Recent research from the lab of Leng Han, PhD, assistant professor of biochemistry and molecular biology, on “Multi-omics prediction of immune-related adverse events during checkpoint immunotherapy,” has been published in the October edition of Nature Communications.
“Immune checkpoint blockade (ICB) therapies have achieved striking benefits in a wide spectrum of cancer types,” Han said. “But there are also rising concerns about increased frequency and potential severity and fatality of immune related adverse events (irAEs) caused by ICB in the research community.”
In search of predictive biomarkers of irAEs, Han’s lab analyzed data from a pharmacovigilance database, US Food and Drug Administration Adverse Event Reporting System (FAERS), and multi-omics data from The Cancer Genome Atlas (TCGA). Their research identified a bivariate model of LCP1 and ADPGK that could predict irAEs and also provided a framework to study potential biomarkers by pharmacovigilance and multi-omics data. The authors also validated the model consists of LCP1 and ADPGK in an independent patient-level cohort.
The study combined the power of pharmacovigilance and multi-omics data, and suggested that LCP1 and ADPGK might enable a pre-risk check of patients before receiving anti-PD-1/PD-L1 agents with further study. Due to a limitation in obtaining a large patient level cohort, Han said that further studies with a larger sample size are necessary to deeply explore biomarkers of irAEs.
“It may require multiple years of multicenter efforts to obtain a large number of patient samples with or without irAEs from clinical trials,” Han said. “We aim to collect more multi-dimensional data, such as longitude biopsy of irAE affected tissues and tumor samples, to further study our irAE predicting model in cancer immunotherapy. The comprehensive patient level data will provide research community unprecedented power to accelerate investigation of irAEs.”
Contributors to the research included, Ying Jing, PhD, postdoctoral fellow in the Han Lab; Youqiong Ye, PhD; Lixia Diao, PhD; Steven H. Lin, MD, PhD and Gordon B. Mills, MD, PhD.