Predictive Tool for Melanoma Could Guide Immunotherapy Choices
Source: OncLive, December 2024
Genomic heterogeneity and ploidy identify patients with intrinsic resistance to PD-1 blockade in metastatic melanoma.
In previous research, Dana-Farber researchers found that a tumor’s genomic heterogeneity, meaning the tumor’s propensity to develop new mutations, and low genomic ploidy, a measure of the number of chromosomes in the cells, predicts resistance to immune checkpoint blockade with anti-PD-1 therapy.
This study builds on that research by developing a predictive machine learning model that uses genomic heterogeneity and ploidy to predict resistance to anti-PD-1 therapy. The team of clinical investigators and computational biologists refined and validated the model in four cohorts including two clinical trials. The interpretable machine-learning algorithm employs a simple decision tree to robustly predict which patients are likely to resist immune checkpoint blockade with anti-PD-1 therapy.