Cells become cancerous due to changes in their genetic makeup. In cancers, an altered amino acid due to a tumor mutation can result in proteins that are identified as "foreign" by the immune system. An MHC molecule will bind to these "foreign" peptide fragments, also called neoantigens. There are 2 classes of MHC molecules. While the MHC I complex is found in all cells with a nucleus, MHC II complexes are mostly found in antigen presenting cells (APCs), such as macrophages, B cells, and dendritic cells. The MHC molecule then presents the neoantigen on the cell's surface. If an immune cell, such as a T-cell, is able to bind to the neoantigen, it can then destroy the tumor cell. However, there are molecules that act as checkpoints on certain immune cells that have to be activated or inactivated to start an immune response. This ensures that healthy cells are not being killed. However, sometimes cancer cells can find ways to use these checkpoints to avoid being attacked. An example of immunotherapy which has had clinical successes is checkpoint blockade inhibition, which means blocking the activity of immune checkpoint proteins in order to release the "brakes" on the immune system to increase its ability to destroy cancer cells. Studies have found that there is a correlation between mutational load and response to immunotherapy. The goal of this project is to create a pipeline that identifies tumor neoantigens. This involved researching various softwares and implementing them to work together. This project involved developing a neoantigen prediction pipeline, which works with TGen's genomics pipeline, to help understand a patient's immune response. The neoantigen prediction pipeline first creates two protein fastas from the high quality non-synonymous mutations, frameshifts, codon insertions, and codon deletions from vcfmerger. One of the protein fastas includes the mutations, while the other one does not representing the wildtype protein. The pipeline then predicts both classes of HLA genotypes of the MHC molecules using DNA or RNA expression in the form of fastqs. The protein fastas and each HLA are fed into IEDB to obtain peptide-MHC binding predictions. Wildtype peptides and neoantigens with low binding affinities are then removed. RNA expression information is then added into the final text file from dseq and sailfish files from TGen's genomics pipeline.

Immunosignaturing is a new immunodiagnostic technology that uses random-sequence peptide microarrays to profile the humoral immune response. Though the peptides have little sequence homology to any known protein, binding of serum antibodies may be detected, and the pattern correlated to disease states. The aim of my dissertation is to analyze the factors affecting the binding patterns using monoclonal antibodies and determine how much information may be extracted from the sequences. Specifically, I examined the effects of antibody concentration, competition, peptide density, and antibody valence. Peptide binding could be detected at the low concentrations relevant to immunosignaturing, and a monoclonal's signature could even be detected in the presences of 100 fold excess naive IgG. I also found that peptide density was important, but this effect was not due to bivalent binding. Next, I examined in more detail how a polyreactive antibody binds to the random sequence peptides compared to protein sequence derived peptides, and found that it bound to many peptides from both sets, but with low apparent affinity. An in depth look at how the peptide physicochemical properties and sequence complexity revealed that there were some correlations with properties, but they were generally small and varied greatly between antibodies. However, on a limited diversity but larger peptide library, I found that sequence complexity was important for antibody binding. The redundancy on that library did enable the identification of specific sub-sequences recognized by an antibody. The current immunosignaturing platform has little repetition of sub-sequences, so I evaluated several methods to infer antibody epitopes. I found two methods that had modest prediction accuracy, and I developed a software application called GuiTope to facilitate the epitope prediction analysis. None of the methods had sufficient accuracy to identify an unknown antigen from a database. In conclusion, the characteristics of the immunosignaturing platform observed through monoclonal antibody experiments demonstrate its promise as a new diagnostic technology. However, a major limitation is the difficulty in connecting the signature back to the original antigen, though larger peptide libraries could facilitate these predictions.