Our body’s immune system recognizes things that are different, making tumours that show fewer differences (vs. healthy cells or tissue) harder to recognize, resulting in less vigorous immune activation. These tumours with so-called low tumour mutational burden (TMB) are less responsive to therapies that are aimed at using a patient’s own defences to rid the body of the tumour, by either removing the brakes or stimulating the immune system, as well as other therapies that rely on tumour-associated signals. Strategies and tools to identify the few differences that are present in these situations are important for the development of patient-specific immunotherapy, and immunopetidomics is poised as an excellent tool for this challenge. One of the hurdles in the development of immunopeptidomic tools is the heterogeneity of the tumour-associated signals (i.e., antigenic peptides) between patients, tissues, and diseases, resulting in challenges with traditional database search strategies for MS data. Researchers in the Guha laboratory at the National Institute for Health (NIH) are working to remove this hurdle by combing de novo peptide antigen sequencing and custom database strategies. This epitope profiling pipeline, which was presented in Molecular and Cellular Biology, uses the powerful de novo sequencing algorithms of PEAKS software and takes into consideration important post-translational modifications (PTM), sequence variants identified by whole-exome sequencing, and antigenic peptides that originate from noncoding regions of RNA. Further, by focusing on tumours with low TMB they show that a significant number of tumour-associated antigens can be identified from these therapy-resistant tumours. The use of PEAKS PTM search allowed for extensive profiling of PTM in the immunopeptides and the resultant data suggested that certain position and types of PTMs may be more prevalent, and that some PTM peptides may be discoverable by MS that are not predicted by known HLA binding algorithms. To demonstrate the strength of the MS-based epitope identification, a subset of the identified peptides were validated using HLA class I-binding assays.

How was PEAKS used?

The patient- and cell line–specific protein sequence databases (DBs) were generated using a variety of tools, as described in the Material and Methods. The de novo– assisted DB search workflow in PEAKS Studio was used with these custom databases or standard human proteome DB. As HLA peptides are not artificially digested prior to analysis the “no enzyme” setting was employed, as was the PTM search, which included 650 different variable modifications. High quality de novo sequenced peptides were selected by using the average local confidence (ALC) score filter set at 50%. Peptides were quantified using the MS1 peak area–based label-free quantitation method in the PEAKS Q module.

Qi YA, Maity TK, Cultraro CM, Misra V, Zhang X, Ade C, Gao S, Milewski D, Nguyen KD, Ebrahimabadi MH, Hanada KI, Khan J, Sahinalp C, Yang JC, Guha U. Proteogenomic Analysis Unveils the HLA Class I-Presented Immunopeptidome in Melanoma and EGFR-Mutant Lung Adenocarcinoma. Mol Cell Proteomics. 2021 Aug 13;20:100136. doi:10.1016/j.mcpro.2021.100136. Epub ahead of print. PMID: 34391887.

Abstract

Immune checkpoint inhibitors and adoptive lymphocyte transfer-based therapies have shown great therapeutic potential in cancers with high tumor mutational burden (TMB), such as melanoma, but not in cancers with low TMB, such as mutant epidermal growth factor receptor (EGFR)-driven lung adenocarcinoma. Precision immunotherapy is an unmet need for most cancers, particularly for cancers that respond inadequately to immune checkpoint inhibitors. Here, we employed large-scale MS-based proteogenomic profiling to identify potential immunogenic human leukocyte antigen (HLA) class I-presented peptides in melanoma and EGFR-mutant lung adenocarcinoma. Similar numbers of peptides were identified from both tumor types. Cell line and patient-specific databases (DBs) were constructed using variants identified from whole-exome sequencing. A de novo search algorithm was used to interrogate the HLA class I immunopeptidome MS data. We identified 12 variant peptides and several classes of tumor-associated antigen-derived peptides. We constructed a cancer germ line (CG) antigen DB with 285 antigens. This allowed us to identify 40 class I-presented CG antigen-derived peptides. The class I immunopeptidome comprised more than 1000 post-translationally modified (PTM) peptides representing 58 different PTMs, underscoring the critical role PTMs may play in HLA binding. Finally, leveraging de novo search algorithm and an annotated long noncoding RNA (lncRNA) DB, we developed a novel lncRNA-encoded peptide discovery pipeline to identify 44 lncRNA-derived peptides that are presented by class I. We validated tandem MS spectra of select variant, CG antigen, and lncRNA-derived peptides using synthetic peptides and performed HLA class I-binding assays to demonstrate binding to class I proteins. In summary, we provide direct evidence of HLA class I presentation of a large number of variant and tumor-associated peptides in both low and high TMB cancer. These results can potentially be useful for precision immunotherapies, such as vaccine or adoptive cell therapies in melanoma and EGFR-mutant lung cancers.