Acute Temporal Changes of MRI-Tracked Tumor Vascular Parameters after Combined Anti-angiogenic and Radiation Treatments in a Rat Glioma Model: Identifying Signatures of Synergism

Abstract

In this study we used magnetic resonance imaging (MRI) biomarkers to monitor the acute temporal changes in tumor vascular physiology with the aim of identifying the vascular signatures that predict response to combined anti-angiogenic and radiation treatments. Forty-three athymic rats implanted with orthotopic U-251 glioma cells were studied for approximately 21 days after implantation. Two MRI studies were performed on each animal, pre- and post-treatment, to measure tumor vascular parameters. Two animal groups received treatment comprised of Cilengitide, an anti-angiogenic agent and radiation. The first group received a subcurative regimen of Cilengitide 1 h before irradiation, while the second group received a curative regimen of Cilengitide 8 h before irradiation. Cilengitide was given as a single dose (4 mg/kg; intraperitoneal) after the pretreatment MRI study and before receiving a 20 Gy radiation dose. After irradiation, the post-treatment MRI study was performed at selected time points: 2, 4, 8 and 12 h (n = ≥5 per time point). Significant changes in vascular parameters were observed at early time points after combined treatments in both treatment groups (1 and 8 h). The temporal changes in vascular parameters in the first group (treated 1 h before exposure) resembled a previously reported pattern associated with radiation exposure alone. Conversely, in the second group (treated 8 h before exposure), all vascular parameters showed an initial response at 2–4 h postirradiation, followed by an apparent lack of response at later time points. The signature time point to define the “synergy” of Cilengitide and radiation was 4 h postirradiation. For example, 4 h after combined treatments using a 1 h separation (which followed the subcurative regimen), tumor blood flow was significantly decreased, nearly 50% below baseline (P = 0.007), whereas 4 h after combined treatments using an 8 h separation (which followed the curative regimen), tumor blood flow was only 10% less than baseline. Comparison between the first and second groups further revealed that most other vascular parameters were maximally different 4 h after combined treatments. In conclusion, the data are consistent with the assertion that the delivery of radiation at the vascular normalization time window of Cilengitide improves radiation treatment outcome. The different vascular responses after the different delivery times of combined treatments in light of the known tumor responses under similar conditions would indicate that timing has a crucial influence on treatment outcome and long-term survival. Tracking acute changes in tumor physiology after monotherapy or combined treatments appears to aid in identifying the beneficial timing for administration, and perhaps has predictive value. Therefore, judicial timing of treatments may result in optimal treatment response.

Toward a noninvasive estimate of interstitial fluid pressure by dynamic contrast‐enhanced MRI in a rat model of cerebral tumor

“…bevacizumab was administered as a single dose intravenously for 30 minutes at a dose of 10 mg/kg and delivered at selected time points of 2, 4, 8, 12, and 24 hours before the MRI study. In the combined therapy, Cilengitide was given as a single dose (4 mg/kg, intraperitoneal) prior to a 20‐Gy single fraction radiation exposure as described previously . In the RT monotherapy group, RT was administered as a 10‐Gy single dose of irradiation at selected time points of 24 and 48 hours before the MRI study …”

“…In the combined therapy, Cilengitide was given as a single dose (4 mg/kg, intraperitoneal) prior to a 20-Gy single fraction radiation exposure as described previously. 21 In the RT monotherapy group, RT was administered as a 10-Gy single dose of irradiation at selected time points of 24 and 48 hours before the MRI study. 22…”

Glioblastoma: Vascular Habitats Detected at Preoperative Dynamic Susceptibility-weighted Contrast-enhanced Perfusion MR Imaging Predict Survival

“…DSC quantification involves the computation of the hemodynamic indexes obtained from the kinetic analysis of the T2* concentration time curves retrieved from the first pass of an intravenously injected paramagnetic contrast agent (12). Such indexes have demonstrated powerful capabilities for a wide range of applications such as tumor grading (13,14), neovascularization assessment (15,16), early response to treatment assessment (17,18), recurrence versus radionecrosis (19,20) and prediction of clinical outcome (22)(23).…”

Unsupervised learning for vascular heterogeneity assessment of glioblastoma based on magnetic resonance imaging: The Hemodynamic Tissue Signature

“…Glioblastoma is characterized by a robust angiogenesis, strong vascular proliferation and an aberrant microvasculature (Alves et al, 2011;Hardee and Zagzag, 2012;Kargiotis et al, 2006). Numerous studies have focused on the analysis of perfusion indices to assess tumor grading (Law et al, 2003;Emblem et al, 2008), early response to treatment assessment (Elmghirbi et al, 2017;Vidiri et al, 2012), recurrence vs radionecrosis (Hu et al, 2009;Barajas et al, 2009) or clinical outcome prediction (Mangla et al, 2010;Jain et al, 2014). More recent studies addressed the local characterization of sub-regions within the glioblastoma using DSC, DCE or Magnetic Resonance Spectroscopy Imaging (MRSI) (Artzi et al, 2014;Akbari et al, 2014;Sawlani et al, 2010;Raschke et al, 2019).…”

“…Section 2.2.2 performs an in depth description of the techniques employed in this thesis to estimate the perfusion parametric maps from the DSC sequence. Such perfusion indexes have demonstrated powerful capabilities for a wide range of applications such as tumor grading (Law et al, 2003;Emblem et al, 2008), neovascularization assessment (Thompson et al, 2011;Tykocinski et al, 2012), early response to treatment assessment (Vidiri et al, 2012;Elmghirbi et al, 2017), recurrence versus radionecrosis (Hu et al, 2009;Barajas et al, 2009) and prediction of clinical outcome (Mangla et al, 2010;Jain et al, 2014).…”