Immune checkpoint inhibitors (ICI) have become the standard of care as a first- or second-line systemic treatment for patients with various cancer types. Unfortunately, many patients do not respond to this treatment and the occurrence of immune-related adverse events varies widely. There is an urgent need for robust predictive biomarkers for immune checkpoint inhibitor response to select patients with likely clinical benefit from this costly treatment.
Several biomarkers, aimed at predicting response to immune checkpoint inhibitors, have been proposed, including PD-L1 expression, tumor mutation burden (TMB), infiltration of cytotoxic T-cells in the tumor, Microsatellite Instability (MSI) and expression of various immune gene signatures. However, these individual biomarkers have suboptimal performance and multiple complementary omics technologies are required to properly quantify them. Integration of these multi-omic biomarkers has proven valuable in increasing the accuracy and robustness of immune checkpoint inhibitor response prediction.
The featured speakers have developed a computational pipeline that determines the expressed mutation burden (eTMB), MSI status, fraction of infiltrating immune cells and various immune gene expression signatures directly from the RNA-sequencing profile of the tumor. Each biomarker is quantified using a dedicated algorithm that applies machine learning (eTMB and MSI) or computational deconvolution (infiltrating immune cells) and integrates external data sources to maximize performance. Algorithm performance has been validated on large cohorts of tumor RNA-sequencing data with matching gold standard quantification of said biomarkers. Moreover, integrating these biomarkers improves prediction of response to checkpoint inhibition therapy.
Register for this webinar to learn how this computational approach enables the quantification of various immune checkpoint inhibitor response biomarkers from a single omics layer (RNA-seq) and improves response prediction.
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.
Who Should Attend?
Clinical and translational researchers from industry and academia interested in learning:
- The state-of-the-art in mass spectrometry-based proteomics
- How to incorporate proteomics into:
- Preclinical translational research (e.g., target discovery, lead compound characterization)
- Clinical trials (e.g., pharmacodynamic, mechanism of action, correlative studies)
- Diagnostic testing
What You Will Learn
Attendees 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
CellCarta is the leading provider of specialized precision medicine laboratory services to the biopharmaceutical industry. Leveraging its integrated analytical platforms in immunology, histopathology, proteomics and genomics, as well as related specimen collection and logistics services, CellCarta supports the entire drug development cycle, from discovery to late-stage clinical trials.
The company operates globally with 10 facilities located in Canada, USA, Belgium, Australia, and China.
For more information on how CellCarta can partner with you, please contact us: www.cellcarta.com
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