High-grade gliomas are the most common primary brain tumors in adults. Despite immense efforts in the development of improved therapies, high-grade gliomas continue to be among the most devastating and deadliest of all human cancers. Brain tumors are the second leading cause of death in children under age 15, in young adults up to age 34, and are a leading cause of cancer-related deaths overall in the USA and Europe each year [1, 2]. Use of pre-clinical cancer models, in conjunction with non-invasive imaging methods, is critical for the diagnosis, staging, and development/monitoring of new therapies for high-grade glioma.
Tumor hypoxia is of particular interest in both basic and clinical oncology due to its negative effects on cancer therapies and promotion of cancer metastases. Hypoxia is defined by a deficiency of intracellular oxygen resulting from a reduction in oxygen supply and/or increased demand for oxygen, a consequence of increased metabolism due to rapid tumor growth . Hypoxia is involved in many aspects of tumor development, particularly angiogenesis, and heterogeneous growth and phenotypes, in many different types of tumors. However, highly aggressive brain tumors, particularly GBM with its substantial heterogeneity, have not been studied thoroughly . A robust and reliable measurement of tumor hypoxia in vivo would (i) provide insight into the pathophysiology and natural course of tumor progression, (ii) aid the planning of radio- and chemotherapy treatments, (iii) assist in prediction and early detection of treatment response, and (iv) accelerate targeted cancer therapies that minimize the negative impacts of hypoxia [5, 6]. Though a variety of techniques have been proposed to non-invasively quantify tumor hypoxia, none have been established clinically. The method used for quantifying tumor hypoxia is based on the recently proposed MRI-based qBOLD model that provides maps (quantitative parameter images) in vivo of OEF and deoxyhemoglobin-containing blood volume (DBV) .
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Oxygen consumption can be described by the OEF parameter, defined as the percent of the oxygen removed from the blood by tissue during its passage through the capillary network. Furthermore, the BOLD effect exists because deoxygenated blood has a different magnetic susceptibility than oxygenated blood, which is similar to its surrounding tissue. Separation of the hemodynamic contributions (i.e., OEF and DBV) of the BOLD signal in pathological tissue can provide important physiological information on the evaluation of tumor hypoxia.
MATERIALS AND METHODS
DBT and U87 cell implantation. All surgical procedures were conducted under the guidelines of the Washington University Institutional Animal Care and Use Committee. Mouse DBT glioblastoma cells (10 K cells in DMEM media) were implanted into the brains of female Balb/c mice as previously described . Human U87 astrocytoma cells (50 K cells in DMEM media) were implanted into the brains of female nude mice as previously described [9, 10]. Mice were anesthetized with isoflurane prior to intracranial DBT cell implantation. After a midline scalp incision, a 1-mm cutting burr was used to make a craniostomy 2 mm from the midline, 2 mm anterior to the bregma, and 3 mm deep. Mice were secured in a stereotactic frame and 8 ïL of DBT tumor cell suspension (total of 10 K cells) or 10 ïL of U87 tumor cell suspension (total of 50 K cells) were aspirated into a Hamilton syringe attached to the frame. The syringe was inserted into the brain and the tumor cells were injected over three minutes. The craniostomy hole was secured with bone wax and the incision was closed with a dermal adhesive. DBT cells were obtained from Keith Rich's lab and U87 cells were obtained from Joshua Rubin's lab.
Preparation of Mice for MRI. Prior to each imaging experiment, mice were anesthetized with isoflurane/O2 [2 % (v/v)], and maintained on isoflurane [1 % (v/v)] in a gas mixture of 20 % O2 / 80 % N2 throughout the experiment.
qBOLD MRI. A 3D version of gradient echo sampling of spin echo sequence (GESSE)  was employed. Data acquisition was performed using the following acquisition parameters: FOV 18 ï‚´ 18 ï‚´ 18 mm3, sampling matrix of 64 ï‚´ 64 ï‚´ 32, TR of 200 msec, NEX of 8, 52 minutes of total imaging time. The spin echo occurs at the 12th of 50 echoes. All 3D MR data were filtered by a Hanning filter to improve SNR and to reduce the Fourier leakage. The MR signal was analyzed using a qBOLD model  that includes signals from tissue, deoxygenated blood, and CSF. In particular, the free induction decay of MR signal from the brain tissue was described in terms of the BOLD model  and the signal from the deoxygenated blood was modeled as originating from a network of randomly oriented cylindrical blood vessels . All experiments were performed on a dedicated 4.7 T Varian small-animal MRI scanner using an actively decoupled volume and surface coil.
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T2-weighted anatomic images. The brain of each mouse was imaged with a multislice T2-weighted spin-echo pulse sequence (echo time (TE) = 30 ms; repetition time (TR) = 2 s; field of view (FOV) = 15 Ã- 15 mm2; matrix= 256 Ã- 128; 21 slices; slice thickness = 500 ïm; NT = 4; total imaging time = 17 min).
T1-weighted anatomic images. (TE = 20 ms; TR = 1000 ms; FOV= 15 Ã- 15 mm2; matrix = 256 Ã- 128; 21 slices; slice thickness = 500 ïm; NT = 4; total imaging time = 8.5 min).
Histological Studies. At the conclusion of MRI examinations, mice were perfused with 4% paraformaldehyde in PBS. The brain was removed and embedded in paraffin. Serial sections (5-ïm thickness) of the brain were then prepared. Selected slices were stained with H&E and examined by light microscopy.
RESULTS AND DISCUSSION
We report the first measurement of hypoxia/OEF in tumors using the qBOLD method. The technique allows in vivo monitoring of oxygenation in growing tumors, and could have significant impact on the study of tumor growth and the development of pre-clinical therapeutics in small-animal models of brain tumors and on the diagnosis and treatment of human tumors.
Representative signal-intensity evolution profiles from two selected voxels in healthy tissue and tumor tissue obtained with the GESSE sequence are illustrated in Figure 1. The signal contributions from ISF/CSF and intravascular venous blood are also shown in Figure 1e,f. Only the real part of the blood signal is shown as it contributes most to the total magnitude signal . Figure c shows the extravascular brain tissue signals after removing the effect of R2 decay and contributions from ISF/CSF and intravascular blood. The estimated fitting parameters were: DBV of 8 % (healthy) and 8 % (tumor), OEF of 22 % (healthy) and 123 % (tumor), ISF (CSF) frequency shift of 12.29 (healthy) Hz and 7.80 (tumor) with 7 % (healthy) and 9 % (tumor) contribution to the total signal at the spin-echo time.
Figure . Representative data and fitting curves obtained with the 3D GESSE sequence (matrix = 64 x 64, TR = 210 ms) for a mouse implanted with U87 cells 27 days post implantation. Contributions from all compartments are shown in: (a) Signals (square) and the fitted profiles (dashed line) for voxels in the tumor area (red line) with DBV = 8 % and OEF = 123 % and the healthy tissue area (green line) with DBV = 8 % and OEF = 22 %; (b) T1-weighted image reconstructed from the GESSE sequence with selected voxels shown by a square; (c) The extravascular signal contribution after removing the signal from ISF/CSF and intravascular blood, and adjusting for the R2 decay (multiplying by the factor exp[+ R2 TE]). The black solid lines correspond to the extrapolated signal profile from the asymptotic behavior at long echo times (TEs), demonstrating the quadratic behavior around the spin echo; (d) fitting residual; (e) magnitudes of the ISF/CSF signals; and (f) Real parts of the intravascular blood signals. In all plots the x-axis corresponds to a GRE time elapsed from the SE time (TE = 72 ms), and the y-axes represent signals in relative units. The echo spacing is 1.44 ms.
Figure . DBT brain tumor in mouse on POD 13. The top leftmost image is a T2-weighted anatomic image. The parametric maps obtained with a (64 x64) GESSE sequence are OEF (%), R2' of tissue (s-1) which depends on the SE time in the GESSE sequence, whole mount H&E staining of mouse brain with tumor, DBV fraction (%), and R2* of brain tissue (s-1). shows maps of the estimated brain parameters from the GESSE study using a 64 x 64 sampling matrix with a total acquisition time of 52 minutes and shows a representative T2-weighted image and maps of the estimated OEF and DBV values with correlation to histology. It well known that hypoxia limit the response of tumor cells to radiotherapy [14, 15] and resistant to chemotherapy . Hence, we can expect that the region of tumor growth will demonstrate an elevated OEF compared to healthy tissue. Our data also shows some evidence (see Figure . DBT brain tumor in mouse on POD 13. The top leftmost image is a T2-weighted anatomic image. The parametric maps obtained with a (64 x64) GESSE sequence are OEF (%), R2' of tissue (s-1) which depends on the SE time in the GESSE sequence, whole mount H&E staining of mouse brain with tumor, DBV fraction (%), and R2* of brain tissue (s-1). ) that DBV decreases when OEF increases. This is consistent with the fact that the high OEF corresponds to lower blood flow hence decreased blood volume. The OEF reflects the balance between oxygen delivery and tissue oxygen consumption, thus the noninvasive measurement of OEF could have significant implications for numerous clinical and research investigations. These regional OEF measurements can provide new tools for researchers and clinicians that can be readily implemented on commonly available scanners. Previous studies have validated the OEF measurement in rat brain  and human studies were performed using a whole body 3.0 T Trio MRI scanner in agreement with PET studies . Our current study was designed to show that the typically hypoxic regions of high-grade glioma in murine models will exhibit a measurable increase in OEF compared to normal brain.
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Figure . DBT brain tumor in mouse on POD 13. The top leftmost image is a T2-weighted anatomic image. The parametric maps obtained with a (64 x64) GESSE sequence are OEF (%), R2' of tissue (s-1) which depends on the SE time in the GESSE sequence, whole mount H&E staining of mouse brain with tumor, DBV fraction (%), and R2* of brain tissue (s-1).
FYI: OEF map from Figure 2, with scaling backed off, max value ~140.
Figure . Oxygen extraction fraction (OEF) parametric maps (top), T1-weighted images (middle), and T2-weighted anatomic images of a time course study for a mouse implanted with U87 cells. Increased OEF is observed in the tumor regions. (Mark tumors in figure, do we want DBV?) Histology of a mouse 62 days post-implantation of U87 cells (same mouse as in Figure . Oxygen extraction fraction (OEF) parametric maps (top), T1-weighted images (middle), and T2-weighted anatomic images of a time course study for a mouse implanted with U87 cells. Increased OEF is observed in the tumor regions. (Mark tumors in figure, do we want DBV?)). Whole mount, shows clear tumor, 20 x magnification, and 40 x magnification images show necrosis and micro-hemorrhage (?) ask pathologist to be sure.
Figure . Oxygen extraction fraction (OEF) parametric maps (top), T1-weighted images (middle), and T2-weighted anatomic images of a time course study for a mouse implanted with U87 cells. Increased OEF is observed in the tumor regions. (Mark tumors in figure, do we want DBV?) shows a time course study of days 50, 55, and 62 post-implantation of the U87 cells with corresponding OEF maps. Results show clear correlation of elevated OEF with tumor progression. The elevated OEF values also correlate well with histology shown in Error: Reference source not found. Histology confirms presence of high grade tumor were enhanced OEF is observed. The mouse was perfused immediately following the last data point to ensure the truest representation of the mouse brain during the time of the MRI scan.
Mice were imaged using the GESSE sequence following the injection of the DBT cells. Figure 5 shows maps of the estimated OEF and shows a representative single slice from an anatomic T2-weighted image of a mouse on day 13 post implantation of DBT cells. The color bar on the left shows OEF in %. It is clear that in the tumor area there is elevated OEF. The scale of OEF is in shown in percent. Values of over 100% were observed for OEF in tumor regions, but this is likely due to blood pooling (from hematoma in the brain) affecting T2*. Similar results of elevation in OEF in actively growing tumors were obtained for other mice imaged in this study, with regions of high OEF correlating to both size and location with tumors in histology. These results serve to establish the feasibility and importance of qBOLD-based methods for quantitatively measuring hypoxia in developing tumors in mouse models of brain tumors. We anticipate that these methods can be readily translated to clinical studies of patients with high-grade brain tumors.
In patients with tumors, the potential to non-invasively identify and image regions of hypoxia is of particular interest because while tumors outgrow their blood supply, hypoxic regions develop within the tumor. These hypoxic regions may be relatively less sensitive to traditional therapeutic interventions. Identification of these heterogeneous regions of relative hypoxia within tumors in vivo could have significant implications for the clinical management of patients by allowing specific targeting of different types or intensities of therapy to these hypoxic areas. Our qBOLD-based method is well suited for measuring hypoxic regions because lack of oxygen supply leads to elevated OEF. Hence, enhanced OEF contrast in growing tumors serves as an imaging based biomarker for tumor hypoxia.
Our investigations measuring hypoxia in mouse models of brain cancer (DBT and U87 cell lines) demonstrates clear elevations in OEF, where tumor boundaries were defined via anatomical images and histology (see Figs x and y). These results establish the qBOLD-based methods for quantitative and non-invasive measurements of hypoxia in developing tumors in mouse models of brain cancer that could potentially be used to accurately predict treatment response.
Although there are significant differences in tumor and vessel growth patterns between DBT and human gliomas, these results support the use of qBOLD MRI in human brain tumors. Of significance, we observed elevated OEF levels which are commonly attributed to increase in hypoxia. In cases where a T1 contrast agent was used, postcontrast enhancement was not always observed where OEF was found to be elevated, indicating that elevated OEF/hypoxia could occur before disruption of the BBB. Widely accepted that hypoxia is critical in theâ€¦and is a key factor in tumor malignancy and metastatic potential (cite).
Comprehensive investigations to understand the mechanism of tumor hypoxia, develop methods to quantify OEF, and determine the role of hypoxia in tumor biology and malignancy.
Ultimately the goal is to develop effective therapeutics for highly malignant cancers such as gliomas.
In most studies, quantification of OEF is done by invasive electrodes (etc)â€¦laborious histopathological analysis
Furthermore, histology is inherently static and destructive, does not provide longitudinal data in individual tumors, and analysis is limited to # of biopsy specimens, cannot provide a global measure of total tumor hypoxia.
Advanced MRI technique that can show physical maps that complement anatomical information provided by traditional imaging methods
Results demonstrate that it is possible to make non-invasive measurements of mouse brain tumor hypoxia using qBOLD MRI.
In particular, qBOLD MRI provides a means of obtaining high-spatial resolution maps of hemodynamic parameters (OEF and DBV).
qBOLD provides a non-invasive approach to assess tumor hypoxia
Performing qBOLD studies in mouse models of brain cancer can provide essential in vivo data on tumor growth (?) and development, as well as therapeutic response of brain tumors.
In summary, in this study we utilized an MR signal model of brain tissue, the qBOLD model, to quantify OEF in mouse models of glioma. Using MR images of mouse brain obtained with a modified GESSE sequence, our model-based multivariable curve fitting approach quantifies brain parameters such as DBV, OEF, ISF/CSF volume fraction, and frequency shift simultaneously.
Our results demonstrate the first measurement of hypoxia / OEF in tumors using the qBOLD method. The qBOLD MRI technique analytically correlates the BOLD effect to hemodynamic parameters yielding relevant physiological information about the status of the tumor and normal tissue and provides unique, cooperative information that will indeed be a valuable tool in the determination of improved cancer therapy. The utilization of this novel technique will have a significant impact on the study of tumor growth and the development of pre-clinical therapeutics in small-animal models of brain tumors and on the diagnosis and treatment of human tumors. This technique could also be useful in the diagnosis and treatment of other pathologies such as brain edema or to map cerebral DBV and OEF for functional activation for BOLD fMRI studies.
Accurate, non-invasive measurements of hemodynamic and perfusion parameters (and insight about the relationship of these parameters) will have considerable implications in the clinical setting. This method can be readily translated to clinical studies of patients with high-grade brain tumors to provide an improvement in cancer treatment and targeted cancer therapy  . Studies are currently underway in tumors outside of the brain.
DBT cells were provided by Liya Yuan and Keith Rich originally from (?).
Erin Smith (BLI)
Joseph J.H. Ackerman for helpful discussions