Liu2019 - Logistic Regression models to predict intrinsic resistance to anti-PD1 ICB in Ipilimumab treated and Ipilimumab naive patients with metastatic melanoma.

ABSTRACT: In this comprehensive study, the authors have developed concise models integrating clinical, genomic and transcriptomic features to predict intrinsic resistance to anti-PD1 Immune Checkpoint Blockade (ICB) treatment in individual tumors. It's important to note that their validation was performed in smaller, independent cohorts, constrained by data availability. The authors have developed two Logistic Regression based models for Ipilimumab treated and Ipilimumab naive patients with metastatic melanoma. The main predictive features for the Ipilimumab treated patients are MHC-II HLA, LDH at treatment initiation and the presence of lymph node metastases (LN met), chosen using forward selection methodology. The main predictive features for the Ipilimumab naive patients are tumor heterogeneity, tumor ploidy and tumor purity, chosen using forward selection methodology. Please note that in these models, the output ‘1’ means progressive disease (PD) and ‘0’ means non-PD. The original GitHub repository can be accessed at https://github.com/vanallenlab/schadendorf-pd1

SUBMITTER: Divyang Deep Tiwari  

PROVIDER: MODEL2310150001 | BioModels | 2023-11-17

REPOSITORIES: BioModels