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.
Nature, Published online: 25 February 2025; doi:10.1038/d41586-025-00598-y
A junk-food splurge can change your brain activity. Plus, hear from US postdocs and PhD students hit hard by Trump’s crackdown on science.
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
Nature, Published online: 24 February 2025; doi:10.1038/d41586-025-00265-2
Lack of screening and limited treatment options mean that low- and middle-income countries face higher death rates despite having fewer cases than wealthier nations.
Oncogene, Published online: 26 February 2025; doi:10.1038/s41388-025-03314-2
Molecular mechanisms altering cell identity in cancer
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
Oncogene, Published online: 22 February 2025; doi:10.1038/s41388-025-03309-z
Single-cell multiomics reveals a gene regulatory circuit driving leukemia cell differentiation
Gemini is Google’s long-promised, next-gen generative AI model family.
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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
Cancer Cell. 2025 Feb 14:S1535-6108(25)00030-3. doi: 10.1016/j.ccell.2025.01.012. Online ahead of print.
ABSTRACT
Recent advances have shed light on the molecular heterogeneity of small cell lung cancer (SCLC), yet the spatial organizations and cellular interactions in tumor immune microenvironment remain to be elucidated. Here, we employ co-detection by indexing (CODEX) and multi-omics profiling to delineate the spatial landscape for 165 SCLC patients, generating 267 high-dimensional images encompassing over 9.3 million cells. Integrating CODEX and genomic data reveals a multi-positive tumor cell neighborhood within ASCL1+ (SCLC-A) subtype, characterized by high SLFN11 expression and associated with poor prognosis. We further develop a cell colony detection algorithm (ColonyMap) and reveal a spatially assembled immune niche consisting of antitumoral macrophages, CD8+ T cells and natural killer T cells (MT2) which highly correlates with superior survival and predicts improving immunotherapy response in an independent cohort. This study serves as a valuable resource to study SCLC spatial heterogeneity and offers insights into potential patient stratification and personalized treatments.
PMID:39983726 | DOI:10.1016/j.ccell.2025.01.012
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
NPJ Precis Oncol. 2025 Feb 24;9(1):50. doi: 10.1038/s41698-025-00834-8.
ABSTRACT
Disulfidptosis, a newly discovered cell death mode distinct from other programmed cell death in lung and kidney cancer cells, is defined as extensive disulfide bonds to actin cytoskeleton proteins, leading to actin contraction and cytoskeletal disruption cell death. New cell death pattern discoveries often drive advances in tumor research. Therefore, the present study attempted to decipher the manifestation and importance of disulfidptosis in pan-cancer. Combining Clinical specimen immunofluorescence staining, single-cell analyses, and spatial transcriptome analyses, we demonstrated the manifestation of disulfidptosis in pan-cancer. Multi-omics analysis has revealed that genomic variants and DNA methylation in DRGs can affect the prognosis of patients with pan-cancer. The nomogram based on the DRGs Score model could accurately predict the prognosis of patients with pan-cancer. PF-562271, EHT-1864, and IPA-3 are potential therapeutic agents targeting disulfidptosis. Collectively, this study deciphered for the first time the importance of disulfidptosis for pan-cancer and developed the DRGs Score model that can assist clinicians in accurately predicting the prognosis and guiding individualized treatment of pan-cancer patients.
PMID:39994355 | DOI:10.1038/s41698-025-00834-8
Front Immunol. 2025 Feb 7;16:1479550. doi: 10.3389/fimmu.2025.1479550. eCollection 2025.
ABSTRACT
BACKGROUND: To construct a prediction model consisting of metabolites and proteins in peripheral blood plasma to predict whether patients with unresectable stage III and IV non-small cell lung cancer can benefit from immunotherapy before it is administered.
METHODS: Peripheral blood plasma was collected from unresectable stage III and IV non-small cell lung cancer patients who were negative for driver mutations before receiving immunotherapy. Then we classified samples according to the follow-up results after two courses of immunotherapy and non-targeted metabolomics and proteomics analyses were performed to select different metabolites and proteins. Finally, potential biomarkers were picked out by applying machine learning methods including random forest and stepwise regression and prediction models were constructed by logistic regression.
RESULTS: The presence of metabolites and proteins in peripheral blood plasma was causally associated with both non-small cell lung cancer and PD-L1/PD-1 expression levels. A total of 2 differential metabolites including 5-sulfooxymethylfurfural and Anthranilic acid and 2 differential proteins including Immunoglobulin heavy variable 1-45 and Microfibril-associated glycoprotein 4 were selected as reliable biomarkers. The area under the curve (AUC) of the prediction model built on clinical risks was merely 0.659. The AUC of metabolomics prediction model was 0.977 and the AUC of proteomics was 0.875 while the AUC of the integrative-omics prediction model was 0.955.
CONCLUSIONS: Metabolic and protein biomarkers in peripheral blood both have high efficacy and reliability in the prediction of immunotherapy sensitivity in unresectable stage III and IV non-small cell lung cancer, but validation in larger population-based cohorts is still needed.
PMID:39991162 | PMC:PMC11842339 | DOI:10.3389/fimmu.2025.1479550
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