Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade.

ABSTRACT: Patients with metastatic triple-negative breast cancer (TNBC) show variable responses to PD-1 inhibition. Efficient patient selection by predictive biomarkers would be desirable but is hindered by the limited performance of existing biomarkers. Here, we leveraged in silico patient cohorts generated using a quantitative systems pharmacology model of metastatic TNBC, informed by transcriptomic and clinical data, to explore potential ways to improve patient selection. We evaluated and quantified the performance of 90 biomarker candidates, including various cellular and molecular species, at different cutoffs by a cutoff-based biomarker testing algorithm combined with machine learning-based feature selection. Combinations of pretreatment biomarkers improved the specificity compared to single b

SUBMITTER: Arulraj T 

PROVIDER: S-EPMC11551325 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature