Functional Characterization of Spinocerebellar Ataxia Associated Dynorphin A Mutant Peptides

Mutations in the prodynorphin gene (PDYN) are associated with the development of spinocerebellar ataxia type 23 (SCA23). Pathogenic missense mutations are localized predominantly in the PDYN region coding for the dynorphin A (DynA) neuropeptide and lead to persistently elevated mutant peptide levels with neurotoxic properties. The main DynA target in the central nervous system is the kappa opioid receptor (KOR), a member of the G-protein coupled receptor family, which can elicit signaling cascades mediated by G-protein dissociation as well as β-arrestin recruitment. To date, a thorough analysis of the functional profile for the pathogenic SCA23 DynA mutants at KOR is still missing. To elucidate the role of DynA mutants, we used a combination of assays to investigate the differential activation of G-protein subunits and β-arrestin. In addition, we applied molecular modelling techniques to provide a rationale for the underlying mechanism. Our results demonstrate that DynA mutations, associated with a severe ataxic phenotype, decrease potency of KOR activation, both for G-protein dissociation as well as β-arrestin recruitment. Molecular modelling suggests that this loss of function is due to disruption of critical interactions between DynA and the receptor. In conclusion, this study advances our understanding of KOR signal transduction upon DynA wild type or mutant peptide binding.

IMMU-06. TARGETING IDH1 MUTANT GRADE II RECURRENT GLIOMAS USING A PEPTIDE VACCINATION STRATEGY

INTRODUCTION While primary GBM is largely heterogeneous and devoid of homogeneously expressed neoantigens, mutant IDH1 (R132H) is a uniformly expressed hallmark in >70% of low grade gliomas. As such, IDH1 mutations represent a potentially valuable vaccination target. METHODS Here, we report an update on the immunogenicity results of the mutant IDH1 peptide vaccine alone and in combination with temozolomide (TMZ). In the phase I RESIST clinical trial (NCT02193347), patients with recurrent and resectable IDH1 R132H mutant grade 2 glioma received peptide vaccinations composed of 500 µg of mutant IDH1 peptide and 150 µg of GM-CSF mixed 1:1 with Montanide adjuvant prior to surgical resection. Vaccines 1, 2, and 3 were given 15 (+/-) 3 days apart. 7-12 days after vaccine 3, patients underwent standard of care tumor (SOC) resection. After resection, patients with grade 2 gliomas were given up to 15 doses of peptide vaccine in combination with TMZ regimens while patients with transformed grade 3 gliomas were given up to 15 doses of peptide vaccine in combination with SOC radiation therapy + TMZ regimens. T cell responses against the mutant peptide were measured after vaccine 3 using IFN-γ ELISPOT and intracellular flow cytometry for IL-2, TNFα,and IFNγ. RESULTS 3/20 patients were taken off the study before completion of study related activities. 1/20 patients progressed before completion of all vaccines. Out of 134 total doses of vaccine delivered, only one dose generated a grade 2 or higher injection site reaction according to the CTCAE guidelines. Vaccination with the mutant peptide led to an overall increase in IFN-γ+ spot-forming splenocytes specific to the mutant peptide (p=0.0408). CONCLUSION Administering the mutant IDH1 peptide vaccine in patients with recurrent IDH-mutant gliomas was able to induce anti-IDH1 R132H immune responses in this initial phase I study.

MUTANT P21 PEPTIDES COULD ACT AS AN IMPROVED CYCLIN A INHIBITORS FOR CANCER THERAPY: AN IN SILICO VALIDATION

Objective: The present study delineates the generation of mutant peptide library from a known anticancer peptide, p21 and in silico evaluation for their affinity towards cyclin. A substrate binding groove. Methods: Mutant peptide library was created based on their AntiCP score and was docked with cyclin A using ClusPro2.0 web server. The docked structures were further simulated into an aqueous environment using Gromacs 4.5.6. Visualization was performed using PyMol software and interaction analysis was done using Discovery Studio Visualizer 4.1 Client and LigPlot plus tool. Results: A total of 57 mutant peptides were generated; out of which only 3 namely, K3C (Lys3Cys), K3F (Lys3Phe), and K3W (Lys3Trp) had a greater affinity for cyclin A than WILD p21 peptide (HSKRRLIFS). Molecular dynamic simulation studies showed that the peptides remained docked into the substrate binding groove throughout the run. Among all the peptides, K3C showed a significantly higher negative binding energy with cyclin A as compared to WILD. Conclusion: The overall results suggested that K3C mutant peptide had ~30 % higher affinity towards cyclin A and thus, could further be explored for its anticancer potential. The study also provides an insight into the crucial interactions governing the recognition of substrate binding groove of cyclin A for the development of novel peptide-based anticancer therapeutics.

Development of a Neoantigen Prediction Tool for Patient Stratification in Immuno-Oncology Trials

Immunotherapeutic agents are quickly becoming a routine aspect of treatment paradigms. However, despite clinical successes, cancer immunotherapy faces major challenges. One such challenge is that efficacy in most patients is unpredictable. Many immunotherapy treatments have demonstrated efficacy in only select cancer types. Variability in patient response indicates that immunotherapy needs to be patient-specific in order to be most effective. Identifying biomarkers that have value in predicting benefit from treatment with immunotherapy has been difficult. Few predictive biomarkers for immunotherapy treatments are robustly validated for use in clinical trials. Pivotal trials reveal that treatment benefits with checkpoint blockers is not solely restricted to PD-L1-positive patients, indicating the existence of other unknown biomarkers that could be predictive of response. Similarly, tumor mutational burden (TMB) has limitations, as it is not able to effectively segregate patients that are likely to respond to immunotherapeutic agents in tumor types that are relatively mutationally dormant. Increasingly, evidence suggests that tumor immunogenicity may be a strong biomarker for immunotherapy patient selection. In short, the abundance of predicted immunogenic mutations may be useful in predicting patients likely to benefit from checkpoint blockade and related immunotherapies. To address this need for a more specific biomarker, we have designed an assay and bioinformatic work-flow utilizing a multimodal neo-antigen prediction approach that combines data on somatic variants, RNA expression, and compatibility of resultant epitope with host HLA type. In summary, whole exome and whole transcriptome sequencing are performed on a patient tumor sample, and HLA typing is performed on a matched germline patient sample. The somatic variants (tumor-specific mutations) identified by exome sequencing are compared to the RNA sequencing data to identify the most prevalent variants in the transcriptome occurring in the most highly expressed regions. These highly expressed mutations are most likely to be translated into mutant peptides that can interact with MHC molecules and be subsequently presented on the tumor cell surface as neoantigens. The subject's HLA type is then determined using the seq2hla computational tool. Next, a molecular modeling tool, NetMHC4.0, compares the structures of the candidate mutant peptides to the HLA molecule structures and generates a goodness of fit prediction. A higher binding affinity between mutant peptide and HLA molecule corresponds to a greater likelihood of this complex existing on the cell surface as a neoantigen. This data - the DNA sequencing, RNA expression and binding affinity calculation - is combined via a series of filters to generate an immunogenicity score associated with each tumor mutation / predicted mutant peptide. These candidate neoantigens are then returned as a rank order list for each case. This information then can be used to guide targeted therapies and to stratify patients with higher immunogenicity scores for immunotherapy. To test our bioinformatic pipeline, we utilized a subset of multiple myeloma samples. Such analysis yielded a rank list of predicted neoantigens for each tumor sample, with associated immunogenicity scores for each prediction. Additionally, TMB was calculated for these samples. We compared the number of predicted neoantigens from our workflow to the TMB of the tumors as a proxy for this assay's performance against a current clinically utilized biomarker (TMB). The numbers of predicted neoantigens for the samples ranged from 19 to 61 (Average number of 41 neoantigens per sample), and the TMB scores for these samples respectively were between 7 and 13 mutations per megabase. Comparing these results using Pearson Correlation method yields a strong R squared value of 0.91. Among top ranking neoantigens were peptides associated with TP53, SIK3, ATM and NOTCH2 genes among others, and representing known frequently mutated genes in multiple myeloma. Therefore, our neoantigen predictor demonstrates promise as a reliable tool to identify markers of tumor immunogenicity. These preliminary results suggest that further validation of our process is warranted and may yield a new method for use in patient stratification and response prediction in immuno-oncology trials. Disclosures No relevant conflicts of interest to declare.

Generation of MANAbodies specific to HLA-restricted epitopes encoded by somatically mutated genes

Mutant epitopes encoded by cancer genes are virtually always located in the interior of cells, making them invisible to conventional antibodies. We here describe an approach to identify single-chain variable fragments (scFvs) specific for mutant peptides presented on the cell surface by HLA molecules. We demonstrate that these scFvs can be successfully converted to full-length antibodies, termed MANAbodies, targeting “Mutation-Associated Neo-Antigens” bound to HLA. A phage display library representing a highly diverse array of single-chain variable fragment sequences was first designed and constructed. A competitive selection protocol was then used to identify clones specific for mutant peptides bound to predefined HLA types. In this way, we obtained two scFvs, one specific for a peptide encoded by a common KRAS mutant and the other by a common epidermal growth factor receptor (EGFR) mutant. The scFvs bound to these peptides only when the peptides were complexed with HLA-A2 (KRAS peptide) or HLA-A3 (EGFR peptide). We converted one scFv to a full-length antibody (MANAbody) and demonstrate that the MANAbody specifically reacts with mutant peptide–HLA complex even when the peptide differs by only one amino acid from the normal, WT form.

Molecular Dynamics Folding Simulation of β-hairpin Protein (1E0Q)

The structure and trajectories of the mutant peptide of ubiquitin (PDB ID: 1E0Q) has been studied using Molecular Dynamics (MD) simulation. The simulation was performed using AMBER 11 utilizing force field 99 for 50 ns at constant temperature 325 K. The purpose of this study is to investigate the protein folding pathway of protein 1E0Q. In this simulation, the protein 1E0Q has folded into its near native β-hairpin structure within 5 ns. The RMSD value as compared to the NMR structure from the first residue to 17 residues is 2.17 Å. It has been observed that Gly 10 had been responsible to promote β-turn which caused the structure to turn into β-hairpin. In secondary structure analysis, it is shown that the residue from Thr 6 to Lys 11 has formed a bend in the structure. Two beta strands has also been found comprising residues Glu 2 to Lys 5 and Ile 13 to Glu 16.