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. 2024 May 2;40(5):btae205.
doi: 10.1093/bioinformatics/btae205.

NeoAgDT: optimization of personal neoantigen vaccine composition by digital twin simulation of a cancer cell population

Anja Mösch  1 , Filippo Grazioli  1 , Pierre Machart  1 , Brandon Malone  1
Affiliations

NeoAgDT: optimization of personal neoantigen vaccine composition by digital twin simulation of a cancer cell population

Anja Mösch et al. Bioinformatics. .

Abstract

Motivation: Neoantigen vaccines make use of tumor-specific mutations to enable the patient's immune system to recognize and eliminate cancer. Selecting vaccine elements, however, is a complex task which needs to take into account not only the underlying antigen presentation pathway but also tumor heterogeneity.

Results: Here, we present NeoAgDT, a two-step approach consisting of: (i) simulating individual cancer cells to create a digital twin of the patient's tumor cell population and (ii) optimizing the vaccine composition by integer linear programming based on this digital twin. NeoAgDT shows improved selection of experimentally validated neoantigens over ranking-based approaches in a study of seven patients.

Availability and implementation: The NeoAgDT code is published on Github: https://github.com/nec-research/neoagdt.

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Conflict of interest statement

A.M. and P.M. are employees of NEC Laboratories GmbH. F.G. and B.M. were employees of NEC Laboratories GmbH at the time the research was conducted.

Figures

Figure 1.
Figure 1.
Workflow of NeoAgDT from sequencing data to vaccine composition. Variant/mutation calling and gene expression steps are not part of the pipeline but are shown for general understanding of data flow. NeoAgDT input data are shown as individual boxes (green) in the large box at the bottom left. For this publication, pVACtools (Hundal et al. 2020) is used for the predictions, which are indicated by a separate box containing the three related green boxes. However, it is also possible to use other tools.
Figure 2.
Figure 2.
Representation of MinSum optimization, Equation (18) as a network flow problem.
Figure 3.
Figure 3.
MinMax optimization as a network flow problem. Representation of the optimization of Equation (21) as a network flow problem.
Figure 4.
Figure 4.
Distributions of the response probabilities for all simulated cells, given the optimal vaccine composition. The population size has been set to 10 000 and the MinSum objective has been optimized. For each patient, a total of 10 cell populations are considered.
Figure 5.
Figure 5.
Coverage ratio refers to the proportion of responding cells at the response probability threshold indicated on the x-axis. The population size has been set to 10 000 and the MinSum objective has been optimized for computing the vaccine composition. Confidence intervals are over 10 repeated simulations.
Figure 6.
Figure 6.
Vaccine optimization run time expressed as a function of the cell population size. The y-axis presents a logarithmic scale. Runtime is computed for the following cell population sizes: 10; 100; 500; 1000; 5000; 10 000. Results are averaged over 10 runs.
Figure 7.
Figure 7.
Intersection over union (IoU) of 10 different optimal vaccine compositions computed for different simulated cell populations. The plot depicts how the IoU varies as a function of the cell population size. Results are computed for the following cell population sizes: 10; 100; 500; 1000; 5000; 10 000.
See this image and copyright information in PMC

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