14-protein plasma signature predicts lung cancer risk years before diagnosis and identifies candidates for preventive therapy
By
William Hill1,65,68 Send email to [email protected]
Summary
This article presents a scientific study on lung cancer risk prediction using a 14-protein plasma signature identified through machine learning. The signature can predict lung cancer more than 5 years before diagnosis and was validated across eight cohorts. It builds on findings from the CANTOS trial, which showed that IL-1β inhibition (canakinumab) reduces lung cancer incidence but has a high number needed to treat (NNT) in unselected populations. The protein signature helps identify individuals who would benefit most from anti-IL-1β-based lung cancer risk reduction. The research demonstrates how diverse tumor-promoting factors (including smoking and particulate matter exposure) converge on an alveolar transitional state underlying lung tumorigenesis, enabling molecular cancer prevention approaches.
Source
bsky14-protein plasma signature predicts lung cancer risk years before diagnosis and identifies candidates for preventive therapycell.comKey quotes
· 3 pulledPredicting lung cancer risk would enhance prevention trials.
Using machine learning, we identified a 14-protein plasma signature predicting lung cancer more than 5 years before diagnosis.
The signature, validated across eight cohorts, was elevated in current smokers and individuals exposed to particulate matter (PM).
You might also wanna read
New plasma proteomics model improves thrombosis risk prediction in cancer patients and identifies IL-17-driven endothelial activation as therapeutic target
This article presents research by Karagkouni and colleagues who developed a new cancer-associated venous thromboembolism (VTE) prediction mo
scim.ag·15d ago
Anti-inflammatory drugs show promise for lung cancer prevention, new research finds
New research indicates that existing anti-inflammatory drugs show promise for preventing lung cancer, moving beyond traditional lifestyle-ba
economist.com·20d ago
Machine Learning Model Shows Promise for Predicting Liver Cancer Risk in Young Adults
Researchers developed and validated machine learning models to predict early-onset hepatocellular carcinoma (HCC) risk in young adults using
mdsc.pe·5d ago
Gemma-Based AI Model Identifies Potential Cancer Therapy Pathway Through Conditional Immune Amplification
Researchers used a new 27 billion parameter foundation model called C2S-Scale 27B, built on Google's Gemma family of open models, to discove
blog.google·8mo ago
Navigating Biomarker Conversations: Empowering Patient Understanding in Gastroesophageal Cancer
mdsc.pe·18d ago
Computational Design of Peptide Masks Enables Conditionally Active Miniprotein Therapeutics
This article presents a novel computational approach for designing conditionally active miniprotein binders through de novo peptide masks. T
pubs.acs.org·11d ago
Comments
Sign in to join the conversation.
No comments yet. Be the first.