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Biomarkers, omics and artificial intelligence for early detection of pancreatic cancer

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

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Systems-level design principles of metabolic rewiring in an animal

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.
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Rare disease gene association discovery in the 100,000 Genomes Project

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.
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Technology shapes relationships. Relationships shape technology.

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

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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: 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
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Single-cell multiomics reveals a gene regulatory circuit driving leukemia cell differentiation

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
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Imaging and outcome correlates of ctDNA methylation markers in prostate cancer: a comparative, cross-sectional [⁶⁸Ga]Ga-PSMA-11 PET/CT study

To validate the clinical utility of a previously identified circulating tumor DNA methylation marker (meth-ctDNA) panel for disease detection and survival outcomes, meth-ctDNA markers were compared to PSA leve...
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金蝶徐嘉祺:抓住AI红利,构建新质生产力

2月27日,金蝶云数智化转型大会在广州举办。会上,金蝶中国助理总裁、广东省公司总经理徐嘉祺表示:预测未来的最好方式是创造它,技术不断创新,科技不断进步,我们不应只是追随,更应做到引领。AI时代,金蝶也将秉承“予力企业,成就不凡”的使命,融合数字技术和管理智慧,予力全球每一个企业和个人,帮助更多企业抓住AI红利,构建新质生产力,一起为中国经济发展贡献力量。
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Comprehensive resource describes functions of more than 20,000 human genes

A comprehensive encyclopedia of the known functions of all protein-coding human genes has just been completed and released. Researchers used large-scale evolutionary modeling to integrate data on human genes with genetic data collected from other organisms. This has culminated in a searchable public resource that lists the known functions of more than 20,000 genes using the most accurate and complete evidence available.
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Automatic cell analysis with the help of artificial intelligence

Identifying and delineating cell structures in microscopy images is crucial for understanding the complex processes of life. This task is called 'segmentation' and it enables a range of applications, such as analyzing the reaction of cells to drug treatments, or comparing cell structures in different genotypes. It was already possible to carry out automatic segmentation of those biological structures but the dedicated methods only worked in specific conditions and adapting them to new conditions was costly. An international research team has now developed a method by retraining the existing AI-based software Segment Anything on over 17,000 microscopy images with over 2 million structures annotated by hand.
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