Activating the immune system: Novel workflows for neoantigen prioritization
Jack DiGiovanna1, Joseph Szustakowski2, Isaac Neuhaus2, Sujaya Srinivasan2, Ariella Sasson2, Ariella Sasson2, Ana Mijalkovic Lazic1, Danielle Greenawalt2, Han Chang2, Stefan Kirov2, and Brandi Davis-Dusenbery1
1Seven Bridges, 2Bristol-Myers Squibb
In silico characterization of neoantigens is critical for evaluating the immunogenic state of tumors. Emerging literature suggests that specific neoantigens can elicit anti-tumor immune responses , and that the total number neoantigens expressed by a tumor (neoantigen burden) may be predictive of clinical response to immuno-therapy. Neoantigen burden is emerging as a promising biomarker for patient selection and indication prioritization. While several groups have described pipelines for neoantigen discovery from tumor exome sequencing data, those approaches are computationally intensive. Moreover, research in this area is ongoing and best practices have yet to emerge.
We will describe a neoantigen prediction workflow co-developed by Seven Bridges (SB) and BMS. The workflow uses Whole Exome Sequencing via the Sentieon toolkit to generate somatic variant calls. A novel SB tool then extracts candidate targets which are filtered based on RNA expression levels. Targets are matched to the patient’s Human Leukocyte Antigen (HLA) type. Finally, an IEDB T-Cell Epitopes Processing Tool prioritizes epitope candidates based upon peptide processing. This workflow on the SB Platform rapidly generates a patient-specific ranked list of neoantigen candidates.
 Schumacher T.N. and Schreiber R.D., Science, 03 Apr 2015, Vol. 348, Issue 6230, pp. 69-74