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. 2015 Aug 6;12(109):20150388.
doi: 10.1098/rsif.2015.0388.

In Silico Analysis Suggests Differential Response to Bevacizumab and Radiation Combination Therapy in Newly Diagnosed Glioblastoma

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Free PMC article

In Silico Analysis Suggests Differential Response to Bevacizumab and Radiation Combination Therapy in Newly Diagnosed Glioblastoma

Andrea Hawkins-Daarud et al. J R Soc Interface. .
Free PMC article

Abstract

Recently, two phase III studies of bevacizumab, an anti-angiogenic, for newly diagnosed glioblastoma (GBM) patients were released. While they were unable to statistically significantly demonstrate that bevacizumab in combination with other therapies increases the overall survival of GBM patients, there remains a question of potential benefits for subpopulations of patients. We use a mathematical model of GBM growth to investigate differential benefits of combining surgical resection, radiation and bevacizumab across observed tumour growth kinetics. The differential hypoxic burden after gross total resection (GTR) was assessed along with the change in radiation cell kill from bevacizumab-induced tissue re-normalization when starting therapy for tumours at different diagnostic sizes. Depending on the tumour size at the time of treatment, our model predicted that GTR would remove a variable portion of the hypoxic burden ranging from 11% to 99.99%. Further, our model predicted that the combination of bevacizumab with radiation resulted in an additional cell kill ranging from 2.6×10(7) to 1.1×10(10) cells. By considering the outcomes given individual tumour kinetics, our results indicate that the subpopulation of patients who would receive the greatest benefit from bevacizumab and radiation combination therapy are those with large, aggressive tumours and who are not eligible for GTR.

Keywords: bevacizumab; glioblastoma; hypoxia; mathematical model; radiation; surgery.

Figures

Figure 1.
Figure 1.
Simulation of an aggressive tumour (net invasion rate D = 305 mm2 yr−1 and net proliferation rate ρ = 83 1 yr−1) with anti-angiogenic therapy started at different time points in the tumour evolution. (a) Snapshots of the simulated tumour composition just prior to and after two weeks of anti-angiogenic therapy (treatment was started when the tumour size was 2.5 cm, the same as in the second column of b). One can see that the tumour is predicted to continue growing during therapy, however, the percentage of the tumour that is hypoxic is diminished under anti-angiogenic therapy. (b) For three different tumour sizes on T2-weighted MRI at the start of therapy, the top row shows the tumour composition as the total number of each phenotype of cell during the tumour progression, highlighting the time of treatment with the grey box. The second row presents the same data but as a percentage of the tumour cells being comprised of each phenotype. Solid lines are from treated simulations, whereas the dashed lines show the tumour dynamics if untreated for comparison. In all cases, the hypoxia is seen to diminish during therapy, however, the relative change depends on the size of the tumour.
Figure 2.
Figure 2.
(a) Design of virtual experiment 1. (b) Absolute change in the number of hypoxic tumour cells after 14 days of anti-angiogenic therapy. The tumour size at the start of therapy is characterized by RT2, which is the radius presented on the T2-weighted MRI image. Each block of grid cells represents the full suite of growth kinetics tested with each grid cell representing a different ‘tumour’ defined by its net proliferation and net invasion rates.
Figure 3.
Figure 3.
(a) Design of virtual experiment 2. (b) Comparison of the hypoxic burden between tumours two weeks into anti-angiogenic treatment and a virtual control having experienced no treatment. The largest benefit in terms of number of cells potentially salvaged from hypoxia and thus, increased radiosensitivity was observed in large tumours with high proliferation and high invasion rates.
Figure 4.
Figure 4.
(a) Design of virtual experiment 3. (b) Radiation cell kill determined through the LQ model accounting for OER effects of hypoxic cells. Both colour bars in this figure are in terms of log base 10 to highlight the different orders of magnitude of cell kill. The third row highlights the additional cell kill due to combination therapy as the subtraction of the middle row from the top row, and thus has a different colour bar.
Figure 5.
Figure 5.
(a) Design of virtual experiment 4. (b) Comparison between hypoxic cell burden with or without gross total resection.

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References

    1. Stupp R, et al. 2005. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N. Engl. J. Med. 352, 987–996. (10.1056/NEJMoa043330) - DOI - PubMed
    1. Norden AD, et al. 2008. Bevacizumab for recurrent malignant gliomas: efficacy, toxicity, and patterns of recurrence. Neurology 70, 779–787. (10.1212/01.wnl.0000304121.57857.38) - DOI - PubMed
    1. Iwamoto FM, Abrey LE, Beal K, Gutin PH, Rosenblum MK, Reuter VE, Deangelis LM, Lassman AB. 2009. Patterns of relapse and prognosis after bevacizumab failure in recurrent glioblastoma. Neurology 73, 1200–1206. (10.1212/WNL.0b013e3181bc0184) - DOI - PMC - PubMed
    1. Gilbert MR, et al. 2014. A randomized trial of bevacizumab for newly diagnosed glioblastoma. N. Engl. J. Med. 370, 699–708. (10.1056/NEJMoa1308573) - DOI - PMC - PubMed
    1. Chinot OL, et al. 2014. Bevacizumab plus radiotherapy–temozolomide for newly diagnosed glioblastoma. N. Engl. J. Med. 370, 709–722. (10.1056/NEJMoa1308345) - DOI - PubMed

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