Description

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 [1], 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.

[1] Schumacher T.N. and Schreiber R.D., Science, 03 Apr 2015, Vol. 348, Issue 6230, pp. 69-74

DOI: 10.1126/science.aaa4971