The first step in our treatment process is the identification of our cancer’s neoantigens. These would vary from patient to patient and would have to be Identified experimentally. Identification of neoantigens begins by first sequencing the genome of the patient's tumors using massive parallel sequencing(MPS). MPS is a system which readily identifies tumor cell mutations by comparison of the genome of the cancer cells to the genome of a somatic cell (Gubin et al 2015). This method of genome analysis has been shown to be an effective means to identify cancer cell gene mutations (Shiraishi et al 2011).We will use a hybrid exome sequencing technique which allows for the analysis of only genes which encode proteins and allow sequencing on a time scale that is relevant to clinical treatment (Hodges et al 2009). Once the tumor genome has been sequenced and analyzed to identify mutations we will then determine which of the tumors mutations are in oncogenes capable of binding to the MHC protein within the cell (Gubin et al 2015). This will be accomplished by utilizing bioinformatic databases and softwares, specifically the NetMHCpan algorithm system which identifies a wide range probable MHC binding sequences in Human and nonhuman primates (Nielsen et al 2007).We will then harvest lymphocytes from the patient and test them for neoantigen binding specificity in vitro and select T-cells with tumor suppressing ability that possess the receptor for one of the neoantigens we derive from or cancer cell genome analysis. Following this we would grow these cells in culture to create a large amount of tumor infiltrating lymphocytes (TIL) (Perica et al 2012). These TIL’s will be further modified to improving their binding specificity and resistance to T-cell suppression.
Gubin MM, Artyomov MN, Mardis ER,and Schreiber RD. 2015. Tumor neoantigens: building a framework for personalized cancer immunotherapy. The Journal of Clinical Investigation 125(9): 3413–3421. National Center for Biotechnology Information[NCBI]. <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4588307/>. Accessed 2017 April 24.
Hodges E, Rooks M, Xuan Z, Bhattacharjee A, Gordon DB, Brizuela L, McCombie WR, and Hannon GJ. 2009. Hybrid selection of discrete genomic intervals on custom-designed microarrays for massively parallel sequencing. Nature Protocol 4(6): 960-974. National Center for Biotechnology Information [NCBI]. <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2990409/>. Accessed 2017 April 24.
Nielsen M, Lundegaard C, Blicher T, Lamberth K, Harndahl M, Justesen S, Røder G, Peters B, Sette A, Lund O, Buus S. 2007. NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence. PLOS one 2(8): e796. National Center for Biotechnology Information [NCBI]. <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1949492/>. Accessed 2017 April 24.
Shiraishi T,Terada N, Zeng Y, Suyama T, Luo J, Trock B, Kulkarni P, and Getzenberg RH. 2011. Cancer/Testis antigens as potential predictors of biochemical recurrence of prostate cancer following radical prostatectomy. Journal of Translational Medicine 9: 153. National Center for Biotechnology Information [NCBI]. <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3184272/>. Accessed 2017 April 24.