Immunoinformatics Approach for Non-Small Cell Lung Cancer Mohammad M.Tarek Armed Forces College of Medicine (AFCM) Cairo, Egypt.

Big Data! “it’s not about the data” Gary king An estimated number of 158,080 deaths are expected to occur in 2016 1 out of 4 of deaths to be caused by cancer. (NSCLC) it accounts for about 84% of lung cancer cases. The Surveillance, Epidemiology, and End Results (SEER) Cancer Treatment Centers of America® (CTCA)

Immunotherapy Using the power of Immune system to fight Cancer Ancient Egyptians and Immunotherapy. William Coley’s Experiments. Immune checkpoint modulators “nivolumab, ipilimumab and pembrolizumab” Immune cell therapy – CAR-T Therapeutic Antibodies rituximab , MPDL3280A in melanoma sipuleucel-T, for castration-resistant prostate cancer.

Bioinformatics of Antigen Prediction GEO – NIH cell lines Mining X antigen family member 1b (XAGE-1b) was shown to be typically overexpressed in adenocarcinoma cases of NSCLC. XAGE-1b is considered as one of the most immunogenic antigens and a promising target for lung adenocarcinoma immunotherapy

Sequence analysis The antigenic protein sequence of 9kD cancer/testis-associated protein XAGE-1b protein was retrieved from NCBI Database, in order to study the antigenicity and solvent accessible regions which permit's potential vaccine targets to recognize active sites against NSCLC

Homology modeling and quality estimation Template PDB Acc. No. Sequence Identity Sequence similarity GMQE QMEAN4 Residues % in favorite regions A 4IFD 42.00 0.40 0.39 -1.18 87.9% B 3U1K 38.30 0.38 0.31 -6.25 91.9%   C 2AE8 32.39 0.36 0.a58 -3.89 73.2%   D 4JTU 32.20 0.48 -5.21 84.2%   E 1E3P 30.00 0.37 0.10 -4.62 88.5%   F EJ43 28.57 0.34 0.26 -2.22 90.0%   G 2ZKQ 28.17 0.35 0.58 -3.85 90.0% H 4PBN 25.00 0.44 -3.44 88.7%  

Modeling – Solvent Mapping

B-cell Epitopes Developers have applied this tool to a variety of proteins to predict B-cell epitopes and results came out with 75% accuracy No. Chain Start End Peptide No. of residues Score 1 A 13 21 VGILHLGSR 9 0.67 2 40 52 CKSCISQTPGINL 0.66 3 59 69 KSCISQTPGHC 11 0.544 No. Residues Score 1 A:C40, A:K41, A:S42, A:I44, A:S45, A:Q46, A:T47, A:P48, A:G49 A:I50, A:N51, A:L52 13 0.66 2 A:V13, A:G14, A:I15, A:L16, A:H17, A:L18, A:G19, A:S20, A:R21, A:K23 10 0.652 3 A:I63, A:P64, A:K65, A:E66, A:E67, A:H68 6 0.644 4 A:K59, A:V60, A:K61 0.6 5 A:I25, A:I27, A:S31 0.589

Hydrophobicity antigenicity and solvent accessible regions which permit's potential vaccine targets to recognize active sites against NSCLC.

Proper Vaccine Design

T-HELPER CELL EPITOPES Length From To Score/Percentile Rank Restricted Allele SPKKKNQQL 9 3 11 0.2 HLA-B*08:01 GVKVKIIPK 57 65 0.3 HLA-A*30:01 ILHLGSRQK 15 23 0.35 HLA-A*03:01 RQKKIRIQL 21 29 0.45 HLA-A*31:01 RSQCATWKV 30 38 2.9 HLA-B*57:01 KIRIQLRSQ 24 32 5.2 HLA-B*07:02 GSGVKVKII 55 63 6.7 HLA-B*58:01 KSCISQTPG 41 49 13 HLA-B*15:01 KKKNQQLKV 5 HLA-B*51:01 VKVKIIPKE 58 66 HLA-B*53:01

T-CYTOTOXIC CELL EPITOPES Core Epitope Length From To IC50 Restricted Allele Peptide IRIQLRSQC 9 25 33 5.91 HLA-DRB1*12:01 RQKKIRIQLRSQCAT ILHLGSRQK 15 23 6.26 HLA-DRB5*01:01 KVGILHLGSRQKKIR INLDLGSGV­­­ 50 58 54.38 HLA-DRB1*13:02 GINLDLGSGVKVKII LRSQCATWK 29 37 84.35 HLA-DRB1*11:01 IRIQLRSQCATWKVI LDLGSGVKV 52 60 100.79 HLA-DRB1*07:01 RIQLRSQCA 26 34 210.77 HLA-DRB1*04:05 KIRIQLRSQCATWKV GILHLGSRQ 14 22 329.88 HLA-DRB1*09:01 LKVGILHLGSRQKKI QLKVGILHL 10 18 421.45 HLA-DRB1*15:01 KNQQLKVGILHLGSR KKIRIQLRS 31 698.29 HLA-DRB1*08:02 CATWKVICKSCISQT

Population Coverage

Docking and Drug Design Schrodinger, LLC.

NGS Data Analysis- Coding variants classifier  immune epitopes generated due to mutations, from Next Generation Sequencing data  Model evaluation ANN (Multilayer perceptron) Accuracy 93.6% Sensitivity 0.909 Precision 0.833 16

Thank You!