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Theranostics 2021; 11(11):5313-5329. doi:10.7150/thno.56595 This issue Cite

Research Paper

Deep-learning and MR images to target hypoxic habitats with evofosfamide in preclinical models of sarcoma

Bruna V. Jardim-Perassi1✉#, Wei Mu1#, Suning Huang1,2, Michal R. Tomaszewski1, Jan Poleszczuk3,4, Mahmoud A. Abdalah5, Mikalai M. Budzevich6, William Dominguez-Viqueira6, Damon R. Reed7, Marilyn M. Bui8, Joseph O. Johnson9, Gary V. Martinez1,6,10, Robert J. Gillies1✉

1. Department of Cancer Physiology, Moffitt Cancer Center, Tampa, US.
2. Current Address: Guangxi Medical University Cancer Hospital, Nanning Guangxi, China.
3. Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, US.
4. Current Address: Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Poland.
5. Quantitative Imaging Core, Moffitt Cancer Center, Tampa, Florida.
6. Small Animal Imaging Laboratory, Moffitt Cancer Center, Tampa, Florida.
7. Department of Interdisciplinary Cancer Management, Adolescent and Young Adult Program, Moffitt Cancer Center, Tampa, Florida.
8. Department of Pathology, Moffitt Cancer Center, Tampa, Florida.
9. Analytic Microscopy Core, Moffitt Cancer Center, Tampa, Florida.
10. Current Address: Department of Imaging Physics, The University of Texas M.D. Anderson Cancer Center.
#These authors contributed equally to this work.

✉ Corresponding authors: Bruna V. Jardim-Perassi, Department of Cancer Physiology, Moffitt Cancer Center, 12902 USF Magnolia Drive Tampa, FL 33612. Phone: (813) 745-3225, E-mail: bruna.perassiorg; Robert J. Gillies, Department of Cancer Physi More

Citation:
Jardim-Perassi BV, Mu W, Huang S, Tomaszewski MR, Poleszczuk J, Abdalah MA, Budzevich MM, Dominguez-Viqueira W, Reed DR, Bui MM, Johnson JO, Martinez GV, Gillies RJ. Deep-learning and MR images to target hypoxic habitats with evofosfamide in preclinical models of sarcoma. Theranostics 2021; 11(11):5313-5329. doi:10.7150/thno.56595. https://www.thno.org/v11p5313.htm
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Abstract

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Rationale: Hypoxic regions (habitats) within tumors are heterogeneously distributed and can be widely variant. Hypoxic habitats are generally pan-therapy resistant. For this reason, hypoxia-activated prodrugs (HAPs) have been developed to target these resistant volumes. The HAP evofosfamide (TH-302) has shown promise in preclinical and early clinical trials of sarcoma. However, in a phase III clinical trial of non-resectable soft tissue sarcomas, TH-302 did not improve survival in combination with doxorubicin (Dox), possibly due to a lack of patient stratification based on hypoxic status. Therefore, we used magnetic resonance imaging (MRI) to identify hypoxic habitats and non-invasively follow therapies response in sarcoma mouse models.

Methods: We developed deep-learning (DL) models to identify hypoxia, using multiparametric MRI and co-registered histology, and monitored response to TH-302 in a patient-derived xenograft (PDX) of rhabdomyosarcoma and a syngeneic model of fibrosarcoma (radiation-induced fibrosarcoma, RIF-1).

Results: A DL convolutional neural network showed strong correlations (>0.76) between the true hypoxia fraction in histology and the predicted hypoxia fraction in multiparametric MRI. TH-302 monotherapy or in combination with Dox delayed tumor growth and increased survival in the hypoxic PDX model (p<0.05), but not in the RIF-1 model, which had a lower volume of hypoxic habitats. Control studies showed that RIF-1 resistance was due to hypoxia and not other causes. Notably, PDX tumors developed resistance to TH-302 under prolonged treatment that was not due to a reduction in hypoxic volumes.

Conclusion: Artificial intelligence analysis of pre-therapy MR images can predict hypoxia and subsequent response to HAPs. This approach can be used to monitor therapy response and adapt schedules to forestall the emergence of resistance.

Keywords: hypoxia, tumor microenvironment, deep learning, hypoxia-activated prodrugs, in-vivo imaging

Citation styles

APA
Jardim-Perassi, B.V., Mu, W., Huang, S., Tomaszewski, M.R., Poleszczuk, J., Abdalah, M.A., Budzevich, M.M., Dominguez-Viqueira, W., Reed, D.R., Bui, M.M., Johnson, J.O., Martinez, G.V., Gillies, R.J. (2021). Deep-learning and MR images to target hypoxic habitats with evofosfamide in preclinical models of sarcoma. Theranostics, 11(11), 5313-5329. https://doi.org/10.7150/thno.56595.

ACS
Jardim-Perassi, B.V.; Mu, W.; Huang, S.; Tomaszewski, M.R.; Poleszczuk, J.; Abdalah, M.A.; Budzevich, M.M.; Dominguez-Viqueira, W.; Reed, D.R.; Bui, M.M.; Johnson, J.O.; Martinez, G.V.; Gillies, R.J. Deep-learning and MR images to target hypoxic habitats with evofosfamide in preclinical models of sarcoma. Theranostics 2021, 11 (11), 5313-5329. DOI: 10.7150/thno.56595.

NLM
Jardim-Perassi BV, Mu W, Huang S, Tomaszewski MR, Poleszczuk J, Abdalah MA, Budzevich MM, Dominguez-Viqueira W, Reed DR, Bui MM, Johnson JO, Martinez GV, Gillies RJ. Deep-learning and MR images to target hypoxic habitats with evofosfamide in preclinical models of sarcoma. Theranostics 2021; 11(11):5313-5329. doi:10.7150/thno.56595. https://www.thno.org/v11p5313.htm

CSE
Jardim-Perassi BV, Mu W, Huang S, Tomaszewski MR, Poleszczuk J, Abdalah MA, Budzevich MM, Dominguez-Viqueira W, Reed DR, Bui MM, Johnson JO, Martinez GV, Gillies RJ. 2021. Deep-learning and MR images to target hypoxic habitats with evofosfamide in preclinical models of sarcoma. Theranostics. 11(11):5313-5329.

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