Targeted Cancer Vaccine Therapy

General Papers Development of Neoantigen - Targeted Cancer Vaccine Therapy YAMASHITA Yoshiko, ONOUE Kousuke, TANAKA Yuki, MALONE Brandon Abstract Cancer immunotherapy is expected to be the fourth cancer therapy after surgery, chemotherapy and radiation treatment. Under this trend, NEC Corporation realizes that the mutation of cancer varies depending on individual patients and has thus proceeded to develop vaccine therapies targeting the tumor-specific antigens (hereinafter “neoantigens”) of each patient as cases of individualized medicine. This paper introduces the technique for iden- tifying the neoantigen that can induce the strongest immune response of each patient based on effective use of genome analysis and AI technologies. Clinical trials of an individualized cancer immunotherapy using the technique introduced here have already been started. Keywords Individualized medicine, cancer immunotherapy, cancer vaccine, neoantigen, AI 1. Introduction Surgical therapy Chemotherapy Cancer therapies can be broadly classified into the following three methods: “surgical therapy” that removes cancerous lesions; “chemotherapy” that destroys or re- duces the growth of cancer cells using anticancer drugs and “radiation therapy” that irradiates cancer lesions to destroy the cancer cells. Each of them has advanced through a long history but still presents advantages and disadvantages even at present. In addition to the three therapies, “cancer immunotherapy” has recently Radiation therapy Immunotherapy 4th Pillar been attracting attention as the fourth therapy (Fig. 1). This therapy originally controls the growth and progress Fig. 1 Cancer therapies. of cancer by enhancing the body’s innate immunity to cancer. It is expected to be a treatment that can act throughout the whole body, provide sustained effects 2. Mechanism of Cancer Immunity, Cancer Immunotherapy and present low side effects. In the rest of this paper, chapter 2 outlines the mechanism of cancer immunity Genes are damaged by various contents of tobacco and cancer immunotherapy, chapter 3 describes NEC’s and food such as carcinogenic substances and active approach toward cancer vaccine therapy, and chapter 4 oxygen, so mutated cells are produced every day in the introduces the neoantigen prediction method used in the body. The immune system protects the body by surveil- development of vaccine targeting tumor-specific anti- ling and eliminating abnormal cells, but cancer could be gens (hereinafter “neoantigen”). formed with the help of the cancer escape mechanism if 136 NEC Technical Journal／Vol.15 No.1／Special Issue on NEC Value Chain Innovation General Paper s Development of Neoantigen - Targeted Cancer Vaccine Therapy functioning of immunity and inhibit the attack of im- activation mune cells. 2 3 3. NEC’s approach to cancer vaccine therapy Tumor antigens Lymph node Among cancer immunotherapies, NEC is focusing on cancer vaccine therapy. Recent advancement of Dying T cell 1 Cancer cell 4 next-generation sequencing and bioinformatics has made it clear that mutation is extremely variable among infiltration into tumors patients. NEC has therefore started development of Cancer cancer vaccine therapy as an instance of individual- 5 cell ized medicine, which targets the neoantigen of each individual patient1). The vaccine development process Fig. 2 Cancer immune cycle. conceived by NEC involves harvesting tumor and normal tissues from the patient, and obtaining the whole exome sequencing data and RNA sequencing data using cancer cells slip through the immune attack and prolifer- next-generation sequencing. The obtained sequencing ate and the body can no longer maintain immune dom- data and RNA sequencing data are analyzed by using an inance. Fig. 2 shows the induction of immune response originally-developed neoantigen prediction system to se- to the cancer cell. Cancer cells release cancer antigens lect the neoantigen, and then synthesized with vaccine on their cell surfaces (Fig. 2-1). The released cancer that incorporates the neoantigen. Finally the synthesized antigens are phagocytized by antigen presenting cells vaccine is administered to the patient (Fig. 3). such as dendritic cells, which then migrate to lymph nodes through the lymph ducts and place the peptides, 4. NeoAntigen Prediction Technique Developed by NEC which are fragments of the cancer antigens, on the ma- jor histocompatibility complex (MHC) class I and II mol- Fig. 4 shows the processing flow of the neoantigen ecules to present them to T-cells (Fig. 2-2). The T-cell prediction technique developed by NEC. First, the whole recognizes the cancer-antigen peptide presented on the exome sequence (WXS) data of tumor and normal tissues MHC molecules by means of the T-cell receptor (TCR) are mapped onto a reference genome sequence. Tu- and activates itself (Fig. 2-3). The activated T-cells mi- mor-specific mutations are detected by comparing the re- grate through the blood vessel toward the cancerous sults of each. Peptides with nine residues containing this tissue (Fig. 2-4) and then exit from the blood vessels mutation are used as candidate epitopes. Also, the WXS to infiltrate the cancer cell tissues. The cancer cells also data of the normal cell is used to identify the HLA type. present their own cancer-antigen peptide on the MHC Next, the scores associated with the immune re- molecules so the T-cells recognize the cancer-antigen sponses to the candidate epitopes are calculated. These peptide/MHC complex via TCRs and attack/eliminate the scores include the following prediction values: machine cancer cells (Fig. 2-5). learning predictions of the binding affinity between the Cancer immunotherapy has variations including the candidate epitope and HLA molecule2), machine learn- following: “BRM therapy (immunomodulating thera- ing predictions of whether or not the candidate epitope py),” the oldest immune therapy method administering a foreign object into the body to activate the immune response; “immune cell therapy,” in which immune cells such as lymphocytes and dendritic cells are removed Normal from the patient’s body, cultured in vitro and returned into the body; “cancer vaccine therapy,” that injects part of the cancer antigen; “gene-modified T-cell therapy,” that extracts the T-cell from the patient’s body, modi- Sampling Administration fies the gene so that the cancer cell can be recognized, NeoAntigen Individualized cultures the T-cell and returns it into the patient’s body Prediction system Vaccine production again; “immune checkpoint therapy,” which inhibits Tumor transmission of immunosuppression signals by coupling with the molecule used by the cancer cell to brake the Fig. 3 Flow of vaccine preparation. NEC Technical Journal／Vol.15 No.1／Special Issue on NEC Value Chain Innovation 137 General Papers Development of Neoantigen - Targeted Cancer Vaccine Therapy will elicit an immune response, machine learning pre- Embedding space 6 6 diction of whether or not the candidate epitope will be 5 EP2 5 EP2 presented on the cell surface. These scores also include self-similarity, mutation frequency, the RNA expres- 3 Score-based 3 scaling 2 sion level, and so on. Then the total score is calculated 2 1 3) 1 4 by means of a graph-based relational learning out of 4 multiple scores mentioned above for each candidate EP1 EP1 epitope. In graph-based relational learning, a graph consists of vertices representing epitopes and edges Fig. 7 Score-based scaling of embedded vector sizes. Proprietary which connect two vertices with high similarity (Fig. 5). database Next, the score vectors of the candidate epitopes as- HLA HLA binding signed to the vertexes are converted into dimensionally WXS typing prediction compressed vectors (embedded vectors) using a deep Normal Immune response prediction neural network. The parameters of the deep neural net- Antigen presentation prediction Graph-based Diversity work are retrained based on the difference between the Variant Relational Ranking calling Ranking Learning list embedded vectors calculated on each vertex and those Self-similarity Sampling calculated on the adjacent vertices. This processing is DNA mutation frequency repeated until the differences converge, and the em- RNA mutation frequency WXS bedded vector is obtained (Fig. 6). The embedded vec- RNA expression RNA level tors are used as the feature to obtain the total immune Tumor response prediction score, and then the size of each Fig. 4 Processing flow of NEC’s neoantigen prediction. embedded vector is changed to the size of the predicted score (Fig. 7). The diverse epitopes with large scores are then selected from the embedding space in order to select the vaccine candidates. 22 5. Conclusion 5 11 In this paper, the authors described three main sub- jects: the mechanism of cancer immunity and cancer 44 33 immunotherapies, NEC’s approach toward cancer vac- 66 cine therapy, and the neoantigen prediction technique developed by NEC. This technique is not only applicable Fig. 5 Example of epitope relationship graph. to individualized cancer vaccines but also to the design of anti-COVID-19 vaccines4). Score vector The clinical trials of an individualized cancer immuno- (a2, b2, c2, d2,…) therapy using this technique have already been started. In 2 the future, the data accumulated through the trial period Score vector DNN will be verified to see the types of patients for whom the (a1 b1, c1, d1,…) 1 Embedded vector therapy is effective. For patients who find the therapy ef- (EP1 , EP2 , EP3 ,…) 1 1 1 fective, we shall

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