Citations

Abstract Background Glioblastoma (GBM) is the most common primary malignant adult brain tumor. Temozolomide (TMZ) is the standard of care and is most effective in GBMs that lack the DNA repair protein O6-methylguanine-DNA methyltransferase (MGMT). Moreover, even initially responsive tumors develop a secondary resistance to TMZ and become untreatable. Since aberrant epidermal growth factor receptor (EGFR) signaling is widespread in GBM, EGFR inhibition has been tried in multiple clinical trials without success. We recently reported that inhibiting EGFR leads to increased secretion of tumor necrosis factor (TNF) and activation of a survival pathway in GBM. Here, we compare the efficacy of TMZ versus EGFR plus TNF inhibition in an orthotopic mouse model of GBM. Methods We use an orthotopic model to examine the efficacy of TMZ versus EGFR plus TNF inhibition in multiple subsets of GBMs, including MGMT methylated and unmethylated primary GBMs, recurrent GBMs, and GBMs rendered experimentally resistant to TMZ. Results The efficacy of the 2 treatments was similar in MGMT methylated GBMs. However, in MGMT unmethylated GBMs, a combination of EGFR plus TNF inhibition was more effective. We demonstrate that the 2 treatment approaches target distinct and non-overlapping pathways. Thus, importantly, EGFR plus TNF inhibition remains effective in TMZ-resistant recurrent GBMs and in GBMs rendered experimentally resistant to TMZ. Conclusion EGFR inhibition combined with a blunting of the accompanying TNF-driven adaptive response could be a viable therapeutic approach in MGMT unmethylated and recurrent EGFR-expressing GBMs.

Abstract O6-methylguanine-DNA methyltransferase (MGMT) is an enzyme that removes alkyl groups at the O6-position of guanine in DNA. MGMT expression is reduced or absent in many tumor types derived from a diverse range of tissues, most notably in glioma. Low MGMT expression confers significant sensitivity to DNA alkylating agents such as temozolomide, providing a natural therapeutic index over normal tissue. In this study, we sought to identify novel approaches that could maximally exploit the therapeutic index between tumor cells and normal tissues based on MGMT expression, as a means to enhance selective tumor cell killing. Temozolomide, unlike other alkylators, activated the ataxia telangiectasia and Rad3–related (ATR)–checkpoint kinase 1 (Chk1) axis in a manner that was highly dependent on MGMT status. Temozolomide induced growth delay, DNA double-strand breaks, and G2–M cell-cycle arrest, which led to ATR-dependent phosphorylation of Chk1; this effect was dependent on reduced MGMT expression. Treatment of MGMT-deficient cells with temozolomide increased sensitivity to ATR inhibitors both in vitro and in vivo across numerous tumor cell types. Taken together, this study reveals a novel approach for selectively targeting MGMT-deficient cells with ATR inhibitors and temozolomide. As ATR inhibitors are currently being tested in clinical trials, and temozolomide is a commonly used chemotherapeutic, this approach is clinically actionable. Furthermore, this interaction potently exploits a DNA-repair defect found in many cancers. Significance: Monofunctional alkylating agents sensitize MGMT-deficient tumor cells to ATR inhibitors.

AbstractAccurate pathologic diagnoses and molecularly informed treatment decisions for a wide variety of cancers depend on robust clinical molecular testing that uses genomic, epigenomic, and transcriptomic-based tools. Nowhere is this more essential than in the workup of brain tumors, as emphasized by the incorporation of molecular criteria into the 2016 World Health Organization classification of central nervous system tumors and the updated official guidelines of the National Comprehensive Cancer Network. Despite the medical necessity of molecular testing in brain tumors, access to and utilization of molecular diagnostics is still highly variable across institutions, and a lack of reimbursement for such testing remains a significant obstacle. The objectives of this review are (i) to identify barriers to adoption of molecular testing in brain tumors, (ii) to describe the current molecular tools recommended for the clinical evaluation of brain tumors, and (iii) to summarize how molecular data are interpreted to guide clinical care, so as to improve understanding and justification for their coverage in the routine workup of adult and pediatric brain tumor cases.

Abstract Background Predictive molecular biomarkers to select optimal treatment for patients with glioblastoma and other cancers are lacking. New strategies are needed when large randomized trials with correlative molecular data are not feasible. Methods Gene signatures (GS) were developed from 31 orthotopic glioblastoma patient-derived xenografts (PDXs), treated with standard therapies, to predict benefit from radiotherapy (RT-GS), temozolomide (Chemo-GS), or the combination (ChemoRT-GS). Independent validation was performed in a heterogeneously treated clinical cohort of 502 glioblastoma patients with overall survival as the primary endpoint. Multivariate Cox analysis was used to adjust for confounding variables and evaluate interactions between signatures and treatment. Results PDX models recapitulated the clinical heterogeneity of glioblastoma patients. RT-GS, Chemo-GS, and ChemoRT-GS were correlated with benefit from treatment in the PDX models. In independent clinical validation, higher RT-GS scores were associated with increased survival only in patients receiving RT (P = 0.0031, hazard ratio [HR] = 0.78 [0.66–0.92]), higher Chemo-GS scores were associated with increased survival only in patients receiving chemotherapy (P < 0.0001, HR = 0.66 [0.55–0.8]), and higher ChemoRT-GS scores were associated with increased survival only in patients receiving ChemoRT (P = 0.0001, HR = 0.54 [0.4–0.74]). RT-GS and ChemoRT-GS had significant interactions with treatment on multivariate analysis (P = 0.0009 and 0.02, respectively), indicating that they are bona fide predictive biomarkers. Conclusions Using a novel PDX-driven methodology, we developed and validated 3 platform-independent molecular signatures that predict benefit from standard of care therapies for glioblastoma. These signatures may be useful to personalize glioblastoma treatment in the clinic and this approach may be a generalizable method to identify predictive biomarkers without resource-intensive randomized trials.