Citation: Savsani, K.; Dakshanamurthy, S. Novel Methodology for the Design of Personalized Cancer Vaccine Targeting Neoantigens: Application to Pancreatic Ductal Adenocarcinoma. Diseases 2024 ,12, 149. https:// doi.org/10.3390/diseases12070149 Academic Editor: Maurizio Battino Received: 15 May 2024 Revised: 3 July 2024 Accepted: 8 July 2024 Published: 11 July 2024 Copyright: ©2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). diseases Article Novel Methodology for the Design of Personalized Cancer Vaccine Targeting Neoantigens: Application to Pancreatic Ductal Adenocarcinoma Kush Savsani1 and Sivanesan Dakshanamurthy2,* 1Department of Surgery, Virginia Commonwealth University, Richmond, VA 23219, USA 2Lombardi Comprehensive Cancer Center, Georgetown University School of Medicine, Washington, DC 20007, USA *Correspondence: sd233@georgetown.edu Abstract: Personalized cancer vaccines have emerged as a promising avenue for cancer treatment or prevention strategies. This approach targets the specific genetic alterations in individual patient’s tumors, offering a more personalized and effective treatment option. Previous studies have shown that generalized peptide vaccines targeting a limited scope of gene mutations were ineffective, em- phasizing the need for personalized approaches. While studies have explored personalized mRNA vaccines, personalized peptide vaccines have not yet been studied in this context. Pancreatic ductal adenocarcinoma (PDAC) remains challenging in oncology, necessitating innovative therapeutic strate- gies. In this study, we developed a personalized peptide vaccine design methodology, employing RNA sequencing (RNAseq) to identify prevalent gene mutations underlying PDAC development in a patient solid tumor tissue. We performed RNAseq analysis for trimming adapters, read alignment, and somatic variant calling. We also developed a Python program called SCGeneID, which validates the alignment of the RNAseq analysis. The Python program is freely available to download. Using chromosome number and locus data, SCGeneID identifies the target gene along the UCSC hg38 reference set. Based on the gene mutation data, we developed a personalized PDAC cancer vaccine that targeted 100 highly prevalent gene mutations in two patients. We predicted peptide-MHC binding affinity, immunogenicity, antigenicity, allergenicity, and toxicity for each epitope. Then, we selected the top 50 and 100 epitopes based on our previously published vaccine design methodology. Finally, we generated pMHC-TCR 3D molecular model complex structures, which are freely available to download. The designed personalized cancer vaccine contains epitopes commonly found in PDAC solid tumor tissue. Our personalized vaccine was composed of neoantigens, allowing for a more precise and targeted immune response against cancer cells. Additionally, we identified mutated genes, which were also found in the reference study, where we obtained the sequencing data, thus validating our vaccine design methodology. This is the first study designing a personalized peptide cancer vaccine targeting neoantigens using human patient data to identify gene mutations associated with the specific tumor of interest. Keywords: personalized cancer vaccines; neoantigens; pancreatic ductal adenocarcinoma; peptide based personalized cancer vaccine; MHC; HLA; TCR 1. Introduction Personalized cancer vaccines are a rising innovation in the field of vaccine design [ 1]. These vaccines induce an antigen-specific CD8+and CD4+T-cell response to enhance anti-tumor activity based on a patient’s individual tumor. Technological innovation has led to the ability to rapidly sequence and analyze patient genome data, which led to the selection of gene targets and on-demand production of personalized therapy [ 2]. A phase I clinical trial synthesized personalized mRNA vaccines against PDAC from solid tumors, which led to improved disease-free survival [ 3]. The trial analyzed a patient Diseases 2024 ,12, 149. https://doi.org/10.3390/diseases12070149 https://www.mdpi.com/journal/diseases Diseases 2024 ,12, 149 2 of 14 population who underwent surgical resection of PDAC tumors. Future development of personalized cancer vaccines directly to demonstrate significant efficacy in patients without major surgical intervention. Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer and is projected to be the second-leading cause of cancer mortality by 2030 [ 4,5]. Current clinical therapies involve neoadjuvant therapy followed by possible surgical resection [ 6]. However, patients with PDAC suffer from poor prognosis, with a median survival rate of 22.1 months and an actual survival rate of 17.0% [ 7]. PDAC is often diagnosed late, and as a result, surgical resection may not be a viable option for many patients [ 8]. As the cancer progresses and possible treatment options decrease, survival outcomes also significantly worsen. The five-year survival rate for patients diagnosed with late-stage PDAC is less than 10% [8]. PDAC progresses as a complex activation of driver genes and inactivation of tumor suppressor genes [ 9]. Commonly mutated genes observed in PDAC include KRAS, TP53, CDNK2A, DPC4/SMAD4, and BRCA2. Studies of key mutations in these genes are con- ducted with the goal of developing targeted gene therapies. One particular mutation, the KRAS G12D mutation, is present in over 40% of PDAC patients [ 10]. However, this specific mutation has been found not to be significantly associated with overall survival outcomes. The TP53 gene is mutated in about 50% of PDAC patients [ 11]. These mutations include gain-of-function point mutations and null mutations as a result of deletions. Mutations of the CDNK2A gene have been found to be significantly associated with poorer survival outcomes for patients with PDAC compared to mutations of KRAS and TP53 [12,13]. Several PDAC vaccines are under development, and clinical trials are being conducted using a variety of immunologic targeting methods [ 14]. These methods include cell-based, protein-based, microorganism-based, DNA-based, exosome-based, and peptide-based vac- cines. Peptide-based vaccines have been growing in popularity due to their ability to be quickly and cheaply developed and their flexibility in patient populations [ 15]. For PDAC, the first peptide vaccine to undergo clinical trials was a KRAS-targeting peptide co-administered with GM-CSF to promote a greater immune response [ 16]. The vaccine successfully induced specific immune responses in 58% of patients, contributing to a longer survival time for treated patients. Other peptide vaccines targeting survivin, gas- trin, VEGFR-1, VEGFR-2, and WT1 have been ineffective in inducing immune response or contributing to significantly improved survival [ 14,16–20]. However, the design of personalized-based peptide cancer vaccines is completely absent. This study focuses on the development of a design protocol to create personalized peptide vaccines with application to PDAC. The protocol identifies genetic variants using RNAseq analysis and designs a personalized peptide vaccine using a vaccine development protocol and omics pipeline previously developed by our group [21]. 2. Materials and Methods 2.1. Patient Genomic Data We obtained patient genomic data from the Gene Expression Omnibus (GEO) database [ 22],a publicly accessible repository of comprehensive microarray, next-generation sequencing, and other forms of high-throughput functional genomic data. For this study, we specifically collected raw Illumina sequencing data pertaining to human patient solid tumor samples. These samples were part of a detailed study focused on analyzing long-term heterogeneity in patients with pancreatic ductal adenocarcinoma (PDAC) [ 23]. This study included genomic data from a cohort of 19 patients, consisting of 10 long-term and 9 short-term survivors, providing a diverse basis for examining genetic variations linked to survival outcomes. For the objectives of this study, we selected one patient classified as a short-term survivor and one patient classified as a long-term survivor to design personalized vaccines, serving as a proof-of-concept for our approach. This selection was strategic, allowing us to explore the potential of personalized medicine in cases with poorer prognoses and to evaluate the efficacy of targeted therapies based Diseases 2024 ,12, 149 3 of 14 on genomic insights. The design and development of the vaccine were personalized to the unique genetic profile of the chosen patient, focusing on the anomalies most likely to influence tumor behavior and treatment response. To confirm that our personalized vaccine design was rigorous and potentially effective, we compared the targeted genetic components of the vaccine to key genes previously identified as significant in the sur- vival of PDAC patients by Bhardwaj et al. [ 23]. This comparison enabled us to validate our personalized vaccine design approach and increase the therapeutic relevance of the vaccine design. This properly controlled process of data selection and comparison with established genetic markers supports our vaccine design methodology, which is detailed further in the section below. 2.2. RNAseq Analysis of Patient Data We performed an RNAseq analysis using the Partek Flow genomic analysis suite, as shown in Figure 1, which outlines our comprehensive RNAseq workflow to obtain and confirm variant data. Initially, we imported the raw sequence data in a fastq format into Partek Flow. This format is widely used for storing the output from high-throughput sequencing instruments and contains both nucleotide sequence data and corresponding quality scores. Following data importation, the first computational step involved trimming the Illumina sequencing adapters. These adapters, which are artificial sequences added during library preparation, can interfere with the analysis if not removed, as they may be misinterpreted as part of the genomic sequence. After trimming, we aligned the reads to a reference genome using the Burrows–Wheeler Aligner (BWA) algorithm version 0.7.18. BWA is a software tool that efficiently aligns relatively short sequences (such as those from Illumina sequencers) against a long reference sequence, such as a complete genome. This alignment is important for locating the genomic origins of each read and is fundamental to identifying variations from the reference sequence. In the post-alignment, we executed somatic variant calling using the Strelka algorithm, which was specifically designed to detect somatic variants with high sensitivity and accuracy in tumor-normal paired samples. This step was important for identifying potentially significant genetic mutations that could be relevant in the context of disease, herein cancer. To ensure the reliability of our findings, we manually inspected each significant gene variant using the Integrative Genomics Viewer (IGV) (Partek Inc., Chesterfield, Missouri). IGV is an interactive visualization tool that allows us to visually explore genomic data, thus facilitating the validation of computa- tional predictions through a critical human-oversight step. We excluded gene variants of inadequate quality from further analysis. This quality control step is key to avoiding false positives that could skew the results of downstream applications, such as vaccine develop- ment. Finally, we focused our efforts on analyzing single nucleotide polymorphisms (SNPs) that held potential for inclusion in our vaccine development process. SNPs, being the most common type of genetic variation among cancer patients, provide valuable insights into genetic variability, which can be exploited to design targeted vaccines. Diseases 2024 , 12, x FOR PEER REVIEW 4 of 15 Figure 1. RNAseq analysis work ﬂow using Partek Flow suite. Created using BioRender.com. 3. Gene Annotation Con ﬁrmation Using SCGeneID Python Program Development and Applic ation of SCGeneID After obtaining and processing genomic data through Partek Flow, we advanced to the next step by developing a Python program named ‘SCGeneID’. The code for this in-novative tool is comprehensively detailed in Supplementary File S1 and is freely available for download. SCGeneID was speci ﬁcally designed to enhance ou r analytical capabilities in gene annotation by using both chromosome number and locus information. Using the hg38 reference set accessible via the UCSC Genome Browser [24], SCGeneID systemati- cally identi ﬁes corresponding gene names based on their chromosomal location. The tool operates by exploiting web-scra ping techniques to extract relevant genomic data directly from the browser. Once the data are retrieve d, SCGeneID processes this information to generate a detailed output that includes a table forma tted with chromosome numbers, locus details, and the names of associated ge nes. This functionality not only streamlines the gene identi ﬁcation process but also warrants a ccuracy by referencing updated ge- nomic data. The application of SCGeneID in our study was twofold. Primarily, it served to externally validate the a lignment accuracy and overall reliability of our RNAseq anal- ysis process. By cross-verifying the gene anno tations provided by Partek Flow with those extracted by SCGeneID, we could con ﬁrm the consistency and validity of our results. Ad- ditionally, as shown in Figure 2, we employed a modi ﬁed version of SCGeneID to specif- ically extract a list of genes from a given variant ﬁle. This adaptation was particularly im- portant for our personalized vaccine as it allo wed us to focus on particular genomic vari- ants of interest, facilitating a more targeted approach in our subsequent analyses. Figure 1. RNAseq analysis workflow using Partek Flow suite. Created using BioRender.com. Diseases 2024 ,12, 149 4 of 14 3. Gene Annotation Confirmation Using SCGeneID Python Program Development and Application of SCGeneID After obtaining and processing genomic data through Partek Flow, we advanced to the next step by developing a Python program named ‘SCGeneID’. The code for this innovative tool is comprehensively detailed in Supplementary File S1 and is freely available for download. SCGeneID was specifically designed to enhance our analytical capabilities in gene annotation by using both chromosome number and locus information. Using the hg38 reference set accessible via the UCSC Genome Browser [ 24], SCGeneID systematically identifies corresponding gene names based on their chromosomal location. The tool operates by exploiting web-scraping techniques to extract relevant genomic data directly from the browser. Once the data are retrieved, SCGeneID processes this information to generate a detailed output that includes a table formatted with chromosome numbers, locus details, and the names of associated genes. This functionality not only streamlines the gene identification process but also warrants accuracy by referencing updated genomic data. The application of SCGeneID in our study was twofold. Primarily, it served to externally validate the alignment accuracy and overall reliability of our RNAseq analysis process. By cross-verifying the gene annotations provided by Partek Flow with those extracted by SCGeneID, we could confirm the consistency and validity of our results. Additionally, as shown in Figure 2, we employed a modified version of SCGeneID to specifically extract a list of genes from a given variant file. This adaptation was particularly important for our personalized vaccine as it allowed us to focus on particular genomic variants of interest, facilitating a more targeted approach in our subsequent analyses. Diseases 2024 , 12, x FOR PEER REVIEW 5 of 15 Figure 2. SCGeneID Python program work ﬂow. Created using BioRender.com.