited by sampling constraints and an inability to capture tumor heterogeneity. In this study,
Liquid biopsy, specifically circulating tumor DNA (ctDNA) analysis, has emerged as a transformative tool in precision oncology, providing real-time, minimally invasive characterizations of the tumor and tumor dynamics. While tissue biopsy is a critical tool for baseline diagnosis of malignancy, it is often limited by sampling constraints and an inability to capture tumor heterogeneity. In this study, we explored the clinical utility of serial ctDNA testing in guiding therapeutic decisions across a cohort of 30 patients with diverse solid tumors. Our real-world analysis demonstrates that ctDNA profiling meaningfully influenced treatment escalation, de-escalation, disease monitoring, and early relapse prediction. Cases where ctDNA positivity indicated minimal residual disease prompted timely escalation of therapy, while ctDNA clearance allowed safe treatment de-intensification, minimizing toxicity without compromising outcomes. Longitudinal ctDNA monitoring provided a dynamic, non-invasive method for assessing treatment response and detecting recurrence months before radiological progression. Our study highlights the potential of integrating liquid biopsy into routine clinical practice to enable dynamic treatment monitoring, early detection of therapeutic resistance, and more informed, personalized decision-making across various cancer types.
Nature Medicine, Published online: 09 September 2025; doi:10.1038/s41591-025-03926-xThis Review of the WHO’s International Clinical Trials Registry Platform presents a snapshot of the global cancer trial landscape and provides critical empirical evidence to inform policy, practice and investment.
This Review of the WHO’s International Clinical Trials Registry Platform presents a snapshot of the global cancer trial landscape and provides critical empirical evidence to inform policy, practice and investment.
Front Biosci (Landmark Ed). 2025 Aug 30;30(8):44091. doi: 10.31083/FBL44091.ABSTRACTZhang et al.'s recent article utilizes comprehensive single-cell data to identify differences in tumor cell populations, highlighting the CKS1B+ malignant cell subcluster as a potential target for immunotherapy. It develops a prognostic and immunotherapeutic signature (PIS) based on this subcluster, demonstrating good performance in predicting lung adenocarcinoma (LUAD) prognosis. The study also validates the rol
Front Biosci (Landmark Ed). 2025 Aug 30;30(8):44091. doi: 10.31083/FBL44091.
ABSTRACT
Zhang et al.'s recent article utilizes comprehensive single-cell data to identify differences in tumor cell populations, highlighting the CKS1B+ malignant cell subcluster as a potential target for immunotherapy. It develops a prognostic and immunotherapeutic signature (PIS) based on this subcluster, demonstrating good performance in predicting lung adenocarcinoma (LUAD) prognosis. The study also validates the role of PSMB7 in LUAD progression. However, there are areas for improvement. There is a lack of clarity regarding the relationship between the CKS1B+ malignant cell subcluster and the PIS, particularly in terms of why PSMB7 was selected for functional studies. The sequencing data are retrospectively obtained from public databases and lack prospective clinical validation. It is suggested to collect LUAD patient tissues for RT-qPCR and RNA-seq analysis and seek external multi-center validations. Additionally, integrating emerging multi-omics methods is recommended to further validate the findings. Despite these limitations, the study represents progress in understanding LUAD and treatment strategies, and continuous evaluation and refinement of multi-omics and machine learning methods are expected for future research and clinical practice.
“My watch saved my life.”
Liam — not his real name — is a 75-year-old retired teacher in Boston. Two years ago, his son-in-law gave him an Apple Watch. Soon after, it began flagging something strange: possible atrial fibrillation.Read the rest…
Liam — not his real name — is a 75-year-old retired teacher in Boston. Two years ago, his son-in-law gave him an Apple Watch. Soon after, it began flagging something strange: possible atrial fibrillation.
DOVER, N.H. — Not long after he woke from surgery in June, Bill Stewart made a pact with his newest organ. He wasn’t sure how long the thing would last. The doctors had been up-front from the get-go: It could be three months or six, one year or four. Still, the uncertainty hit him as he started getting back preliminary lab results, which were okay but left room for improvement. “My pig kidney and I had a little conversation while I was laying there. I just basically said, ‘I’m going to do every
DOVER, N.H. — Not long after he woke from surgery in June, Bill Stewart made a pact with his newest organ. He wasn’t sure how long the thing would last. The doctors had been up-front from the get-go: It could be three months or six, one year or four. Still, the uncertainty hit him as he started getting back preliminary lab results, which were okay but left room for improvement. “My pig kidney and I had a little conversation while I was laying there. I just basically said, ‘I’m going to do everything I can to make sure that you stay healthy, and I appreciate you doing everything you can to keep me upright and breathing,” Stewart said.
It’s been almost three months, and Stewart is home, back to work, and has even been able to go e-biking on a lakeside trail with his wife, blessedly untethered to the grueling schedules of dialysis for the first time in years, all thanks to a gene-edited Yucatan miniature pig named Lavender.
Stewart is the most recent recipient of a pig kidney — but chances are, he won’t hold that distinction for long. On Monday, eGenesis, a Cambridge-based biotechnology company, announced that it had been cleared by the Food and Drug Administration to begin a trial of kidneys from donor pigs that have been CRISPR’d to make their organs more human-friendly. Now, Massachusetts researchers will be performing more surgeries like Stewart’s to see whether these animal parts could serve as a lifeline for people with end-stage renal disease.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Introducing: our 35 Innovators Under 35 list for 2025
The world is full of extraordinary young people brimming with ideas for how to crack tough problems. Every year, we recognize 35 such individuals from around the world—all of whom are under the age of 35.
These scientists, inventors, and entrepreneurs are working to help mitigate climate change, ac
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Introducing: our 35 Innovators Under 35 list for 2025
The world is full of extraordinary young people brimming with ideas for how to crack tough problems. Every year, we recognize 35 such individuals from around the world—all of whom are under the age of 35.
These scientists, inventors, and entrepreneurs are working to help mitigate climate change, accelerate scientific progress, and alleviate human suffering from disease. Some are launching companies while others are hard at work in academic labs. They were selected from hundreds of nominees by expert judges and our newsroom staff.
Get to know them all—including our 2025 Innovator of the Year—in these profiles.
Why basic science deserves our boldest investment
—Julia R. Greer is a materials scientist at the California Institute of Technology, a judge for MIT Technology Review’s Innovators Under 35 and a former honoree (in 2008).
A modern chip the size of a human fingernail contains tens of billions of silicon transistors, each measured in nanometers—smaller than many viruses. These tiny switches form the infrastructure behind nearly every digital device in use today.
Much of the fundamental understanding that moved transistor technology forward came from federally funded university research. But that funding is under increasing pressure, thanks to deep budget cuts proposed by the White House.
These losses have forced some universities to freeze graduate student admissions, cancel internships, and scale back summer research opportunities—making it harder for young people to pursue scientific and engineering careers.
In an age dominated by short-term metrics and rapid returns, it can be difficult to justify research whose applications may not materialize for decades. But those are precisely the kinds of efforts we must support if we want to secure our technological future. Read the full story.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 The US is considering annual chip supply permits in China For South Korean companies Samsung and SK Hynix, specifically. (Bloomberg $) + US lawmakers still hold power over chips in China. (CNN)
2 America has recorded its first case of screwworm in over 50 years And the warming climate is making it easier for the flies to thrive. (Vox) + Experts fear an approaching public health emergency. (The Guardian)
3 Drone warfare is dominating Ukraine’s frontline Amid relentless assaults, overhead and land drones are being put to work. (The Guardian) + How cutting-edge drones forced land-locked tanks to evolve. (NYT $) + On the ground in Ukraine’s largest Starlink repair shop. (MIT Technology Review)
4 OpenAI is working out why chatbots hallucinate so much Examining a model’s incentives provides some clues. (Insider $) + Models’ tendency to confidently present falsehoods as fact is a big problem. (TechCrunch) + Why does AI hallucinate? (MIT Technology Review)
5 How one man is connecting Silicon Valley to the Middle East’s AI boom If you want to build a data center, Zachary Cefaratti is your man. (FT $) + The data center boom in the desert. (MIT Technology Review)
6 The first OpenAI-backed movie is coming to theaters next year The animated Critterz is hoping for a Cannes Film Festival debut. (WSJ $) + A Disney director tried—and failed—to use an AI Hans Zimmer to create a soundtrack. (MIT Technology Review)
7 Who wants to live forever? These billionaires are confident their cash will pave the way to longer lives. (WSJ $) + Putin says organ transplants could grant immortality. Not quite. (MIT Technology Review)
8 Tesla isn’t focused on selling cars any more The company’s latest Master Plan is all about humanoid robots. (The Atlantic $) + The board is willing to offer Musk a $1 trillion pay package if he delivers. (Wired $) + Uber is gearing up to test driverless cars in Germany. (The Verge) + China’s EV giants are betting big on humanoid robots. (MIT Technology Review)
9 Do aliens go on holiday? Scientists wonder whether tourism could be a potential drive for them to visit us. (New Yorker $) + How these two UFO hunters became go-to experts on America’s “mystery drone” invasion. (MIT Technology Review)
10 Vodafone’s new TikTok influencer isn’t real It’s yet another example of AI avatars being used in ads. (The Verge) + Synthesia’s AI clones are more expressive than ever. Soon they’ll be able to talk back. (MIT Technology Review)
Quote of the day
“Silicon Valley totally effed up in overhyping LLMs.”
—Palantir CEO Alex Karp criticizes those who fueled the AI hype around large language models, Semafor reports.
One more thing
Puerto Rico’s power struggles
On the southeastern coast of Puerto Rico lies the country’s only coal-fired power station, flanked by a mountain of toxic ash. The plant, owned by the utility giant AES, has long plagued this part of Puerto Rico with air and water pollution.
Before the coal plant opened Guayama had on average just over 103 cancer cases per year. In 2003, the year after the plant opened, the number of cancer cases in the municipality surged by 50%, to 167.
In 2022, the most recent year with available data, cases hit a new high of 209. The question is: How did it get this bad? Read the full story.
—Alexander C. Kaufman
We can still have nice things
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)
+ What’s up with tennis players’ strange serving rituals? + If constant scrolling is turning your hands into gnarled claws, this stretch should help. + How to land a genuine bargain on Facebook Marketplace. + This photographer tracks down people who featured in pictures decades before, and persuades them to recreate their poses. Heartwarming stuff
Nature Biotechnology, Published online: 09 September 2025; doi:10.1038/s41587-025-02772-zAntibody–bottlebrush conjugates expand the options for drug cargos compared to antibody–drug conjugates.
Int J Biol Macromol. 2025 Sep 5;327(Pt 2):147473. doi: 10.1016/j.ijbiomac.2025.147473. Online ahead of print.ABSTRACTOsteosarcoma (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 meta
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.
bioRxiv [Preprint]. 2025 Aug 25:2025.08.21.671519. doi: 10.1101/2025.08.21.671519.ABSTRACTLung 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
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.
bioRxiv [Preprint]. 2025 Aug 25:2025.08.21.671519. doi: 10.1101/2025.08.21.671519.ABSTRACTLung 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
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.
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 st
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, 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.
A 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.
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
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.
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.
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.
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.
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.
Nature, Published online: 03 September 2025; doi:10.1038/d41586-025-02835-wIberian harvester ants can produce males that are genetically different species — plus, new insights into preventing deadly bridge collapses.
Nature, Published online: 03 September 2025; doi:10.1038/s41586-025-09478-xDNA-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.
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.
World J Methodol. 2025 Dec 20;15(4):105516. doi: 10.5662/wjm.v15.i4.105516. eCollection 2025 Dec 20.ABSTRACTBACKGROUND: 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 cond
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.
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