Exploring RELNs Epitopes Expressed on HER2+ Breast Cancer to Design B and CTL Associated Brain Metastases Cancer Vaccines Using Immunoinformatics Approach,
ABSTRACT
Metastases of HER2+ breast cancer (MBC+) to the brain, responsible for 90% of all cancer deaths, are strongly related to overexpress Reelin proteins (RELNs). Typically, patients diagnozed with MBC+ develop brain metastases only months to several years after first diagnosis. Recently, only radiotherapy and/or neurological surgery regarded as the best option for treatments but they are limitted by exact tumorous boundaries and tumor recalcitrance.
Cancer immunotherapy through multi-epitope cancer vaccines has to be a promising treatment of immunology to the field oncology. However, testing all cancer vaccine properties such as identification vaccine candidate antigenic stimuli and the best vaccine administration in biological model condition can be difficult because a high number of variable need to be considered at the same time. Nowadays, the advance 3D structure protein determination and high peptide database system collection may lead to design all posible computational models used in addition to biological model by mean of immunoinformatics approach. Since RELNs
are landmark for MBC+ brain metastases, it is critical to explore promising epitopes for cancer vaccine. The indispensable and signifant step to design cancer vaccine is the recognizing competent continuous-discontinuous B-cell epitopes and Cytotoxoc T lymphocyte. In this study, the B-cell and cytotoxic T lymphocyte (CTL) epitopes derived from RELNs were screened and mapped computationally. A total of 20 continuous peptides (Lin1-Lin20) and 4 discontinuous peptides (Dis1-Dis4) B cell epitopes that may induce protective neutralizing antibodies were obtained by using IEDB server based on Kolaskar and Tongaonkar antigenicity (A), Parker hydrophobicity prediction (P), Karplus and Schulz flexibility prediction (K) and Emini surface accessibility prediction (E). Additionally, a total of 11 peptides (CTL-epitope1 to CTL-epitope 11) were identified as Cytotoxic T cell (CTL) epitopes by using NetCTL server and CTLepitope1 peptide were found to have the highest affinity and the best fitted into MHC class 1 binding site through molecular protein-peptide docking approach and both residues Q155 and T73 were found to be binding site CTL-epitopes to MHC class I molecule. These findings, based on immuno-informatics analysis, can be conducted in further experimental HER2+ breast cancer brain metastases study.
Keywords: cancer vaccines, molecular docking, Immunoinformatics, metastases, RELNs
Figure 4. 3D molecular interaction analysis of predicted CTL-epitopes 1 (blue) derived RELN docked to MHC-1 HLA-A*02:1 (white) filtered under 3.5Å and 20 degrees constraints relaxed areas.
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