Personal response to immune checkpoint inhibitors of patients with advanced melanoma explained by a computational model of cellular immunity, tumor growth, and drug

English version

Autoři: D. Perlstein ^aff001; O. Shlagman ^aff001; Y. Kogan ^aff001; K. Halevi-Tobias ^aff001; A. Yakobson ^aff002; I. Lazarev ^aff002; Z. Agur ^aff001
Působiště autorů: Institute for Medical Biomathematics (IMBM), Bene Ataroth, Israel ^aff001; Department of Oncology, Soroka University Medical Center, Be'er Sheba, Israel ^aff002
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0226869

Souhrn

Immune checkpoint inhibitors, such as pembrolizumab, are transforming clinical oncology. Yet, insufficient overall response rate, and accelerated tumor growth rate in some patients, highlight the need for identifying potential responders. To construct a computational model, identifying response predictors, and enabling immunotherapy personalization. The combined dynamics of cellular immunity, pembrolizumab, and the melanoma cancer were modeled by a set of ordinary differential equations. The model relies on a scheme of T memory stem cells, progressively differentiating into effector CD8⁺ T cells, and additionally includes T cell exhaustion, reinvigoration and senescence. Clinical data of a pembrolizumab-treated patient with advanced melanoma (Patient O’) were used for model calibration and simulations. Virtual patient populations, varying in one parameter or more, were generated for retrieving clinical studies. Simulations captured the major features of Patient O’s disease, displaying a good fit to her clinical data. A temporary increase in tumor burden, as implied by the clinical data, was obtained only when assuming aberrant self-renewal rates. Variation in effector T cell cytotoxicity was sufficient for simulating dynamics that vary from rapid progression to complete cure, while variation in tumor immunogenicity has a delayed and limited effect on response. Simulations of a-specific clinical trial were in good agreement with the clinical results, demonstrating positive correlations between response to pembrolizumab and the ratio of reinvigoration to baseline tumor load. These results were obtained by assuming inter-patient variation in the toxicity of effector CD8+ T cells, and in their intrinsic division rate, as well as by assuming that the intrinsic division rate of cancer cells is correlated with the baseline tumor burden. In conclusion, hyperprogression can result from lower patient-specific effector cytotoxicity, a temporary increase in tumor load is unlikely to result from real tumor growth, and the ratio of reinvigoration to tumor load can predict personal response to pembrolizumab. Upon further validation, the model can serve for immunotherapy personalization.

Klíčová slova:

Cell cycle and cell division – Cell differentiation – Immune response – Cancer treatment – T cells – Cytotoxic T cells – Melanomas – Cancer immunotherapy

Zdroje

1. Society TAC. Cancer Facts & Figures 2016. Atlanta, GA: The American Cancer Society, 2016.

2. American Cancer Society I. Survival Rates for Melanoma Skin Cancer, by Stage 2018. Available from: https://www.cancer.org/cancer/melanoma-skin-cancer/detection-diagnosis-staging/survival-rates-for-melanoma-skin-cancer-by-stage.html.

3. Sweetlove M, Wrightson E, Kolekar S, Rewcastle GW, Baguley BC, Shepherd PR, et al. Inhibitors of pan-PI3K Signaling Synergize with BRAF or MEK Inhibitors to Prevent BRAF-Mutant Melanoma Cell Growth. Frontiers in Oncology. 2015;5(135). doi: 10.3389/fonc.2015.00135

4. Robert C, Ribas A, Hamid O, Daud A, Wolchok JD, Joshua AM, et al. Durable Complete Response After Discontinuation of Pembrolizumab in Patients With Metastatic Melanoma. J Clin Oncol. 2018;36(17):1668–74. doi: 10.1200/JCO.2017.75.6270 29283791.

5. Hodi FS, Hwu WJ, Kefford R, Weber JS, Daud A, Hamid O, et al. Evaluation of Immune-Related Response Criteria and RECIST v1.1 in Patients With Advanced Melanoma Treated With Pembrolizumab. J Clin Oncol. 2016;34(13):1510–7. doi: 10.1200/JCO.2015.64.0391 26951310; PubMed Central PMCID: PMC5070547 online at www.jco.org. Author contributions are found at the end of this article.

6. Seidel JA, Otsuka A, Kabashima K. Anti-PD-1 and Anti-CTLA-4 Therapies in Cancer: Mechanisms of Action, Efficacy, and Limitations. Front Oncol. 2018;8:86. doi: 10.3389/fonc.2018.00086 29644214; PubMed Central PMCID: PMC5883082.

7. Kang SP, Gergich K, Lubiniecki GM, de Alwis DP, Chen C, Tice MAB, et al. Pembrolizumab KEYNOTE-001: an adaptive study leading to accelerated approval for two indications and a companion diagnostic. Annals of Oncology. 2017;28(6):1388–98. doi: 10.1093/annonc/mdx076 30052728

8. Michot JM, Bigenwald C, Champiat S, Collins M, Carbonnel F, Postel-Vinay S, et al. Immune-related adverse events with immune checkpoint blockade: a comprehensive review. Eur J Cancer. 2016;54:139–48. doi: 10.1016/j.ejca.2015.11.016 26765102.

9. Postow MA, Callahan MK, Wolchok JD. Immune checkpoint blockade in cancer therapy. Journal of Clinical Oncology. 2015;33(17):1974–82. doi: 10.1200/JCO.2014.59.4358 25605845

10. Kato S, Goodman A, Walavalkar V, Barkauskas DA, Sharabi A, Kurzrock R. Hyperprogressors after Immunotherapy: Analysis of Genomic Alterations Associated with Accelerated Growth Rate. Clin Cancer Res. 2017;23(15):4242–50. doi: 10.1158/1078-0432.CCR-16-3133 28351930; PubMed Central PMCID: PMC5647162.

11. Champiat S, Dercle L, Ammari S, Massard C, Hollebecque A, Postel-Vinay S, et al. Hyperprogressive Disease Is a New Pattern of Progression in Cancer Patients Treated by Anti-PD-1/PD-L1. Clin Cancer Res. 2017;23(8):1920–8. doi: 10.1158/1078-0432.CCR-16-1741 27827313.

12. Chubachi S, Yasuda H, Irie H, Fukunaga K, Naoki K, Soejima K, et al. A Case of Non-Small Cell Lung Cancer with Possible "Disease Flare" on Nivolumab Treatment. Case Rep Oncol Med. 2016;2016:1075641. doi: 10.1155/2016/1075641 28116195; PubMed Central PMCID: PMC5223009 publication of this paper.

13. Chiou VL, Burotto M. Pseudoprogression and Immune-Related Response in Solid Tumors. J Clin Oncol. 2015;33(31):3541–3. doi: 10.1200/JCO.2015.61.6870 26261262; PubMed Central PMCID: PMC4622096.

14. Wang Q, Gao J, Wu X. Pseudoprogression and hyperprogression after checkpoint blockade. Int Immunopharmacol. 2018;58:125–35. doi: 10.1016/j.intimp.2018.03.018 29579717.

15. Trivedi MS, Hoffner B, Winkelmann JL, Abbott ME, Hamid O, Carvajal RD. Programmed death 1 immune checkpoint inhibitors. Clin Adv Hematol Oncol. 2015;13(12):858–68. 27058852.

16. Sharma P, Allison JP. The future of immune checkpoint therapy. Science. 2015;348(6230):56–61. doi: 10.1126/science.aaa8172 25838373.

17. Zarnitsyna VI, Handel A, McMaster SR, Hayward SL, Kohlmeier JE, Antia R. Mathematical Model Reveals the Role of Memory CD8 T Cell Populations in Recall Responses to Influenza. Front Immunol. 2016;7:165. doi: 10.3389/fimmu.2016.00165 27242779; PubMed Central PMCID: PMC4861172.

18. Agur Z, Vuk-Pavlovic S. Personalizing immunotherapy: Balancing predictability and precision. Oncoimmunology. 2012;1(7):1169–71. doi: 10.4161/onci.20955 23170268; PubMed Central PMCID: PMC3494634.

19. Castro M, Lythe G, Molina-Paris C, Ribeiro RM. Mathematics in modern immunology. Interface Focus. 2016;6(2):20150093. doi: 10.1098/rsfs.2015.0093 27051512; PubMed Central PMCID: PMC4759751.

20. Kogan Y, Halevi-Tobias K, Elishmereni M, Vuk-Pavlovic S, Agur Z. Reconsidering the paradigm of cancer immunotherapy by computationally aided real-time personalization. Cancer Res. 2012;72(9):2218–27. doi: 10.1158/0008-5472.CAN-11-4166 22422938.

21. Ahmed R, Bevan MJ, Reiner SL, Fearon DT. The precursors of memory: models and controversies. Nat Rev Immunol. 2009;9(9):662–8. doi: 10.1038/nri2619 19680250.

22. DiSpirito JR, Shen H. Quick to remember, slow to forget: rapid recall responses of memory CD8+ T cells. Cell Res. 2010;20(1):13–23. doi: 10.1038/cr.2009.140 20029390.

23. Gattinoni L, Klebanoff CA, Restifo NP. Paths to stemness: building the ultimate antitumour T cell. Nat Rev Cancer. 2012;12(10):671–84. doi: 10.1038/nrc3322 22996603.

24. Gattinoni L, Lugli E, Ji Y, Pos Z, Paulos CM, Quigley MF, et al. A human memory T cell subset with stem cell-like properties. Nat Med. 2011;17(10):1290–7. doi: 10.1038/nm.2446 21926977; PubMed Central PMCID: PMC3192229.

25. Klebanoff CA, Gattinoni L, Restifo NP. CD8+ T-cell memory in tumor immunology and immunotherapy. Immunol Rev. 2006;211:214–24. doi: 10.1111/j.0105-2896.2006.00391.x 16824130; PubMed Central PMCID: PMC1501075.

26. Martin MD, Badovinac VP. Influence of time and number of antigen encounters on memory CD8 T cell development. Immunol Res. 2014;59(1–3):35–44. doi: 10.1007/s12026-014-8522-3 24825776.

27. Arakelyan L, Belilty G, Dahan N, Harpak H, Kogan Y, Merbl Y, et al. Application of the Virtual Cancer Patient Engine (VCPE) for improving oncological treatment desig. Journal of Clinical Oncology. 2004;V 22 (14 suppl):692.

28. Kleiman M, Sagi Y, Bloch N, Agur Z. Use of virtual patient populations for rescuing discontinued drug candidates and for reducing the number of patients in clinical trials. Altern Lab Anim. 2009;37 Suppl 1:39–45. doi: 10.1177/026119290903701S07 19807203.

29. Agur Z. From the evolution of toxin resistance to virtual clinical trials: the role of mathematical models in oncology. Future Oncol. 2010;6(6):917–27. doi: 10.2217/fon.10.61 20528230.

30. Agur Z, Bloch N, Gorelik B, Kleiman M, Kogan Y, Sagi Y, et al. Developing Oncology Drugs Using Virtual Patients of Vascular Tumor Diseases. 2011. p. 201–37.

31. Bangs A. Predictive biosimulation and virtual patients in pharmaceutical R&D. Studies in health technology and informatics. 2005;111:37–42. 15718695

32. Agur Z. Interactive Clinical Trial Design”: A Combined Mathematical and Statistical Simulation Method for Optimizing Drug Development from Statistical Methods in Healthcare. Faltin FW, Kenett R, F. R, editors: Wiley; 2012.

33. Huang AC, Postow MA, Orlowski RJ, Mick R, Bengsch B, Manne S, et al. T-cell invigoration to tumour burden ratio associated with anti-PD-1 response. Nature. 2017;545(7652):60–5. doi: 10.1038/nature22079 28397821; PubMed Central PMCID: PMC5554367.

34. Shvartser-Beryozkin Y, Yakobson A, Benharroch D, Saute M, Feinmesser M. Malignant Melanoma in Association With a Thymic Nevus in a Patient With a Giant Congenital Nevus. Am J Dermatopathol. 2017;39(7):538–41. doi: 10.1097/DAD.0000000000000817 28033154.

35. Hart D, Shochat E, Agur Z. The growth law of primary breast cancer as inferred from mammography screening trials data. Br J Cancer. 1998;78(3):382–7. doi: 10.1038/bjc.1998.503 9703287; PubMed Central PMCID: PMC2063020.

36. Hochman G, Agur Z. Deciphering Fate Decision in Normal and Cancer Stem Cells: Mathematical Models and Their Experimental Verification in Mathematical Methods and Models in Biomedicine: Springer; 2013.

37. Arakelyan L, Merbl Y, Agur Z. Vessel maturation effects on tumour growth: validation of a computer model in implanted human ovarian carcinoma spheroids. Eur J Cancer. 2005;41(1):159–67. doi: 10.1016/j.ejca.2004.09.012 15618001.

38. Elishmereni M, Kheifetz Y, Shukrun I, Bevan GH, Nandy D, McKenzie KM, et al. Predicting time to castration resistance in hormone sensitive prostate cancer by a personalization algorithm based on a mechanistic model integrating patient data. Prostate. 2016;76(1):48–57. doi: 10.1002/pros.23099 26419619.

39. Kedl RM, Rees WA, Hildeman DA, Schaefer B, Mitchell T, Kappler J, et al. T cells compete for access to antigen-bearing antigen-presenting cells. J Exp Med. 2000;192(8):1105–13. doi: 10.1084/jem.192.8.1105 11034600; PubMed Central PMCID: PMC2195874.

40. Dong H, Strome SE, Salomao DR, Tamura H, Hirano F, Flies DB, et al. Tumor-associated B7-H1 promotes T-cell apoptosis: a potential mechanism of immune evasion. Nat Med. 2002;8(8):793–800. doi: 10.1038/nm730 12091876.

41. Ahmadzadeh M, Johnson LA, Heemskerk B, Wunderlich JR, Dudley ME, White DE, et al. Tumor antigen-specific CD8 T cells infiltrating the tumor express high levels of PD-1 and are functionally impaired. Blood. 2009;114(8):1537–44. doi: 10.1182/blood-2008-12-195792 19423728; PubMed Central PMCID: PMC2927090.

42. Nguyen LT, Ohashi PS. Clinical blockade of PD1 and LAG3—potential mechanisms of action. Nat Rev Immunol. 2015;15(1):45–56. doi: 10.1038/nri3790 25534622.

43. Angelosanto JM, Blackburn SD, Crawford A, Wherry EJ. Progressive loss of memory T cell potential and commitment to exhaustion during chronic viral infection. J Virol. 2012;86(15):8161–70. doi: 10.1128/JVI.00889-12 22623779; PubMed Central PMCID: PMC3421680.

44. Utzschneider DT, Legat A, Fuertes Marraco SA, Carrie L, Luescher I, Speiser DE, et al. T cells maintain an exhausted phenotype after antigen withdrawal and population reexpansion. Nat Immunol. 2013;14(6):603–10. doi: 10.1038/ni.2606 23644506.

45. Kahan SM, Wherry EJ, Zajac AJ. T cell exhaustion during persistent viral infections. Virology. 2015;479-480C:180–93. doi: 10.1016/j.virol.2014.12.033 25620767; PubMed Central PMCID: PMC4424083.

46. Akbar AN, Henson SM. Are senescence and exhaustion intertwined or unrelated processes that compromise immunity? Nat Rev Immunol. 2011;11(4):289–95. doi: 10.1038/nri2959 21436838.

47. Marciniak-Czochra A, Stiehl T, Wagner W. Modeling of replicative senescence in hematopoietic development. Aging (Albany NY). 2009;1(8):723–32. doi: 10.18632/aging.100072 20195386; PubMed Central PMCID: PMC2830082.

48. Kaszubowska L. Telomere shortening and ageing of the immune system. J Physiol Pharmacol. 2008;59 Suppl 9:169–86. 19261979.

49. He S, Nakada D, Morrison SJ. Mechanisms of stem cell self-renewal. Annu Rev Cell Dev Biol. 2009;25:377–406. doi: 10.1146/annurev.cellbio.042308.113248 19575646.

50. Nishino M, Giobbie-Hurder A, Gargano M, Suda M, Ramaiya NH, Hodi FS. Developing a common language for tumor response to immunotherapy: immune-related response criteria using unidimensional measurements. Clin Cancer Res. 2013;19(14):3936–43. doi: 10.1158/1078-0432.CCR-13-0895 23743568; PubMed Central PMCID: PMC3740724.

51. Kamphorst AO, Wieland A, Nasti T, Yang S, Zhang R, Barber DL, et al. Rescue of exhausted CD8 T cells by PD-1-targeted therapies is CD28-dependent. Science. 2017;355(6332):1423–7. doi: 10.1126/science.aaf0683 28280249; PubMed Central PMCID: PMC5595217.

52. Gorelik B, Ziv I, Shohat R, Wick M, Hankins WD, Sidransky D, et al. Efficacy of weekly docetaxel and bevacizumab in mesenchymal chondrosarcoma: a new theranostic method combining xenografted biopsies with a mathematical model. Cancer Res. 2008;68(21):9033–40. doi: 10.1158/0008-5472.CAN-08-1723 18974149; PubMed Central PMCID: PMC3098452.

53. Ferrara R, Mezquita L, Texier M, Lahmar J, Audigier-Valette C, Tessonnier L, et al. Hyperprogressive Disease in Patients With Advanced Non-Small Cell Lung Cancer Treated With PD-1/PD-L1 Inhibitors or With Single-Agent Chemotherapy. JAMA Oncol. 2018;4(11):1543–52. doi: 10.1001/jamaoncol.2018.3676 30193240; PubMed Central PMCID: PMC6248085.

54. Saada-Bouzid E, Defaucheux C, Karabajakian A, Coloma VP, Servois V, Paoletti X, et al. Hyperprogression during anti-PD-1/PD-L1 therapy in patients with recurrent and/or metastatic head and neck squamous cell carcinoma. Ann Oncol. 2017;28(7):1605–11. doi: 10.1093/annonc/mdx178 28419181.

55. Pearson AT, Sweis RF. Hyperprogression-Immunotherapy-Related Phenomenon vs Intrinsic Natural History of Cancer. JAMA Oncol. 2019;5(5):743. doi: 10.1001/jamaoncol.2019.0130 30896751.

56. Lee N, Shin MS, Kang I. T-cell biology in aging, with a focus on lung disease. J Gerontol A Biol Sci Med Sci. 2012;67(3):254–63. doi: 10.1093/gerona/glr237 22396471; PubMed Central PMCID: PMC3297764.

57. Van Acker HH, Capsomidis A, Smits EL, Van Tendeloo VF. CD56 in the Immune System: More Than a Marker for Cytotoxicity? Front Immunol. 2017;8:892. doi: 10.3389/fimmu.2017.00892 28791027; PubMed Central PMCID: PMC5522883.

58. Ozaki Y, Shindoh J, Miura Y, Nakajima H, Oki R, Uchiyama M, et al. Serial pseudoprogression of metastatic malignant melanoma in a patient treated with nivolumab: a case report. BMC Cancer. 2017;17(1):778. doi: 10.1186/s12885-017-3785-4 29162045; PubMed Central PMCID: PMC5696908.

Personal response to immune checkpoint inhibitors of patients with advanced melanoma explained by a computational model of cellular immunity, tumor growth, and drug

Souhrn

Klíčová slova:

Zdroje

PLOS One

Aktuální možnosti diagnostiky a léčby litiáz