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. Author manuscript; available in PMC: 2021 Jun 15.
Published in final edited form as: Clin Cancer Res. 2020 Apr 8;26(13):3135–3144. doi: 10.1158/1078-0432.CCR-19-3817

Multi-parametric MR-PET imaging predicts pharmacokinetics and clinical response to GDC-0084 in patients with recurrent high-grade glioma

Benjamin M Ellingson 1,2,3,4,5,6, Jingwen Yao 1,2,3, Catalina Raymond 1,2, David A Nathanson 13, Ararat Chakhoyan 1,2, Jeremy Simpson 9, James Garner 9, Alan Olivero 8, Lars Mueller 8, Jordi Rodon 11, Elizabeth R Gerstner 12, Timothy F Cloughesy 6,7, Patrick Y Wen 10
PMCID: PMC8204664  NIHMSID: NIHMS1583662  PMID: 32269051

Abstract

Purpose:

GDC-0084 is an oral, brain-penetrant small molecule inhibitor of phosphoinositide 3-kinase (PI3K) and mammalian target of rapamycin (mTOR). Since these two targets alter tumor vascularity and metabolism, respectively, we hypothesized multi-parametric MR-PET could be used to quantify the response, estimate pharmacokinetic (PK) parameters, and predict progression-free survival (PFS) in patients with recurrent malignant gliomas.

Experimental Design:

Multiparametric advanced MR-PET imaging was performed to evaluate physiological response in a first-in-man, multicenter, phase I, dose-escalation study of GDC-0084 (NCT01547546) in 47 patients with recurrent malignant glioma.

Results:

Measured maximum concentration (Cmax) was associated with a decrease in enhancing tumor volume (P=0.0287) and an increase in fractional anisotropy (FA) (P=0.0418). Post-treatment tumor volume, 18F-FDG uptake, Ktrans, and relative cerebral blood volume (rCBV) were all correlated with Cmax. A linear combination of change in 18F-FDG PET uptake, apparent diffusion coefficient (ADC), FA, Ktrans, vp, and rCBV were able to estimate both Cmax (R2=0.4113, P<0.0001) and drug exposure (AUC) (R2=0.3481, P<0.0001). Using this composite multi-parametric MR-PET imaging response biomarker to predict PK, patients with an estimated Cmax >0.1 uM and AUC > 1.25 uM*hr demonstrated significantly longer PFS compared with patients with a lower estimated concentration and exposure (P=0.0039 and P=0.0296, respectively).

Conclusion:

Results from the current study suggest composite biomarkers created from multi-parametric MR-PET imaging targeting metabolic and/or physiologic processes specific to the drug mechanism of action may be useful for subsequent evaluation of treatment efficacy for larger phase II-III studies.

STATEMENT OF TRANSLATIONAL RELEVANCE

While questions regarding brain penetration and target engagement in experimental therapies are typically answered using early phase surgical studies, an alternative strategy is to use advanced imaging in to quantify downstream physiological changes that are theorized to change as a result of target engagement. The current study demonstrates that a combination of MRI and PET imaging can predict pharmacokinetic parameters and progression-free survival of recurrent malignant gliomas treated with GDC-0084, an oral, brain-penetrant small molecule inhibitor of phosphoinositide 3-kinase (PI3K) and mammalian target of rapamycin (mTOR), likely due to the metabolic consequences of mTOR inhibition and the known role of PI3K in angiogenesis and proliferation. Results from the current study suggest multi-parametric MR-PET imaging targeting biologic processes specific to the drug mechanism of action may be useful for evaluation of treatment efficacy for larger phase II-III studies.

INTRODUCTION

Glioblastoma (GBM) is a complex disease with a dismal prognosis of only 12–21 months from initial diagnosis when treated with maximal safe resection followed by radiation therapy combined with temozolomide plus adjuvant temozolomide with or without tumor treating fields (1–3). Despite aggressive initial therapy, almost all patients with GBM relapse and after first line treatment failure there are limited treatment options for GBM.

The Cancer Genome Atlas (TCGA) has identified the phophatidylinositol 3-kinase (PI3K) pathway as one of the most frequently altered pathways, being mutated, amplified, or having loss of signaling proteins in more than 80% of human GBMs (4). While most drugs that inhibit the PI3K/Akt/mTOR pathway have not achieved favorable results, including erlotinib (5), lapatinib (6), everolimus (7), and gefitinib (8,9), this was largely attributed to the inability of these compounds to adequately cross the blood-brain barrier (10–13), resulting in subtherapeutic concentrations within the tumor. 5-(6,6-Dimethyl-4-morpholino-8,9-dihydro-6H-[1,4]oxazino[4,3-e]-purin-2-yl)pyrimidin-2-amine (GDC-0084) is a selective inhibitor of PI3K and mTOR specifically optimized for brain penetration and developed as a potential treatment of GBM (14). Preclinical studies have shown the ability for GDC-0084 to inhibit the proliferation of several glioma cell lines, and careful molecular imaging studies have demonstrated adequate penetration of GDC-0084 within intracranial tumors (14,15). These results suggest GDC-0084 may be efficacious in GBM.

From 2012–2014 an open-label, phase I, dose escalation study was performed in patients with recurrent high-grade gliomas in order to assess the safety and tolerability of GDC-0084. The safety and tolerability were described previously (16). Due to the metabolic consequences of mTOR inhibition (17–19) and the known role of PI3K in angiogenesis (20–22), we hypothesized higher concentrations of GDC-0084 within the brain would result in proportional reductions in both glucose utilization and tumor vascularity. We rationalized that, since GDC-0084 is a brain penetrant agent, pharmacokinetics (PK) would be related to tissue pharmacodynamics (PD) and, therefore, target engagement resulting in physiologic changes would only result when PK parameters were favorable. Thus, the current study examined the dose-dependent, multi-parametric MRI and PET imaging response in this “first-in-man” study to document traditional radiographic response as well as determine whether advanced MR or PET imaging techniques could predict drug PK parameters and progression-free survival (PFS).

METHODS

Patients and Study Design

A classical “3+3” design was used to assess safety, tolerability, and pharmacokinetics of GDC-0084 administered orally once daily in patients with recurrent high-grade glioma in this open-label, multicenter, Phase I, dose-escalation study (NCT01547546). A total of 47 patients with recurrent or progressive high-grade gliomas were enrolled in the Stage 1 (dose escalation) portion of the current study in 4 sites in the United States and Europe (University of California Los Angeles, Dana Farber Cancer Institute, Massachusetts General Hospital, and Hospital Universitario Vall d’Hebron Institute of Oncology). Of these patients, 13 (27.7%) were female and 34 (72.3%) were male. Patients were predominantly white (93.6%) and predominately not of Hispanic or Latino origin (93.6%). The mean age of patients was 49.7 years (range 29–73 years) at baseline. Patients received a dose of 2mg (N=7), 4mg (N=4), 8mg (N=5), 15mg (N=6), 20mg (N=4), 30mg (N=7), 45mg (N=8), or 65mg (N=6) of study drug. Patient characteristics are highlighted in Table 1.

Table 1:

Patient Demographics

  2mg 4mg 8mg 15mg 20mg 30mg 45mg 65mg All Patients
Demographic Characteristics (N=7) (N=4) (N=5) (N=6) (N=4) (N=7) (N=8) (N=6) (N=47)
Age (yr)
 Mean (SD) 53.7 (10.5) 54.0 (16.1) 46.2 (8.0) 53.2 (10.2) 39.0 (10.5) 58.0 (9.8) 47.0 (10.5) 42.7 (11.7) 49.7 (11.6)
 Median 58 61 44 57 38 56 48.5 41.5 50
 Range 32 – 63 30–64 38–59 38–62 30–50 44–73 31–62 29–59 29–73
Sex (F/M) 2/5 1/3 0/5 2/4 4/3 4/3 2/6 0/6 13/34
Race
 Asian 0 0 0 0 1 0 0 0 1
 White 7 4 5 6 2 7 8 5 44
 Other 0 0 0 0 1 0 0 1 2
Ethnicity
 Hispanic or Latino 0 0 0 1 1 0 0 1 3
 Not Hispanic or Latino 7 4 5 5 3 7 8 5 44
Baseline Weight (kg)
 Mean (SD) 81.7 (20.6) 83.2 (20.0) 102.4 (33.2) 78.1 (7.1) 72.7 (10.5) 78.8 (25.1) 82.1 (16.2) 86.4 (20.1) 83.2 (20.3)
 Median 84.6 82.6 89 78.4 70.8 71.8 80.1 84.5 80.1
 Range 45–102 66–102 72–147 67–88 63–87 56–126 62–99 67–123 45–147
Baseline KPS
 70 1 0 1 0 1 2 3 1 9
 80 4 1 1 1 1 3 0 2 13
 90 2 3 3 5 2 2 4 3 24
 100 0 0 0 0 0 0 1 0 1
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All patients who received GDC-0084 were over age 18, signed conformed consent forms at their local sites to contribute to the current study, had a life expectancy >12 weeks from enrollment, and histologically documented recurrent or progressive high-grade gliomas (WHO III-IV gliomas) with Karnofsky performance status ≥ 70 at screening who were at least 12 weeks from completion of concurrent chemoradiation (radiation therapy + concurrent temozolomide). Additionally, all patients included in the current trial had <2mg dexamethasone per day or an equivalent dose of other systemic anti-inflammatory corticosteroid or immunosuppressant prior to enrollment. Additional inclusion and exclusion criteria can be found at (https://clinicaltrials.gov/ct2/show/NCT01547546) and in the published clinical details of the trial (23).

The study protocol was approved by local Institutional Review Boards prior to patient recruitment and was conducted in accordance with the Declaration of Helsinski International Conference on Harmonization E6 Guidelines for Good Clinical Practice. Written informed consent was obtained for all patients prior to performing study-related procedures in accordance with federal and institutional guidelines.

MRI and PET Acquisition

All study patients received baseline MRI and PET scans on approved 1.5T and 3T MRI scanners 14 days prior to initiation of study drug while either not receiving glucocorticoids or on a stable dose (i.e., same daily dose) of glucocorticoids during the 5 consecutive days prior to the baseline scan. MRI and PET scanners were approved by an independent radiological facility (MedQIA, Los Angeles, CA) based on 1) adherence to the targeted acquisition parameters including image resolution (+/−10%); 2) qualitative assessments of image quality to look for motion artifacts, geometric distortions, etc.; and 3) adequate range of quantitative measures in normal-appearing tissue (e.g. T1 and ADC measurements, etc.) both submitted MRI and PET phantoms (for conditional approval) as well as patient examinations (for full approval). The first follow-up time point was within 2 weeks after the first dose of GDC-0084 in 7 of the 47 patients, spread across various dose levels, and 1–2 months after the first dose in 38 of the patients, with 2 patients not having any follow up evaluations. Follow-up images were acquired on the same accredited MRI and PET scanners used at baseline. The MRI protocol (Supplemental Table 1) consisted of axial T2-weighted images, axial T2-weighted FLAIR images, axial 30 direction diffusion tensor images (DTI), 2-point Dixon VIBE sequence for attenuation correction on MR-PET scanners (24), axial pre-contrast T1-weighted turbo spin echo (TSE) images, axial variable flip angle 3D gradient echo (GRE) images for pre-contrast T1 mapping, axial dynamic contrast enhanced (DCE) perfusion MRI images obtained with a single dose of contrast, axial dynamic susceptibility contrast (DSC) perfusion MRI obtained after a second dose of contrast (using the DCE dose as a pre-load of contrast), parameter-matched post-contrast axial T1-weighted TSE images, and a 1–1.5mm isotropic resolution post-contrast 3D T1-weighted inversion recovery prepared gradient echo (IR-GRE) sequence. Supplemental Table 1 outlines the general MRI protocol sequence parameters for 3T.

18F-FDG was synthesized using standard methods (25,26) to an average specific radioactivity of 200 GBq/mmol. PET scans were acquired 60 minutes after injection of 2.0 MBq/kg body weight of 18F-FDG, administered as an isotonic neutral solution. A total of 30 minutes of PET data acquisition was acquired with the PET scanner in 3D mode (average of 6 frames x 5 minutes). At the end of PET image acquisition, a transmission scan was acquired to correct for photon attenuation (for CT/PET scans). PET emission data was corrected for photon attenuation, photon scatter, and random coincidences, and then reconstructed using a standard filtered backprojection technique and a Hanning filter with cutoff frequency of 0.5 cycles per bin, yielding a full width-half maximum of 5 mm.

Fig. 1 illustrates the available data used for the current study. A total of 27 of the 47 patients enrolled received 18F-FDG PET and multi-parametric MRI prior to and following cycle 2 of GDC-0084, while 34 of the 47 patients received DCE and DSC perfusion MRI. The remaining 10 patients received anatomic MRI, DSC perfusion MRI, and diffusion tensor imaging (DTI) or diffusion weighted imaging (DWI) prior to and after administration of GDC-0084, along with every 2 cycles until tumor recurrence or death. Of these patients with available data (outlined in Fig. 1), a subset of high-quality post-treatment imaging data and another subset of high-quality matched pre-treatment and post-treatment imaging data were used for subsequent analysis. Data was excluded if there was no measurable enhancing disease, artifacts relating to excessive patient motion, artifacts due to geometric distortions, signal dropout, incomplete enhancing tumor coverage, corrupt raw data files, incorrect acquisition parameters (outside a range of ~10% variation), patient intolerability or claustrophobia, unavailability of tracers, or technical issues during image acquisition.

Fig. 1: Data flow diagram describing available imaging data for each parameter.

Fig. 1:

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A total of 47 patients were enrolled in the current trial. Of which, all patients had anatomic imaging, but only a subset of patients had MRI and PET imaging data available and of sufficient quality for the current study.

MRI and PET Post-Processing

Definition of Enhancing Tumor.

Contrast-enhanced T1-weighted subtraction maps (Fig. 2) were created using parameter matched pre- and post-contrast axial 2D T1-weighted images and techniques previously described (27–29). These images were then registered to 3D 1–1.5mm isotropic post-contrast T1-weighted images for a common patient reference. Tumor volumes of interest (VOIs) were created and included areas of contrast enhancement on T1 subtraction maps and excluded central necrosis as defined hypointensity on T1 post-contrast and subtraction maps, surrounded by contiguous enhancing disease.

Fig. 2: Example MR-PET imaging response in a 47-year-old female patient with recurrent GBM treated with 45mg of GDC-0084.

Fig. 2:

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A) Baseline, pre-treatment and B) 2 month post-treatment multi-parametric MR-PET images are shown, including T2-weighted fluid attenuated inversion recovery (FLAIR), post-contrast T1-weighted images, T1 digital subtraction maps, normalized 18F-FDG PET SUV maps fused to anatomic MR images, apparent diffusion coefficient (ADC) maps, fractional anisotropy (FA) maps, as well as maps of Ktrans, plasma volume fraction (vp), and relative cerebral blood volume (rCBV). C) Pharmacokinetic characteristics during the first 24 hours after the 1st dose of GDC-0084 in this patient. Red arrows show reduction in contrast enhancing tumor burden after treatment.

18F-FDG.

Standardized uptake value (SUV) maps were calculated (30) and registered to 3D post-contrast T1-weighted images. 18F-FDG SUV within enhancing tumor (defined above) and within a 10mm spherical volume placed in the contralateral hemisphere within normal appearing white matter (NAWM) were measured. The median ratio of 18F-FDG uptake in enhancing tumor to NAWM within the enhancing tumor was calculated and used in subsequent analyses.

Diffusion Imaging.

Apparent diffusion coefficient (ADC) was estimated as the mean diffusivity on DTI or DWI images and fractional anisotropy (FA) measurements were created (31,32). Median ADC and FA within enhancing tumor (defined above) were then calculated for each patient after registration to patient-specific 3D post-contrast T1-weighted images.

Perfusion MRI.

Pre-contrast T1 maps were calculated using variable flip angle data and nonlinear regression in MATLAB® (Version 2018a, The MathWorks, Inc., Natick, Massachusetts, United States). Estimates of Ktrans, the flux rate of contrast from the intravascular to extravascular space often as a surrogate for vascular permeability (33,34), and the plasma volume fraction, vp, were estimated using the Extended Tofts model (35) applied to DCE-MRI data. Estimates of rCBV were obtained using a bidirectional leakage correction algorithm (36,37) applied to DSC-MRI data. The median ratio of rCBV within the enhancing tumor to NAWM (defined as 10mm diameter sphere in the contralateral hemisphere) along with median Ktrans and vp within enhancing tumor were estimated and used in subsequent analyses.

Pharmacokinetic (PK) Analysis

To determine the single dose PK properties of GDC-0084, frequent blood sampling through 24 hours was obtained following a single dose of GDC-0084 administered orally on Day 1 of Cycle 1. A validated LC-MS/MS assay with a lower level of quantification (LLOQ) of 0.00052 μM was used to quantify the concentration of GDC-0084 in plasma samples. Nominal time data were used in the analysis, and the linear up/log down trapezoidal method was used for calculating the area under the concentration−time curve (AUC). All plasma concentration−time data collected in Cycle 1 were analyzed using WinNonlin® (Version 6.4, Pharsight Corp, Mountain View, CA) to estimate PK parameters, which included but were not limited to AUC and Cmax.

Data and Statistical Analysis

The percentage change in median values of multi-parametric MR-PET imaging parameters within contrast enhancing tumor were evaluated per oral dose (2mg – 65mg). A correlation matrix was calculated for the percentage change in imaging measurements to understand the interrelationship between the different MR-PET imaging parameters. All MR-PET imaging parameters were then independently correlated with Cmax and AUC from PK evaluation to explore any associations using a level of significance, α = 0.05, not correcting for any multiple comparisons. Because not all patients had a full set of multi-parametric MR-PET imaging measurements, piecewise cubic spline interpolation was used to impute missing data via MATLAB® (Version 2018a, The MathWorks, Inc., Natick, Massachusetts, United States). A set of multivariable linear regression models based on imputed multi-parametric MR-PET imaging measurements were then trained to predict PK parameters Cmax and AUC. Model predictions of Cmax and AUC were then subsequently used to predict radiographic progression-free survival (PFS) in patients who progressed on study (41 of 47 patients) using univariate log-rank analyses applied to Kaplan-Meier data.

RESULTS

At study enrollment, 33 patients (70.2%) were classified as having glioblastoma (WHO IV) while 14 patients (29.8%) had WHO grade III malignant gliomas. The median time from primary diagnosis was 40.5 months and the median number of prior surgeries was 2.0 (range 1–6) and median number of prior systemic therapies was 3.0 (range 1–5). Investigator assessed RANO (38) evaluations in this phase I dose escalation study suggested the best overall response was 40.4% of patients with stable disease (19 of 47), while 55.3% of patients (26 of 47) experienced rapid disease progression and the remaining patients were not evaluable. A linear trend was observed between the proportion of patients with stable disease at each dose level and the oral dose (R2=0.6362, P=0.0177), as 28.6% of patients treated with 2mg had stable disease, 25% at 4mg, 40% at 8mg, 33.3% at 15mg, 25% at 20mg, 42.9% at 30mg, 37.5% at 45mg and 83.8% of patients (5 of 6) had stable disease at the highest dose level of 65mg. Fig. 2 illustrates an example of a 47-year-old female patient with complete multi-parametric MR-PET imaging treated with an oral dose of 45mg of GDC-0084, demonstrating reduction in contrast enhancing tumor burden along with changes on multi-parametric MR-PET images. Seven of the 27 patients had visible, measureable metabolic response on 18F-FDG PET SUV images according to independent radiological facility determination (see Supplemental Fig. 1 for examples). Thirty-seven patients (78.7%) were on-study for less than 3 months, 7 patients (14.9%) were on-study for 3–6 months, and 3 patients (6.4%) were on-study for 6–12 months.

Correlation Between MR-PET Imaging Measurements

No significant correlations were observed between measurements of change in multi-parametric MR-PET imaging measurements before and after treatment (Supplemental Fig. 2; P>0.05), suggesting these measurements reflect independent characteristics of physiological behavior.

Dose-Dependent Post-Treatment Changes MR-PET Imaging

No statistically significant dose-dependent differences were observed when comparing change in enhancing tumor volume (Fig. 3A; P=0.6121), 18F-FDG uptake (Fig. 3B; P=0.4926), ADC (Fig. 3C; P=0.3233), FA (Fig. 3D; P=0.3518), Ktrans (Fig. 3E; P=0.2951), vp (Fig. 3F; P=0.1685), or rCBV (Fig. 3G; P=0.2108) across low, medium, and high doses (see Supplemental Fig. 3 for data on individual dose levels and patient cohorts).

Fig. 3: Multi-parametric MR-PET imaging responses for various oral dose levels of GDC-0084.

Fig. 3:

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Change in A) contrast enhancing tumor volume, B) median 18F-FDG uptake relative to white matter, C) median ADC, D) median FA, E) median Ktrans, F) median vp, and G) median rCBV for low (2–8mg), medium (15–30mg), and high (45–65mg) oral dose levels of GDC-0084.

Multi-Parametric MR-PET Imaging Prediction of Cmax

A signficant negative correlation was observed between change in enhancing tumor volume and Cmax (Fig. 4A; R2=0.1295, P=0.0287), while a significant positive association was observed between Cmax and both change in FA (Fig. 4D; R2=0.2482, P=0.0418) and vp (Fig. 4F; R2=0.3919, P=0.0032). No signficant linear associations were observed between Cmax and percentage change in 18F-FDG uptake, ADC, Ktrans, or rCBV. A multivariable linear regression model could estimate, but tended to slightly underestimate, measured Cmax (Fig. 4H; R2=0.4113, P<0.0001). Supplemental Table 2 outlines the specific model parameters.

Fig. 4: Correlation between multi-parametric MR-PET imaging responses and Cmax.

Fig. 4:

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Correlation between measured Cmax and change in A) contrast enhancing tumor volume, B) median 18F-FDG uptake relative to white matter, C) median ADC, D) median FA, E) median Ktrans, F) median vp, and G) median rCBV. H) Model predictions of Cmax using a linear combination of multi-parametric MR-PET imaging measurements compared with measured values of Cmax.

Multi-Parametric MR-PET Imaging Prediction of AUC

Next, the relationship between MR-PET imaging measurements and AUC during the first 24 hours were explored. A strong association was observed between Cmax and AUC (Supplemental Fig. 4; R2=0.8794, P<0.0001). No significant correlations were observed between the percentage change in MR-PET measurements after GDC-0084 and measured AUC (Fig. 5); however, a decrease in contrast enhancing tumor volume (Fig. 5A; R2=0.1035, P=0.0522), increase in FA (Fig. 5D; R2=0.2003, P=0.0716), and increase in vp (Fig. 5F; R2=0.1917, P=0.0535) trended toward a higher AUC. A multivariate linear regression model created from MR-PET imaging measurements within contrast enhancing tumor before and after the first dose of GDC-0084 was able to predict AUC within the first 24 hours (Fig. 5H; R2=0.3421, P<0.0001). Supplemental Table 3 outlines the specific model parameters.

Fig. 5: Correlation between multi-parametric MR-PET imaging responses and AUC.

Fig. 5:

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Correlation between measured AUC and change in A) contrast enhancing tumor volume, B) median 18F-FDG uptake relative to white matter, C) median ADC, D) median FA, E) median Ktrans, F) median vp, and G) median rCBV. H) Model predictions of AUC using a linear combination of multi-parametric MR-PET imaging measurements compared with measured values of Cmax.

Association Between Multi-Parametric MR-PET Imaging and Progression-Free Survival

The linear regression models created from MR-PET imaging measurements were used to determine whether estimates of Cmax or AUC could be used to predict PFS in patients who continued to receive drug until radiographic progression (41 of 47). Using an empirical threshold of Cmax=0.1 μM, which was approximately the cohort median measured Cmax, a linear combination of multi-parametric MR-PET imaging measurements before and after GDC-0084 could be used to stratify long and short PFS (Fig. 6A; Log-rank, P=0.0039, HR=0.4176). Similarly, model estimate of AUC=1.25 μMxhr estimated from a linear combination of MR-PET measurement responses could predict PFS (Fig. 6B; Log-rank, P=0.0296, HR=0.4679). Interestingly, measured Cmax from blood was not proportional to PFS (Cox Univariate, P=0.6162) and a similar threshold of Cmax=0.1 μM did not result in a difference in PFS (Fig. 6C; Log-rank, P=0.8111). Similarly, measured AUC was not proportional to PFS (Cox Univariate, P=0.6168) and grouping patients based on a measured AUC=1.25 μMxhr did not show a significant difference in PFS (Fig. 6D; Log-rank, 0.5977).

Fig. 6: Difference in progression-free survival (PFS) between multi-parametric MR-PET imaging estimates of high and low concentration and exposure to GDC-0084.

Fig. 6:

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A) Difference in PFS between imaging estimates of high Cmax (>0.1 uM) and low Cmax (<0.1 uM) (Log-rank, P=0.0039). B) Difference in PFS between imaging estimates of high AUC (>1.25 uM*hr) and low AUC (<1.25 uM*hr) (Log-rank, P=0.0296).

Additional Observations

In addition to changes in multi-parametric MR-PET We observed a statistically significant association between Cmax and post-treatment measurements of contrast enhancing tumor burden (Supplemental Fig. 5A; R2=0.1304, P=0.0221), 18F-FDG uptake (Supplemental Fig. 5B; R2=0.1902, P=0.0293), Ktrans (Supplemental Fig. 5C; R2=0.3046, P=0.0078), and rCBV (Supplemental Fig. 5D; R2=0.1649, P=0.0155).

DISCUSSION

Although the PI3K pathway is altered in more than 80% of GBM, many have questioned the ability to target this pathway based on the large number of failed clinical trials (39). GDC-0084 was specifically optimized to cross the blood-brain barrier while maintaining adequate potency and selectivity (14). In vitro and preclinical studies have demonstrated efficacy and brain penetrance of GDC-0084 (40), suggesting this agent may demonstrate activity in human GBM. Since successful mTOR (17–19) and PI3K inhibition (20–22) are thought to reduce glucose utilization and reduce tumor vascularity, respectively, we hypothesized multi-parametric MR-PET imaging using a combination of 18F-FDG PET, along with diffusion and perfusion MRI, may be useful for non-invasively characterizing the multifaceted response to GDC-0084 in patients with malignant gliomas and potentially useful for predicting drug concentration and exposure.

Results from the current study appear to at least partially support this hypothesis, although individual imaging measurements showed mostly trends and the individual comparisons were not corrected for multiple testing. There appeared to be trends toward dose-dependent effects of GDC-0084 on the volume of contrast enhancement, indicating that change in enhancing tumor burden remains an important measurement of drug efficacy(41). Similar to reductions in contrast enhancing tumor burden, estimates of Ktrans, often used as a surrogate for vascular permeability, also trended toward a reduction in proportion to Cmax, supporting the notion that PI3K inhibition using GDC-0084 would result in reduction in abnormal vascularity or vascular characteristics. Results also suggested an increase in FA may be associated with higher drug concentrations and exposure. This may suggest reduction in edema and reemergence of white matter fibers within edematous tissue after treatment with GDC-0084. Surprisingly, no strong association was observed between change in 18F-FDG uptake and PK parameters. This may be due in part to the fact that 18F-FDG SUV measurement during static PET scanning reflects accumulation of 18F-FDG, the mechanisms of which are complex (42–44) and include both tumor-related (e.g. glucose metabolism, vascular fraction, tumor size, hypoxia, etc.) and non-tumor-related mechanisms (e.g. high serum glucose, inflammation, etc.). It is important to note that we did observe global decreases in 18F-FDG uptake in many patients as illustrated in Supplemental Fig. 1C and 1E, which may suggest brain penetration and mTOR inhibition throughout the brain. Additionally, a correlation between Cmax and post-treatment estimates of 18F-FDG, tumor volume, Ktrans, and rCBV (Supplemental Fig. 5) were detected, which appears consistent with our original hypotheses.

Since repeated brain surgeries are not realistic to quantify drug penetration in most malignant glioma patients and traditional PK studies and “phase 0” or “window of opportunity” studies can be both time consuming and expensive, there remains an unmet need in neuro-oncology for non-invasive biomarkers that can be used to estimate drug PK characteristics. In the current study we created a simple model for predicting both Cmax and AUC using a linear combination of all available multi-parametric MR-PET imaging parameters. Then, using the non-invasive imaging estimates of drug concentration and exposure, we were able to predict patients with more favorable PFS. While preliminary, results from the current study suggest composite biomarkers created from multi-parametric MR-PET imaging targeting metabolic and/or physiologic processes specific to the drug mechanism of action may be useful for subsequent evaluation of treatment efficacy in larger phase II-III studies. And while machine learning and artificial intelligence techniques hold the promise of similarly predicting features like drug penetration and outcome using non-invasive imaging information, these approaches require large amounts of data to generalize these characteristics, which will not be available when testing novel drugs. Thus, the current study suggests a simple linear combination of multi-parametric MRI and PET imaging measurements can effectively predict PK parameters and PFS. Interestingly, actual measures of drug concentration and exposure from blood did not appear to predict PFS, suggesting that estimations of exposure using a combination of imaging features may provide added value over direct blood PK measurements. It is conceivable this may be due, in part, to changes in imaging measurements reflecting drug penetration and target engagement in individual patients as opposed systemic drug exposure, although this is only speculative.

There are a number of important limitations to the current study that should be addressed. First, no multiple comparisons corrections were performed when evaluating the correlation between PK parameters and multiple imaging measurements. Since we did not observe a strong correlation between the various imaging measurements and because this was a small pilot study, we felt as though a conservative approach to correcting for multiple comparisons (e.g. Bonferroni correction) would inhibit our ability to identify potentially meaningful associations between individual imaging measurements and PK parameters. Future studies with more patients and a targeted small number of specific imaging measurements may be useful for refining these associations. Secondly, despite great efforts to standardize image acquisition, there were a large variety in 18F-FDG PET SUV measurements in both phantom calibration (results not shown) as well as in tumor and normal brain tissue. To overcome these challenges, we chose to normalize 18F-FDG uptake in the enhancing tumor to that of normal white matter. However, as illustrated in Supplemental Fig. 1C and E, global changes in 18F-FDG uptake may occur and may actually reflect brain penetration of GDC-0084 and meaningful inhibition of mTOR. Thus, more sophisticated techniques for isolating the changes in 18F-FDG metabolism within the tumor from that of background tissue, or even considering global changes in glucose utilization as a potentially meaningful indicator of mTOR inhibition, may be important. Similarly, despite great efforts to standardize acquisition of DTI and perfusion MRI, some studies were not in compliance and were not usable in subsequent analyses. Consequently, not all patients had all imaging measurements available, therefore results should be interpreted with caution and findings should be replicated in an independent cohort. Also, greater efforts to balance the needed complexity of multi-parametric MR-PET imaging studies with what is practical at various sites is critical to ensure similarly designed trials can quantify needed parameters while maximizing the amount of available data. Lastly, the patients in the current trial were heavily pretreated and therefore the single-agent anti-tumor activity may have been significantly limited. It is conceivable that GDC-0084 may have more clinical activity in patients who are less heavily treated or in the first-line setting where tumors are less heterogenous and aggressive.

Another vital set of limitations that should be addressed are the assumptions that a favorable blood PK is closely linked with tissue PK/PD and that favorable tissue PK/PD is necessary to cause a tumor response. While we rationalized that a favorable blood PK is a necessary, but not sufficient, condition for a physiological drug effect, tissue drug concentrations and biological effects depend on a number of complex characteristics including properties of the particular drug, regional perfusion, blood clearance, drug metabolism, and genetic or epigenetic differences within the tumor. It is conceivable that these complex interactions were responsible for some of the variability we observed when relating imaging parameters with PK measurements as well as our observations that blood PK was not directly predictive of PFS, whereas imaging response which presumably reflected biological changes imposed by direct target engagement by the drug, was predictive of PFS. Thus, results from the current study should be interpreted with some caution until more comprehensive studies have been conducted to isolate these specific effects.

CONCLUSION

A combination of multi-parametric MR-PET imaging parameters aimed to targeting metabolic and physiologic changes resulting from mTOR and PI3K inhibition can be used to estimate GDC-0084 pharmacokinetics and predict progression-free survival in patients with recurrent high-grade gliomas.

Supplementary Material

Figure S5

Supplemental Fig. 5: Association between post-treatment measurements and Cmax. Correlation between Cmax and post-treatment measurement of A) contrast enhancing tumor volume, B) relative 18F-FDG uptake, C) Ktrans, and D) rCBV.

Table S1
Table S2
Table S3
Figure S4

Supplemental Fig. 4: Correlation between Cmax and AUC.

Figure S1

Supplemental Fig. 1: Examples of metabolic responders to GDC-0084 as indicated by a decrease in 18F-FDG PET uptake. A-B) Examples of a clear reduction in focal 18F-FDG uptake within the tumor. C-E) Example of more subtle reduction in 18F-FDG uptake after GDC-0084. C and E) demonstrate both focal (tumor) and global (gray matter) reductions in 18F-FDG uptake.

Figure S2

Supplemental Fig. 2: Correlation between multi-parametric MR-PET imaging changes before and after GDC-0084. A) Pearson’s correlation coefficient (R) and B) resulting p-values describing the association between all imaging measurements. No significant correlations (P<0.05) were observed.

Figure S3

Supplemental Fig. 3: Post-treatment multi-parametric MR-PET imaging measurements and responses for each oral dose of GDC-0084. Post-treatment measurements of A) contrast enhancing tumor volume, B) median 18F-FDG uptake relative to white matter, C) median ADC, D) median FA, E) median Ktrans, F) median vp, and G) median rCBV and change in measurements of H) contrast enhancing tumor volume, I) median 18F-FDG uptake relative to white matter, J) median ADC, K) median FA, L) median Ktrans, M) median vp, and N) median rCBV for dose levels of 2, 4, 8, 15, 20, 30, 45, and 65mg.

Footnotes

Disclosure of potential conflicts of interest:

BME: Consulting/Advisory: MedQIA, Genentech/Roche, Agios, Siemens, Janssen, Medicenna, Imaging Endpoints, Novogen, Northwest Biopharmaceuticals, Image Analysis Group, Oncoceutics, Beigene, Tocagen, VBL Therapeutics. Research Grants: Siemens, Janssen, VBL Therapeutics.

JY: None

CR: None

DAN: None

AC: None

JS: Employee of Kazia Therapeutics Limited.

JG: Employee of Kazia Therapeutics Limited.

AO: Employee of Genentech, Inc., shareholder of F. Hoffmann La Roche, Ltd.

LM: Employee of Genentech, Inc., shareholder of F. Hoffmann La Roche, Ltd.

JR: Non-financial support and reasonable reimbursement for travel: European Journal of Cancer, Vall d’Hebron Institut of Oncology, Chinese University of Hong Kong, SOLTI, Elsevier, Glaxo Smith Kline. Consulting and travel fees: Novartis, Eli Lilly, Orion Pharmaceuticals, Servier Pharmaceuticals, Peptomyc, Merck Sharp & Dohme, Kelun Pharmaceutical/Klus Pharma, Spectrum Pharmaceuticals Inc, Pfizer, Roche Pharmaceuticals, Ellipses Pharma (including serving on the scientific advisory board from 2015-present). Research funding: Bayer, Novartis. Serving as investigator in clinical trials: Spectrum Pharmaceuticals, Tocagen, Symphogen, BioAtla, Pfizer, GenMab, CytomX, Kelun-Biotech, Takeda-Millenium, Glaxo Smith Kline, IPSEN. Travel fees: ESMO, US Department of Defense, Louisiana State University, Hunstman Cancer Institute, Cancer Core Europe, Karolinska Cancer Institute and King Abdullah International Medical Research Center (KAIMRC).

ERG: None

TC: Advisory role: Abbvie, Agios, Amgen, Bayer, Boehinger Ingelheim, Boston Biomedical, Celgene, Deciphera, Del Mar Pharmaceuticals Genentech/Roche, GW Pharma, Karyopharm, Kiyatec, Medscape ,Merck, Odonate Therapeutics, Pascal Biosciences, Tocagen, Trizel, VBI , VBL Therapeutics. Stock options: Notable labs. Board of Directors: Global Coalition for Adaptive Research (501c3).

PYW: Consulting or Advisory role: AbbVie, Agios, Angiochem, AstraZeneca, Cavion, Celldex, Exelixis, Astra Zeneca, Bayer, Blue Earth Diagnostics, Immunomic Therapeutics, Karyopharm, Kiyatec, Merck, Prime Oncology, Puma, Taiho, Tocagen, Vascular Biogenics, Deciphera, VBI Vaccines. Research support: Agios, Astra Zeneca, Beigene, Eli Lily, Genentech/Roche, GlaxoSmithKline, Karyopharm Therapeutics, Midatech, Momenta Pharmaceuticals, Kazia, MediciNova, Merck, Novartis, Novocure, Regeneron, Oncoceutics, Prime Oncology, Sanofi, Sigma-Tau-Aventis, Vascular Biogenics. VBI Vaccines. Speakers’ Bureau: Merck, Prime Oncology. DSMB: Tocagen.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S5

Supplemental Fig. 5: Association between post-treatment measurements and Cmax. Correlation between Cmax and post-treatment measurement of A) contrast enhancing tumor volume, B) relative 18F-FDG uptake, C) Ktrans, and D) rCBV.

NIHMS1583662-supplement-Figure_S5.tiff (1.5MB, tiff)
Table S1
NIHMS1583662-supplement-Table_S1.docx (18KB, docx)
Table S2
NIHMS1583662-supplement-Table_S2.docx (64.8KB, docx)
Table S3
NIHMS1583662-supplement-Table_S3.docx (63.8KB, docx)
Figure S4

Supplemental Fig. 4: Correlation between Cmax and AUC.

NIHMS1583662-supplement-Figure_S4.pdf (671.9KB, pdf)
Figure S1

Supplemental Fig. 1: Examples of metabolic responders to GDC-0084 as indicated by a decrease in 18F-FDG PET uptake. A-B) Examples of a clear reduction in focal 18F-FDG uptake within the tumor. C-E) Example of more subtle reduction in 18F-FDG uptake after GDC-0084. C and E) demonstrate both focal (tumor) and global (gray matter) reductions in 18F-FDG uptake.

NIHMS1583662-supplement-Figure_S1.pdf (3.7MB, pdf)
Figure S2

Supplemental Fig. 2: Correlation between multi-parametric MR-PET imaging changes before and after GDC-0084. A) Pearson’s correlation coefficient (R) and B) resulting p-values describing the association between all imaging measurements. No significant correlations (P<0.05) were observed.

NIHMS1583662-supplement-Figure_S2.pdf (1,018.7KB, pdf)
Figure S3

Supplemental Fig. 3: Post-treatment multi-parametric MR-PET imaging measurements and responses for each oral dose of GDC-0084. Post-treatment measurements of A) contrast enhancing tumor volume, B) median 18F-FDG uptake relative to white matter, C) median ADC, D) median FA, E) median Ktrans, F) median vp, and G) median rCBV and change in measurements of H) contrast enhancing tumor volume, I) median 18F-FDG uptake relative to white matter, J) median ADC, K) median FA, L) median Ktrans, M) median vp, and N) median rCBV for dose levels of 2, 4, 8, 15, 20, 30, 45, and 65mg.

NIHMS1583662-supplement-Figure_S3.pdf (3.9MB, pdf)

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