Processes 2019, 7,37
Currently, only 1 in 10s of clinical trials results in drugs that make it to the market [165]. The
process takes 10–12 years, costing billions of dollars, sometimes with low effectiveness when used
by real patients [136]. Although virtual clinical trials with virtual patients and virtual cohorts cannot
replace clinical trials, they can inform design of such trials to improve success rates and increase
the efficacy of the process of drug development. Virtual trials can also better address the need
for personalized therapy [166]. Finally, combining agent-based models and data-driven artificial
intelligence (AI) methods (e.g., machine learning, including deep learning), we can better understand
the gap between preclinical findings and clinical outcomes.
In summary, in silico modeling and specifically agent-based modeling are powerful tools of
cancer systems biology and cancer immune systems biology. Combined with novel measurement
methodologies and increasing amounts and sophistication of data available from clinical trials,
they should bring a better mechanistic understanding and predictive capabilities of therapeutic
interventions in cancer, including immunotherapies. The field is ripe for conducting predictive virtual
clinical trials as a prerequisite to clinical trials in patients.
Author Contributions: All authors conceptualized the work, participated in draft preparation, and editing and
finalizing the manuscript. K.A.N., C.G., and S.J. contributed equally to the paper.
Acknowledgments: This work was supported by the National Institutes of Health grants R01CA138264,
U01CA212007 and R01CA196701 and American Cancer Society postdoctoral fellowship PF-13-174-01-CSM (KAN).
The authors thank Amanda Figueroa for her expert drawing of Figure 1. The authors thank Mohammad Jafarnejad
and Richard Sove for critical comments on the manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Hanahan, D.; Weinberg, R.A. Hallmarks of cancer: The next generation. Cell 2011, 144, 646–674. [CrossRef]
[PubMed]
2.
Paget, S. The distribution of secondary growths in cancer of the breast. Lancet 1889, 133, 571–573. [CrossRef]
3.
Wei, S.C.; Duffy, C.R.; Allison, J.P. Fundamental mechanisms of immune checkpoint blockade therapy.
Cancer Discov. 2018, 8, 1069–1086. [CrossRef][PubMed]
4.
Chen, D.S.; Mellman, I. Elements of cancer immunity and the cancer-Immune set point. Nature 2017, 541,
321–330. [CrossRef][PubMed]
5.
Quail, D.F.; Joyce, J.A. Microenvironmental regulation of tumor progression and metastasis. Nat. Med. 2013,
19, 1423–1437. [CrossRef][PubMed]
6.
Ansell, S.M.; Vonderheide, R.H. Cellular composition of the tumor microenvironment. Am. Soc. Clin. Oncol.
Educ. B 2013, 33, e91–e97. [CrossRef]
7.
Pitt, J.M.; Marabelle, A.; Eggermont, A.; Soria, J.C.; Kroemer, G.; Zitvogel, L. Targeting the tumor
microenvironment: Removing obstruction to anticancer immune responses and immunotherapy. Ann. Oncol.
2016, 27, 1482–1492. [CrossRef]
8.
Crespo, I.; Coukos, G.; Doucey, M.; Xenarios, I. Modelling approaches to discovery in the tumor
microenvironment. J. Cancer Immunol. Ther. 2018, 1, 23–37.
9.
Netea, M.G.; Joosten, L.A.B.; Latz, E.; Mills, K.H.G.; Natoli, G.; Stunnenberg, H.G.; O’Neill, L.A.J.; Xavier, R.J.
Trained immunity: A program of innate immune memory in health and disease. Science 2016, 352, aaf1098.
[CrossRef]
10. Netea, M.G.; Quintin, J.; Van Der Meer, J.W.M. Trained immunity: A memory for innate host defense.
Cell Host Microbe 2011, 9, 355–361. [CrossRef]
11. Kumar, H.; Kawai, T.; Akira, S. Pathogen recognition by the innate immune system. Int. Rev. Immunol. 2011,
30, 16–34. [CrossRef]
12. Gajewski, T.F.; Schreiber, H.; Fu, Y.-X. Innate and adaptive immune cells in the tumor microenvironment.
Nat. Immunol. 2013, 14, 1014–1022. [CrossRef]
13. Dykes, S.S.; Hughes, V.S.; Wiggins, J.M.; Fasanya, H.O.; Tanaka, M.; Siemann, D. Stromal cells in breast
cancer as a potential therapeutic target. Oncotarget 2018, 9, 23761–23779. [CrossRef][PubMed]
55
Currently, only 1 in 10s of clinical trials results in drugs that make it to the market [165]. The
process takes 10–12 years, costing billions of dollars, sometimes with low effectiveness when used
by real patients [136]. Although virtual clinical trials with virtual patients and virtual cohorts cannot
replace clinical trials, they can inform design of such trials to improve success rates and increase
the efficacy of the process of drug development. Virtual trials can also better address the need
for personalized therapy [166]. Finally, combining agent-based models and data-driven artificial
intelligence (AI) methods (e.g., machine learning, including deep learning), we can better understand
the gap between preclinical findings and clinical outcomes.
In summary, in silico modeling and specifically agent-based modeling are powerful tools of
cancer systems biology and cancer immune systems biology. Combined with novel measurement
methodologies and increasing amounts and sophistication of data available from clinical trials,
they should bring a better mechanistic understanding and predictive capabilities of therapeutic
interventions in cancer, including immunotherapies. The field is ripe for conducting predictive virtual
clinical trials as a prerequisite to clinical trials in patients.
Author Contributions: All authors conceptualized the work, participated in draft preparation, and editing and
finalizing the manuscript. K.A.N., C.G., and S.J. contributed equally to the paper.
Acknowledgments: This work was supported by the National Institutes of Health grants R01CA138264,
U01CA212007 and R01CA196701 and American Cancer Society postdoctoral fellowship PF-13-174-01-CSM (KAN).
The authors thank Amanda Figueroa for her expert drawing of Figure 1. The authors thank Mohammad Jafarnejad
and Richard Sove for critical comments on the manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Hanahan, D.; Weinberg, R.A. Hallmarks of cancer: The next generation. Cell 2011, 144, 646–674. [CrossRef]
[PubMed]
2.
Paget, S. The distribution of secondary growths in cancer of the breast. Lancet 1889, 133, 571–573. [CrossRef]
3.
Wei, S.C.; Duffy, C.R.; Allison, J.P. Fundamental mechanisms of immune checkpoint blockade therapy.
Cancer Discov. 2018, 8, 1069–1086. [CrossRef][PubMed]
4.
Chen, D.S.; Mellman, I. Elements of cancer immunity and the cancer-Immune set point. Nature 2017, 541,
321–330. [CrossRef][PubMed]
5.
Quail, D.F.; Joyce, J.A. Microenvironmental regulation of tumor progression and metastasis. Nat. Med. 2013,
19, 1423–1437. [CrossRef][PubMed]
6.
Ansell, S.M.; Vonderheide, R.H. Cellular composition of the tumor microenvironment. Am. Soc. Clin. Oncol.
Educ. B 2013, 33, e91–e97. [CrossRef]
7.
Pitt, J.M.; Marabelle, A.; Eggermont, A.; Soria, J.C.; Kroemer, G.; Zitvogel, L. Targeting the tumor
microenvironment: Removing obstruction to anticancer immune responses and immunotherapy. Ann. Oncol.
2016, 27, 1482–1492. [CrossRef]
8.
Crespo, I.; Coukos, G.; Doucey, M.; Xenarios, I. Modelling approaches to discovery in the tumor
microenvironment. J. Cancer Immunol. Ther. 2018, 1, 23–37.
9.
Netea, M.G.; Joosten, L.A.B.; Latz, E.; Mills, K.H.G.; Natoli, G.; Stunnenberg, H.G.; O’Neill, L.A.J.; Xavier, R.J.
Trained immunity: A program of innate immune memory in health and disease. Science 2016, 352, aaf1098.
[CrossRef]
10. Netea, M.G.; Quintin, J.; Van Der Meer, J.W.M. Trained immunity: A memory for innate host defense.
Cell Host Microbe 2011, 9, 355–361. [CrossRef]
11. Kumar, H.; Kawai, T.; Akira, S. Pathogen recognition by the innate immune system. Int. Rev. Immunol. 2011,
30, 16–34. [CrossRef]
12. Gajewski, T.F.; Schreiber, H.; Fu, Y.-X. Innate and adaptive immune cells in the tumor microenvironment.
Nat. Immunol. 2013, 14, 1014–1022. [CrossRef]
13. Dykes, S.S.; Hughes, V.S.; Wiggins, J.M.; Fasanya, H.O.; Tanaka, M.; Siemann, D. Stromal cells in breast
cancer as a potential therapeutic target. Oncotarget 2018, 9, 23761–23779. [CrossRef][PubMed]
55