Depth of Radiographic Response and Time to Tumor Regrowth Predicts Overall Survival Following Anti-VEGF Therapy in Recurrent Glioblastoma

Notwithstanding the importance of VEGF in brain tumor biology (1, 2) and promising initial responses to a variety of anti-VEGF therapies (3–7), confirmatory randomized phase II–III trials have not shown an overall survival (OS) benefit for patients with recurrent glioblastoma and there appears to be no direct association between objective response rate (ORR) and median OS when examining the literature (refs. 5, 8–15; Fig. 1A). However, anecdotally, there are patients who experience robust responses to anti-VEGF therapy and subsequently appear to have a survival benefit when treated with anti-VEGF therapies, including those with specific diffusion MR phenotypes (16–18). Furthermore, bevacizumab is used with increasing frequency as the control arm in recurrent glioblastoma trials, or in combination trials with new therapeutics. Thus, a diagnostic tool for identifying patients having a beneficial response to anti-VEGF therapy, beyond traditional measures of simple tumor shrinkage vis-à-vis response rate, may have high clinical, scientific, social, and economic impact, as this costly form of therapy could be withheld until other options have been exhausted.

Because of the fact most patients with recurrent glioblastoma treated with anti-VEGF therapies show some radiographic response, we theorized a mathematical model that characterizes both this initial response component along with a rebound, or regrowth phase may be appropriate for describing the temporal behavior of these tumors. Consistent with this concept, evidence suggests tumor regrowth rate during experimental treatment estimated using a biexponential model that incorporates both growth and regression rates showed a strong association with OS via serial serum prostate-specific antigen (19) and tumor volume measurements (20, 21) in prostate and metastatic colorectal cancers, respectively. Thus, we hypothesized a comparable biexponential model could be used to model recurrent glioblastoma response to antiangiogenic therapy, and parameters associated with this model will be predictive of OS. Furthermore, we theorized the use of T1-weighted digital subtraction maps (22, 23), where precontrast images are subtracted voxel-by-voxel from postcontrast images to highlight areas of subtle enhancement, would significantly improve the accuracy of tumor measurements in the context of antiangiogenic therapies (22, 23). This study aimed to test this hypothesis by investigating the association between model-derived parameters describing enhancing tumor volumetric dynamics and OS in two phase II trials (training) and one phase III trial (validation) of recurrent glioblastoma treated with anti-VEGF therapies including bevacizumab and cabozantinib.

A total of 276 patients with anti-VEGF treatment–naïve recurrent glioblastoma with measurable enhancing tumor (>1 cm³) and at least three time points including baseline available from two separate multicenter phase II clinical trials were included in this study as training data. Among these 276 patients were 139 patients treated with bevacizumab with or without irinotecan as part of the BRAIN trial (ref. 5; Roche/Genentech, AVF3708g; NCT00345163), an open-label, multicenter (11 sites), randomized, noncomparative phase II trial performed to assess the effectiveness of bevacizumab or bevacizumab (10 mg/kg every 2 weeks) and irinotecan hydrochloride (340 mg/m² or 125 mg/m²) with or without concomitant enzyme-inducing antiepileptic drugs. The remaining 137 patients were treated with cabozantinib monotherapy as part of XL184–201 (12), a multicenter (8 sites), phase II, open-label, uncontrolled study of cabozantinib (XL184; Exelixis; NCT00704288), a tyrosine kinase inhibitor with principal targets of MET, VEGF receptors, AXL, and RET, at a dose of 140 or 100 mg (free base equivalent weight, oral, daily) at first or second relapse. Along with these 276 phase II patients, an additional 74 patients with measurable enhancing tumor and at least three time points including baseline in the bevacizumab control arm in the phase III GLOBE trial (ref. 13; VBL Therapeutics; NCT02511405), a randomized controlled trial comparing the efficacy and safety of upfront combination of ofranergene obadenovec (VB-111) and bevacizumab versus bevacizumab monotherapy (10 mg/kg every 2 weeks), were included as a validation cohort. All patients enrolled in all trials signed institutional review board–approved written consent at the respective study sites and all studies were conducted in accordance with the Declaration of Helsinki. Additional information for these respective trials can be found in Friedman and colleagues (ref. 5; BRAIN), Wen and colleagues (ref. 12; XL184–201), and Cloughesy and colleagues (GLOBE; ref. 13).

All anatomic MR images were acquired for all patients using a 1.5T or 3T MR scanner and included study-specific standardized pre- and postcontrast T1-weighted images and 2D axial T2-weighted FLAIR images. A subset of patients had diffusion MRI data at baseline available for analysis. For this study, T1-weighted images pre- and postadministration of gadolinium-based contrast agents were used to quantify enhancing volumes, and consisted of either 2D axial turbo spin echo images with a slice thickness of 3–6-mm with an interslice gap of 0–2.5 mm, or a 3D inversion-prepared gradient echo with a 1–1.5-mm isotropic voxel size, consistent with international recommendations (24). In a subset of patients with diffusion MRI available, diffusion-weighted images (DWI) were acquired before injection of contrast with TE/TR = 80–110 msec/4–10 sec, NEX = 1, slice thickness = 5 with 0–1 mm interslice gap, matrix size = 128×128, and FOV = 220–256 mm using a monopolar spin-echo echo-planar preparation. Apparent diffusion coefficient (ADC) maps were calculated offline from the acquired DWIs using b = 0 s/mm² and b = 1,000 s/mm² images and used for subsequent analyses.

Contrast-enhanced T1-weighted digital subtraction maps were used to extract contrast-enhancing tumors while excluding blood products and necrotic lesions within the tumor as described previously (22, 23, 25). First, pre- and postcontrast T1-weighted images were coregistered using a six-degree-of-freedom rigid transformation and a mutual information cost function using FSL software (flirt; FMRIB Software Library; http://www.fmrib.ox.ac.uk/fsl/). Then, Gaussian normalization of image intensity for both nonenhanced and contrast-enhanced T1-weighted images was performed using custom code courtesy of the National Institute of Mental Health Magnetoencephalography Core Facility (3dNormalize; NIMH MEG Core, https://megcore.nih.gov/index.php/3dNormalize). Next, bias field correction was performed (FAST; FMRIB Software Library; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FAST) and voxel-by-voxel subtraction between normalized nonenhanced and contrast-enhanced T1-weighted images was performed. Image voxels with a positive (greater than zero) change in normalized contrast enhancement signal intensity (i.e., voxels increasing in MR signal after contrast agent administration) were isolated within the lesion area, and estimates of tumor volume were obtained by combining areas of contrast enhancement on T1 subtraction maps with any regions of central necrosis (defined as being enclosed by contiguous, positive enhancing disease). A team of trained lab technologists generated initial segmentations, and all final volumes were reviewed by a single investigator (B.M. Ellingson) who was blinded to other relevant metrics until study completion.

A Kruskal–Wallis test and Dunn test for multiple comparisons were used to test differences in estimates of g, d, TTG, and DpR across trial datasets used in this study. Pearson's correlation analyses and nonlinear regression were used to investigate the association between parameters. Optimal thresholds for each variable were determined from phase II data by finding the minimum log-rank HR and corresponding P values with respect to OS in patients categorized as “high” versus “low”, while changing the variable thresholds through the range of values as described previously (16). Reported P values for phase II data were calculated using the approach described by Lausen and Schumacher (29). These thresholds were then validated using the phase III dataset to confirm these are meaningful cutoffs for identifying survival differences between patients. Next, univariate Cox regression analysis was performed to assess the association between OS and continuous estimates of g, d, TTG, and DpR. Additional multivariate Cox regression analysis were performed by including age and baseline tumor volume in addition to each model parameter. Finally, the association between estimates of g, d, TTG, and DpR were compared between pretreatment diffusion MR phenotypes (higher or lower than ADC_L = 1.24 μm²/ms) using unpaired t tests. Statistical significance was set at P < 0.05 and P values were adjusted for multiple comparisons when stated. Statistical analyses were performed using a combination of GraphPad Prism (v7.0e; GraphPad Software), Python 3.11.1, and Matlab (MATLAB 2022b, The MathWorks).

Data generated by others were used under license by the authors. The data analyzed in this study are available from Roche/Genentech, Exelixis, and VBL Therapeutics. Restrictions apply to the availability of these data, which were used under license for this study. Data are available from the authors upon reasonable request with the permission of Roche/Genentech, Exelixis, and VBL Therapeutics.

A total of 255 of the eligible 276 phase II patients (92.4%) and 63 of the eligible 74 phase III patients (85.1%) had sufficient model fit to experimental data and were included in subsequent analyses. Patient demographic data for the included patients can be found in Table 1. Figure 1C illustrates an example patient with a strong radiographic response to bevacizumab in the phase II group, suggestive by a relatively low regression rate, d, and tumor regrowth rate, g. This is contrasted with an example patient in Fig. 1D showing rapid failure on bevacizumab as evidence by a rapid regression, d, and subsequently high regrowth rate, g, and short TTG.

g was significantly higher in patients treated with cabozantinib compared with bevacizumab monotherapy (Fig. 2A; phase II BRAIN Trial, P_adj = 0.01; phase III VB111 control arm, P_adj= 0.0003) and bevacizumab in combination with irinotecan (P_adj= 0.0047). Also, d was significantly higher in patients treated with cabozantinib compared with bevacizumab monotherapy (Fig. 2B; phase II, P_adj= 0.0004; phase III, P_adj< 0.0001). Consequently, TTG was shorter for patients treated with cabozantinib compared with bevacizumab with or without irinotecan as part of the BRAIN trial (Fig. 2C; monotherapy, P_adj= 0.0318; combination, P_adj= 0.0008), but not when evaluated with respect to phase III data (P > 0.05). Also, DpR was significantly higher in patients treated with combination bevacizumab plus irinotecan compared with bevacizumab monotherapy in the phase III trial (Fig. 2D; P_adj= 0.0343). Together, these results suggest slightly different volumetric responses across the different therapeutic arms.

The optimal cutoffs for each model parameter were chosen by quantifying the minimum log-rank HR and P values for increasing thresholds (Fig. 3A–D). The optimal cutoff for the largest difference in OS between groups was g = 0.07 months⁻¹ (Fig. 3A), d = 0.11 months⁻¹ (Fig. 3B), TTG = 3.8 months (Fig. 3C), and DpR = 11.3% or 35.2% (Fig. 3D; note that DpR is different than simply percentage change from baseline because the ratio is log-transformed.). An apparent log–log correlation was observed between TTG and g (Fig. 3E), indicating that patients with short regrowth rates have longer time to tumor regrowth. In addition, a significant linear correlation was observed between d and g (Fig. 3F; Pearson correlation, R² = 0.6769, P < 0.0001).

Kaplan–Meier curves applied to univariate Cox analyses for the combined phase II data was used to visualize the significant OS advantage in patients with lower g (Supplementary Table S1; Fig. 4A; threshold g < 0.07 months⁻¹; HR = 0.3121, P_adj < 0.001), lower d (Fig. 4B; threshold d < 0.11 months⁻¹; HR = 0.3543, P_adj < 0.001), longer TTG (Fig. 4C; threshold TTG > 3.8 months; HR = 0.3078, P_adj= 0.011), and deeper response using a threshold of DpR > 11.3% (Fig. 4D; HR = 0.6389, P_adj= 0.027) or DpR > 35.2% (Fig. 4E; HR = 0.4682, P = 0.0018) based on the optimized thresholds. These same trends toward longer survival were observed within each individual phase II trial (Supplementary Table S2; Supplementary Fig. S1), with the exception of DpR in the phase II cabozantinib monotherapy trial (P = 0.7162). When these same thresholds were applied to an independent phase III dataset (Table 2), results confirmed the OS differences based on g (Fig. 4F; HR = 0.2362, P < 0.0001), d (Fig. 4G; HR = 0.3724, P = 0.0082), TTG (Fig. 4H; HR = 0.2214, P < 0.0001), and DpR using a threshold of 11.3% (Fig. 4I; HR = 0.478, P = 0.0177). However, a survival advantage in patients with a DpR > 35.2% was not demonstrated with this independent dataset (Fig. 4J; HR = 0.2453, P = 0.1280).

While these optimal values were chosen to maximize the difference in survival between groups, Figure 3A–D suggests most thresholds for g, d, TTG, and DpR result in a significant survival difference. Univariate Cox regression analysis using continuous values confirmed these findings (Supplementary Tables S1, S4, and S5), showing a significant survival advantage in patients demonstrating a slower g (phase II, P < 0.0001; phase III, P < 0.0001), slower d (phase II, P < 0.0001; phase III, P = 0.0004), longer TTG (phase II, P < 0.0001; phase III, P = 0.0001), and larger DpR (phase II, P = 0.0010; phase III, P = 0.0076) in the combined phase II and independent phase III trials. Individual phase II trials also showed similar trends (Supplementary Table S3), again with the exception of DpR in the phase II cabozantinib monotherapy trial (P = 0.8476).

To verify that g, d, TTG, and DpR were prognostic factors for survival independent of both age and baseline tumor volume, multivariable Cox regression was performed. Using continuous values, data from combined phase II trials confirmed that g (Supplementary Table S3; Cox, P < 0.0001), d (P < 0.0001), TTG (P < 0.0001), and DpR (P = 0.0025) were independent predictors of survival, which was verified using the independent phase III dataset (g, P < 0.0001; d, P = 0.0004; TTG, P < 0.0001; DpR, P = 0.0043). Importantly, baseline enhancing tumor volume was a strong independent prognostic factor for all Cox evaluations (P < 0.0001). In addition to continuous values, we verified that high- and low-risk groups defined using the optimal cut-off values were also predictors of survival independent of age and baseline tumor volume. Similar to continuous values, results from phase II trials confirmed and phase III trials verified that g < 0.07 months⁻¹ (Table 2; phase II, P < 0.0001; phase III, P < 0.0001), d < 0.11 months⁻¹ (phase II, P < 0.0001; phase III, P = 0.0108), TTG (phase II, P < 0.0001; phase III, P < 0.0001), and DpR (phase II, P = 0.0004; phase III, P = 0.0084) were independent predictors of survival.

A composite index based on the combination of DpR and TTG was then created to further stratify risk for early death in recurrent glioblastoma patients treated with anti-VEGF therapy (Fig. 5). Patients were stratified based on whether they had a favorable DpR (>11.3%), favorable TTG (>3.8 months), or both a favorable DpR and TTG. Results for the combined phase II datasets illustrated a significantly longer OS in patients exhibiting both a high DpR and TTG compared with those showing either a favorable DpR or TTG (Fig. 5A; mOS = 18.2 vs. 11.3 months; HR = 0.5567, Log rank, P < 0.0001) or those with neither a favorable DpR or TTG (Fig. 5A; mOS = 18.2 vs. 7.5 months; HR = 0.2859, P < 0.0001). In addition, patients exhibiting either a favorable DpR or TTG had a significantly longer OS compared with those showing neither a high DpR or TTG (Fig. 5A; mOS = 11.3 vs. 7.5 months; HR = 0.5530, P < 0.0001). These observations were then confirmed using the independent phase III dataset, where patients exhibiting both a favorable DpR and TTG had a significantly longer OS compared with patients showing either a favorable DpR or TTG (Fig. 5B; mOS = undefined vs. 8.4 months; HR = 0.2226, P < 0.0001) and patients exhibiting either a favorable DpR or TTG showing a longer OS compared with those illustrating neither (Fig. 5B; mOS = 11.3 vs. 8.4 months; HR = 0.3411, P = 0.0002). Importantly, phase III data did not confirm the previously observed survival difference between patients illustrating both high DpR and TTG and those showing either a high DpR or TTG (Fig. 5B; HR = 0.6565, P = 0.3376).

Finally, we tested whether response characteristics were intrinsic to diffusion MR phenotypes known to be predictive of anti-VEGF response (16–18). Phase II data in patients treated with bevacizumab with or without irinotecan showed that patients with a favorable diffusion MR phenotype (ADC_L > 1.24 μm²/ms) had a significantly lower g (Supplementary Fig. S1A; P = 0.0185), lower d (P = 0.0055), and longer TTG (P = 0.0055). Phase II data in patients treated with cabozantinib similarly showed that lower g (Supplementary Fig. S1B; P = 0.0395) and longer TTG (P = 0.0031) was associated with a favorable diffusion MR phenotype.

While anti-VEGF agents have not shown a significant survival advantage compared to cytotoxic chemotherapies in recurrent glioblastoma, bevacizumab received regulatory approval and is used often for clinical care in the end stages of glioblastoma as well as a control arm or in combination with experimental therapy in clinical trials (30). As inexpensive bioequivalents of bevacizumab are becoming available, clinicians are increasingly using anti-VEGF therapies to manage vasogenic edema, inflammation, and neurologic symptoms without the side effects of corticosteroids. Given the high reported response rate and lack of apparent association between RANO response and survival benefit in antiangiogenic agents (15), however, the field has been skeptical to use anatomic imaging to monitor these patients.

Results from this study confirm that the combination of T1-weighted digital subtraction maps and mathematical modeling of the volumetric response can be used to identify patients who have a significant survival benefit when treated with anti-VEGF therapies, including bevacizumab and cabozantinib. Results pooled from multiple clinical trials indicate that tumors with more rapid regression rates after anti-VEGF treatment also tend to have a more rapid rebound or regrowth rate. These rapidly responding tumors also tend to have a shorter TTG, or durability of response, and have a significantly shorter survival. Multivariable Cox regression analysis confirmed these observations, validating that all volumetric response measures were independent predictors of OS (i.e., g, d, TTG, and DpR), even when controlling for both age and baseline tumor volume. Data also clearly demonstrates that the combination of the depth of response, DpR, larger than 11% and the durability of response, TTG, longer than 3.8 months was meaningful in terms of predicting long-term survival in patients treated with anti-VEGF therapies.

In addition to confirming that anatomic changes were meaningfully associated with survival benefit in anti-VEGF treatment, results from the current study also confirmed that patients with favorable diffusion MR phenotypes prior to treatment largely reflected the same patients with a favorable radiographic response and survival benefit. Our previous work has shown that diffusion MRI is one of the strongest predictive factors for anti-VEGF treatment outcome in recurrent glioblastoma (16–18), and appears to be associated with increased expression of molecules that modulate the stiffness of the extracellular matrix, namely decorin (31). This study adds to this previous body of literature and specifically suggests that diffusion MRI, or perhaps intratumoral decorin expression, may be associated with benefit from anti-VEGF therapy as evidenced through a longer TTG and slower g.

While the proposed biexponential model appears sufficient to characterize the response to antiangiogenic agents, it may have utility in other treatments. However, the average objective response rate for recurrent GBM ranges between 3% and 8% for cytotoxic, biologic, and immunotherapies (15) therefore, d, DpR, and TTG may be of limited use in these (non-antiangiogenic) therapies. However, the model is flexible such that if a patient doesn't have tumor shrinkage (d = 0), a slow tumor regrowth rate, g, may be of use to quantify tumor control and therapeutic benefit.

While results from this study have important clinical and trial implications, there are some critical limitations that should be discussed. First, this study was retrospective and some of the trials were conducted 10–15 years ago, so IDH mutation status and other genetic factors were not available for all the patients. Thus, inclusion of other tumor types may have slightly contaminated our study cohort. Second, not all patients treated in the trials were evaluable using the approaches outlined in this study, as adequate images were required and at least three time points including baseline were required to estimate the response parameters. Despite this limitation, a total of 255 of the eligible 276 phase II patients (92.4%) and 63 of the eligible 74 phase III (85.1%) could be evaluated, suggesting the outlined approach for characterizing volumetric response may not be as restrictive as initially thought.

Estimates of volumetric d, g, TTG, and DpR are significant and independent predictors of overall survival in recurrent glioblastoma treated with anti-VEGF therapy. In addition, patients with favorable diffusion MRI characteristics prior to treatment had a significantly longer TTG and slower tumor regrowth rate than patients with tumors exhibiting restricted diffusion. This information is useful for interpreting activity of antiangiogenic agents in recurrent glioblastoma.

B.M. Ellingson reports grants from NIH during the conduct of the study; B.M. Ellingson also reports grants and personal fees from Janssen and Neosoma, as well as personal fees from Medicenna, MedQIA, Servier, Siemens, Imaging Endpoints, Kazia, Chimerix, Sumitomo Dainippon Pharma Oncology, ImmunoGenesis, Ellipses Pharma, Monteris, Alpheus Medical, Sagimet Biosciences, Sapience Therapeutics, and Global Coalition for Adaptive Research outside the submitted work. C.J. Morris reports grants from NIH (NIGMS training grant T32 GM008042) during the conduct of the study. N.S. Cho reports grants from NIH during the conduct of the study. L.E. Abrey reports other support from Roche during the conduct of the study, as well as other support from Novartis and InCephalo outside the submitted work. J. Garcia was an employee of Roche/Genentech during the conduct of the study. D.T. Aftab reports personal fees, nonfinancial support, and other support from Exelixis outside the submitted work; in addition, D.T. Aftab has a patent for the use of cabozantinib in treating cancer pending and issued. C. Hessel reports personal fees from Exelixis during the conduct of the study. T. Rachmilewitz Minei reports personal fees from VBL Therapeutics during the conduct of the study, as well as personal fees from BioLineRx outside the submitted work. D. Harats reports other support from VBL outside the submitted work. D.A. Nathanson reports grants from UCLA during the conduct of the study. P.Y. Wen reports grants and personal fees from AstraZeneca, Servier, and Chimerix; grants from VBI Vaccines, Vascular Biogenics, Merck, Black Diamond, ERASCA, Kazia, MediciNova, and Bristol Myers Squibb; and personal fees from Day One Bo, Novocure, Genenta, Sagimet, Sapience, Prelude Therapeutics outside the submitted work. T.F. Cloughesy reports personal fees from Sagimet, Clinical Care Options, Ideology Health, Servier, Jubilant Therapeutics, Sonalasense, Gan & Lee, BrainStorm, Sapience, Inovio, DNATrix, Tyme, SDP, Novartis, Roche, Kintara, Bayer, Merck, Boehringer Ingelheim, VBL Therapeutics, Amgen, Kiyatec, Medefield, Pascal Bioscience, Bluerock, Vida Ventures, Lista Therapeutics, StemLine, Tocagen, Karyopharm, Agios, Novocure, Global Coalition for Adaptive Research, Katmai, Erasca, and Chimerix outside the submitted work; in addition, T.F. Cloughesy has a patent for 62/819,322 issued, licensed, and with royalties paid from Katmai. T.F. Cloughesy also reports the following: cofounder, major stock holder, consultant, and board member of Katmai Pharmaceuticals; holds stock for Erasca; member of the board and paid consultant for the 501c3 Global Coalition for Adaptive Research; holds stock in Chimerix and receives milestone payments and possible future royalties; member of the scientific advisory board for Break Through Cancer; and member of the scientific advisory board for Cure Brain Cancer Foundation. The Regents of the University of California (TFC. employer) has licensed intellectual property coinvented by TFC to Katmai Pharmaceuticals. No disclosures were reported by the other authors.

B.M. Ellingson: Conceptualization, resources, formal analysis, supervision, funding acquisition, writing–original draft, project administration, writing–review and editing. A. Hagiwara: Conceptualization, software, formal analysis, writing–review and editing. C.J. Morris: Software, formal analysis, writing–original draft, writing–review and editing. N.S. Cho: Data curation, software, formal analysis, writing–review and editing. S. Oshima: Software, formal analysis, writing–review and editing. F. Sanvito: Software, formal analysis, writing–review and editing. T.C. Oughourlian: Formal analysis, writing–review and editing. D. Telesca: Formal analysis, validation. C. Raymond: Resources, data curation, software, validation, writing–review and editing. L.E. Abrey: Resources, funding acquisition, project administration, writing–review and editing. J. Garcia: Resources, data curation, funding acquisition, writing–review and editing. D.T. Aftab: Resources, data curation, funding acquisition, writing–review and editing. C. Hessel: Resources, data curation, writing–review and editing. T. Rachmilewitz Minei: Resources, data curation, funding acquisition, writing–review and editing. D. Harats: Resources, data curation, funding acquisition, writing–review and editing. D.A. Nathanson: Conceptualization, funding acquisition, writing–review and editing. P.Y. Wen: Conceptualization, writing–review and editing. T.F. Cloughesy: Conceptualization, resources, funding acquisition, writing–review and editing.

This work was supported by National Brain Tumor Society (NBTS) and the Sontag Foundation; NIH-NIGMS Training Grant T32 GM008042 (to C.J Morris and N.S. Cho); NIH/NINDS R01NS078494 (to B.M. Ellingson); NIH/NCI R01CA270027 (to B.M. Ellingson and T.F. Cloughesy); NIH/NCI P50CA211015 (to B.M. Ellingson and T.F. Cloughesy); ABTA ARC1700002 (B.M. Ellingson)

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