Crick, UCL Find 14-Protein Blood Signature That Halves Lung Cancer Risk With Canakinumab
Updated
Updated · ERC · Jun 4
Crick, UCL Find 14-Protein Blood Signature That Halves Lung Cancer Risk With Canakinumab
3 articles · Updated · ERC · Jun 4
Summary
More than 48,000 UK Biobank samples helped researchers identify a 14-protein blood signature that, alongside age, smoking and lung-disease history, predicted lung cancer diagnosis within five years.
Eight global validation datasets showed the signature was higher in people who later developed lung cancer, including non-smokers, suggesting it captures risk missed by screening based mainly on age and smoking history.
Mouse and patient analyses linked the signature to an inflammatory lung state driven by IL-1β and pollution exposure, not to existing tumors; blocking IL-1β reduced KAC cells and slowed early tumor development.
In 4,651 participants from Novartis's CANTOS trial, people with a high baseline signature saw lung cancer risk nearly halved on canakinumab, with a number needed to treat of 55.
The findings, published in Cell, point to a potential LDL-like biomarker for precision lung-cancer prevention and possibly other inflammation-linked lung diseases such as COPD and idiopathic pulmonary fibrosis.
Could a simple anti-inflammatory pill stop lung cancer before it even starts?
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Revolutionizing Lung Cancer Prevention: 14-Protein Blood Signature Enables Early Risk Prediction and Precision Therapy
Overview
A major breakthrough was announced on June 4, 2026: a new blood protein signature can predict lung cancer risk years before diagnosis. This innovation could transform early detection and prevention, especially since current screening mostly targets older smokers and misses many at-risk people, such as never-smokers and those exposed to pollution. Since lung cancer is the most commonly diagnosed cancer worldwide and survival rates remain low, this new approach offers hope for more effective, inclusive screening and earlier intervention, potentially improving outcomes for many who are currently overlooked by existing methods.