Longitudinal plasma proteomic profiling of patients with non-small cell lung cancer undergoing immune checkpoint blockade.

ABSTRACT:

Background

Immune checkpoint inhibitors (ICIs) have revolutionized the cancer therapy landscape due to long-term benefits in patients with advanced metastatic disease. However, robust predictive biomarkers for response are still lacking and treatment resistance is not fully understood.

Methods

We profiled approximately 800 pre-treatment and on-treatment plasma proteins from 143 ICI-treated patients with non-small cell lung cancer (NSCLC) using ELISA-based arrays. Different clinical parameters were collected from the patients including specific mutations, smoking habits, and body mass index, among others. Machine learning algorithms were used to identify a predictive signature for response. Bioinformatics tools were used for the identification of patient subtypes and analysis

SUBMITTER: Harel M 

PROVIDER: S-EPMC9207924 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature