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

2. 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)

3. 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.

4. 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

5. 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

6. 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%  

7. Modeling – Solvent Mapping

8. 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

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

10. Proper Vaccine Design

11. 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

12. 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

14. Population Coverage

15. Docking and Drug Design Schrodinger, LLC.

16. 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
Precision 16

17. Thank You!