Abstract 4512: High-throughput identification of neoepitopes for the development of patient-specific cancer immunotherapies
Authors: Nguyen Andrew , Sanborn J Zachary , Vaske Charles J. , Rabizadeh Shahrooz , Niazi Kayvan , Soon-Shiong Patrick , Benz Steven C. 
Published: July 2016

Abstract
Introduction: Immunotherapies such as checkpoint inhibitors, CAR T cells, NK cells, and therapeutic vaccines are revolutionizing cancer medicine. Although these agents have resulted in remarkable responses in some patients, many fail to respond, suggesting that a patient-specific approach is needed for immunotherapies to realize their full potential. Here we report the analysis of whole genome sequencing (WGS) and RNA sequencing (RNAseq) data from The Cancer Genome Atlas (TCGA) to identify neoepitopes that may serve as viable immunotherapeutic targets and that could be used to develop next-generation, patient-specific cancer immunotherapies.
Methods: HLA-A compatible neoepitopes were identified by creating all permutations of 9-mer amino acid strings derived from identified single nucleotide variants (SNVs) occurring in expressed genes. All neoepitopes were filtered against a database of 9-mers created using every known human protein along with common variations obtained through dbSNP to reduce off-target effects. HLA typing was determined from WGS data using the HLA forest algorithm. NetMHC 3.4 was used to obtain the predicted binding affinities of neoepitopes to HLA-A alleles
Results: We analyzed 750 patients across 23 cancer types using WGS data and RNAseq data, when available. Mutational and neoepitope loads varied across cancer types, with skin cutaneous melanoma and thyroid carcinoma having the highest and lowest mutational and neoepitope loads, respectively. Neoepitope counts identified by WGS correlated with neoepitope expression identified by RNAseq across a wide variety of cancers (Pearson's r = 0.99 for all cancers combined). The distribution of HLA-A and HLA-DRB1 alleles determined from TCGA were generally comparable to that in the US population; however, the frequency of HLA-DRB1*15:01 (associated with several diseases) was ∼2-fold higher than the US population. Among patients who expressed HLA-A*02:01 (n = 143), the numbers of predicted neoepitopes identified by WGS, neoepitopes expressed per RNAseq, and neoepitopes having binding affinity to HLA-A*02:01 were 23272, 9619, and 138, respectively. Almost all neoepitopes were unique to each patient, with a maximum of only 2-shared neoepitopes among any cancer type. Among 26 triple negative breast cancer (TNBC) samples, the numbers of predicted neoepitopes, expressed neoepitopes, and neoepitopes with affinity to HLA-A*02:01 were 17925, 8184, and 228, respectively. There were no shared neoepitopes among TNBC samples. Across all cancers, ∼6% of neoepitopes occurred in cancer driver genes.
Conclusions: Neoepitopes are rarely shared among cancers, and almost all are unique to each patient. For patients with limited treatment options and poor outcomes, such as TNBC, high-throughput identification of neoepitopes could serve as the basis for the development of next-generation, patient-specific immunotherapies.
Citation Format: Andrew Nguyen, J Zachary Sanborn, Charles J. Vaske, Shahrooz Rabizadeh, Kayvan Niazi, Patrick Soon-Shiong, Steven C. Benz. High-throughput identification of neoepitopes for the development of patient-specific cancer immunotherapies. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4512.