A highly predictive autoantibody-based biomarker panel for prognosis in early-stage NSCLC with potential therapeutic implications.

ABSTRACT:

Background

Lung cancer is the leading cause of cancer-related death worldwide. Surgical resection remains the definitive curative treatment for early-stage disease offering an overall 5-year survival rate of 62%. Despite careful case selection, a significant proportion of early-stage cancers relapse aggressively within the first year post-operatively. Identification of these patients is key to accurate prognostication and understanding the biology that drives early relapse might open up potential novel adjuvant therapies.

Methods

We performed an unsupervised interrogation of >1600 serum-based autoantibody biomarkers using an iterative machine-learning algorithm.

Results

We identified a 13 biomarker signature that was highly predictive for survivorship in post-operativ

SUBMITTER: Patel AJ 

PROVIDER: S-EPMC8770460 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature