65 Identification of frequently presented non-mutated tumor-specific immunogens for the development of both off-the-shelf and personalized vaccines without need for tumor biopsy

BackgroundVaccines have little chance of destroying heterogeneous tumor cells since they rarely induce polyclonal T-cell responses against the tumor. The main challenge is the accurate identification of tumor targets recognizable by T cells. Presently, 6–8% of neoepitopes selected based on the patients‘ tumor biopsies are confirmed as real T cell targets.1 2. To overcome this limitation, we developed a computational platform called Personal Antigen Selection Calculator (PASCal) that identifies frequently presented immunogenic peptide sequences built on HLA-genetics and tumor profile of thousands of real individuals.3 Here we show the performance of PASCal for the identification of both shared and personalized tumor targets in metastatic colorectal cancer (mCRC) and breast cancer subjects.MethodsExpression frequency of the tumor-specific antigens (TSAs) ranked in PASCal’s database (based on 7,548 CRC specimen) was compared to the RNA-sequencing data of CRC tumors obtained from TCGA. Using PASCal, 12 shared PEPIs (epitopes restricted to at least 3 HLA class I alleles of a subject from an in silico cohort) derived from 7 TSAs were selected as frequent targets (calculated probability: average 2.5 [95%CI 2.4–2.8] TSAs/patient). Spontaneous immune responses against each of the twelve 9mer peptides were determined by ELISpot using PBMCs of 10 mCRC subjects who participated in the OBERTO-101 study.4 PEPIs selected for a breast cancer subject based on her HLA genotype were also tested.ResultsEach of the 106 tumors analyzed expressed at least 13, average 15 of the 20 top-ranked TSAs in PASCal’s database confirming their prevalence in CRC. 7/10 subjects had spontaneous CD8+ T-cell responses against at least one peptide selected with PASCal. Each peptide (12/12) was recognized by at least one patient. Patients‘ T-cells reacted with average 3.6/12 (30%) peptides confirming the expression of average 2.8 [95%CI 1.0–4.6] TSAs (n=10). After HLA-matching, among the subjects for whom we could select at least 4 PEPIs (average 5) from the list of 12 peptides (n=6), average 2.5 (50%) peptides were positive. Of the 12 PEPIs selected with PASCal for a breast cancer subject, we detected spontaneous T-cell responses against 9 PEPIs, indicating that at least 75% of the selected peptides were present in the subject’s tumor and were recognized by T-cells.ConclusionsPASCal platform accommodates both tumor- and patient heterogeneity and identifies non-mutated tumor targets that may trigger polyclonal cytotoxic T-cell responses. It is a rapid tool for the design of both off-the-shelf and personalized cancer vaccines negating the need for tumor biopsy.ReferencesWells DK, van Buuren MM, Dang KK, et al. Key parameters of tumor epitope immunogenicity revealed through a consortium approach improve neoantigen prediction. Cell 2020:183(3):818–34.e13.Bulik-Sullivan B, Busby J, Palmer CD, et al. Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification. Nat Biotech 2018:37:55–63.Somogyi E, Csiszovszki Z, Lorincz O, et al. 1181PDPersonal antigen selection calculator (PASCal) for the design of personal cancer vaccines. Annal Oncol 2019:30(Supplement_5):v480-v81.Hubbard J, Cremolini C, Graham R, et al. P329 PolyPEPI1018 off-the shelf vaccine as add-on to maintenance therapy achieved durable treatment responses in patients with microsatellite-stable metastatic colorectal cancer patients (MSS mCRC). J ImmunoTher Cancer 2019:7(1):282.

Primary vitreoretinal lymphomas display a remarkably restricted immunoglobulin gene repertoire

Primary vitreoretinal lymphoma (PVRL) is a high-grade lymphoma affecting the vitreous and/or the retina. The vast majority of cases are histopathologically classified as diffuse large B-cell lymphoma (DLBCL) and considered a subtype of primary central nervous system lymphoma (PCNSL). To obtain more insight into the ontogenetic relationship between PVRL and PCNSL, we adopted an immunogenetic perspective and explored the respective immunoglobulin gene repertoire profiles from 55 PVRL cases and 48 PCNSL cases. In addition, considering that both entities are predominantly related to activated B-cell (ABC) DLBCL, we compared their repertoire with that of publicly available 262 immunoglobulin heavy variable domain gene rearrangement sequences from systemic ABC-type DLBCLs. PVRL displayed a strikingly biased repertoire, with the IGHV4-34 gene being used in 63.6% of cases, which was significantly higher than in PCNSL (34.7%) or in DLBCL (30.2%). Further repertoire bias was evident by (1) restricted associations of IGHV4-34 expressing heavy chains, with κ light chains utilizing the IGKV3-20/IGKJ1 gene pair, including 5 cases with quasi-identical sequences, and (2) the presence of a subset of stereotyped IGHV3-7 rearrangements. All PVRL IGHV sequences were highly mutated, with evidence of antigen selection and ongoing mutations. Finally, half of PVRL and PCNSL cases carried the MYD88 L265P mutation, which was present in all 4 PVRL cases with stereotyped IGHV3-7 rearrangements. In conclusion, the massive bias in the immunoglobulin gene repertoire of PVRL delineates it from PCNSL and points to antigen selection as a major driving force in their development.

Vaccinomics and adversomics as new trends in vaccinology

Currently used vaccines have been developed based on experimental pre-clinical and clinical trials. Although the widespread availability of vaccines is one of the greatest achievements in public health, the selection of antigens capable of inducing an effective immune response has not been successful for some pathogens to date. Searching for and detecting a relationship between genes or whole genome sequences and the level of immunization response has opened the second “golden age” of vaccinology and led to the development of two new branches: vaccinomics and adversomics. Vaccinomics is a combination of pharmacogenetics, which defines the correlation between single gene polymorphism and immunization response and pharmacogenomics, which characterizes the correlation between genome sequence polymorphism, immunogenicity induced by the vaccine. Adversomics is aimed at developing a strategy for reducing the risk of adverse events by diagnosing potentially high-risk individuals and using modified vaccines. The assumptions and achievements of vaccinomics, initiated by Georg Poland, have influenced the development of a new vaccine antigen selection strategy. This strategy consists of selecting the optimal antigen after characterizing the genetic and epigenetic determinants of the immune system components of all candidate vaccine antigens. Taking into account the role of variability, not only in the pathogen but also in the host in antigen, selection strategies may significantly improve the efficiency of the newly developed vaccines and vaccines currently in use after their respective modifications.