Elham Sherafat

BACKGROUND: Personalized cancer vaccines are emerging as one of the most promising approaches to immunotherapy of advanced cancers. However, only a small proportion of the neoepitopes generated by somatic DNA mutations in cancer cells lead to tumor rejection. Since it is impractical to experimentally assess all candidate neoepitopes prior to vaccination, developing accurate methods for predicting tumor-rejection mediating neoepitopes (TRMNs) is critical for enabling routine clinical use of cancer vaccines. RESULTS: In this paper we introduce Positive-unlabeled Learning using AuTOml (PLATO), a general semi-supervised approach to improving accuracy of model-based classifiers. PLATO generates a set of high confidence positive calls by applying a stringent filter to model-based predictions, then rescores remaining candidates by using positive-unlabeled learning. To achieve robust performance on clinical samples with large patient-to-patient variation, PLATO further integrates AutoML hyper-parameter tuning, classification threshold selection based on spies, and support for bootstrapping. CONCLUSIONS: Experimental results on real datasets demonstrate that PLATO has improved performance compared to model-based approaches for two key steps in TRMN prediction, namely somatic variant calling from exome sequencing data and peptide identification from MS/MS data.

High-throughput DNA and RNA sequencing are revolutionizing precision oncology, enabling personalized therapies such as cancer vaccines designed to target tumor-specific neoepitopes generated by somatic mutations expressed in cancer cells. Identification of these neoepitopes from next-generation sequencing data of clinical samples remains challenging and requires the use of complex bioinformatics pipelines. In this paper, we present GeNeo, a bioinformatics toolbox for genomics-guided neoepitope prediction. GeNeo includes a comprehensive set of tools for somatic variant calling and filtering, variant validation, and neoepitope prediction and filtering. For ease of use, GeNeo tools can be accessed via web-based interfaces deployed on a Galaxy portal publicly accessible at https://neo.engr.uconn.edu/. A virtual machine image for running GeNeo locally is also available to academic users upon request.

Abstract Description The identification and prioritization of cancer-specific neoepitopes from next-generation sequencing data for personalized immunotherapies such as cancer vaccines remains challenging and requires the use of complex bioinformatics approaches. Here, we present GeNeo2, an updated version with enhanced features of the GeNeo toolbox for predicting neoepitopes from matched tumor/normal exome sequencing data coupled with tumor RNA-Seq data (Al Seesi et al., 2023). Unlike GeNeo, which identifies neoepitopes generated by single nucleotide variants, GeNeo2 also predicts neoepitopes generated by somatic indels. A distinguishing feature in GeNeo2 is that it integrates tools for analyzing mass spectrometry immunepeptidomic data, which can reveal neoantigens derived from both canonical and noncanonical sources. Finally, GeNeo2 integrates novel machine-learning approaches to improve the accuracy of somatic variant calling and peptide identification from mass spectrometry data. GeNeo2 tools can be accessed via web-based interfaces deployed on a Galaxy portal accessible at https://neo.engr.uconn.edu/. A virtual machine image for running GeNeo2 locally is also available to academic users upon request. Topic Categories Computational and Systems Immunology (COMP)