Anne Baldock

Gene therapy enhances chemotherapy tolerance and efficacy in glioblastoma patients

BACKGROUND Temozolomide (TMZ) is one of the most potent chemotherapy agents for the treatment of glioblastoma. Unfortunately, almost half of glioblastoma tumors are TMZ resistant due to overexpression of methylguanine methyltransferase (MGMT(hi)). Coadministration of O6-benzylguanine (O6BG) can restore TMZ sensitivity, but causes off-target myelosuppression. Here, we conducted a prospective clinical trial to test whether gene therapy to confer O6BG resistance in hematopoietic stem cells (HSCs) improves chemotherapy tolerance and outcome. METHODS We enrolled 7 newly diagnosed glioblastoma patients with MGMT(hi) tumors. Patients received autologous gene-modified HSCs following single-agent carmustine administration. After hematopoietic recovery, patients underwent O6BG/TMZ chemotherapy in 28-day cycles. Serial blood samples and tumor images were collected throughout the study. Chemotherapy tolerance was determined by the observed myelosuppression and recovery following each cycle. Patient-specific biomathematical modeling of tumor growth was performed. Progression-free survival (PFS) and overall survival (OS) were also evaluated. RESULTS Gene therapy permitted a significant increase in the mean number of tolerated O6BG/TMZ cycles (4.4 cycles per patient, P < 0.05) compared with historical controls without gene therapy (n = 7 patients, 1.7 cycles per patient). One patient tolerated an unprecedented 9 cycles and demonstrated long-term PFS without additional therapy. Overall, we observed a median PFS of 9 (range 3.5-57+) months and OS of 20 (range 13-57+) months. Furthermore, biomathematical modeling revealed markedly delayed tumor growth at lower cumulative TMZ doses in study patients compared with patients that received standard TMZ regimens without O6BG. CONCLUSION These data support further development of chemoprotective gene therapy in combination with O6BG and TMZ for the treatment of glioblastoma and potentially other tumors with overexpression of MGMT. TRIAL REGISTRATION Clinicaltrials.gov NCT00669669. FUNDING R01CA114218, R01AI080326, R01HL098489, P30DK056465, K01DK076973, R01HL074162, R01CA164371, R01NS060752, U54CA143970.

Invasion and proliferation kinetics in enhancing gliomas predict IDH1 mutation status

BACKGROUND Glioblastomas with a specific mutation in the isocitrate dehydrogenase 1 (IDH1) gene have a better prognosis than gliomas with wild-type IDH1. METHODS Here we compare the IDH1 mutational status in 172 contrast-enhancing glioma patients with the invasion profile generated by a patient-specific mathematical model we developed based on MR imaging. RESULTS We show that IDH1-mutated contrast-enhancing gliomas were relatively more invasive than wild-type IDH1 for all 172 contrast-enhancing gliomas as well as the subset of 158 histologically confirmed glioblastomas. The appearance of this relatively increased, model-predicted invasive profile appears to be determined more by a lower model-predicted net proliferation rate rather than an increased model-predicted dispersal rate of the glioma cells. Receiver operator curve analysis of the model-predicted MRI-based invasion profile revealed an area under the curve of 0.91, indicative of a predictive relationship. The robustness of this relationship was tested by cross-validation analysis of the invasion profile as a predictive metric for IDH1 status. CONCLUSIONS The strong correlation between IDH1 mutation status and the MRI-based invasion profile suggests that use of our tumor growth model may lead to noninvasive clinical detection of IDH1 mutation status and thus lead to better treatment planning, particularly prior to surgical resection, for contrast-enhancing gliomas.

From Patient-Specific Mathematical Neuro-Oncology to Precision Medicine

Gliomas are notoriously aggressive, malignant brain tumors that have variable response to treatment. These patients often have poor prognosis, informed primarily by histopathology. Mathematical neuro-oncology (MNO) is a young and burgeoning field that leverages mathematical models to predict and quantify response to therapies. These mathematical models can form the basis of modern “precision medicine” approaches to tailor therapy in a patient-specific manner. Patient-specific models (PSMs) can be used to overcome imaging limitations, improve prognostic predictions, stratify patients, and assess treatment response in silico. The information gleaned from such models can aid in the construction and efficacy of clinical trials and treatment protocols, accelerating the pace of clinical research in the war on cancer. This review focuses on the growing translation of PSM to clinical neuro-oncology. It will also provide a forward-looking view on a new era of patient-specific MNO.

Response Classification Based on a Minimal Model of Glioblastoma Growth Is Prognostic for Clinical Outcomes and Distinguishes Progression from Pseudoprogression

Glioblastoma multiforme is the most aggressive type of primary brain tumor. Glioblastoma growth dynamics vary widely across patients, making it difficult to accurately gauge their response to treatment. We developed a model-based metric of therapy response called Days Gained that accounts for this heterogeneity. Here, we show in 63 newly diagnosed patients with glioblastoma that Days Gained scores from a simple glioblastoma growth model computed at the time of the first postradiotherapy MRI scan are prognostic for time to tumor recurrence and overall patient survival. After radiation treatment, Days Gained also distinguished patients with pseudoprogression from those with true progression. Because Days Gained scores can be easily computed with routinely available clinical imaging devices, this model offers immediate potential to be used in ongoing prospective studies.

Patient-Specific Metrics of Invasiveness Reveal Significant Prognostic Benefit of Resection in a Predictable Subset of Gliomas

Object Malignant gliomas are incurable, primary brain neoplasms noted for their potential to extensively invade brain parenchyma. Current methods of clinical imaging do not elucidate the full extent of brain invasion, making it difficult to predict which, if any, patients are likely to benefit from gross total resection. Our goal was to apply a mathematical modeling approach to estimate the overall tumor invasiveness on a patient-by-patient basis and determine whether gross total resection would improve survival in patients with relatively less invasive gliomas. Methods In 243 patients presenting with contrast-enhancing gliomas, estimates of the relative invasiveness of each patients tumor, in terms of the ratio of net proliferation rate of the glioma cells to their net dispersal rate, were derived by applying a patient-specific mathematical model to routine pretreatment MR imaging. The effect of varying degrees of extent of resection on overall survival was assessed for cohorts of patients grouped by tumor invasiveness. Results We demonstrate that patients with more diffuse tumors showed no survival benefit (P = 0.532) from gross total resection over subtotal/biopsy, while those with nodular (less diffuse) tumors showed a significant benefit (P = 0.00142) with a striking median survival benefit of over eight months compared to sub-totally resected tumors in the same cohort (an 80% improvement in survival time for GTR only seen for nodular tumors). Conclusions These results suggest that our patient-specific, model-based estimates of tumor invasiveness have clinical utility in surgical decision making. Quantification of relative invasiveness assessed from routinely obtained pre-operative imaging provides a practical predictor of the benefit of gross total resection.

Tumor cells in search for glutamate: an alternative explanation for increased invasiveness of IDH1 mutant gliomas

We thank the authors for a very thoughtful letter and agree that there are a number of different mechanisms through which isocitrate dehydrogenase (IDH) mutation, the downstream 2-hydroxyglutarate (2HG), can lead to a number of different state changes within tumor cells. The acidification of the tumor microenvironment was solely an interpretation of our Fig. 1. Schematic overview of the proposed effects of mutated IDH1 on cellular metabolism. a-KG is reduced to 2-HG, which when exported out of the cell could lead to acidification of tumor microenvironment. This may promote local invasive growth of tumor cells. We postulate the hypothesis that, because of depletion of cytosolic a-KG, glutamate is imported via EAAT2 and converted to a-KG by GDH (thick arrows). In this way glutamate could act as a chemotactic source that also promotes invasive tumor cell growth. Letters to the editor

Use of a novel patient-specific model of glioma growth kinetics to elucidate underlying biology as measured by gene expression microarray.

71 Background: Gliomas are heterogeneous diseases with a wide distribution of growth kinetics that can be estimated prior to treatment and that are prognostic for patient outcome after treatment. Coherent molecular data sets have been made available through cooperative projects such as REMBRANDT and TCGA. We apply our novel patient specific method of measuring the net proliferation and diffusion rate from routinely available preoperative MRI sequences on patients included in these publicly available data sets to assess the underlying biology with imaging. METHODS The normalized microarray data from REMBRANDT (n=475) was used to discover a set of genes differentially expressed among GBM patients when compared with lower grade gliomas (n=853). 647 of these genes were also assessed with probesets in TCGA (n=466). Of these 466 patients, 84 also had preoperative MRI imaging available through The Cancer Imaging Archive (TCIA), for which net diffusion (D) proliferation (ρ) were estimated. Differential gene expression comparing these patients was performed. RESULTS 37 genes were differentially expressed with D. Genes implicated in cell adhesion, ECM maintenance and the production of focal adhesions are negatively correlated with D. Genes positively correlated with D are related to cell motility and pseudopodia formation. When considering ρ, 20 genes were found to be differentially expressed. A subset of these genes is related to hypoxia and therapy resistance. Some genes are also increased after radiation in cell-lines. Clustering on these genes revealed two classes; one with a survival advantage (p=0.0002). CONCLUSIONS This work demonstrates the potential to assess underlying differences in biology in a heterogeneous disease through patient specific assessment of routinely available imaging. We find that more diffuse tumors will under-express genes involved in focal-adhesions and production of ECM, while they express genes in pathways related to motility and pseudopodia formation. Furthermore, tumors with high ρ are seen to express genes related to treatment resistance, which may explain worse survival in these patients. Future work will verify these markers in model organisms.