# Application of Machine Learning Algorithms for Prediction of Tumor T-Cell Immunogens
## 1 Introduction
### Computational Methods for Tumor T-Cell Antigen Prediction
#### 1.2.2 Other Prediction Tools  
The Landscape of Effective Neoantigens Software (LENS) offers predictions for tumor-specific and tumor-associated antigens, considering diverse genomic alterations such as single nucleotide variants, insertions and deletions, fusion events, splice variants, cancer-tests antigens, overexpressed self-antigens, as well as viral and endogenous retroviral elements [18]. It is a pipeline of over two thousand separate tools for predicting the full suite of tumor antigens from genomics data. As an open-source project, it offers extendibility through both the introduction of new modules, tools, and reference datasets, along with refinement of its tumor antigen prioritization capabilities. OpenVax provides a computational workflow designed for the identification of somatic variants, prediction of neoantigens, and the curation of personalized cancer vaccine contents [19]. It performs alignment of the tumor and normal DNA samples as well as the tumor RNA. A prioritization of the identified somatic variants is made based on expression and predicted MHC Class-I binding affinity after multi-step processing via several computational programs. OpenVax has been used in three clinical trials assessing synthetic long peptides (NCT02721043, NCT03223103, NCT03359239). pVACtools support the identification of altered peptides arising from various mechanisms, including point mutations, in-frame and frameshift insertions and deletions, and gene fusions [20]. It is a pipeline consisting of several steps executed via a modular workflow featuring tools for neoantigen prediction from somatic alterations (pVACseq and pVACfuse), prioritization, and selection using a graphical web-based interface (pVACviz), and the design of DNA vector-based vaccines (pVACvector) and synthetic long peptide vaccines. The Nextflow NEOantigen prediction pipeline (nextNEOpi) represents a comprehensive and fully automated bioinformatic tool for predicting tumor neoantigens from raw DNA and RNA sequencing data [21]. It takes as input raw whole exome sequencing (WES) or whole genome sequencing (WGS) data from matched tumor-normal samples and, optionally, bulk-tumor RNA-seq data. After data preprocessing, nextNEOpi derives the germline and phased somatic mutations, copy number variants, tumor purity and ploidy, and subsequently selects high-confidence variants through majority voting. This method can predict both MHC Class-I and MHC Class-II neoantigens. TIminer (Tumor Immunology Miner) represents a user-friendly framework designed for the facilitation of integrative immunogenomics analyses, particularly focusing on tumor RNA-seq and mutational data at the level of individual patients or samples [22]. It integrates state-of-the-art bioinformatics tools to analyze single-sample RNA-seq data and somatic DNA mutations to characterize the tumor-immune interface including the following: genotyping of HLAs from NGS data; prediction of tumor neoantigens using mutation data and HLA types; characterization of tumor-infiltrating immune cells from bulk RNA-seq data; and quantification of tumor immunogenicity from expression data. Initiated with somatic mutations obtained from WES or WGS data, TIminer predicts mutated proteins arising from nonsynonymous variants, leveraging the Ensemble Variant Effect Predictor (VEP) [23]. Determination of Class-I HLA alleles is achieved through the utilization of Optitype applied to RNA-seq data [24]. Subsequently, the binding affinities of mutated peptides, derived from predicted proteins, to the reconstructed HLA alleles are forecasted using NetMHCpan [25]. Those peptides demonstrating high binding affinity, referred to as strong binders, are subsequently singled out as candidate neoantigens. StackTTCA introduces a novel stacking ensemble learning framework tailored for the identification of tumor T cell antigens [26]. The framework integrates 12 distinct feature-encoding schemes and 13 widely used machine learning methods.