Fig. 1

Integrative genomics and bioinformatics approach for neoantigen discovery and prioritization. a Overview of next-generation sequencing and neoantigen prediction. Whole-exome sequencing was performed on the pretreatment tumor and matched normal samples to identify somatic mutations, which were applied in neoantigen prediction pipeline that evaluates MHC binding, clonal status and gene expression to generate neoantigen specific to the patient’s HLA haploytype (Methods). b Top recurrently mutated genes in the 20 EOC patients, ordered by the numbers of recurrence. Known Cancer Gene Census (CGC) genes are in bold. For genes with recurrence at least 3, all genes are included. For genes with recurrence equals to 2, only known CGC genes are included. Red: truncating mutations, including nonsense SNV or frameshift Indels; Blue: altering mutations, including missense SNV or in-frame Indels. c Summary of neoantigen predictions in the 20 EOC patients, stratified by the MHC class type and gene expression status. There are 100 neoantigen predicted to bind to MHC class I only, 234 to class II only, and 115 to both class I and II, respectively. Among them, 209 are expressed based on RNAseq data. d The neoantigen landscape of Pt #19, as displayed in the Christmas Light Plot (CLP). The CLP incorporate pre-defined criteria for neoantigen prioritization, including MHC binding affinities, expression level, HLA class types, and the mutant clonal status. X-axis: Variant allele fraction (VAF) in WES, which can be used to infer clonal status; Y-axis: the predicted binding affinity of the mutant peptide. Each dot represents a neoantigen with the following characteristics displayed; size: the gene expression level by RNAseq; shape: HLA binding classes (I, II, or both); vertical bar: difference between mutant and wildtype binding affinities; color: stratified based on the mutant versus wildtype binding and mutant expression level (Methods). Gene symbols are displayed for neoantigens selected for screening