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Published in: Medical Oncology 5/2024

01-05-2024 | Pancreatic Cancer | Original Paper

Design and evaluation of a multiepitope vaccine for pancreatic cancer using immune-dominant epitopes derived from the signature proteome in expression datasets

Authors: Sooram Banesh, Nupoor Patil, Vihadhar Reddy Chethireddy, Arnav Bhukmaria, Prakash Saudagar

Published in: Medical Oncology | Issue 5/2024

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Abstract

Pancreatic cancer is a highly aggressive and often lethal malignancy with limited treatment options. Its late-stage diagnosis and resistance to conventional therapies make it a significant challenge in oncology. Immunotherapy, particularly cancer vaccines, has emerged as a promising avenue for treating pancreatic cancer. Multi-epitope vaccines, designed to target multiple epitopes derived from various antigens associated with pancreatic cancer, have gained attention as potential candidates for improving therapeutic outcomes. In this study, we have explored transcriptomics and protein expression databases to identify potential upregulated proteins in pancreatic cancer cells. After examining a total of 21,054 proteins from various databases, it was discovered that 143 proteins expressed differently in malignant and healthy cells. The CTL, HTL and BCE epitopes were predicted for the shortlisted proteins. 51,840 vaccine constructs were created by concatenating CTL, HTL, and B-cell epitopes in the respective sequences. The best 86 structures were selected from a set of 51,840 designs after they were analyzed for vaxijenicity, allergenicity, toxicity, and antigenicity scores. In further simulation of the immune response using constructs, it was found that 41417, 37961, and 40841 constructs could produce a strong immune response when injected. Further, it was found that construct 37961 showed stronger interaction and stability with TLR-9 as determined from the large-scale molecular dynamics simulations. Moreover, the 37961 construct has shown interactions with TLR-9 suggests its potential in inducing immune response. In addition, construct 37961 has shown 100% predicted solubility in the E. coli expression system. Overall, the study indicates the designed construct 37961 has the potential to induce an anti-tumor immune response and long-standing protection pending further studies.

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Literature
1.
go back to reference Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.PubMedCrossRef Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.PubMedCrossRef
2.
go back to reference Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17–48.PubMedCrossRef Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17–48.PubMedCrossRef
3.
go back to reference Jones S, Zhang X, Parsons DW, Lin JC-H, Leary RJ, Angenendt P, et al. Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science. 2008;321(5897):1801–6.PubMedPubMedCentralCrossRef Jones S, Zhang X, Parsons DW, Lin JC-H, Leary RJ, Angenendt P, et al. Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science. 2008;321(5897):1801–6.PubMedPubMedCentralCrossRef
4.
go back to reference Hayashi A, Hong J, Iacobuzio-Donahue CA. The pancreatic cancer genome revisited. Nat Rev Gastroenterol Hepatol. 2021;18(7):469–81.PubMedCrossRef Hayashi A, Hong J, Iacobuzio-Donahue CA. The pancreatic cancer genome revisited. Nat Rev Gastroenterol Hepatol. 2021;18(7):469–81.PubMedCrossRef
5.
go back to reference Gheorghe G, Diaconu CC, Ionescu V, Constantinescu G, Bacalbasa N, Bungau S, et al. Risk factors for pancreatic cancer: emerging role of viral hepatitis. J Personal Med. 2022;12(1):83.CrossRef Gheorghe G, Diaconu CC, Ionescu V, Constantinescu G, Bacalbasa N, Bungau S, et al. Risk factors for pancreatic cancer: emerging role of viral hepatitis. J Personal Med. 2022;12(1):83.CrossRef
6.
go back to reference Wang DS, Chen DL, Ren C, Wang ZQ, Qiu MZ, Luo HY, et al. ABO blood group, hepatitis B viral infection and risk of pancreatic cancer. Int J Cancer. 2012;131(2):461–8.PubMedCrossRef Wang DS, Chen DL, Ren C, Wang ZQ, Qiu MZ, Luo HY, et al. ABO blood group, hepatitis B viral infection and risk of pancreatic cancer. Int J Cancer. 2012;131(2):461–8.PubMedCrossRef
8.
go back to reference Conroy T, Desseigne F, Ychou M, Bouché O, Guimbaud R, Bécouarn Y, et al. FOLFIRINOX versus gemcitabine for metastatic pancreatic cancer. N Engl J Med. 2011;364(19):1817–25.PubMedCrossRef Conroy T, Desseigne F, Ychou M, Bouché O, Guimbaud R, Bécouarn Y, et al. FOLFIRINOX versus gemcitabine for metastatic pancreatic cancer. N Engl J Med. 2011;364(19):1817–25.PubMedCrossRef
9.
go back to reference Von Hoff DD, Ervin T, Arena FP, Chiorean EG, Infante J, Moore M, et al. Increased survival in pancreatic cancer with nab-paclitaxel plus gemcitabine. N Engl J Med. 2013;369(18):1691–703.CrossRef Von Hoff DD, Ervin T, Arena FP, Chiorean EG, Infante J, Moore M, et al. Increased survival in pancreatic cancer with nab-paclitaxel plus gemcitabine. N Engl J Med. 2013;369(18):1691–703.CrossRef
10.
go back to reference Pontén F, Jirström K, Uhlen M. The human protein atlas—a tool for pathology. J Pathol: J Pathol Soc Great Br Irel. 2008;216(4):387–93.CrossRef Pontén F, Jirström K, Uhlen M. The human protein atlas—a tool for pathology. J Pathol: J Pathol Soc Great Br Irel. 2008;216(4):387–93.CrossRef
11.
go back to reference Papatheodorou I, Fonseca NA, Keays M, Tang YA, Barrera E, Bazant W, et al. Expression atlas: gene and protein expression across multiple studies and organisms. Nucleic Acids Res. 2018;46(D1):D246–51.PubMedCrossRef Papatheodorou I, Fonseca NA, Keays M, Tang YA, Barrera E, Bazant W, et al. Expression atlas: gene and protein expression across multiple studies and organisms. Nucleic Acids Res. 2018;46(D1):D246–51.PubMedCrossRef
12.
go back to reference Zeng J, Zhang Y, Shang Y, Mai J, Shi S, Lu M, et al. CancerSCEM: a database of single-cell expression map across various human cancers. Nucleic Acids Res. 2022;50(D1):D1147–55.PubMedCrossRef Zeng J, Zhang Y, Shang Y, Mai J, Shi S, Lu M, et al. CancerSCEM: a database of single-cell expression map across various human cancers. Nucleic Acids Res. 2022;50(D1):D1147–55.PubMedCrossRef
13.
go back to reference Tang G, Cho M, Wang X. OncoDB: an interactive online database for analysis of gene expression and viral infection in cancer. Nucleic Acids Res. 2022;50(D1):D1334–9.PubMedCrossRef Tang G, Cho M, Wang X. OncoDB: an interactive online database for analysis of gene expression and viral infection in cancer. Nucleic Acids Res. 2022;50(D1):D1334–9.PubMedCrossRef
14.
go back to reference Thumuluri V, Almagro Armenteros JJ, Johansen AR, Nielsen H, Winther O. DeepLoc 2.0: multi-label subcellular localization prediction using protein language models. Nucleic Acids Res. 2022;50(W1):W228–34.PubMedPubMedCentralCrossRef Thumuluri V, Almagro Armenteros JJ, Johansen AR, Nielsen H, Winther O. DeepLoc 2.0: multi-label subcellular localization prediction using protein language models. Nucleic Acids Res. 2022;50(W1):W228–34.PubMedPubMedCentralCrossRef
15.
go back to reference Hallgren J, Tsirigos KD, Pedersen MD, Almagro Armenteros JJ, Marcatili P, Nielsen H, et al. (2022) DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks. BioRxiv. 2022:2022.04. 08.487609 Hallgren J, Tsirigos KD, Pedersen MD, Almagro Armenteros JJ, Marcatili P, Nielsen H, et al. (2022) DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks. BioRxiv. 2022:2022.04. 08.487609
16.
go back to reference 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(1):1–9.CrossRef 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(1):1–9.CrossRef
17.
go back to reference Reynisson B, Alvarez B, Paul S, Peters B, Nielsen M. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res. 2020;48(W1):W449–54.PubMedPubMedCentralCrossRef Reynisson B, Alvarez B, Paul S, Peters B, Nielsen M. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res. 2020;48(W1):W449–54.PubMedPubMedCentralCrossRef
18.
go back to reference Clifford JN, Høie MH, Deleuran S, Peters B, Nielsen M, Marcatili P. BepiPred-3.0: improved B-cell epitope prediction using protein language models. Protein Sci. 2022;31(12):e4497.PubMedPubMedCentralCrossRef Clifford JN, Høie MH, Deleuran S, Peters B, Nielsen M, Marcatili P. BepiPred-3.0: improved B-cell epitope prediction using protein language models. Protein Sci. 2022;31(12):e4497.PubMedPubMedCentralCrossRef
19.
go back to reference Kolaskar AS, Tongaonkar PC. A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett. 1990;276(1–2):172–4.PubMedCrossRef Kolaskar AS, Tongaonkar PC. A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett. 1990;276(1–2):172–4.PubMedCrossRef
22.
go back to reference Rapin N, Lund O, Bernaschi M, Castiglione F. Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system. PLoS ONE. 2010;5(4):e9862.PubMedPubMedCentralCrossRef Rapin N, Lund O, Bernaschi M, Castiglione F. Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system. PLoS ONE. 2010;5(4):e9862.PubMedPubMedCentralCrossRef
23.
go back to reference Stolfi P, Castiglione F, Mastrostefano E, Di Biase I, Di Biase S, Palmieri G, et al. In-silico evaluation of adenoviral COVID-19 vaccination protocols: assessment of immunological memory up to 6 months after the third dose. Front Immunol. 2022;13: 998262.PubMedPubMedCentralCrossRef Stolfi P, Castiglione F, Mastrostefano E, Di Biase I, Di Biase S, Palmieri G, et al. In-silico evaluation of adenoviral COVID-19 vaccination protocols: assessment of immunological memory up to 6 months after the third dose. Front Immunol. 2022;13: 998262.PubMedPubMedCentralCrossRef
25.
go back to reference Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: making protein folding accessible to all. Nat Methods. 2022;19(6):679–82.PubMedPubMedCentralCrossRef Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: making protein folding accessible to all. Nat Methods. 2022;19(6):679–82.PubMedPubMedCentralCrossRef
26.
go back to reference Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583–9.PubMedPubMedCentralCrossRef Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583–9.PubMedPubMedCentralCrossRef
28.
go back to reference Kozakov D, Hall DR, Xia B, Porter KA, Padhorny D, Yueh C, et al. The ClusPro web server for protein–protein docking. Nat Protoc. 2017;12(2):255–78.PubMedPubMedCentralCrossRef Kozakov D, Hall DR, Xia B, Porter KA, Padhorny D, Yueh C, et al. The ClusPro web server for protein–protein docking. Nat Protoc. 2017;12(2):255–78.PubMedPubMedCentralCrossRef
29.
go back to reference Guex N, Peitsch MC. SWISS-MODEL and the Swiss-Pdb viewer: an environment for comparative protein modeling. Electrophoresis. 1997;18(15):2714–23.PubMedCrossRef Guex N, Peitsch MC. SWISS-MODEL and the Swiss-Pdb viewer: an environment for comparative protein modeling. Electrophoresis. 1997;18(15):2714–23.PubMedCrossRef
30.
go back to reference Krüger DM, Ahmed A, Gohlke H. NMSim web server: integrated approach for normal mode-based geometric simulations of biologically relevant conformational transitions in proteins. Nucleic Acids Res. 2012;40(W1):W310–6.PubMedPubMedCentralCrossRef Krüger DM, Ahmed A, Gohlke H. NMSim web server: integrated approach for normal mode-based geometric simulations of biologically relevant conformational transitions in proteins. Nucleic Acids Res. 2012;40(W1):W310–6.PubMedPubMedCentralCrossRef
31.
go back to reference Agostini F, Cirillo D, Livi CM, Delli Ponti R, Tartaglia GG. cc SOL omics: a webserver for solubility prediction of endogenous and heterologous expression in Escherichia coli. Bioinformatics. 2014;30(20):2975–7.PubMedPubMedCentralCrossRef Agostini F, Cirillo D, Livi CM, Delli Ponti R, Tartaglia GG. cc SOL omics: a webserver for solubility prediction of endogenous and heterologous expression in Escherichia coli. Bioinformatics. 2014;30(20):2975–7.PubMedPubMedCentralCrossRef
32.
go back to reference Grossberg AJ, Chu LC, Deig CR, Fishman EK, Hwang WL, Maitra A, et al. Multidisciplinary standards of care and recent progress in pancreatic ductal adenocarcinoma. CA Cancer J Clin. 2020;70(5):375–403.PubMedPubMedCentralCrossRef Grossberg AJ, Chu LC, Deig CR, Fishman EK, Hwang WL, Maitra A, et al. Multidisciplinary standards of care and recent progress in pancreatic ductal adenocarcinoma. CA Cancer J Clin. 2020;70(5):375–403.PubMedPubMedCentralCrossRef
33.
34.
go back to reference Wiederstein M, Sippl MJ. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 2007;35:W407–10.PubMedPubMedCentralCrossRef Wiederstein M, Sippl MJ. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 2007;35:W407–10.PubMedPubMedCentralCrossRef
35.
go back to reference Tyka MD, Keedy DA, André I, DiMaio F, Song Y, Richardson DC, et al. Alternate states of proteins revealed by detailed energy landscape mapping. J Mol Biol. 2011;405(2):607–18.PubMedCrossRef Tyka MD, Keedy DA, André I, DiMaio F, Song Y, Richardson DC, et al. Alternate states of proteins revealed by detailed energy landscape mapping. J Mol Biol. 2011;405(2):607–18.PubMedCrossRef
36.
go back to reference Gasteiger E, Hoogland C, Gattiker A, Se D, Wilkins MR, Appel RD, et al. Protein identification and analysis tools on the ExPASy server. Totowa: Springer; 2005.CrossRef Gasteiger E, Hoogland C, Gattiker A, Se D, Wilkins MR, Appel RD, et al. Protein identification and analysis tools on the ExPASy server. Totowa: Springer; 2005.CrossRef
37.
go back to reference Kara EE, Comerford I, Fenix KA, Bastow CR, Gregor CE, McKenzie DR, et al. Tailored immune responses: novel effector helper T cell subsets in protective immunity. PLoS Pathog. 2014;10(2): e1003905.PubMedPubMedCentralCrossRef Kara EE, Comerford I, Fenix KA, Bastow CR, Gregor CE, McKenzie DR, et al. Tailored immune responses: novel effector helper T cell subsets in protective immunity. PLoS Pathog. 2014;10(2): e1003905.PubMedPubMedCentralCrossRef
38.
41.
go back to reference Nardin A, Abastado J-P. Macrophages and cancer. Front Biosci. 2008;13(3):494–505. Nardin A, Abastado J-P. Macrophages and cancer. Front Biosci. 2008;13(3):494–505.
43.
go back to reference Helminen O, Huhta H, Kauppila JH, Lehenkari PP, Saarnio J, Karttunen TJ. Localization of nucleic acid-sensing toll-like receptors in human and mouse pancreas. APMIS. 2017;125(2):85–92.PubMedCrossRef Helminen O, Huhta H, Kauppila JH, Lehenkari PP, Saarnio J, Karttunen TJ. Localization of nucleic acid-sensing toll-like receptors in human and mouse pancreas. APMIS. 2017;125(2):85–92.PubMedCrossRef
44.
go back to reference Pahlavanneshan S, Sayadmanesh A, Ebrahimiyan H, Basiri M. Toll-like receptor-based strategies for cancer immunotherapy. J Immunol Res. 2021;2021:1–14.CrossRef Pahlavanneshan S, Sayadmanesh A, Ebrahimiyan H, Basiri M. Toll-like receptor-based strategies for cancer immunotherapy. J Immunol Res. 2021;2021:1–14.CrossRef
45.
go back to reference Ruan M, Thorn K, Liu S, Li S, Yang W, Zhang C, et al. The secretion of IL-6 by CpG-ODN-treated cancer cells promotes T-cell immune responses partly through the TLR-9/AP-1 pathway in oral squamous cell carcinoma. Int J Oncol. 2014;44(6):2103–10.PubMedCrossRef Ruan M, Thorn K, Liu S, Li S, Yang W, Zhang C, et al. The secretion of IL-6 by CpG-ODN-treated cancer cells promotes T-cell immune responses partly through the TLR-9/AP-1 pathway in oral squamous cell carcinoma. Int J Oncol. 2014;44(6):2103–10.PubMedCrossRef
46.
go back to reference Connor AA, Gallinger S. Pancreatic cancer evolution and heterogeneity: integrating omics and clinical data. Nat Rev Cancer. 2022;22(3):131–42.PubMedCrossRef Connor AA, Gallinger S. Pancreatic cancer evolution and heterogeneity: integrating omics and clinical data. Nat Rev Cancer. 2022;22(3):131–42.PubMedCrossRef
47.
go back to reference Pilla L, Rivoltini L, Patuzzo R, Marrari A, Valdagni R, Parmiani G. Multipeptide vaccination in cancer patients. Expert Opin Biol Ther. 2009;9(8):1043–55.PubMedCrossRef Pilla L, Rivoltini L, Patuzzo R, Marrari A, Valdagni R, Parmiani G. Multipeptide vaccination in cancer patients. Expert Opin Biol Ther. 2009;9(8):1043–55.PubMedCrossRef
48.
go back to reference Ray SK, Mukherjee S. Altering landscape of cancer vaccines: unique platforms, research on therapeutic applications and recent patents. Recent Pat Anti-Cancer Drug Discovery. 2023;18(2):133–46.CrossRef Ray SK, Mukherjee S. Altering landscape of cancer vaccines: unique platforms, research on therapeutic applications and recent patents. Recent Pat Anti-Cancer Drug Discovery. 2023;18(2):133–46.CrossRef
49.
go back to reference Gan L-L, Hii L-W, Wong S-F, Leong C-O, Mai C-W. Molecular mechanisms and potential therapeutic reversal of pancreatic cancer-induced immune evasion. Cancers. 2020;12(7):1872.PubMedPubMedCentralCrossRef Gan L-L, Hii L-W, Wong S-F, Leong C-O, Mai C-W. Molecular mechanisms and potential therapeutic reversal of pancreatic cancer-induced immune evasion. Cancers. 2020;12(7):1872.PubMedPubMedCentralCrossRef
50.
go back to reference de Paula PL, da Luz FAC, dos Anjos PB, Brigido PC, de Araujo RA, Goulart LR, et al. Peptide vaccines in breast cancer: the immunological basis for clinical response. Biotechnol Adv. 2015;33(8):1868–77.CrossRef de Paula PL, da Luz FAC, dos Anjos PB, Brigido PC, de Araujo RA, Goulart LR, et al. Peptide vaccines in breast cancer: the immunological basis for clinical response. Biotechnol Adv. 2015;33(8):1868–77.CrossRef
51.
go back to reference Tamiola K, Acar B, Mulder FA. Sequence-specific random coil chemical shifts of intrinsically disordered proteins. J Am Chem Soc. 2010;132(51):18000–3.PubMedCrossRef Tamiola K, Acar B, Mulder FA. Sequence-specific random coil chemical shifts of intrinsically disordered proteins. J Am Chem Soc. 2010;132(51):18000–3.PubMedCrossRef
52.
go back to reference Saber MM, Monir N, Awad AS, Elsherbiny ME, Zaki HF. TLR9: a friend or a foe. Life Sci. 2022;307:120874.PubMedCrossRef Saber MM, Monir N, Awad AS, Elsherbiny ME, Zaki HF. TLR9: a friend or a foe. Life Sci. 2022;307:120874.PubMedCrossRef
53.
go back to reference Lin X, Ye L, Wang X, Liao Z, Dong J, Yang Y, et al. Follicular helper T cells remodel the immune microenvironment of pancreatic cancer via secreting CXCL13 and IL-21. Cancers. 2021;13(15):3678.PubMedPubMedCentralCrossRef Lin X, Ye L, Wang X, Liao Z, Dong J, Yang Y, et al. Follicular helper T cells remodel the immune microenvironment of pancreatic cancer via secreting CXCL13 and IL-21. Cancers. 2021;13(15):3678.PubMedPubMedCentralCrossRef
54.
go back to reference den Haan JM, Arens R, van Zelm MC. The activation of the adaptive immune system: cross-talk between antigen-presenting cells, T cells and B cells. Immunol Lett. 2014;162(2):103–12.CrossRef den Haan JM, Arens R, van Zelm MC. The activation of the adaptive immune system: cross-talk between antigen-presenting cells, T cells and B cells. Immunol Lett. 2014;162(2):103–12.CrossRef
55.
go back to reference Alshaker HA, Matalka KZ. IFN-γ, IL-17 and TGF-β involvement in shaping the tumor microenvironment: the significance of modulating such cytokines in treating malignant solid tumors. Cancer Cell Int. 2011;11(1):1–12.CrossRef Alshaker HA, Matalka KZ. IFN-γ, IL-17 and TGF-β involvement in shaping the tumor microenvironment: the significance of modulating such cytokines in treating malignant solid tumors. Cancer Cell Int. 2011;11(1):1–12.CrossRef
Metadata
Title
Design and evaluation of a multiepitope vaccine for pancreatic cancer using immune-dominant epitopes derived from the signature proteome in expression datasets
Authors
Sooram Banesh
Nupoor Patil
Vihadhar Reddy Chethireddy
Arnav Bhukmaria
Prakash Saudagar
Publication date
01-05-2024
Publisher
Springer US
Published in
Medical Oncology / Issue 5/2024
Print ISSN: 1357-0560
Electronic ISSN: 1559-131X
DOI
https://doi.org/10.1007/s12032-024-02334-4

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