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Prediction Of T-cell Epitopes for Cancer Therapy

  • Author(s): Rao, Arjun Arkal
  • Advisor(s): Haussler, David
  • et al.
Creative Commons Attribution-ShareAlike 4.0 International Public License
Abstract

The human immune system can identify malfunctioning, damaged, or infected cells within the body. The integrity of nucleated cells is communicated to the immune system through short peptides which they display on their exterior using MHC proteins. Mutated peptides produced in cancer cells due to single nucleotide events, short insertion or deletion events, or due to large chromosomal rearrangements can help the immune system identify tumor cells as threats. Adoptive T-cell and Vaccine-based therapies that stimulate autologous T-cells to treat a patient's disease are more specific than standard chemo- and radiotherapies because they directly target tumor-specific mutations. They also potentially protect the body from recurrence of the tumor due to their unique memory potential. However, T-cells are often perceptive to only those cells originating from the patient's body, making this a personalized treatment for the disease. Rapid identification of tumor neoepitopes in a patient diagnosed with cancer will greatly reduce the time taken to develop a personalized therapy.

This thesis primarily covers ProTECT, a computational workflow for the Prediction of Epitopes for Cancer Therapy. ProTECT is an automated, scalable, and reproducible end-to-end pipeline that processes patient sequencing data to produce a ranked list of neoepitopes of therapeutic significance. ProTECT also attempts to predict the state of the MHC presentation pathway in the tumor, and the tumor response to immunotherapies based on previously published immune signatures. I describe ProTECT and demonstrate its features using a cohort of 326 Prostate Adenocarcinoma patients. I then demonstrate its clinical utility through the identification of a neoepitope arising from a hotspot mutant in pediatric Neuroblastoma.

To enable ProTECT, I worked on two auxiliary projects, Toil and TransGene. Toil is a workflow manager developed at the Genomics Institute at UCSC, and is the backbone of the ProTECT workflow. I spent some of my time at UCSC working on enhancing the efficiency of Toil workflows. TransGene is a tool I wrote that translates genomic variants (single nucleotide, short insertions and deletions, and fusion genes) into peptides that are compatible with existing peptide:MHC binding affinity predictors. These methods are also included in this thesis.

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