A New Path to Preventing Cancer

6 hours ago

Researchers discovered a 14-protein blood test using machine learning and high-throughput proteomics to predict lung cancer more than 5 years before diagnosis.
The protein signature, validated in 8 cohorts including non-smokers, reflects lung cell stress from inflammation, surfactant production, epithelial secretion, and matrix remodeling.
The proteins originate from healthy bystander cells sensing precancerous stress, not from tumor cells, with interleukin-1β (IL-1β) identified as a key driver.
The CANTOS trial serendipitously found that blocking IL-1β with canakinumab reduced lung cancer, but with a high number needed to treat (NNT >1,000).
Using the 14-protein signature to identify high-risk individuals in CANTOS lowered the NNT to 50 for preventing one lung cancer case with IL-1β blockade.
The study integrates proteomics, mouse models, organoids, and air pollution (particulate matter) exposure to elucidate mechanisms linking environmental factors and mutations to cancer risk.
This work establishes a template for primary cancer prevention, combining predictive biomarkers with targeted interventions, potentially complementing preventive vaccine strategies.
Resources like the UK Biobank and large trials (e.g., CANTOS) were instrumental, highlighting the importance of clinical cohorts for advancing preventive medicine.

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