Whole exome-seq and RNA-seq data reveal unique neoantigen profiles in Kenyan breast cancer patients

Abstract

BackgroundThe immune response against tumors relies on distinguishing between self and non-self, the basis of cancer immunotherapy. Neoantigens from somatic mutations are central to many immunotherapeutic strategies and understanding their landscape in breast cancer is crucial for targeted interventions. We aimed to profile neoantigens in Kenyan breast cancer patients using genomic DNA and total RNA from paired tumor and adjacent non-cancerous tissue samples of 23 patients. MethodsWe sequenced the genome-wide exome (WES) and RNA, from which somatic mutations were identified and their expression quantified, respectively. Neoantigen prediction focused on human leukocyte antigens (HLA) crucial to cancer, HLA type I. HLA alleles were predicted from WES data covering the adjacent non-cancerous tissue samples, identifying four alleles that were present in at least 50% of the patients. Neoantigens were deemed potentially immunogenic if their predicted median IC50 binding scores were [≤]500nM and were expressed [transcripts per million (TPM) >1] in tumor samples. ResultsAn average of 1465 neoantigens covering 10260 genes had [≤]500nM median IC50 binding score and >1 TPM in the 23 patients and their presence significantly correlated with the somatic mutations (R2 =0.570, P=0.001). Assessing 58 genes reported in the catalog of somatic mutations in cancer (COSMIC, v99) to be commonly mutated in breast cancer, 44 (76%) produced >2 neoantigens among the 23 patients, with a mean of 10.5 ranging from 2 to 93. For the 44 genes, a total of 477 putative neoantigens were identified, predominantly derived from missense mutations (88%), indels (6%), and frameshift mutations (6%). Notably, 78% of the putative breast cancer neoantigens were patient-specific. HLA-C*06:01 allele was associated with the majority of neoantigens (194), followed by HLA-A*30:01 (131), HLA-A*02:01 (103), and HLA-B*58:01 (49). Among the genes of interest that produced putative neoantigens were MUC17, TTN, MUC16, AKAP9, NEB, RP1L1, CDH23, PCDHB10, BRCA2, TP53, TG, and RB1. ConclusionsThe unique neoantigen profiles in our patient group highlight the potential of immunotherapy in personalized breast cancer treatment as well as potential biomarkers for prognosis. The unique mutations producing these neoantigens, compared to other populations, provide an opportunity for validation in a much larger sample cohort.