bioRxivImmunologyPreprint
Immunogenicity of a public neoantigen derived from mutated PIK3CA
S. S. Chandran, J. Ma, M. G. Klatt, F. Dundar, C. Bandlamudi, P. C. Razavi, H. Y. Wen, B. Weigelt, P. Zumbo, S. N. Fu, L. B. Banks, W. D. Bestman, A. Drilon, D. Betel, D. A. Scheinberg, B. M. Baker, C. A. Klebanoff
Abstract
Public neoantigens (NeoAgs) represent an elite class of shared cancer-specific epitopes derived from recurrent mutations in driver genes that are restricted by prevalent HLA alleles. Here, we report on a high-throughput platform combining single-cell transcriptomic and T cell receptor (TCR) sequencing to establish whether mutant (Mut) PIK3CA, among the most common genomically altered driver oncogenes, generates an immunogenic public NeoAg. Using this method, we developed a library of TCRs that recognize an endogenously processed neoepitope containing a common PIK3CA hotspot mutation that is restricted by HLA-A*03:01. Mechanistically, immunogenicity to this public NeoAg arises primarily from enhanced stability of the neopeptide/HLA complex caused by a preferred HLA anchor substitution. Structural studies indicated that the HLA-bound neopeptide presents a relatively "featureless" surface dominated by the peptides backbone. To overcome the challenge of binding such an epitope with high specificity and affinity, we discovered that a lead TCR clinical candidate engages the neopeptide through an extended interface aided by an unusually long {beta}-chain complementarity-determining region 3 (CDR3{beta}) loop. In a pan-cancer cohort of patients with diverse malignancies that express the PIK3CA public NeoAg, we observed spontaneous immunogenicity, NeoAg clonal conservation, and in a limited number of cases, evidence of targeted immune escape. Together, these results establish the immunogenic potential of Mut PIK3CA, creating a framework for off-the-shelf immunotherapies targeting this public NeoAg.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: cell-cell communication
- approach: deep learning
- approach: experimental assay
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