Predicting Immune Checkpoint Inhibitor Response Directly from Tumor RNA-Sequencing Data

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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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.