Targeting hypoxic habitats with hypoxia pro-drug evofosfamide in preclinical models of sarcoma

Abstract

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, TH-302 did not improve survival in combination with doxorubicin (dox), most likely due to a lack of patient stratification based on hypoxic status. Herein, our goal was to develop deep-learning (DL) models to identify hypoxic habitats, using multiparametric (mp) MRI and co-registered histology, and to non-invasively monitor response to TH-302 in a patient-derived xenograft (PDX) of rhabdomyosarcoma and a syngeneic model of fibrosarcoma (RIF-1). A DL convolutional neural network showed strong correlations (>0.81) between the true hypoxic portion in histology and the predicted hypoxic portion 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 lower volume of hypoxic habitats. Control studies showed that RIF-1 resistance was due to hypoxia and not to other causes. Notably, PDX tumors developed resistance to TH-302 under prolonged treatment. In conclusion, response to TH-302 can be attributed to differences in hypoxia status prior therapy. Development of non-invasive MR imaging to assess hypoxia is crucial in determining the effectiveness of TH-302 therapy and to follow response. In further studies, our approach can be used to better plan therapeutic schedules to avoid resistance. One Sentence SummaryDevelopment of non-invasive MR imaging to assess hypoxia is crucial in determining the effectiveness of TH-302 therapy and to follow response.