We use machine learning and neural networks to provide you with advanced analytics to better inform your discovery and translational research programs.
ImmunogenomicsID guides the investigation of critical immuno-oncology genes with information including expression, variant effect impact, and DNA/RNA allelic fractions. Unlike targeted therapies, there tends to be general agreement that it is unlikely that a single predictive biomarker in tumor biopsies will be found for determining response to immunotherapies. Thus, multidimensional biomarker analysis is needed to accurately assess patient response. The Personalis Immunogenomics Engine enables the ability to look across critical areas to characterize tumor biology for focused analysis.
Rapidly evaluate the tumor biology of a sample in key areas including:
- Antigen Presentation
- Translational research empowers a better understanding of the pathways that tumor cells use to evade immune surveillance. Detecting critical mutations in genes such as B2M are important to comprehend the mechanisms of acquired resistance to immunotherapies. B2M deficiency has been shown in Adoptive Cell therapies (Restifo et al., 1996), Checkpoint Inhibitors (Zaretsky et al., 2016) and Neoantigen Vaccine strategies (Sahin et al., 2017)
- Repair and Replication
- Microsatellite instability High (MSI- H) or DNA mismatch repair deficiency tumors are thought to be an important biomarker for patient response. Recently the FDA approved the use of pembrolizumab based upon the tumor’s MSI status.
- Checkpoint Modulators
- The activation of T-cells is regulated by both stimulatory and inhibitory signals. Understanding the tumors checkpoint ligand expression is key to understand the likely mechanisms of tumor escape.