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Designing optimal combination therapy for personalised glioma treatment

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Abstract

Background

Like it happens in other tumours, glioma cells co-evolve in a microenvironment consisting of bona fide tumour cells as well as a range of parenchymal cells, which produces numerous signalling molecules. Recently, the results of an in silico experiment suggested that a combination therapy that would target multiple key cytokines at the same time may be more effective for suppressing the growth of a tumour. The in silico experiments also showed that the optimal combination therapy is very much dependent on a patient’s molecular profile.

Method

In this work, we employ evolutionary algorithms for designing optimal combination therapy tailored to the patient’s tumour microenvironment. Experiments were performed using a state-of-the-art glioma microenvironment model, capable of imitating many characteristics of human glioma development, and many virtual patient profiles.

Conclusions

Results show that the therapies designed by the presented memetic algorithm were very effective in impeding tumour growth and were tailored to the patient’s personal tumour microenvironment.

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Notes

  1. The PSO version in the jMetal framework http://jmetal.sourceforge.net/ was used.

  2. The basic ABC version available from https://abc.erciyes.edu.tr/software.htm was used.

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Acknowledgements

The authors would like to thank Prof. Rong Fan and Dr. Yu Wu for sharing the code of their GBM model. P.M. thanks a generous donation from the Maitland Cancer Appeal.

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  1. School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, 2308, Australia

    Nasimul Noman & Pablo Moscato

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  1. Nasimul Noman
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  2. Pablo Moscato
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Correspondence to Nasimul Noman.

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Noman, N., Moscato, P. Designing optimal combination therapy for personalised glioma treatment. Memetic Comp. 12, 317–329 (2020). https://doi.org/10.1007/s12293-020-00312-7

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