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.
Viewers will gain insights into:
- A unique computational pipeline that defines several characteristics directly from the RNA-sequencing profile of a tumor such as:
- Expressed mutation burden (eTMB)
- Fraction of infiltrating immune cells
- Various immune gene expression signatures
- How this analysis is compatible with formalin-fixed paraffin-embedded (FFPE) tumor samples and does not require a matched germline DNA sample
Pieter Mestdagh, Computational Biology Manager, CellCarta
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.
