EMAN RESEARCH PUBLISHING | <p>Development of Personalized Therapeutics Using Neo7logix Precision Profiling in Lung Cancer</p>

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Development of Personalized Therapeutics Using Neo7logix Precision Profiling in Lung Cancer

Anton Yuryev ^A, John Catanzaro ^B, Md Shamsuddin Sultan Khan ^C

+ Author Affiliations

Journal of Precision Biosciences 1 (1) 016-025 https://doi.org/10.25163/biosciences.112090DB112921119

Submitted: 29 October 2019 Revised: 29 October 2019 Published: 02 November 2019

Abstract

Neo7logix is a biopathway HLA affinity mapping and selection prediction ranking device that combines biological intelligence programming (molecular mapping), knowledge-based systems applications, artificial intelligence and machine learning. The platform integration utilizes all NGS data including WES, WGS, RNAseq and Proteomics. Neo7logix, LLC proprietary architecture is applicable for cancer, autoimmune and neurodegenerative diseases. A precision mapping, ranking and selection profile is then generated to derive a precision-based personalized Immuno-molecular Augmentation therapeutic application that activates the immune system defenses and regulatory mechanisms to intelligently fight the cancer and / or disease process. Neo7logix, LLC can also predict “best fit” drug applications in many disease types. In this paper the focus profiling, mapping, affinity ranking and final selection is in a patient prototype sample lung cancer diagnosis.

Key words: WES, Proteomics, PBIMA, CNS Inflammation
Autoimmune Disease

References

Anastasia P. Nesterova, Anton Yuryev, Eugene A. Klimov, Maria Zharkova, Maria Shkrob, Natalia V. Ivanikova, Sergey Sozin, Vladimir Sobolev. Disease Pathways: An Atlas of Human Disease Signaling Pathways 1st Edition.2019, Elsevier, Amsterdam

Anderson, Robert P, and Bana Jabri. Vaccine against autoimmune disease: antigen-specific immunotherapy. Current opinion in immunology vol. 25,3 (2013): 410-7. doi:10.1016/j.coi.2013.02.004

Antunes DA, Abella JR, Devaurs D, Rigo MM, Kavraki LE. Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes. Curr Top Med Chem. 2018;18(26):2239–2255. doi:10.2174/1568026619666181224101744

Berger MF, Hodis E, Heffernan TP, et al. Melanoma genome sequencing reveals frequent PREX2 mutations. Nature. 2012;485(7399):502–506. Published 2012 May 9. doi:10.1038/nature11071

Efremova M, Finotello F, Rieder D, Trajanoski Z. Neoantigens Generated by Individual Mutations and Their Role in Cancer Immunity and Immunotherapy. Front Immunol. 2017;8:1679. Published 2017 Nov 28. doi:10.3389/fimmu.2017.01679

Guo Y, Lei K, Tang L. Neoantigen Vaccine Delivery for Personalized Anticancer Immunotherapy. Front Immunol. 2018;9:1499. Published 2018 Jul 2. doi:10.3389/fimmu.2018.01499

Hong Y, Fang F, Zhang Q. Circulating tumor cell clusters: What we know and what we expect (Review). Int J Oncol. ;49(6):2206–2216. doi:10.3892/ijo.2016.3747

https://www.linkedin.com/pulse/immunoediting-cancer-landscape-john-catanzaro/

Khong, Hiep, and Willem W Overwijk. “Adjuvants for peptide-based cancer vaccines.” Journal for immunotherapy of cancer vol. 4 56. 20 Sep. 2016, doi:10.1186/s40425-016-0160-y

Kosaloglu-Yalçin Z, Lanka M, Frentzen A, et al. Predicting T cell recognition of MHC class I restricted neoepitopes. Oncoimmunology. 2018;7(11):e1492508. Published 2018 Aug 27. doi:10.1080/2162402X.2018.1492508

Mittal D, Gubin MM, Schreiber RD, Smyth MJ. New insights into cancer immunoediting and its three component phases--elimination, equilibrium and escape. Curr Opin Immunol. 2014;27:16–25. doi:10.1016/j.coi.2014.01.004

Ott G. et al., 1995. MF59. Design and evaluation of a safe and potent adjuvant for human vaccines. Pharm Biotechnol 6: 277-96.

Ott G. et al., 2000. The adjuvant MF59: a 10-year perspective. Methods in Molecular Medicine, Vol 42, 211-228.

Pan RY, Chung WH, Chu MT, et al. Recent Development and Clinical Application of Cancer Vaccine: Targeting Neoantigens. J Immunol Res. 2018;2018:4325874. Published 2018 Dec 19. doi:10.1155/2018/4325874

Pardi, Norbert et al. mRNA vaccines - a new era in vaccinology. Nature reviews. Drug discovery vol. 17,4 (2018): 261-279. doi:10.1038/nrd.2017.243

Reichmuth, Andreas M et al. mRNA vaccine delivery using lipid nanoparticles. Therapeutic delivery vol. 7,5 (2016): 319-34. doi:10.4155/tde-2016-0006

Rock, Kenneth L et al. “Present Yourself! By MHC Class I and MHC Class II Molecules.” Trends in immunology vol. 37,11 (2016): 724-737. doi:10.1016/j.it.2016.08.010

Schappert A, Schneck JP, Suarez L, Oelke M, Schütz C. Soluble MHC class I complexes for targeted immunotherapy. Life Sci. 2018 Sep 15;209:255-258. doi: 10.1016/j.lfs.2018.08.023. Epub 2018 Aug 10

Sivachenko AY, Yuryev A, Daraselia N, Mazo I. Molecular networks in microarray analysis. J Bioinform Comput Biol. 2007 Apr;5(2B):429-56. Review.

Su Y, Rossi R, De Groot AS, Scott DW. Regulatory T cell epitopes (Tregitopes) in IgG induce tolerance in vivo and lack immunogenicity per se. J Leukoc Biol. 2013;94(2):377–383. doi:10.1189/jlb.0912441

Tan Y, Kagan JC. Innate Immune Signaling Organelles Display Natural and Programmable Signaling Flexibility. Cell. 2019 Apr 4;177(2):384-398.e11. doi: 10.1016/j.cell.2019.01.039

Yost SE, Smith EN, Schwab RB, et al. Identification of high-confidence somatic mutations in whole genome sequence of formalin-fixed breast cancer specimens. Nucleic Acids Res. 2012;40(14):e107. doi:10.1093/nar/gks299

Yuryev A, Castillos L. Developing Pathway Collection for Personalized Anti-cancer Therapy. Int J Cancer Clin Res 2016, 3:043

Zhao W, Sher X (2018) Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopes. PLOS Computational Biology 14(11): e1006457. https://doi.org/10.1371/journal.pcbi.1006457

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