SNAF: Comprehensive prediction of splicing neoantigens for targeted cancer immunotherapy
Updated: Sep 29
1. Guangyuan Li, BS, Cincinnati Children's Hospital Medical Center 2. Anukana Bhattacharjee, PhD, Cincinnati Children's Hospital Medical Center 3. Gloria M. Sheynkman, PhD, University of Virginia 4. Nathan Salomonis, PhD, Cincinnati Children's Hospital Medical Center
Immunotherapy has emerged as a crucial strategy to combat cancer by 'reprogramming' a patient's own immune system. While immunotherapy is typically reserved for cancer patients with a high mutational burden, neoantigens produced from post-transcriptional regulation may provide an untapped reservoir of common immunogenic targets for new targeted cancer therapies. To systematically and comprehensively define tumor-specific and likely immunogenic neoantigens from patient RNA-Seq, we developed SNAF (Splicing Neo Antigen Finder), an easy-to-use and open-source computational workflow to predict splicing-derived immunogenic MHC-bound peptides (T cell antigen) and novel transmembrane proteins with altered extracellular epitopes (B cell antigen). This workflow employs a highly accurate deep-learning strategy for immunogenicity prediction (DeepImmuno) in conjunction with new algorithms to rank the tumor specificity of neoantigens. T-cell antigens from SNAF were frequently verified as HLA-presented peptides from Mass Spectrometry (MS) and reproducibly predict survival and response to immunotherapy in melanoma. Shared splicing neoantigens are found in-up-to 90% of cancer patients, predict overall survival in multiple cancer cohorts and characterized by selective amino acid preferences. In addition to T-cell neoantigens, our B-cell focused pipeline was able to identify a novel class of tumor-specific extracellular neo-epitopes which we term ExNeoEpitopes. ExNeoEpitope full-length mRNA predictions were tumor specific and readily validated using long-read isoform sequencing in in vitro. These included ExNeoEpitopes evidenced by MS that produce stable protein structures using domain and 3D in silico modeling. We provide SNAF with interactive exploration tools to promote broad adoption and enable streamlined prioritization of targets for validation.