Genomic analysis of 63,220 tumors reveals insights into tumor uniqueness and targeted cancer immunotherapy strategies

Abstract

Background: The integration of genomics with immunotherapy has potential value for cancer vaccine development. Given the clinical successes of immune checkpoint modulators, interest in cancer vaccines as therapeutic options has been revived. Current data suggest that each tumor contains a unique set of mutations (mutanome), thus requiring the creation of individualized cancer vaccines. However, rigorous analysis of non-individualized cancer immunotherapy approaches across multiple cancer types and in the context of known driver alterations has yet to be reported. We therefore set out to determine the feasibility of a generalizable cancer vaccine strategy based on targeting multiple neoantigens in an HLA-A/B subtype-directed manner.

Methods: A cancer gene-focused, hybrid capture-based genomic analysis was performed on 63,220 unique tumors. Neoantigens were predicted using a combined peptide processing and MHC-I binding prediction tool (IEDB) for all recurrent (>10 tumors) missense alterations and non-frameshift indels for the two most common HLA-A/B subtypes in North American/European populations.

Results: Despite being overwhelmingly unique overall, many mutanomes (~45%) contain at least one mutation from a set of ten mutations chosen to maximize the number of unique tumors. This held true for tumors driven by KRAS G12C (n = 1799), PIK3CA E545K (n = 1713), or EGFR L858R (n = 478) alterations, which define distinct sample subsets. We therefore hypothesized that sets of carefully selected mutations/neoantigens may allow the development of broadly applicable semi-universal cancer vaccines. To test the feasibility of such an approach, antigen processing and MHC-I binding prediction was applied for HLA subtypes A*01:01/B*08:01 and A*02:01/B*44:02. In tumors with a specific HLA type, 0.7 and 2.5% harbored at least one of a set of ten neoantigens predicted to bind to each subtype, respectively. In comparison, KRAS G12C-driven tumors produced similar results (0.8 and 2.6% for each HLA subtype, respectively), indicating that neoantigen targets still remain highly diverse even within the context of major driver mutations.

Conclusions: This "best case scenario" analysis of a large tumor set across multiple cancer types and in the context of driver alterations reveals that semi-universal, HLA-specific cancer vaccine strategies will be relevant to only a small subset of the general population. Similar analysis of whole exome/genome sequencing, although not currently feasible at scale in a clinical setting, will likely uncover further diversity.

Keywords: Cancer vaccines; Genomic profiling; Neoantigens; Poly-epitope.

Fig. 1

Tumor mutanomes are overwhelmingly unique. a The alteration classes in frequently mutated genes across 63,220 tumors. b, c Top cumulative “and” alterations (tumors which contain all alterations from left to right) for b all tumors or c KRAS G12C-driven tumors. d, e The overlap of the top three alteration types across d all tumors or e KRAS G12C-driven tumors

Fig. 2

Sets of alterations shared across many tumors. a Top additive “and/or” alterations were determined by maximizing the number of unique tumors containing one or more alterations (from left to right; i.e., tumors with gene 1 and/or gene 2, etc.). Overlap across variants was determined by four broad categories (Gene, Type, Variant, Missense SNVs/non-frameshift (fs) indels). b Neoantigen prediction strategy incorporating the number of peptides processed and predicted to bind to specific HLA subtypes

Fig. 3

Applicability of poly-neoantigen, non-individualized targeted cancer immunotherapies using peptide processing, and MHC-I binding predictions. Top additive “and/or” alterations predicted to produce an MHC-I neoantigen are shown for all tumors (left) and KRAS G12C-driven tumors (right) for two common HLA-A/B subtypes, A*01:01/B*08:01 (top) and A*02:01/B*44:02 (bottom)