Optimization of chronic lymphocytic leukemia treatment using game theory
https://doi.org/10.37489/2588-0519-2021-3-57-63
Abstract
The current strategy of chronic lymphocytic leukemia (CLL) treatment is based on genetic risk factors such as del(17p), TP53 mutations and/or unmutated variant of IGHV genes. Guidelines recommend the usage of targeted drugs, e.g. ibrutinib, in the first line for patients with unfavorable risk factors due to dismal results of other treatment options. Unfortunately, in real-life treatment decisions are often made without full knowledge of genetic risk factors in the treated patient.
Our aim was to find the optimal therapeutic strategy for such patients, that is, those providing the best 5-year progression-free survival (PFS). Using a relatively simple game theory-based approach we here show, that currently, the used strategy is more advantageous (success rate 71%) compared to administration of immunochemotherapy to all patients (success rate with fludarabine + cyclophosphamide + rituximab — 45%, bendamustine + rituximab — 32%). However, the optimal strategy for CLL treatment in the conditions of unknown genetic risks is the administration of ibrutinib to all patients (success rate 73%). Our simple method can be used for optimization of treatment strategy of any oncologic disease and can be integrated into relevant clinical decision support systems.
About the Authors
A. S. Luchinin
Federal State Budget Institution of Science Kirov Scientific Research Institute of Hematology and Blood Transfusion of Federal Medical Biological Agency
Russian Federation
Russian Federation
Luchinin Alexander S., PhD, Senior Researcher of the Department of Organization and Support of Scientific Research
Kirov
V. V. Strugov
Institute of oncology and hematology, Federal State Budgetary Institution “Almazov National Medical Research Centre” of the Ministry of Health of the Russian Federation
Russian Federation
Russian Federation
Strugov Vladimir V., Researcher at Institute of oncology and hematology
Saint Petersburg
References
1. Osborne MJ. An introduction to game theory. Oxford University Press. 2002.
2. Conlin PL, Chandler JR, Kerr B. Games of life and death: antibiotic resistance and production through the lens of evolutionary game theory. Curr Opin Microbiol. 2014;21:35–44. doi: 10.1016/j.mib.2014.09. PMID: 25271120
3. Gatenby RA, Vincent TL. An evolutionary model of carcinogenesis. Cancer Res. 2003;63(19):6212–20. PMID: 14559806
4. Torkaman A, Charkari NM, Aghaeipour M. An approach for leukemia classification based on cooperative game theory. Anal Cell Pathol (Amst). 2011;34(5):235–46. doi: 10.3233/ACP-2011-0016. PMID: 21988887
5. Stanková K, Brown JS, Dalton WS, Gatenby RA. Optimizing Cancer Treatment Using Game Theory: A Review. JAMA Oncol. 2019;5(1):96–103. doi: 10.1001/jamaoncol.2018.3395. PMID: 30098166
6. Hallek M. Chronic lymphocytic leukemia: 2019 update on diagnosis, risk stratification and treatment. Am J Hematol. 2019;94(11):1266–87. doi: 10.1002/ajh.25595. PMID: 31364186
7. Barbara Eichhorst, Anna-Maria Fink, Jasmin Bahlo, Raymonde Busch, Gabor Kovacs, Christian Maurer, Elisabeth Lange, Hubert Köppler, Michael Kiehl, Martin Sökler, Rudolf Schlag, Ursula Vehling-Kaiser, Georg Köchling, Christoph Plöger, Michael Gregor, Torben Plesner, Marek Trneny, Kirsten Fischer, Harmut Döhner, Michael Kneba, Clemens-Martin Wendtner, Wolfram Klap-per, Karl-Anton Kreuzer, Stephan Stilgenbauer, Sebastian Böttcher, Michael Hallek, international group of investigators; German CLL Study Group (GCLLSG). First-line chemoimmunotherapy with bendamustine and rituximab versus fludarabine, cyclophosphamide, and rituximab in patients with advanced chronic lymphocytic leukaemia (CLL10): an international, open-label, randomised, phase 3, non-inferiority trial. Lancet Oncol. 2016;17(7):928–42. doi: 10.1016/S1470-2045(16)30051-1. PMID: 27216274
8. Jennifer A Woyach, Amy S Ruppert, Nyla A Heerema, Weiqiang Zhao, Allison M Booth, Wei Ding, Nancy L Bartlett, Danielle M Brander, Paul M Barr, Kerry A Rogers, Sameer A Parikh, Steven Coutre, Arti Hurria, Jennifer R Brown, Gerard Lozanski, James S Blachly, Hatice G Ozer, Brittny Major-Elechi, Briant Fruth, Sreenivasa Nattam, Richard A Larson, Harry Erba, Mark Litzow, Carolyn Owen, Charles Kuzma, Jeremy S Abramson, Richard F Little, Scott E Smith, Richard M Stone, Sumithra J Mandrekar, John C Byrd. Ibrutinib regimens versus chemoimmunotherapy in older patients with untreated CLL. N Engl J Med. 2018;379(26):2517–28. doi: 10.1056/NEJMoa1812836. PMID: 30501481
9. Kirsten Fischer, Jasmin Bahlo, Anna Maria Fink, Valentin Goede, Carmen Diana Herling, Paula Cramer, Petra Langerbeins, Julia von Tresckow, Anja Engelke, Christian Maurer, Gabor Kovacs, Marco Herling, Eugen Tausch 2, Karl-Anton Kreuzer, Barbara Eichhorst, Sebastian Böttcher, John F Seymour, Paolo Ghia, Paula Marlton, Michael Kneba, Clemens-Martin Wendtner, Hartmut Döhner, Stephan Stilgenbauer, Michael Hallek. Longterm remissions after FCR chemoimmunotherapy in previously untreated patients with CLL: updated results of the CLL8 trial. Blood. 2016;127(2):208– 15. doi: 10.1182/blood-2015-06-651125. PMID: 26486789
10. Thorsten Zenz, John G Gribben, Michael Hallek, Hartmut Döhner, Michael J Keating, Stephan Stilgenbauer. Risk categories and refractory CLL in the era of chemoimmunotherapy. Blood. 2012;119(18):4101–7. doi: 10.1182/blood-2011-11-312421. PMID: 22394601
11. Jennifer Crombie, Matthew S. Davids. IGHV Mutational Status Testing in Chronic Lymphocytic Leukemia. Am J Hematol. 2017;92(12):1393–7. doi: 10.1002/ajh.24808. PMID: 28589701
Review
For citations:
Luchinin A.S., Strugov V.V. Optimization of chronic lymphocytic leukemia treatment using game theory. Kachestvennaya Klinicheskaya Praktika = Good Clinical Practice. 2021;(3):57-63. (In Russ.) https://doi.org/10.37489/2588-0519-2021-3-57-63
Views:
1595
Email this article 