Cell Death Discovery, Published online: 10 July 2025; doi:10.1038/s41420-025-02615-5
The transcriptomic fingerprint of cancer response to Tumor Treating Fields (TTFields)
Nature, Published online: 08 July 2025; doi:10.1038/d41586-025-02175-9
Scientists are grappling with the connections between stress and health. Plus, the science that underlies escalating heatwaves and a rabies vaccine that can be spread through licks.
Hereditas. 2025 Jul 7;162(1):121. doi: 10.1186/s41065-025-00487-4.
ABSTRACT
Accurate classification of cancer subtypes plays a pivotal role in advancing precision medicine. In this study, we introduce WMRCA + , a novel clustering approach based on a weighted majority rule that integrates multi-omics data and incorporates metabolic gene sets to robustly determine the optimal number of clusters for tumor subtype identification. WMRCA + evaluates clustering performance using ten internal metrics and offers comprehensive functionalities for data preprocessing and visualization. When applied to The Cancer Genome Atlas (TCGA) lung cancer dataset using lipid metabolism-related gene sets, WMRCA + outperformed widely used clustering algorithms-including iCluster, SNF, NMF, CC, and CNMF-achieving an AUC of 0.947. WMRCA + provides robust, interpretable, and biologically meaningful clustering results, offering a valuable tool for improving the accuracy of cancer subtype prediction. The WMRCA + R package is freely available at https://github.com/guojunliu7/WMRCA .
PMID:40624602 | PMC:PMC12235908 | DOI:10.1186/s41065-025-00487-4
Nature, Published online: 08 July 2025; doi:10.1038/d41586-025-02154-0
Expand biobank sampling to three or more generations
Oncogene, Published online: 06 July 2025; doi:10.1038/s41388-025-03486-x
Inhibition of tumor growth using A conjugated nanobody that specifically targets c-MYC
LM Studio has released version 0.3.17, introducing support for the Model Context Protocol (MCP) — a step forward in enabling language models to access external tools and data sources. Originally developed by Anthropic, MCP defines a standardized interface for connecting LLMs to services such as GitHub, Notion, or Stripe, enabling more powerful, contextual reasoning.
By Robert Krzaczyński
In this podcast, Shane Hastie, Lead Editor for Culture & Methods, spoke to Natan Žabkar Nordberg about how effective leadership requires treating people as whole humans, giving trust first, implementing guided autonomy with clear boundaries, and building diverse teams through shared experiences.
By Natan Žabkar Nordberg
J Hazard Mater. 2025 Jun 23;495:139049. doi: 10.1016/j.jhazmat.2025.139049. Online ahead of print.
ABSTRACT
BACKGROUND: Chronic arsenic exposure is a global health concern linked to pulmonary diseases like fibrosis. However, its precise molecular mechanisms remain unclear. This study explored the effects of chronic arsenic exposure on a murine model (via diet) and BEAS-2B cells, focusing on oxidative stress, lipid peroxidation, mitochondrial dysfunction, and ferroptosis-mediated cell death.
METHODS: BEAS-2B cells were exposed to 1 μmol/L NaAsO₂ for 30 passages. Oxidative stress was assessed via ROS quantification, GSH depletion, and T-SOD activity. Lipid peroxidation was measured using BODIPY fluorescence and MDA levels. Mitochondrial dysfunction was determined by mtROS imaging and JC-1 staining. Ferroptosis was analyzed through GPX4 expression and TEM-based mitochondrial integrity. A 14-month murine model evaluated histopathology, metabolomic dysregulation, and gut-lung axis crosstalk.
RESULTS: Arsenic exposure significantly increased ROS, depleted GSH, and reduced T-SOD activity. Lipid peroxidation and mitochondrial dysfunction were evident, with more than 60 % decline in GPX4. Murine lung histology showed alveolar thickening, inflammatory infiltration, and elevated IL-6, TNF-α, and VEGF. Metabolomic analysis revealed disrupted lipid metabolism, correlating with ferroptosis markers (Acetyl-carnitine, L-Acetylcarnitine).
CONCLUSIONS: This was the first study to demonstrate ferroptosis as a key mechanism in arsenic-induced lung epithelial damage using a 14-month murine model and a 30-passage cellular model. We further demonstrated that ferroptosis induced by chronic exposure becomes functionally irreversible, as ferroptosis inhibition by Ferrostatin-1 failed to rescue GPX4 expression, unlike prior acute exposure models.
PMID:40614423 | DOI:10.1016/j.jhazmat.2025.139049
Ann Surg Oncol. 2025 Jul 3. doi: 10.1245/s10434-025-17738-3. Online ahead of print.
ABSTRACT
BACKGROUND: This study aimed to identify the most effective drug therapeutics for patients with the mesenchymal subtype of advanced gastric cancer (AGC). Extensive research employing diverse omics methodologies has unveiled a varied landscape of AGC. Recent progress in next-generation sequencing and other genomic technologies has facilitated a more intricate exploration of AGC at the molecular level. Nonetheless, the optimal treatment for patients with the mesenchymal subtype of gastric cancer remains elusive. Lei's molecular classification of AGC is based on gene expression profiles named "mesenchymal," "immunogenic," "classical," and "metabolic."
PATIENTS AND METHODS: Based on RNA-seq transcriptome, 234 patients were divided into four molecular subtypes: mesenchymal (n = 96), immunogenic (n = 37), metabolic (n = 61), and classic (n = 40).
RESULTS: Among those with mesenchymal-subtype AGC, compared with non-Apatinib group, the Apatinib treatment group demonstrated a significant increase in objective response rate (ORR 89.3% versus 69.3%, p = 0.038; odds ratio (OR) 0.269, 95% confidence interval (CI) (0.073-0.989)); overall survival (OS) 89.3% versus 60.2%, p = 0.010; hazard ratio (HR) 0.241, 95% CI (0.073-0.796)) and disease-free survival (DFS 78.6% versus 52.9%, p = 0.031; HR 0.400, 95% CI (0.167-0.956)). Furthermore, Apatinib significantly reduced the risk of death and recurrence in patients with mesenchymal subtype (OS: HR 0.129, 95% CI (0.030-0.563), p = 0.006; DFS: HR 0.340, 95% CI (0.138-0.833), p = 0.018). However, no significant differences were observed in the ORR, OS, or DFS between patients with metabolic and classical subtypes who underwent combination chemotherapy with additional Apatinib or camrelizumab.
CONCLUSIONS: Our analysis has revealed that, for neoadjuvant therapy in AGC, the mesenchymal subtype stands out as the ideal patient population benefiting from Apatinib.
PMID:40608168 | DOI:10.1245/s10434-025-17738-3
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.
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
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