Nature, Published online: 02 July 2025; doi:10.1038/s41586-025-09096-7
The Somatic Mosaicism across Human Tissues Network aims to create a reference catalogue of somatic mosaicism across different tissues and cells within individuals.
Nature, Published online: 02 July 2025; doi:10.1038/d41586-025-02036-5
The genome of a man buried at Nuwayrat in Middle Egypt about 4,500–4,800 years ago has been sequenced. Compared with published genomes of ancient individuals, most of the man’s ancestry best matches those of older individuals from North Africa, although around one-fifth best matches those of earlier ones from Mesopotamia. The study marks 40 years of progress in ancient-DNA research.
Sci Rep. 2025 Jul 1;15(1):21173. doi: 10.1038/s41598-025-08986-0.
ABSTRACT
Effective clinical management of patients with cancer requires highly accurate diagnosis, precise therapy selection, and highly sensitive monitoring of disease burden. Caris Assure is a multifunctional blood-based assay that couples whole exome and whole transcriptome sequencing on plasma and leukocytes with advanced machine learning techniques to satisfy all three clinical testing needs on one platform. Caris Assure for therapy selection was CLIA validated using 1,910 samples. 376,197 tissue profiles along with 7,061 paired blood and tissue profiles were used to engineer features for three machine learning models. The MCED model was trained on 1,013 patients and validated on an independent set of 2,675 patients. The tissue of origin for MCED model was trained on 1,166 samples and validated using 5-fold cross validation. The MRD & Monitoring model was trained on 3,439 patients and validated on two independent sets of 86 patients for MRD and 101 patients for monitoring. For early detection, sensitivities for stages I-IV cancers (n = 284, 129, 90, 23 respectively) were 83.1%, 86.0%, 84.4%, and 95.7%, all at 99.6% specificity (n = 2149). The diagnostic first-line procedure for tissue of origin was determined for 8 categories with a top-3 accuracy of 85% for stage I and II cancers. Detection of driver mutations for therapy selection from blood collected within 30 days of matched tumor tissue, demonstrated high concordance (PPA of 93.8%, PPV of 96.8%) using CHIP subtraction. For MRD and recurrence monitoring, the disease-free survival of patients whose cancers were predicted to have an event was significantly shorter than those predicted not to have an event using a tumor naïve approach (HR = 33.4, p < 0.005, HR = 4.39, p = 0.008, respectively). The data presented here demonstrate a unified liquid biopsy platform that uses blood-based whole-exome and transcriptome sequencing coupled with artificial intelligence to address the important clinical needs in multi-cancer early detection, monitoring of MRD and recurrent cancers, and precision selection of molecularly targeted therapies.
PMID:40596693 | PMC:PMC12214926 | DOI:10.1038/s41598-025-08986-0
J Am Soc Mass Spectrom. 2025 Aug 6;36(8):1598-1608. doi: 10.1021/jasms.4c00505. Epub 2025 Jul 2.
ABSTRACT
Gastric signet ring cell carcinoma (GSRC) is an aggressive subtype of gastric cancer (GC) with a poor prognosis. The lack of a systematic molecular and metabolic heterogeneity overview has led to slow progress in clinical practice. This study used mass spectrometry imaging (MSI) to investigate the metabolic landscape of GSRC in GC tissue with various differentiation grades. Our comprehensive spatial profiling of metabolites and lipids unveiled distinct metabolic signatures across different tissue subregions. A substantial number of lipidomic biomarkers associated with GSRC were identified, including phosphatidylethanolamine N-methyl (PE-NMe), phosphatidylethanolamine (PE), sphingomyelin (SM), diacylglycerol (DG), phosphatidic acid (PA), and phosphatidylcholine (PC), which may provide insights into its pathogenesis and potential therapeutic targets. Furthermore, multi-omics network analysis revealed intricate metabolic pathways involved in GSRC progression. Our findings highlight the importance of understanding the metabolic heterogeneity of GSRC and pave the way for future studies exploring its clinical implications and therapeutic strategies.
PMID:40600435 | DOI:10.1021/jasms.4c00505
Last week, the technology companies Anthropic and Meta each won landmark victories in two separate court cases that examined whether or not the firms had violated copyright when they trained their large language models on copyrighted books without permission. The rulings are the first we’ve seen to come out of copyright cases of this kind. This is a big deal!
The use of copyrighted works to train models is at the heart of a bitter battle between tech companies and content creators. That battle is playing out in technical arguments about what does and doesn’t count as fair use of a copyrighted work. But it is ultimately about carving out a space in which human and machine creativity can continue to coexist.
There are dozens of similar copyright lawsuits working through the courts right now, with cases filed against all the top players—not only Anthropic and Meta but Google, OpenAI, Microsoft, and more. On the other side, plaintiffs range from individual artists and authors to large companies like Getty and the New York Times.
The outcomes of these cases are set to have an enormous impact on the future of AI. In effect, they will decide whether or not model makers can continue ordering up a free lunch. If not, they will need to start paying for such training data via new kinds of licensing deals—or find new ways to train their models. Those prospects could upend the industry.
And that’s why last week’s wins for the technology companies matter. So: Cases closed? Not quite. If you drill into the details, the rulings are less cut-and-dried than they seem at first. Let’s take a closer look.
In both cases, a group of authors (the Anthropic suit was a class action; 13 plaintiffs sued Meta, including high-profile names such as Sarah Silverman and Ta-Nehisi Coates) set out to prove that a technology company had violated their copyright by using their books to train large language models. And in both cases, the companies argued that this training process counted as fair use, a legal provision that permits the use of copyrighted works for certain purposes.
There the similarities end. Ruling in Anthropic’s favor, senior district judge William Alsup argued on June 23 that the firm’s use of the books was legal because what it did with them was transformative, meaning that it did not replace the original works but made something new from them. “The technology at issue was among the most transformative many of us will see in our lifetimes,” Alsup wrote in his judgment.
In Meta’s case, district judge Vince Chhabria made a different argument. He also sided with the technology company, but he focused his ruling instead on the issue of whether or not Meta had harmed the market for the authors’ work. Chhabria said that he thought Alsup had brushed aside the importance of market harm. “The key question in virtually any case where a defendant has copied someone’s original work without permission is whether allowing people to engage in that sort of conduct would substantially diminish the market for the original,” he wrote on June 25.
Same outcome; two very different rulings. And it’s not clear exactly what that means for the other cases. On the one hand, it bolsters at least two versions of the fair-use argument. On the other, there’s some disagreement over how fair use should be decided.
But there are even bigger things to note. Chhabria was very clear in his judgment that Meta won not because it was in the right, but because the plaintiffs failed to make a strong enough argument. “In the grand scheme of things, the consequences of this ruling are limited,” he wrote. “This is not a class action, so the ruling only affects the rights of these 13 authors—not the countless others whose works Meta used to train its models. And, as should now be clear, this ruling does not stand for the proposition that Meta’s use of copyrighted materials to train its language models is lawful.” That reads a lot like an invitation for anyone else out there with a grievance to come and have another go.
And neither company is yet home free. Anthropic and Meta both face wholly separate allegations that not only did they train their models on copyrighted books, but the way they obtained those books was illegal because they downloaded them from pirated databases. Anthropic now faces another trial over these piracy claims. Meta has been ordered to begin a discussion with its accusers over how to handle the issue.
So where does that leave us? As the first rulings to come out of cases of this type, last week’s judgments will no doubt carry enormous weight. But they are also the first rulings of many. Arguments on both sides of the dispute are far from exhausted.
“These cases are a Rorschach test in that either side of the debate will see what they want to see out of the respective orders,” says Amir Ghavi, a lawyer at Paul Hastings who represents a range of technology companies in ongoing copyright lawsuits. He also points out that the first cases of this type were filed more than two years ago: “Factoring in likely appeals and the other 40+ pending cases, there is still a long way to go before the issue is settled by the courts.”
“I’m disappointed at these rulings,” says Tyler Chou, founder and CEO of Tyler Chou Law for Creators, a firm that represents some of the biggest names on YouTube. “I think plaintiffs were out-gunned and didn’t have the time or resources to bring the experts and data that the judges needed to see.”
But Chou thinks this is just the first round of many. Like Ghavi, she thinks these decisions will go to appeal. And after that we’ll see cases start to wind up in which technology companies have met their match: “Expect the next wave of plaintiffs—publishers, music labels, news organizations—to arrive with deep pockets,” she says. “That will be the real test of fair use in the AI era.”
But even when the dust has settled in the courtrooms—what then? The problem won’t have been solved. That’s because the core grievance of creatives, whether individuals or institutions, is not really that their copyright has been violated—copyright is just the legal hammer they have to hand. Their real complaint is that their livelihoods and business models are at risk of being undermined. And beyond that: when AI slop devalues creative effort, will people’s motivations for putting work out into the world start to fall away?
In that sense, these legal battles are set to shape all our futures. There’s still no good solution on the table for this wider problem. Everything is still to play for.
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
This story has been edited to add comments from Tyler Chou.
Google DeepMind has announced the release of AlphaGenome, a new AI model designed to predict how genetic variants affect gene regulation across the entire genome. It represents a significant advancement in computational genomics by integrating long-range sequence context with base-pair resolution in a single, general-purpose architecture.
By Robert Krzaczyński
Nature, Published online: 01 July 2025; doi:10.1038/d41586-025-02016-9
The decades-old limit on how long human embryos can be grown in culture is under debate. A new road map outlines how to extend the length of culture responsibly.
Nature, Published online: 30 June 2025; doi:10.1038/d41586-025-02034-7
After a quarter of a century, the website remains an essential tool for navigating the genome and understanding its structure, function and clinical impact.
The OWASP Foundation has officially introduced the AI Testing Guide (AITG), a new open-source initiative aimed at assisting organizations in the systematic testing and security of artificial intelligence systems. This guide serves as a fundamental resource for developers, testers, risk officers, and cybersecurity professionals, promoting best practices in AI system security.
By Robert Krzaczyński
Eur J Trauma Emerg Surg. 2025 Jun 27;51(1):238. doi: 10.1007/s00068-025-02922-7.
ABSTRACT
PURPOSE: Obesity is a prevalent disease, but its influence on post-injury biology remains unclear. In this study, we aimed to characterize the independent effect of obesity on the proteomic and metabolomic signatures of trauma.
METHODS: Plasma was obtained on arrival from injured patients at a Level 1 Trauma Center and analyzed with modern mass spectrometry-based proteomics and metabolomics. Samples obtained after start of transfusion were excluded. Patients were stratified by "obesity" (body mass index [BMI]≥30 kg/m2) vs. "no obesity" (BMI < 30 kg/m2). In sub-group analyses, patients were sub-stratified by Low Injury/Low Shock (ISS < 15, base excess [BE]≥-6mEq/L) and High Injury/High Shock (ISS≥15, BE<-6). Multiple regression was used to adjust the omics data for significant covariates prior to performing ome-wide analyses.
RESULTS: There were 183 patients included (48 [26%] with obesity and 135 [74%] without). After covariate-adjustment, multiple proteins and metabolites were correlated with ISS and/or BE and were significantly different from Low Injury/Low Shock to High Injury/High Shock only in patients with obesity. This obesity-specific omics response to injury was characterized by increased inflammation, hypercoagulability, altered nitrogen metabolism, and mitochondrial dysfunction. Patients with obesity also exhibited excessive injury-provoked tissue destruction and organ damage compared to patients without obesity. In injury severity-adjusted analyses, the obesity signature consistently displayed markers of hemolysis, likely reflecting a pre-injury hemolytic propensity.
CONCLUSION: Obesity is independently associated with altered post-injury biology, which likely underlies unique pathology in trauma patients with obesity. Identifying this aberrant response to injury is the first step in developing personalized therapies for this patient population.
PMID:40576654 | DOI:10.1007/s00068-025-02922-7
Nature, Published online: 27 June 2025; doi:10.1038/d41586-025-02011-0
Several groups hope to develop artificial-intelligence models that can predict how cells behave.
Cloudflare has unveiled thirteen new Model Context Protocol (MCP) servers, enhancing the integration of AI agents with its platform. These servers allow AI clients to interact with Cloudflare's services through natural language, streamlining tasks such as debugging, data analysis, and security monitoring.
By Craig Risi
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