Trans-population graph-based coverage optimization of allogeneic cellular therapy
BackgroundPre-clinical development and in-human trials of ‘off-the-shelf’ immune effector cell therapy (IECT) are burgeoning. IECT offers many potential advantages over autologous products. The relevant HLA matching criteria vary from product to product and depend on the strategies employed to reduc...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Immunology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1069749/full |
| _version_ | 1827946694452445184 |
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| author | Sapir Israeli Elizabeth F. Krakow Elizabeth F. Krakow Martin Maiers Martin Maiers Corinne Summers Corinne Summers Corinne Summers Yoram Louzoun |
| author_facet | Sapir Israeli Elizabeth F. Krakow Elizabeth F. Krakow Martin Maiers Martin Maiers Corinne Summers Corinne Summers Corinne Summers Yoram Louzoun |
| author_sort | Sapir Israeli |
| collection | DOAJ |
| description | BackgroundPre-clinical development and in-human trials of ‘off-the-shelf’ immune effector cell therapy (IECT) are burgeoning. IECT offers many potential advantages over autologous products. The relevant HLA matching criteria vary from product to product and depend on the strategies employed to reduce the risk of GvHD or to improve allo-IEC persistence, as warranted by different clinical indications, disease kinetics, on-target/off-tumor effects, and therapeutic cell type (T cell subtype, NK, etc.).ObjectiveThe optimal choice of candidate donors to maximize target patient population coverage and minimize cost and redundant effort in creating off-the-shelf IECT product banks is still an open problem. We propose here a solution to this problem, and test whether it would be more expensive to recruit additional donors or to prevent class I or class II HLA expression through gene editing.Study designWe developed an optimal coverage problem, combined with a graph-based algorithm to solve the donor selection problem under different, clinically plausible scenarios (having different HLA matching priorities). We then compared the efficiency of different optimization algorithms – a greedy solution, a linear programming (LP) solution, and integer linear programming (ILP) -- as well as random donor selection (average of 5 random trials) to show that an optimization can be performed at the entire population level.ResultsThe average additional population coverage per donor decrease with the number of donors, and varies with the scenario. The Greedy, LP and ILP algorithms consistently achieve the optimal coverage with far fewer donors than the random choice. In all cases, the number of randomly-selected donors required to achieve a desired coverage increases with increasing population. However, when optimal donors are selected, the number of donors required may counter-intuitively decrease with increasing population size. When comparing recruiting more donors vs gene editing, the latter was generally more expensive. When choosing donors and patients from different populations, the number of random donors required drastically increases, while the number of optimal donors does not change. Random donors fail to cover populations different from their original populations, while a small number of optimal donors from one population can cover a different population.DiscussionGraph-based coverage optimization algorithms can flexibly handle various HLA matching criteria and accommodate additional information such as KIR genotype, when such information becomes routinely available. These algorithms offer a more efficient way to develop off-the-shelf IECT product banks compared to random donor selection and offer some possibility of improved transparency and standardization in product design. |
| first_indexed | 2024-03-13T11:04:30Z |
| format | Article |
| id | doaj.art-7e49a1f35754430a8c49d3c705a4013f |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-03-13T11:04:30Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-7e49a1f35754430a8c49d3c705a4013f2023-05-16T08:47:48ZengFrontiers Media S.A.Frontiers in Immunology1664-32242023-05-011410.3389/fimmu.2023.10697491069749Trans-population graph-based coverage optimization of allogeneic cellular therapySapir Israeli0Elizabeth F. Krakow1Elizabeth F. Krakow2Martin Maiers3Martin Maiers4Corinne Summers5Corinne Summers6Corinne Summers7Yoram Louzoun8Department of Mathematics, Bar-Ilan University, Ramat Gan, IsraelClinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, United StatesDepartment of Medical Oncology, University of Washington, Seattle, WA, United StatesDepartment of Bioinformatics, Center for Blood and Marrow Transplant Research, Minneapolis, MN, United StatesDepartment of Bioinformatics, National Marrow Donor Program/Be The Match, Minneapolis, MN, United StatesClinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, United StatesDepartment of Medical Oncology, University of Washington, Seattle, WA, United StatesPediatric Hematology/Oncology Department, Seattle Children’s Hospital, Seattle, WA, United StatesDepartment of Mathematics, Bar-Ilan University, Ramat Gan, IsraelBackgroundPre-clinical development and in-human trials of ‘off-the-shelf’ immune effector cell therapy (IECT) are burgeoning. IECT offers many potential advantages over autologous products. The relevant HLA matching criteria vary from product to product and depend on the strategies employed to reduce the risk of GvHD or to improve allo-IEC persistence, as warranted by different clinical indications, disease kinetics, on-target/off-tumor effects, and therapeutic cell type (T cell subtype, NK, etc.).ObjectiveThe optimal choice of candidate donors to maximize target patient population coverage and minimize cost and redundant effort in creating off-the-shelf IECT product banks is still an open problem. We propose here a solution to this problem, and test whether it would be more expensive to recruit additional donors or to prevent class I or class II HLA expression through gene editing.Study designWe developed an optimal coverage problem, combined with a graph-based algorithm to solve the donor selection problem under different, clinically plausible scenarios (having different HLA matching priorities). We then compared the efficiency of different optimization algorithms – a greedy solution, a linear programming (LP) solution, and integer linear programming (ILP) -- as well as random donor selection (average of 5 random trials) to show that an optimization can be performed at the entire population level.ResultsThe average additional population coverage per donor decrease with the number of donors, and varies with the scenario. The Greedy, LP and ILP algorithms consistently achieve the optimal coverage with far fewer donors than the random choice. In all cases, the number of randomly-selected donors required to achieve a desired coverage increases with increasing population. However, when optimal donors are selected, the number of donors required may counter-intuitively decrease with increasing population size. When comparing recruiting more donors vs gene editing, the latter was generally more expensive. When choosing donors and patients from different populations, the number of random donors required drastically increases, while the number of optimal donors does not change. Random donors fail to cover populations different from their original populations, while a small number of optimal donors from one population can cover a different population.DiscussionGraph-based coverage optimization algorithms can flexibly handle various HLA matching criteria and accommodate additional information such as KIR genotype, when such information becomes routinely available. These algorithms offer a more efficient way to develop off-the-shelf IECT product banks compared to random donor selection and offer some possibility of improved transparency and standardization in product design.https://www.frontiersin.org/articles/10.3389/fimmu.2023.1069749/fullcoverageHLANKT cellmodeltherapies |
| spellingShingle | Sapir Israeli Elizabeth F. Krakow Elizabeth F. Krakow Martin Maiers Martin Maiers Corinne Summers Corinne Summers Corinne Summers Yoram Louzoun Trans-population graph-based coverage optimization of allogeneic cellular therapy Frontiers in Immunology coverage HLA NK T cell model therapies |
| title | Trans-population graph-based coverage optimization of allogeneic cellular therapy |
| title_full | Trans-population graph-based coverage optimization of allogeneic cellular therapy |
| title_fullStr | Trans-population graph-based coverage optimization of allogeneic cellular therapy |
| title_full_unstemmed | Trans-population graph-based coverage optimization of allogeneic cellular therapy |
| title_short | Trans-population graph-based coverage optimization of allogeneic cellular therapy |
| title_sort | trans population graph based coverage optimization of allogeneic cellular therapy |
| topic | coverage HLA NK T cell model therapies |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1069749/full |
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