| Literature DB >> 31069153 |
Muzamil Yaqub Want1, Anna Konstorum2, Ruea-Yea Huang1, Vaibhav Jain1, Satoko Matsueda1, Takemasa Tsuji1, Amit Lugade1, Kunle Odunsi1, Richard Koya1, Sebastiano Battaglia1,3.
Abstract
Ovarian cancer (OC) has an overall modest number of mutations that facilitate a functional immune infiltrate able to recognize tumor mutated antigens, or neoantigens. Although patient-derived xenografts (PDXs) can partially model the tumor mutational load and mimic response to chemotherapy, no study profiled a neoantigen-driven response in OC PDXs. Here we demonstrate that the genomic status of the primary tumor from an OC patient can be recapitulated in vivo in a PDX model, with the goal of defining autologous T cells activation by neoantigens using in silico, in vitro and in vivo approaches. By profiling the PDX mutanome we discovered three main clusters of mutations defining the expansion, retraction or conservation of tumor clones based on their variant allele frequencies (VAF). RNASeq analyses revealed a strong functional conservation between the primary tumor and PDXs, highlighted by the upregulation of antigen presenting pathways. We tested in vitro a set of 30 neoantigens for recognition by autologous T cells and identified a core of six neoantigens that define a potent T cell activation able to slow tumor growth in vivo. The pattern of recognition of these six neoantigens indicates the pre-existence of anti-tumor immunity in the patient. To evaluate the breadth of T cell activation, we performed single cell sequencing profiling the TCR repertoire upon stimulation with neoantigenic moieties and identified sequence motifs that define an oligoclonal and autologous T cell response. Overall, these results indicate that OC PDXs can be a valid tool to model OC response to immunotherapy.Entities:
Keywords: Immunogenomics; Neoantigen; PDX models; TCR; genomics; immunotherapy; ovarian cancer
Year: 2019 PMID: 31069153 PMCID: PMC6492964 DOI: 10.1080/2162402X.2019.1586042
Source DB: PubMed Journal: Oncoimmunology ISSN: 2162-4011 Impact factor: 7.723