Understanding the specific genetic and molecular mechanisms behind tumorigenesis is critical for the discovery and development of new cancer therapies. In this webinar, you’ll learn how to select the most relevant model to test your therapy based on in-depth model characterization, and how to exploit this high-throughput bioinformatic data to understand the transcriptomic response of compound treatments.
You’ll explore this technology through applications presented in two case studies:
- EGFR inhibitor testing including model selection based on molecular data, in vitro 2D/3D compound screening, in vivo experiments and biomarker analysis.
- Single and combinatorial treatment of a-PD1 and a-CTLA4 in CT26 and MC38 mice. The transcriptomic response after immune checkpoint inhibitor treatment and a gene signature predicting treatment response are elucidated using differential gene expression analysis, pathway analysis and machine learning algorithms.
Hagen Klett, PhD
Bioinformatics Scientist, Discovery Services