The interdependence of immunology and cancer biology has been strengthened with recent advances in immunogenomic analysis through computational approaches.
We aim to take advantage of the data generated from high-throughput sequencing technologies like NGS to systematically identify neoantigens across cancers, antigen presentation states, biomarkers of response, and genetic and epigenetic states of the tumor and host.
TIGL has developed and provides:
- Bulk transcriptome analysis
- Single-cell transcriptome analysis
- TCR analysis
- Methylation state and RBS detection
- Neoantigen prediction
Tumor-specific neoantigens are identified using algorithms to predict which mutated peptides are likely to bind to patient-specific MHC class I molecules. TIGL has refined these prediction algorithms incorporating mass spectrometry-based rules as described in the Detection of antigen-specific immunity section. Analysis of large-scale WES data using this pipeline has led to numerous insights regarding the drivers of immune responses across cancer and treatment settings.
Sachet A. Shukla, PhD, Computational Lead Scientist
Juliet Forman, Computational Associate
Yue Hou, PhD, Bioinformatics Analyst
Sonia Moreno, Computational Associate