Nature, Published online: 05 March 2025; doi:10.1038/s41586-025-08626-7
Inhibition of cyclooxygenase 1 releases T cells from immunosuppression by platelet-derived thromboxane A2, thereby enhancing the immune response against metastasis.
Nature, Published online: 05 March 2025; doi:10.1038/s41586-025-08689-6
In the Multicellularity Long Term Evolution Experiment, diploid yeast evolve to be tetraploid under selection for larger multicellular size, revealing how whole-genome duplication can arise due to its immediate benefits, persist under selection, and fuel long-term innovations via aneuploidy.
Nature, Published online: 04 March 2025; doi:10.1038/d41586-025-00618-x
As the European Union’s Artificial Intelligence Act takes effect, AI systems that mimic how human teams collaborate can improve trust in high-risk situations, such as clinical medicine.
Oncogene, Published online: 01 March 2025; doi:10.1038/s41388-025-03322-2
Preclinical application of a CD155 targeting chimeric antigen receptor T cell therapy for digestive system cancers
bioRxiv [Preprint]. 2025 Feb 17:2025.02.13.637721. doi: 10.1101/2025.02.13.637721.
ABSTRACT
Cell type-specific regulatory programs that drive type 1 diabetes (T1D) in the pancreas are poorly understood. Here we performed single nucleus multiomics and spatial transcriptomics in up to 32 non-diabetic (ND), autoantibody-positive (AAB+), and T1D pancreas donors. Genomic profiles from 853,005 cells mapped to 12 pancreatic cell types, including multiple exocrine sub-types. Beta, acinar, and other cell types, and related cellular niches, had altered abundance and gene activity in T1D progression, including distinct pathways altered in AAB+ compared to T1D. We identified epigenomic drivers of gene activity in T1D and AAB+ which, combined with genetic association, revealed causal pathways of T1D risk including antigen presentation in beta cells. Finally, single cell and spatial profiles together revealed widespread changes in cell-cell signaling in T1D including signals affecting beta cell regulation. Overall, these results revealed drivers of T1D progression in the pancreas, which form the basis for therapeutic targets for disease prevention.
PMID:40027657 | PMC:PMC11870426 | DOI:10.1101/2025.02.13.637721
Cancer Innov. 2025 Feb 28;4(2):e160. doi: 10.1002/cai2.160. eCollection 2025 Apr.
ABSTRACT
BACKGROUND: Lung adenocarcinoma (LUAD) is a highly heterogeneous cancer type with a poor prognosis. Accurate subtype identification can help guide its treatment. The traditional subtype identification methods using a single-omics approach make it difficult to comprehensively characterize the molecular features of LUAD. Identification of subtypes through multi-omics association strategies can effectively supplement the shortcomings of single-omics information.
METHODS: In this study, we used the Generative Adversarial Network (GAN) to mine transcriptomic, proteomic, and epigenomic information and generate an integrated data set. The newly integrated data were then used to identify LUAD immune subtypes. In the improved GAN (MOGAN) method, we not only integrated multiple omics datasets but also included the interactions between proteins and genes and between methylation and genes. Thus, we achieved effective complementarity of multi-omics information.
RESULTS: Two subtypes, MOGANTPM_S1 and MOGANTPM_S2, were identified using immune cell infiltration analysis and the integrated multi-omics data. MOGANTPM_S1 patients displayed higher immune cell infiltration, better prognosis, and sensitivity to immune checkpoint inhibitors (ICIs), while MOGANTPM_S2 had lower immune cell infiltration, poorer prognosis, and were insensitive to ICIs. Therefore, immunotherapy was more suitable for MOGANTPM_S1 patients in clinical practice. In addition, this study developed a LUAD subtype diagnostic model using the transcriptomic and proteomic features of five genes, which can be used to guide clinical subtype diagnosis.
CONCLUSIONS: In summary, the MOGAN method was applied to integrate three omics data types and successfully identify two LUAD immune subtypes with significant survival differences. This classification method may be useful for LUAD treatment decisions.
PMID:40026873 | PMC:PMC11868734 | DOI:10.1002/cai2.160
Nature, Published online: 26 February 2025; doi:10.1038/s41586-025-08584-0
This Review summarizes advances in mass-spectrometry-based proteomics and explores the potential applications of these technologies in the clinic.
Semin Cancer Biol. 2025 Jun;111:76-88. doi: 10.1016/j.semcancer.2025.02.009. Epub 2025 Feb 20.
ABSTRACT
Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in its late stages when treatment options are limited. Unlike other common cancers, there are no population-wide screening programmes for PDAC. Thus, early disease detection, although urgently needed, remains elusive. Individuals in certain high-risk groups are, however, offered screening or surveillance. Here we explore advances in understanding high-risk groups for PDAC and efforts to implement biomarker-driven detection of PDAC in these groups. We review current approaches to early detection biomarker development and the use of artificial intelligence as applied to electronic health records (EHRs) and social media. Finally, we address the cost-effectiveness of applying biomarker strategies for early detection of PDAC.
PMID:39986585 | DOI:10.1016/j.semcancer.2025.02.009
Nature, Published online: 26 February 2025; doi:10.1038/s41586-025-08636-5
Systems-level Worm Perturb-Seq of metabolic genes reveals design principles of transcriptional metabolic rewiring, many of which can be explained by a compensation–repression model.
Nature, Published online: 26 February 2025; doi:10.1038/s41586-025-08623-w
A rare variant burden analytical framework for Mendelian diseases was developed and applied to data from the 100,000 Genomes Project, identifying 69 probable new disease–gene associations.
Greetings from a cold winter day.
As I write this letter, we are in the early stages of President Donald Trump’s second term. The inauguration was exactly one week ago, and already an image from that day has become an indelible symbol of presidential power: a photo of the tech industry’s great data barons seated front and center at the swearing-in ceremony.
Elon Musk, Sundar Pichai, Jeff Bezos, and Mark Zuckerberg all sat shoulder to shoulder, almost as if on display, in front of some of the most important figures of the new administration. They were not the only tech leaders in Washington, DC, that week. Tim Cook, Sam Altman, and TikTok CEO Shou Zi Chew also put in appearances during the president’s first days back in action.
These are tycoons who lead trillion-dollar companies, set the direction of entire industries, and shape the lives of billions of people all over the world. They are among the richest and most powerful people who have ever lived. And yet, just like you and me, they need relationships to get things done. In this case, with President Trump.
Those tech barons showed up because they need relationships more than personal status, more than access to capital, and sometimes even more than ideas. Some of those same people—most notably Zuckerberg—had to make profound breaks with their own pasts in order to forge or preserve a relationship with the incoming president.
Relationships are the stories of people and systems working together. Sometimes by choice. Sometimes for practicality. Sometimes by force. Too often, for purely transactional reasons.
That’s why we’re exploring relationships in this issue. Relationships connect us to one another, but also to the machines, platforms, technologies, and systems that mediate modern life. They’re behind the partnerships that make breakthroughs possible, the networks that help ideas spread, and the bonds that build trust—or at least access. In this issue, you’ll find stories about the relationships we forge with each other, with our past, with our children (or not-quite-children, as the case may be), and with technology itself.
Rhiannon Williams explores the relationships people have formed with AI chatbots. Some of these are purely professional, others more complicated. This kind of relationship may be novel now, but it’s something we will all take for granted in just a few years.
Also in this issue, Antonio Regalado delves into our relationship with the ecological past and the way ancient DNA is being used not only to learn new truths about who we are and where we came from but also, potentially, to address modern challenges of climate and disease.
In an extremely thought-provoking piece, Jessica Hamzelou examines people’s relationships with the millions of IVF embryos in storage. Held in cryopreservation tanks around the world, these embryos wait in limbo, in ever growing numbers, as we attempt to answer complicated ethical and legal questions about their existence and preservation.
Turning to the workplace, Rebecca Ackermann explores how our relationships with our employers are often mediated through monitoring systems. As she writes, what may be more important than the privacy implications is how the data they collect is “shifting the relationships between workers and managers” as algorithms “determine hiring and firing, promotion and ‘deactivation.’” Good luck with that.
Thank you for reading. As always, I value your feedback. So please, reach out and let me know what you think. I really don’t want this to be a transactional relationship.
Warmly,
Mat Honan
Editor in Chief
mat.honan@technologyreview.com
Oncogene, Published online: 26 February 2025; doi:10.1038/s41388-025-03308-0
Using prognostic signatures and machine learning to identify core features associated with response to CDK4/6 inhibitor-based therapy in metastatic breast cancer
Gemini is Google’s long-promised, next-gen generative AI model family.
© 2024 TechCrunch. All rights reserved. For personal use only.
Oncol Res Treat. 2025 Feb 25:1-11. doi: 10.1159/000544838. Online ahead of print.
ABSTRACT
BACKGROUND: Current breast cancer (BC) surveillance is limited to the detection of local, locoregional or contralateral recurrence. This is based on two outdated studies from the 1990s and ignores current evidence on liquid biopsies, particularly circulating tumor DNA (ctDNA).
SUMMARY: ctDNA has been shown to be a reliable prognostic biomarker in early BC surveillance. It can be detected using a tumor-informed or a tumor-agnostic approach. However, conclusive evidence for a survival benefit from ctDNA-guided follow-up, as needed for a paradigm shift in BC surveillance, is still lacking. According to current studies, the lead time, i.e. the time from biomarker detection to clinically overt relapse, can be up to several months. This stage of MRD (minimal or molecular residual disease) offers a new therapeutic window, and, currently, several studies are evaluating the efficacy of treatments initiated within this therapeutic window, based on a positive biomarker finding. Liquid biopsy might also open up the possibility of de-escalating therapy in patients with a negative biomarker result.
PMID:39999817 | DOI:10.1159/000544838
Discov Oncol. 2025 Feb 25;16(1):238. doi: 10.1007/s12672-025-01981-x.
ABSTRACT
The fight against lung adenocarcinoma (LUAD) is challenged by tumor microenvironment (TME)-mediated drug resistance, which limits effective treatment. This study examines the LUAD TME and identifies four distinct subtypes through multi-omics profiling: immune-rich, immune-exhausted, stromal-dominant, and TME-desert. Each subtype has unique molecular features, tumor diversity, and links to clinical outcomes. Immune-rich subtypes respond better to immune checkpoint inhibitors, while stromal-dominant and TME-desert subtypes show resistance to treatment and poor prognosis. Molecular analysis uncovers subtype-specific mutations, chromosomal instability, and altered signaling pathways, pointing to potential therapeutic targets. In silico drug screening identifies promising treatments for resistant subtypes. These findings, validated in independent cohorts, highlight the critical role of the TME in drug resistance and treatment response, providing insights for personalized treatment strategies in LUAD.
PMID:40000527 | PMC:PMC11861463 | DOI:10.1007/s12672-025-01981-x
STAR Protoc. 2025 Feb 11;6(1):103635. doi: 10.1016/j.xpro.2025.103635. Online ahead of print.
ABSTRACT
We introduce a protocol for generating 3D organoids from circulating tumor cells (CTCs), enabling longitudinal functional and molecular analyses in pancreatic cancer patients, including those with unresectable disease, which constitutes the majority of cases. We outline the process for isolating and characterizing CTCs from the blood of pancreatic cancer patients and provide detailed instructions for initiating, passaging, and phenotyping CTC-derived organoids. Additionally, we describe techniques for utilizing these organoids in drug screening with a focus on stemness-related pathways. For complete details on the use and execution of this protocol, please refer to Tang et al.1.
PMID:39946239 | PMC:PMC11870243 | DOI:10.1016/j.xpro.2025.103635
The panelists discuss how generative AI is boosting productivity, redefining the developer experience, and affecting software development in 2025.
By Christian Bonzelet, Jessica Andersson, Garima Bajpai, Shobhit Verma, Renato Losio
In this article, based on a talk at QCon San Francisco 2024, author Mandy Gu shares some of the ways her company uses GenAI to enhance productivity and the lessons they learned along the way, including failed bets and features that were rolled back because of low user adoption. Most important, they learned to focus on building tools that were aligned with business goals.
By Mandy Gu
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