Durable complete response to neoantigen-loaded dendritic-cell vaccine following anti-PD-1 therapy in metastatic gastric cancer

Zengqing Guo, Yuan Yuan, Chao Chen, Jing Lin, Qiwang Ma, Geng Liu, Yan Gao, Ying Huang, Ling Chen, Li-Zhu Chen, Yu-Fang Huang, Hailun Wang, Bo Li, Yu Chen, Xi Zhang, Zengqing Guo, Yuan Yuan, Chao Chen, Jing Lin, Qiwang Ma, Geng Liu, Yan Gao, Ying Huang, Ling Chen, Li-Zhu Chen, Yu-Fang Huang, Hailun Wang, Bo Li, Yu Chen, Xi Zhang

Abstract

Neoantigens are ideal targets for dendritic cell (DC) vaccines. So far, only a few neoantigen-based DC vaccines have been investigated in clinical trials. Here, we reported a case of a patient with metastatic gastric cancer who received personalized neoantigen-loaded monocyte-derived dendritic cell (Neo-MoDC) vaccines followed by combination therapy of the Neo-MoDC and immune checkpoint inhibitor (ICI). The patient developed T cell responses against neoantigens after receiving the Neo-MoDC vaccine alone. The following combination therapy triggered a stronger immune response and mediated complete regression of all tumors for over 25 months till October, 2021. Peripheral blood mononuclear cells recognized seven of the eight vaccine neoantigens. And the frequency of neoantigen-specific T cell clones increased obviously after vaccination. Overall, this report describing a complete tumor regression in a gastric cancer patient mediated by Neo-MoDC vaccine in combination with ICI, and suggesting a promising treatment for patients with metastatic gastric cancer.

Conflict of interest statement

The authors declare no competing interests.

© 2022. The Author(s).

Figures

Fig. 1. The combination immunotherapy of Neo-MoDC…
Fig. 1. The combination immunotherapy of Neo-MoDC vaccine and nivolumab mediated rapid complete regression.
a Treatment schema. Cyclophosphamide (CTX) 300 mg/m2 was administrated two days before the first dose of Neo-MoDC vaccine. b The changes of tumor marker CA125 during the treatment. c Tumor lesions shrank during treatment and the follow-ups. Two vertical dotted lines denote day 126 (lymph node < 10 mm) and day 231 (tumor complete regression by PET/CT). d PET/CT image of right ovary (Left panels, yellow circles) and PET image (right panel) show complete regression on day 231. e Representative images of targeted tumor lesions during treatment. Yellow arrows represent targeted tumor lesions. Blue arrows denote complete regressed tumor lesions.
Fig. 2. Neoantigen-specific immunological monitoring.
Fig. 2. Neoantigen-specific immunological monitoring.
a Scans of ELISPOT (left panel) and quantification (right panel) results showed day62 PBLs (after DC vaccination, before ICI treatment) and day245 PBLs (after the combination treatment) generated more IFN-γ spots than day-6 PBLs (before vaccination) when induced by ASP pools 1 or 2. ASP pool 1, 15mer assay mutant peptides pool of TMEM38B F251V, TIGD3 V118M, ZZEF1 R898H, and URB1 R421W; ASP pool 2, 15mer assay mutant peptide pool of DLEC1 R331C, TMTC1 C313Y, MTA2 R92W, and TDP1 K112Rfs.101. The positive control for ELISPOT was PMA/ionomycin at the concentration of 1 ng/mL PMA and 500 ng/mL ionomycin. The control is media only. b Mapping of CD8+ T cells response to each 15mer peptide. Each 27mer mutant peptide was cutting into four 15mer peptides (denoted by blue, red, black, and orange bars) whose start and end positions in the 27mer peptide are 1-to-15, 5-to-19, 9-to-23, and 13-to-27 (also shown in Supplementary Fig. 3). Dotted line, 2 folds of mock (media only); *, mutant epitope with SFC number > 2 folds than that of mock. c ELISPOT assays showed that 5 out of 6 tested mutant peptides, but not their corresponding WT peptides, specifically activated CD8+ T cells. Data shown here were the mean value of two replicates. d Total frequency of neoantigen-specific TCRB clones increased during vaccination. e Neoantigen-specific TCRB clones, whose frequencies were in top 10 in tumor tissue, changed in PBLs during vaccination.

References

    1. Alloatti A, et al. Critical role for Sec22b-dependent antigen cross-presentation in antitumor immunity. J. Exp. Med. 2017;214:2231–2241. doi: 10.1084/jem.20170229.
    1. Sanchez-Paulete AR, et al. Cancer Immunotherapy with Immunomodulatory Anti-CD137 and Anti-PD-1 Monoclonal Antibodies Requires BATF3-Dependent Dendritic Cells. Cancer Disco. 2016;6:71–79. doi: 10.1158/-15-0510.
    1. Broz ML, et al. Dissecting the tumor myeloid compartment reveals rare activating antigen-presenting cells critical for T cell immunity. Cancer Cell. 2014;26:638–652. doi: 10.1016/j.ccell.2014.09.007.
    1. Barry KC, et al. A natural killer-dendritic cell axis defines checkpoint therapy-responsive tumor microenvironments. Nat. Med. 2018;24:1178–1191. doi: 10.1038/s41591-018-0085-8.
    1. Santos PM, Butterfield LH. Dendritic Cell-Based Cancer Vaccines. J. Immunol. 2018;200:443–449. doi: 10.4049/jimmunol.1701024.
    1. Steinman RM. Decisions about dendritic cells: past, present, and future. Annu Rev. Immunol. 2012;30:1–22. doi: 10.1146/annurev-immunol-100311-102839.
    1. Wculek SK, et al. Dendritic cells in cancer immunology and immunotherapy. Nat. Rev. Immunol. 2020;20:7–24. doi: 10.1038/s41577-019-0210-z.
    1. Anguille S, Smits EL, Lion E, van Tendeloo VF, Berneman ZN. Clinical use of dendritic cells for cancer therapy. Lancet Oncol. 2014;15:e257–e267. doi: 10.1016/S1470-2045(13)70585-0.
    1. Samstein RM, et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat. Genet. 2019;51:202–206. doi: 10.1038/s41588-018-0312-8.
    1. McGrail DJ, et al. High tumor mutation burden fails to predict immune checkpoint blockade response across all cancer types. Ann. Oncol. 2021;32:661–672. doi: 10.1016/j.annonc.2021.02.006.
    1. Havel JJ, Chowell D, Chan TA. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat. Rev. Cancer. 2019;19:133–150. doi: 10.1038/s41568-019-0116-x.
    1. Zou W, Wolchok JD, Chen L. PD-L1 (B7-H1) and PD-1 pathway blockade for cancer therapy: Mechanisms, response biomarkers, and combinations. Sci. Transl. Med. 2016;8:328rv324. doi: 10.1126/scitranslmed.aad7118.
    1. Carreno BM, et al. Cancer immunotherapy. A dendritic cell vaccine increases the breadth and diversity of melanoma neoantigen-specific T cells. Science. 2015;348:803–808. doi: 10.1126/science.aaa3828.
    1. Bassani-Sternberg M, et al. A Phase Ib Study of the Combination of Personalized Autologous Dendritic Cell Vaccine, Aspirin, and Standard of Care Adjuvant Chemotherapy Followed by Nivolumab for Resected Pancreatic Adenocarcinoma-A Proof of Antigen Discovery Feasibility in Three Patients. Front Immunol. 2019;10:1832. doi: 10.3389/fimmu.2019.01832.
    1. Sarivalasis A, et al. A Phase I/II trial comparing autologous dendritic cell vaccine pulsed either with personalized peptides (PEP-DC) or with tumor lysate (OC-DC) in patients with advanced high-grade ovarian serous carcinoma. J. Transl. Med. 2019;17:391. doi: 10.1186/s12967-019-02133-w.
    1. Fujimura T, et al. Diagnostic laparoscopy, serum CA125, and peritoneal metastasis in gastric cancer. Endoscopy. 2002;34:569–574. doi: 10.1055/s-2002-33228.
    1. Yamamoto M, Baba H, Toh Y, Okamura T, Maehara Y. Peritoneal lavage CEA/CA125 is a prognostic factor for gastric cancer patients. J. Cancer Res Clin. Oncol. 2007;133:471–476. doi: 10.1007/s00432-006-0189-2.
    1. Eisenhauer EA, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1) Eur. J. Cancer. 2009;45:228–247. doi: 10.1016/j.ejca.2008.10.026.
    1. Janjigian YY, et al. CheckMate-032 Study: Efficacy and Safety of Nivolumab and Nivolumab Plus Ipilimumab in Patients With Metastatic Esophagogastric Cancer. J. Clin. Oncol. 2018;36:2836–2844. doi: 10.1200/JCO.2017.76.6212.
    1. Muro K, et al. Pembrolizumab for patients with PD-L1-positive advanced gastric cancer (KEYNOTE-012): a multicentre, open-label, phase 1b trial. Lancet Oncol. 2016;17:717–726. doi: 10.1016/S1470-2045(16)00175-3.
    1. Janjigian YY, et al. First-line nivolumab plus chemotherapy versus chemotherapy alone for advanced gastric, gastro-oesophageal junction, and oesophageal adenocarcinoma (CheckMate 649): a randomised, open-label, phase 3 trial. Lancet. 2021;398:27–40. doi: 10.1016/S0140-6736(21)00797-2.
    1. Shitara K, et al. Pembrolizumab versus paclitaxel for previously treated, advanced gastric or gastro-oesophageal junction cancer (KEYNOTE-061): a randomised, open-label, controlled, phase 3 trial. Lancet. 2018;392:123–133. doi: 10.1016/S0140-6736(18)31257-1.
    1. Kang YK, et al. Nivolumab plus chemotherapy versus placebo plus chemotherapy in patients with HER2-negative, untreated, unresectable advanced or recurrent gastric or gastro-oesophageal junction cancer (ATTRACTION-4): a randomised, multicentre, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol. 2022;23:234–247. doi: 10.1016/S1470-2045(21)00692-6.
    1. McGranahan N, et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science. 2016;351:1463–1469. doi: 10.1126/science.aaf1490.
    1. Riaz N, et al. Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab. Cell. 2017;171:934–949. doi: 10.1016/j.cell.2017.09.028.
    1. Tran E, et al. T-Cell Transfer Therapy Targeting Mutant KRAS in Cancer. N. Engl. J. Med. 2016;375:2255–2262. doi: 10.1056/NEJMoa1609279.
    1. Rosenthal R, et al. Neoantigen-directed immune escape in lung cancer evolution. Nature. 2019;567:479–485. doi: 10.1038/s41586-019-1032-7.
    1. Marty R, et al. MHC-I Genotype Restricts the Oncogenic Mutational Landscape. Cell. 2017;171:1272–1283. doi: 10.1016/j.cell.2017.09.050.
    1. Luksza M, et al. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature. 2017;551:517–520. doi: 10.1038/nature24473.
    1. Anagnostou V, et al. Evolution of Neoantigen Landscape during Immune Checkpoint Blockade in Non-Small Cell Lung Cancer. Cancer Disco. 2017;7:264–276. doi: 10.1158/-16-0828.
    1. Niknafs N, Beleva-Guthrie V, Naiman DQ, Karchin R. SubClonal Hierarchy Inference from Somatic Mutations: Automatic Reconstruction of Cancer Evolutionary Trees from Multi-region Next Generation Sequencing. PLoS Comput Biol. 2015;11:e1004416. doi: 10.1371/journal.pcbi.1004416.
    1. Chen Y, et al. SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data. Gigascience. 2018;7:1–6.
    1. Cibulskis K, et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 2013;31:213–219. doi: 10.1038/nbt.2514.
    1. Kim S, et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. methods. 2018;15:591–594. doi: 10.1038/s41592-018-0051-x.
    1. Shamsani J, et al. A plugin for the Ensembl Variant Effect Predictor that uses MaxEntScan to predict variant spliceogenicity. Bioinformatics. 2018;35:2315–2317. doi: 10.1093/bioinformatics/bty960.
    1. Lundegaard C, et al. NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11. Nucleic Acids Res. 2008;36:W509–W512. doi: 10.1093/nar/gkn202.
    1. Nielsen M, Andreatta M. NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets. Genome Med. 2016;8:33. doi: 10.1186/s13073-016-0288-x.
    1. Liu G, et al. PSSMHCpan: a novel PSSM-based software for predicting class I peptide-HLA binding affinity. Gigascience. 2017;6:1–11.
    1. Zhang H, Lund O, Nielsen M. The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding. Bioinformatics. 2009;25:1293–1299. doi: 10.1093/bioinformatics/btp137.
    1. Nielsen M, Lundegaard C, Lund O. Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinforma. 2007;8:238. doi: 10.1186/1471-2105-8-238.
    1. Wang L, et al. A Comprehensive Analysis of the T and B Lymphocytes Repertoire Shaped by HIV Vaccines. Front. Immunol. 2018;9:2194. doi: 10.3389/fimmu.2018.02194.
    1. Zhang W, Du Y, Su Z, Wang C. IMonitor: A Robust Pipeline for TCR and BCR Repertoire Analysis. Genetics. 2015;201:459–472. doi: 10.1534/genetics.115.176735.
    1. Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34:i884–i890. doi: 10.1093/bioinformatics/bty560.

Source: PubMed

3
Iratkozz fel