Development of multi-epitope vaccine constructs for non-small cell lung cancer (NSCLC) against USA human leukocyte antigen background: an immunoinformatic approach toward future vaccine designing

ABSTRACT

Objectives

The design of peptide-based vaccines for cancer is a promising immunotherapy that can induce a cancer-specific cytotoxic response in tumor cells.

Methods

Herein, we used the immunoinformatic approach in designing a multi-epitope vaccine targeting G-protein coupled receptor 87 (GPCR-87), cystine/glutamate transporter (SLC7A11), Immunoglobulin binding protein 1 (IGBP1), and thioredoxin domain-containing protein 5 (TXNDC5), which can potentially contribute to NSCLC. The MHC-I and MHC-II epitopes selected for the fusion construct were evaluated for their antigenic and non-allergenic natures via VaxiJen and AllerTop.

Results

A total of five epitopes, four class-I (FIFYLKNIV, CRYTSVLFY, RYLKVVKPF, and RQAKIQRYK), and one class-II (NQVRGYPTLLWFRDG), having combined USA population coverage of 100%, were used to make ten possible multi-epitope fusion constructs. In these constructs, PADRE, a universal T-helper epitope, and RSO9, a TLR4 agonist, were fused as adjuvants. The molecular docking analysis revealed that two constructs were showing significant binding affinities toward HLA-A*02:01, the most prevalent HLA allele in USA. Moreover, MD simulations marked one construct as a promising therapeutic candidate.

Conclusion

The multi-epitope vaccine constructs designed using immunogenic, and non-allergenic peptides of NSCLS tumor-associated proteins are likely to pose significant therapeutic efficacies in cancer immunotherapy due to their high binding affinities toward HLA molecules.

KEYWORDS:

• Non-small cell lung cancer (NSCLC)
• multi-epitope vaccine
• immunoinformatics
• g-protein coupled receptor 87 (GPCR-87)
• cystine/glutamate transporter (SLC7A11)
• immunoglobulin binding protein 1 (IGBP1)
• thioredoxin domain-containing protein 5 (TXNDC5)

Acknowledgments

The authors are thankful for the online available open access databases, which were used in this research.

Author contributions

Naeem Mahmood Ashraf and Malik Siddique Mahmood were involved in the study conception and design. Sana Batool, Duaa Bin-T-Abid, and Hina Batool have performed computational analysis. Arslan Hamid and Azmat Ullah Khan performed MD simulations. Hina Batool, Naeem Mahmood Ashraf, Malik Siddique Mahmood, Mahjabeen Saleem and Saher Shahid participated in the interpretation of the data. Hina Batool, Sana Batool, Mahjabeen Saleem, Saher Shahid, and Malik Siddique Mahmood participated in paper drafting and revising it critically for intellectual content. All authors have approved the final version to be published and agree to be accountable for all aspects of the work.

Data Availability Statement:

The data that supports the findings of this study are available in the supplementary material of this article.

Declaration of Interests

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Ethical statement

This study is a computational work so humans or animal were not used during this study. Therefore, there is no need of ethical approval. Being an in-silico study, there is also no need for informed consent.

Additional information

Funding

This paper was not funded