June 6, 2023
Various biomarkers, such as PD-L1 expression, tumor mutation burden (TMB), cytotoxic T-cell infiltration, Microsatellite Instability (MSI), and immune gene signatures, have been proposed to predict immune checkpoint inhibitor response. However, individual biomarkers have limited accuracy. To overcome this, a computational pipeline has been developed to quantify biomarkers including expressed mutation burden (eTMB), MSI status, infiltrating immune cells, and immune gene expression signatures from tumor RNA-sequencing data. Machine learning and computational deconvolution algorithms enhance performance, validated on large cohorts.
Integration of these biomarkers improves the prediction of checkpoint inhibition therapy response.
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Pieter Mestdagh is a Computational Biology Manager at CellCarta, and Professor at Ghent University, Belgium. He holds master’s degrees in industrial engineering (2004) and in bioscience engineering (2006) and obtained a PhD in biomedical sciences (2011). He is the author of more than 100 scientific articles in international journals and co-inventor on several European patents.