Targeting the tumor mutanome for personalized vaccination in lung cancer

Cancer is characterized by an accumulation of somatic mutations, of which a significant subset can generate cancer-specific neoepitopes that are recognized by autologous T cells. Neoepitopes are emerging as important targets for cancer immunotherapy, including personalized cancer vaccination strategies. We used whole-exome and RNA sequencing analysis to identify potential neoantigens for a patient with non-small cell lung cancer. Sequencing revealed a low tumor mutational burden: 2,219 sequence variants were identified from the primary tumor, of which 23 were expressed in the transcriptome, involving 18 gene products. We assessed autologous T-cell reactivity to the candidate neoantigens using a long peptide approach and could demonstrate spontaneous T-cell responses to 5/18 (28%) mutated gene variants. Further, analysis of the TCR repertoire of neoantigen-specific CD4+ and CD8+ T cells revealed TCR clonotypes that were expanded in both blood and tumor tissue. In parallel, the 18 gene variants were incorporated into a Modified Vaccinia Ankara-based vaccine, which was evaluated in the transgenic HLA-A*02-restricted HHD mouse model. Following vaccination, de novo T-cell responses were generated to 4/18 (22%) mutated gene variants, of which 2 were also observed in the autologous setting; we determined the MHC restriction of the T-cell responses and used in silico prediction tools to determine the likely neoepitopes. Our study demonstrates the feasibility of efficiently identifying tumor-specific neoantigens that can be targeted by vaccination in tumors with a low mutational burden, and promises successful clinical application, with trials currently underway. Overall design: The immune profile of the patient of interest's primary lung tumor (TP01; Fase461) and parasternal metastasis (TP02; Fase548) was contextualized using whole tumor RNA-seq data from a cohort of 18 lung tumor samples (submitted here); plus 47 consecutively collected HNSCC tumor samples that have previously reported (data available in GEO accession number GSE72536)