Open Access
Issue
Math. Model. Nat. Phenom.
Volume 21, 2026
Article Number 13
Number of page(s) 28
Section Mathematical physiology and medicine
DOI https://doi.org/10.1051/mmnp/2026004
Published online 13 April 2026
  1. S.L. Topalian, G.J. Weiner and D.M. Pardoll, Cancer immunotherapy comes of age. J. Clin. Oncol. 29 (2011) 4828–4836. [Google Scholar]
  2. W. Alexander, The checkpoint immunotherapy revolution. Pharm. Ther. 41 (2016) 185–191. [Google Scholar]
  3. A.D. Waldman, J.M. Fritz and M.J. Lenardo, A guide to cancer immunotherapy: from T cell basic science to clinical practice. Nat. Rev. Immunol. 20 (2020) 651–668. Publishing Group. [Google Scholar]
  4. J.D. Wolchok, A. Hoos, S. O'Day, J.S. Weber, O. Hamid, C. Lebbe, M. Maio, M. Binder, O. Bohnsack, G. Nichol, R. Humphrey and F.S. Hodi, Guidelines for the evaluation of immune therapy activity in solid tumors: immune-related response criteria. Clin. Cancer Res. 15 (2009) 7412–7420. [Google Scholar]
  5. J.H.A. Creemers and J. Textor, Leveraging mathematical models to improve the statistical robustness of cancer immunotherapy trials. Curr. Opin. Syst. Biol. 40 (2025) 100540. [Google Scholar]
  6. D.M. Pardoll, The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer 12 (2012) 252–264. [Google Scholar]
  7. E.I. Buchbinder and A. Desai, CTLA-4 and PD-1 pathways: similarities, differences, and implications of their inhibition. Am. J. Clin. Oncol. 39 (2016) 98–106. [Google Scholar]
  8. J.A. Seidel, A. Otsuka and K. Kabashima, Anti-PD-1 and Anti-CTLA-4 therapies in cancer: mechanisms of action, efficacy, and limitations. Front. Oncol. 8 (2018) 86. [Google Scholar]
  9. A. Sancho-Araiz, V. Mangas-Sanjuan and I.F. Troconiz, The role of mathematical models in immuno-oncology: challenges and future perspectives. Pharmaceutics 13 (2021) 1016. [Google Scholar]
  10. J.D. Butner, P. Dogra, C. Chung, R. Pasqualini, W. Arap, J. Lowengrub, V. Cristini and Z. Wang, Mathematical modeling of cancer immunotherapy for personalized clinical translation. Nat. Comput. Sci. 2 (2022) 785–796. [Google Scholar]
  11. J.D. Butner, D. Elganainy, C.X. Wang, Z. Wang, S.-H. Chen, N.F. Esnaola, R. Pasqualini, W. Arap, D.S. Hong, J. Welsh, E.J. Koay and V. Cristini, Mathematical prediction of clinical outcomes in advanced cancer patients treated with checkpoint inhibitor immunotherapy. Sci. Adv. 6 (2020) eaay6298. [Google Scholar]
  12. O. Milberg, C. Gong, M. Jafarnejad, I.H. Bartelink, B. Wang, P. Vicini, R. Narwal, L. Roskos and A.S. Popel, A QSP model for predicting clinical responses to monotherapy, combination and sequential therapy following CTLA-4, PD-1, and PD-L1 checkpoint blockade. Sci. Rep. 9 (2019) 11286. [Google Scholar]
  13. X. Lai and A. Friedman, Combination therapy for melanoma with BRAF/MEK inhibitor and immune checkpoint inhibitor: a mathematical model. BMC Syst. Biol. 11 (2017) 70. [Google Scholar]
  14. D. Perlstein, O. Shlagman, Y. Kogan, K. Halevi-Tobias, A. Yakobson, I. Lazarev and Z. Agur, Personal response to immune checkpoint inhibitors of patients with advanced melanoma explained by a computational model of cellular immunity, tumor growth, and drug. PLoS One 14 (2019) e0226869. [Google Scholar]
  15. N. Tsur, Y. Kogan, E. Avizov-Khodak, D. Vaeth, N. Vogler, J. Utikal, M. Lotem and Z. Agur, Predicting response to pembrolizumab in metastatic melanoma by a new personalization algorithm. J. Transi. Med. 17 (2019) 338. [Google Scholar]
  16. Y. Qin, M. Huo, X. Liu and S.C. Li, Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy. Front. Immunol. 15 (2024) 1368749. [Google Scholar]
  17. H. Wang, O. Milberg, I.H. Bartelink, P. Vicini, B. Wang, R. Narwal, L. Roskos, C.A. Santa-Maria and A.S. Popel, In silico simulation of a clinical trial with anti-CTLA-4 and anti-PD-L1 immunotherapies in metastatic breast cancer using a systems pharmacology model. Roy. Soc. Open Sci. 6 (2019) 190366. [Google Scholar]
  18. M.A. Benchaib, A. Bouchnita, V. Volpert and A. Makhoute, Mathematical Modeling reveals that the administration of EGF can promote the elimination of lymph node metastases by PD-1/PD-L1 blockade. Front. Bioeng. Biotechnol. 7 (2019) 104. [Google Scholar]
  19. X. Lai and A. Friedman, Combination therapy of cancer with cancer vaccine and immune checkpoint inhibitors: a mathematical model. PLoS One 12 (2017) e0178479. [Google Scholar]
  20. A. Friedman and X. Lai, Combination therapy for cancer with oncolytic virus and checkpoint inhibitor: a mathematical model. PLoS One 13 (2018) e0192449. [Google Scholar]
  21. E. Nikolopoulou, S.E. Eikenberry, J.L. Gevertz and Y. Kuang, Mathematical modeling of an immune checkpoint inhibitor and its synergy with an immunostimulant. Discrete Continuous Dyn. Syst. B 26 (2021) 2133–2159. [Google Scholar]
  22. X. Lai and A. Friedman, How to schedule VEGF and PD-1 inhibitors in combination cancer therapy? BMC Syst. Biol. 13 (2019) 30. [Google Scholar]
  23. E. Nikolopoulou, L.R. Johnson, D. Harris, J.D. Nagy, E.C. Stites and Y. Kuang, Tumour-immune dynamics with an immune checkpoint inhibitor. Lett. Biomath. 5 (2018) S137-S159. [Google Scholar]
  24. C.Y. Zheng and P.S. Kim, Mathematical model for delayed responses in immune checkpoint blockades. Bull. Math. Biol. 83 (2021) 106. [Google Scholar]
  25. W. Zhang, C.Y. Zheng and P.S. Kim, Bifurcations of a cancer immunotherapy model explaining the transient delayed response and various other responses. Commun. Nonlinear Sci. Numer. Simul. 135 (2024) 108047. [Google Scholar]
  26. D. Tang, R. Kang, H.J. Zeh III and M.T. Lotze, High-mobility group box 1 and cancer. Biochim. Biophys. Acta Gene Regul. Mech. 1799 (2010) 131–140. [Google Scholar]
  27. G.P. Sims, D.C. Rowe, S.T. Rietdijk, R. Herbst and A.J. Coyle, HMGB1 and RAGE in inflammation and cancer. Annu. Rev. Immunol. 28 (2010) 367–388. [Google Scholar]
  28. O.S. Qureshi, Y. Zheng, K. Nakamura, K. Attridge, C. Manzotti, E.M. Schmidt, J. Baker, L.E. Jeffery, S. Kaur, Z. Briggs, T.Z. Hou, C.E. Futter, G. Anderson, L.S. Walker and D.M. Sansom, Trans-endocytosis of CD80 and CD86: a molecular basis for the cell extrinsic function of CTLA-4. Science 332 (2011) 600–603. [CrossRef] [Google Scholar]
  29. Y. Yang, G. Jin, Y. Pang, Y. Huang, W. Wang, H. Zhang, G. Tuo, P. Wu, Z. Wang and Z. Zhu, Comparative efficacy and safety of nivolumab and nivolumab plus ipilimumab in advanced cancer: a systematic review and meta-analysis. Front. Pharmacol. 11 (2020) ID 40. [Google Scholar]
  30. R.J. Tesi, MDSC; the most important cell you have never heard of. Trends Pharmacol. Sci. 40 (2019) 4–7. [Google Scholar]
  31. K. Oleinika, R.J. Nibbs, G.J. Graham and A.R. Fraser, Suppression, subversion and escape: the role of regulatory T cells in cancer progression. Clin. Exp. Immunol. 171 (2013) 36–45. [Google Scholar]
  32. L. Hornyak, N. Dobos, G. Koncz, Z. Karanyi, D. Páll, Z. Szab', G. Halmos and L. Szekvolgyi, The role of indoleamine- 2,3-dioxygenase in cancer development, diagnostics, and therapy. Front. Immunol. 9 (2018) ID 151. [Google Scholar]
  33. E.J. Wherry, S.-J. Ha, S.M. Kaech, W.N. Haining, S. Sarkar, V. Kalia, S. Subramaniam, J.N. Blattman, D.L. Barber and R. Ahmed, Molecular signature of CD8+ T cell exhaustion during chronic viral infection. Immunity 27 (2007) 670–684. [Google Scholar]
  34. M. Ahmadzadeh, L.A. Johnson, B. Heemskerk, J.R. Wunderlich, M.E. Dudley, D.E. White and S.A. Rosenberg, Tumor antigen-specific CD8 T cells infiltrating the tumor express high levels of PD-1 and are functionally impaired. Blood 114 (2009) 1537–1544. [Google Scholar]
  35. S.L. Topalian, F.S. Hodi, J.R. Brahmer, S.N. Gettinger, D.C. Smith, D.F. McDermott, J.D. Powderly, R.D. Carvajal, J.A. Sosman, M.B. Atkins, P.D. Leming, D.R. Spigel, S.J. Antonia, L. Horn, C.G. Drake, D.M. Pardoll, L. Chen, W.H. Sharfman, R.A. Anders, J.M. Taube, T.L. McMiller, H. Xu, A.J. Korman, M. Jure-Kunkel, S. Agrawal, D. McDonald, G.D. Kollia, A. Gupta, J.M. Wigginton and M. Sznol, Safety, activity, and immune correlates of anti - PD-1 antibody in cancer. N. Engl. J. Med. 366 (2012) 2443–2454. [Google Scholar]
  36. K.E. Pauken, M.A. Sammons, P.M. Odorizzi, S. Manne, J. Godec, O. Khan, A.M. Drake, Z. Chen, D.R. Sen, M. Kurachi, R.A. Barnitz, C. Bartman, B. Bengsch, A.C. Huang, J.M. Schenkel, G. Vahedi, W.N. Haining, S.L. Berger and E.J. Wherry, Epigenetic stability of exhausted T cells limits durability of reinvigoration by PD-1 blockade. Science 354 (2016) 1160–1165. [CrossRef] [PubMed] [Google Scholar]
  37. D.E. Dolan and S. Gupta, PD-1 pathway inhibitors: changing the landscape of cancer immunotherapy. Cancer-Control 21 (2014) 231–237. [Google Scholar]
  38. A. Swaika, W.A. Hammond and R.W. Joseph, Current state of anti-PD-L1 and anti-PD-1 agents in cancer therapy. Mol. Immunol. 67 (2015) 4–17. [Google Scholar]
  39. Y. Iwai, J. Hamanishi, K. Chamoto and T. Honjo, Cancer immunotherapies targeting the PD-1 signaling pathway. J. Biomed. Sci. 24 (2017) 26. [Google Scholar]
  40. S.T. Paijens, A. Vledder, M. de Bruyn and H.W. Nijman, Tumor-infiltrating lymphocytes in the immunotherapy era. Cell. Mol. Immunol. 18 (2021) 842–859. [Google Scholar]
  41. J.A. Carlson, Tumor doubling time of cutaneous melanoma and its metastasis. Am. J. Dermatopathol. 25 (2003) 291–299. [Google Scholar]
  42. U. Del Monte, Does the cell number 10 9 still really fit one gram of tumor tissue? Cell Cycle 8 (2009) 505–506. [Google Scholar]
  43. S. Narod, Disappearing breast cancers. Curr. Oncol. 19 (2012) 59–60. [Google Scholar]
  44. D.M. Catron, A.A. Itano, K.A. Pape, D.L. Mueller and M.K. Jenkins, Visualizing the first 50 hr of the primary immune response to a soluble antigen. Immunity 21 (2004) 341–347. [Google Scholar]
  45. J.P. Medema, J. de Jong, L.T.C. Peltenburg, E.M.E. Verdegaal, A. Gorter, S.A. Bres, K.L.M.C. Franken, M. Hahne, J.P. Albar, C.J.M. Melief and R. Offringa, Blockade of the granzyme B/perforin pathway through overexpression of the serine protease inhibitor PI-9/SPI-6 constitutes a mechanism for immune escape by tumors. Proc. Natl. Acad. Sci. U.S.A. 98 (2001) 11515–11520. [Google Scholar]
  46. C.A. Lazarski, F.A. Chaves, S.A. Jenks, S. Wu, K.A. Richards, J.M. Weaver and A.J. Sant, The kinetic stability of MHC class II: peptide complexes is a key parameter that dictates immunodominance. Immunity 23 (2005) 29–40. [Google Scholar]
  47. M. Wieczorek, E.T. Abualrous, J. Sticht, M. Alvaro Benito, S. Stolzenberg, F. Noe and C. Freund, Frontiers | Major histocompatibility complex (MHC) class I and MHC class II proteins: conformational plasticity in antigen presentation. Front. Immunol. 8 (2017) ID 292. [Google Scholar]
  48. N.S. Wilson, Dendritic cells constitutively present self-antigens in their immature state in vivo and regulate antigen presentation by controlling the rates of MHC class II synthesis and endocytosis. Blood 103 (2004) 2187–2195. [Google Scholar]
  49. K. Kay, K. Dolcy, R. Bies and D.K. Shah, Estimation of solid tumor doubling times from progression-free survival plots using a novel statistical approach. AAPS J. 21 (2019) 27. [Google Scholar]
  50. S. Eskelin, S. Pyrhonen, P. Summanen, M. Hahka-Kemppinen and T. Kivela, Tumor doubling times in metastatic malignant melanoma of the uvea. Ophthalmology 107 (2000) 1443–1449. [Google Scholar]
  51. M.-P. Revel, A. Merlin, S. Peyrard, R. Triki, S. Couchon, G. Chatellier and G. Frija, Software volumetric evaluation of doubling times for differentiating benign versus malignant pulmonary nodules. Am. J. Roentgenol. 187 (2006) 135–142. [Google Scholar]
  52. L. Bozzacco, H. Yu, H.A. Zebroski, J. Dengjel, H. Deng, S. Mojsov and R.M. Steinman, Mass spectrometry analysis and quantitation of peptides presented on the MHC II molecules of mouse spleen dendritic cells. J. Proteome Res. 10 (2011) 5016–5030. [Google Scholar]
  53. L.E. Stopfer, A.D. D'Souza and F.M. White, 1,2,3, MHC: a review of mass-spectrometry-based immunopeptidomics methods for relative and absolute quantification of pMHCs. Immunooncol. Technol. 11 (2021) 100042. [Google Scholar]
  54. Z. Liu and P.A. Roche, macropinocytosis in phagocytes: regulation of MHC class-II-restricted antigen presentation in dendritic cells. Front. Physiol. 6 (2015) ID 1. [Google Scholar]
  55. C.C. Norbury, Drinking a lot is good for dendritic cells. Immunology 117 (2006) 443–451. [Google Scholar]
  56. A.D. McLellan, G.C. Starling, L.A. Williams, B.D. Hock and D.N.J. Hart, Activation of human peripheral blood dendritic cells induces the CD86 co-stimulatory molecule. Eur. J. Immunol. 25 (1995) 2064–2068. [Google Scholar]
  57. S.C. Jones, V. Brahmakshatriya, G. Huston, J. Dibble and S.L. Swain, TLR-activated dendritic cells enhance the response of aged naïve CD4 T cells via an IL-6 dependent mechanism. J. Immunol. 185 (2010) 6783–6794. [Google Scholar]
  58. P. Pozenel, K. Zajc and U. Svajger, Factor of time in dendritic cell (DC) maturation: short-term activation of DCs significantly improves type 1 cytokine production and T cell responses. J. Transi. Med. 22 (2024) 541. [Google Scholar]
  59. C.M. Shuford, R.R. Sederoff, V.L. Chiang and D.C. Muddiman, Peptide production and decay rates affect the quantitative accuracy of protein cleavage isotope dilution mass spectrometry (PC-IDMS). Mol. Cell. Proteomics: MCP 11 (2012) 814–823. [Google Scholar]
  60. K. Palucka and J. Banchereau, Cancer immunotherapy via dendritic cells. Nat. Rev. Cancer 12 (2012) 265–277. [Google Scholar]
  61. R. Saenz, D. Futalan, L. Leutenez, F. Eekhout, J.F. Fecteau, S. Sundelius, S. Sundqvist, M. Larsson, T. Hayashi, B. Minev, D. Carson, S. Esener, B. Messmer and D. Messmer, TLR4-dependent activation of dendritic cells by an HMGB1-derived peptide adjuvant. J. Transl. Med. 12 (2014) 211. [Google Scholar]
  62. P. Rovere-Querini, A. Capobianco, P. Scaffidi, B. Valentinis, F. Catalanotti, M. Giazzon, I.E. Dumitriu, S. Muller, M. Iannacone, C. Traversari, M.E. Bianchi and A.A. Manfredi, HMGB1 is an endogenous immune adjuvant released by necrotic cells. EMBO Rep. 5 (2004) 825–830. [Google Scholar]
  63. E.D. Peltz, E.E. Moore, P.C. Eckels, S.S. Damle, Y. Tsuruta, J.L. Johnson, A. Sauaia, C.C. Silliman, A. Banerjee and E. Abraham, HMGB1 is markedly elevated within 6 hours of mechanical trauma in humans. Shock 32 (2009) 17–22. [Google Scholar]
  64. T. Bonaldi, Monocytic cells hyperacetylate chromatin protein HMGB1 to redirect it towards secretion. EMBO J. 22 (2003) 5551–5560. [Google Scholar]
  65. P. Bonaventura, T. Shekarian, V. Alcazer, J. Valladeau-Guilemond, S. Valsesia-Wittmann, S. Amigorena, C. Caux and S. Depil, cold tumors: a therapeutic challenge for immunotherapy. Front. Immunol. 10 (2019) ID 168. [Google Scholar]
  66. K. Brummel, A.L. Eerkens, M. de Bruyn and H.W. Nijman, Tumour-infiltrating lymphocytes: from prognosis to treatment selection. Br. J. Cancer 128 (2023) 451–458. [Google Scholar]
  67. R.J. Steptoe, R.K. Patel, V.M. Subbotin and A.W. Thomson, Comparative analysis of dendritic cell density and total number in commonly transplanted organs: morphometric estimation in normal mice. Transplant Immunol. 8 (2000) 49–56. [Google Scholar]
  68. Y. Ma, G.V. Shurin, Z. Peiyuan and M.R. Shurin, Dendritic cells in the cancer microenvironment. J. Cancer 4 (2012) 36–44. [Google Scholar]
  69. J.M.T. Janco, P. Lamichhane, L. Karyampudi and K.L. Knutson, Tumor-infiltrating dendritic cells in cancer pathogenesis. J. Immunol. 194 (2015) 2985–2991. [Google Scholar]
  70. G.T. Belz, L. Zhang, M.D.H. Lay, F. Kupresanin and M.P. Davenport, Killer T cells regulate antigen presentation for early expansion of memory, but not naive, CD8+ T cell. Proc. Natl. Acad. Sci. U.S.A. 104 (2007) 6341–6346. [Google Scholar]
  71. A. Ladanyi, J. Kiss, B. Somlai, K. Gilde, Z. Fejos, A. Mohos, I. Gaudi and J. Timar, Density of DC-LAMP+ mature dendritic cells in combination with activated T lymphocytes infiltrating primary cutaneous melanoma is a strong independent prognostic factor. Cancer Immunol. Immunother. CII 56 (2007) 1459–1469. [Google Scholar]
  72. A.K. Abbas, A.H. Lichtman and S. Pillai, Cellular and Molecular Immunology, 8th edn. Elsevier Saunders, Philadelphia, PA (2015). [Google Scholar]
  73. K. Murphy, Janeway's Immunobiology, 9th edn. Garland Science, New York (2017). [Google Scholar]
  74. J.A. McBride and R. Striker, Imbalance in the game of T cells: what can the CD4/CD8 T-cell ratio tell us about HIV and health? PLoS Pathogens 13 (2017) ID 4. [Google Scholar]
  75. M. Liu, X. Wang, L. Wang, X. Ma, Z. Gong, S. Zhang and Y. Li, Targeting the IDO1 pathway in cancer: from bench to bedside. J. Hematol. Oncol. 11 (2018) 100. [Google Scholar]
  76. S. Ostrand-Rosenberg, L.A. Horn and S.T. Haile, The programmed death-1 immune-suppressive pathway: barrier to antitumor immunity. J. Immunol. 193 (2014) 3835–3841. [Google Scholar]
  77. R.J. De Boer, D. Homann and A.S. Perelson, Different dynamics of CD4 + and CD8 + T cell responses during and after acute lymphocytic choriomeningitis virus infection. J. Immunol. 171 (2003) 3928–3935. [Google Scholar]
  78. M.A. Cox, S.M. Kahan and A.J. Zajac, Anti-viral CD8 T cells and the cytokines that they love. Virology 435 (2013) 157–169. [Google Scholar]
  79. M. Berktas, H. Guducuoglu, H. Bozkurt, K.T. Onbasi, M.G. Kurtoglu and S. Andic, Change in serum concentrations of interleukin-2 and interferon-gamma during treatment of tuberculosis. J. Int. Med. Res. 32 (2004) 324–330. [Google Scholar]
  80. J. Pilch, G. Namyslowski, W. Scierski, P. Urbaniec and I. Sowináska-Krzyzanowska, Interleukin 2 concentration changes in the laryngeal cancer patients during the surgical treatment. Otolaryngol. Polska 60 (2006) 331–336. [Google Scholar]
  81. S.A. Rosenberg, IL-2: the first effective immunotherapy for human cancer. J. Immunol. 192 (2014) 5451–5458. [Google Scholar]
  82. A. Megahed, P. Faulhaber, T. Phillips and H. Koon, Delayed response in ipilimumab therapy. J. Community Support. Oncol. 12 (2014) 109–110. [Google Scholar]
  83. D.O. Khair, H.J. Bax, S. Mele, S. Crescioli, G. Pellizzari, A. Khiabany, M. Nakamura, R.J. Harris, E. French, R.M. Hoffmann, I.P. Williams, A. Cheung, B. Thair, C.T. Beales, E. Touizer, A.W. Signell, N.L. Tasnova, J.F. Spicer, D.H. Josephs, J.L. Geh, A. MacKenzie Ross, C. Healy, S. Papa, K.E. Lacy and S.N. Karagiannis, Combining immune checkpoint inhibitors: established and emerging targets and strategies to improve outcomes in melanoma. Front. Immunol. 10 (2019) ID 453. [Google Scholar]
  84. K. Palucka and J. Banchereau, Dendritic cell-based cancer therapeutic vaccines. Immunity 39 (2013) 38. [Google Scholar]
  85. R.L. Sabado, S. Balan and N. Bhardwaj, Dendritic cell-based immunotherapy. Cell Res. 27 (2017) 74–95. [Google Scholar]
  86. J. Constantino, C. Gomes, A. Falcao, B.M. Neves and M.T. Cruz, Dendritic cell-based immunotherapy: a basic review and recent advances. Immunol. Res. 65 (2017) 798–810. [Google Scholar]
  87. S.K. Wculek, F.J. Cueto, A.M. Mujal, I. Melero, M.F. Krummel and D. Sancho, Dendritic cells in cancer immunology and immunotherapy. Nat. Rev. Immunol. 20 (2020) 7–24. [Google Scholar]
  88. S.J. Schuster, J. Svoboda, E.A. Chong, S.D. Nasta, A.R. Mato, A. Anak, J.L. Brogdon, I. Pruteanu-Malinici, V. Bhoj, D. Landsburg, M. Wasik, B.L. Levine, S.F. Lacey, J.J. Melenhorst, D.L. Porter and C.H. June, Chimeric antigen receptor T cells in refractory B-cell lymphomas. N. Engl. J. Med. 377 (2017) 2545–2554. [Google Scholar]
  89. M.M. Honikel and S.H. Olejniczak, Co-Stimulatory Receptor signaling in CAR-T cells. Biomolecules 12 (2022) 1303. [Google Scholar]
  90. H. Conroy, N.A. Marshall and K.H.G. Mills, TLR ligand suppression or enhancement of Treg cells? A double-edged sword in immunity to tumours. Oncogene 27 (2008) 168–180. [Google Scholar]
  91. S. Chakraborty, J. Ye, H. Wang, M. Sun, Y. Zhang, X. Sang and Z. Zhuang, Application of toll-like receptors (TLRs) and their agonists in cancer vaccines and immunotherapy. Front. Immunol. 14 (2023) ID 451. [Google Scholar]
  92. S. Dushyanthen, P.A. Beavis, P. Savas, Z.L. Teo, C. Zhou, M. Mansour, P.K. Darcy and S. Loi, Relevance of tumor-infiltrating lymphocytes in breast cancer. BMC Med. 13 (2015) 202. [Google Scholar]
  93. K.A. Hunter, M.A. Socinski and L.C. Villaruz, PD-L1 Testing in guiding patient selection for PD-1/PD-L1 inhibitor therapy in lung cancer. Mol. Diagn. Ther. 22 (2018) 1–10. [Google Scholar]
  94. A. Renner, M. Burotto and C. Rojas, Immune Checkpoint inhibitor dosing: can we go lower without compromising clinical efficacy? J. Global Oncol. 5 (2019) ID 64. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.