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
Cell Death Discovery, Published online: 05 February 2025; doi:10.1038/s41420-025-02312-3
Impaired RelA signaling and lipid metabolism dysregulation in hepatocytes: driving forces in the progression of metabolic dysfunction-associated steatotic liver disease
Nature, Published online: 05 February 2025; doi:10.1038/s41586-024-08398-6
PELO–HBS1L and SKI complexes in the human mRNA quality control pathway exhibit a synthetic lethal interaction and may represent novel targets for the development of cancer therapies.
Nat Comput Sci. 2025 Feb 7. doi: 10.1038/s43588-024-00764-8. Online ahead of print.
ABSTRACT
Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally scalable analytical pipeline for functionally informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits in 61,838 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered and replicated new associations with lipid traits missed by single-trait analysis.
PMID:39920506 | DOI:10.1038/s43588-024-00764-8
Front Microbiol. 2025 Jan 22;15:1516667. doi: 10.3389/fmicb.2024.1516667. eCollection 2024.
ABSTRACT
Microbiome research, the study of microbial communities in diverse environments, has seen significant advances due to the integration of deep learning (DL) methods. These computational techniques have become essential for addressing the inherent complexity and high-dimensionality of microbiome data, which consist of different types of omics datasets. Deep learning algorithms have shown remarkable capabilities in pattern recognition, feature extraction, and predictive modeling, enabling researchers to uncover hidden relationships within microbial ecosystems. By automating the detection of functional genes, microbial interactions, and host-microbiome dynamics, DL methods offer unprecedented precision in understanding microbiome composition and its impact on health, disease, and the environment. However, despite their potential, deep learning approaches face significant challenges in microbiome research. Additionally, the biological variability in microbiome datasets requires tailored approaches to ensure robust and generalizable outcomes. As microbiome research continues to generate vast and complex datasets, addressing these challenges will be crucial for advancing microbiological insights and translating them into practical applications with DL. This review provides an overview of different deep learning models in microbiome research, discussing their strengths, practical uses, and implications for future studies. We examine how these models are being applied to solve key problems and highlight potential pathways to overcome current limitations, emphasizing the transformative impact DL could have on the field moving forward.
PMID:39911715 | PMC:PMC11794229 | DOI:10.3389/fmicb.2024.1516667
Curr Genomics. 2025;26(1):48-80. doi: 10.2174/0113892029308243240709073945. Epub 2024 Jul 18.
ABSTRACT
BACKGROUND: The damage in the liver and hepatocytes is where the primary liver cancer begins, and this is referred to as Hepatocellular Carcinoma (HCC). One of the best methods for detecting changes in gene expression of hepatocellular carcinoma is through bioinformatics approaches.
OBJECTIVE: This study aimed to identify potential drug target(s) hubs mediating HCC progression using computational approaches through gene expression and protein-protein interaction datasets.
METHODOLOGY: Four datasets related to HCC were acquired from the GEO database, and Differentially Expressed Genes (DEGs) were identified. Using Evenn, the common genes were chosen. Using the Fun Rich tool, functional associations among the genes were identified. Further, protein-protein interaction networks were predicted using STRING, and hub genes were identified using Cytoscape. The selected hub genes were subjected to GEPIA and Shiny GO analysis for survival analysis and functional enrichment studies for the identified hub genes. The up-regulating genes were further studied for immunohistopathological studies using HPA to identify gene/protein expression in normal vs HCC conditions. Drug Bank and Drug Gene Interaction Database were employed to find the reported drug status and targets. Finally, STITCH was performed to identify the functional association between the drugs and the identified hub genes.
RESULTS: The GEO2R analysis for the considered datasets identified 735 upregulating and 284 downregulating DEGs. Functional gene associations were identified through the Fun Rich tool. Further, PPIN network analysis was performed using STRING. A comparative study was carried out between the experimental evidence and the other seven data evidence in STRING, revealing that most proteins in the network were involved in protein-protein interactions. Further, through Cytoscape plugins, the ranking of the genes was analyzed, and densely connected regions were identified, resulting in the selection of the top 20 hub genes involved in HCC pathogenesis. The identified hub genes were: KIF2C, CDK1, TPX2, CEP55, MELK, TTK, BUB1, NCAPG, ASPM, KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A, NUSAP1, DLGAP5, PBK, and CCNB2. Further, GEPIA and Shiny GO analyses provided insights into survival ratios and functional enrichment studied for the hub genes. The HPA database studies further found that upregulating genes were involved in changes in protein expression in Normal vs HCC tissues. These findings indicated that hub genes were certainly involved in the progression of HCC. STITCH database studies uncovered that existing drug molecules, including sorafenib, regorafenib, cabozantinib, and lenvatinib, could be used as leads to identify novel drugs, and identified hub genes could also be considered as potential and promising drug targets as they are involved in the gene-chemical interaction networks.
CONCLUSION: The present study involved various integrated bioinformatics approaches, analyzing gene expression and protein-protein interaction datasets, resulting in the identification of 20 top-ranked hubs involved in the progression of HCC. They are KIF2C, CDK1, TPX2, CEP55, MELK, TTK, BUB1, NCAPG, ASPM, KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A, NUSAP1, DLGAP5, PBK, and CCNB2. Gene-chemical interaction network studies uncovered that existing drug molecules, including sorafenib, regorafenib, cabozantinib, and lenvatinib, can be used as leads to identify novel drugs, and the identified hub genes can be promising drug targets. The current study underscores the significance of targeting these hub genes and utilizing existing molecules to generate new molecules to combat liver cancer effectively and can be further explored in terms of drug discovery research to develop treatments for HCC.
PMID:39911278 | PMC:PMC11793067 | DOI:10.2174/0113892029308243240709073945
Cell Metab. 2025 Jan 28:S1550-4131(24)00495-9. doi: 10.1016/j.cmet.2024.12.013. Online ahead of print.
ABSTRACT
Immune checkpoint blockade (ICB) therapy has revolutionized cancer treatment but remains effective in only a subset of patients. Emerging evidence suggests that the gut microbiome and its metabolites critically influence ICB efficacy. In this study, we performed a multi-omics analysis of fecal microbiomes and metabolomes from 165 patients undergoing anti-programmed cell death protein 1 (PD-1)/programmed death ligand 1 (PD-L1) therapy, identifying microbial and metabolic entities associated with treatment response. Integration of data from four public metagenomic datasets (n = 568) uncovered cross-cohort microbial and metabolic signatures, validated in an independent cohort (n = 138). An integrated predictive model incorporating these features demonstrated robust performance. Notably, we characterized five response-associated enterotypes, each linked to specific bacterial taxa and metabolites. Among these, the metabolite phenylacetylglutamine (PAGln) was negatively correlated with response and shown to attenuate anti-PD-1 efficacy in vivo. This study sheds light on the interplay among the gut microbiome, the gut metabolome, and immunotherapy response, identifying potential biomarkers to improve treatment outcomes.
PMID:39909032 | DOI:10.1016/j.cmet.2024.12.013
Discov Oncol. 2025 Jan 29;16(1):100. doi: 10.1007/s12672-025-01826-7.
ABSTRACT
Non-small cell lung cancer (NSCLC) remains a dire disease being the first cause of cancer death among both genders. Early-stage NSCLC often has better treatment outcomes despite it being a highly heterogeneous disease. So far, the neo-adjuvant chemotherapy strategies have led to a small benefit with an improvement of 5% in overall survival as an absolute benefit. Recently, the introduction of immune checkpoint inhibitors combined with chemotherapy has shown robust efficacy in terms of event-free survival and overall survival. Thus, these combinations are today considered a new standard of care in early-stage NSCLC. The application of these strategies to all-comer population lead to confounding definitive results regarding the efficacy and predictive biomarkers are urgently needed balancing the promise of healing than toxicities. At present, the clinical staging TNM system guides the clinical choice, however it is not entirely sufficient. Circulant tumoral DNA (ctDNA) emerged as a promising prognostic and predictive biomarker that may guide the future perioperative strategy and pave the way to personalized medicine also in this exciting field. This narrative review aims to put in the context the employment of ctDNA, give some perspective and suggestions weighing the pros and cons of this technique for our tomorrow clinical practice.
PMID:39881042 | PMC:PMC11780067 | DOI:10.1007/s12672-025-01826-7
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