A new landscape for systemic pharmacotherapy of recurrent glioblastoma?

Glioblastoma is the most common and aggressive primary malignant brain tumor in adult patients [...

Thrombocytopenia limits the feasibility of salvage lomustine chemotherapy in recurrent glioblastoma: a secondary analysis of EORTC 26101

BACKGROUND Thrombocytopenia represents the main cause of stopping alkylating chemotherapy for toxicity. Here, we explored the incidence, and the consequences for treatment exposure and survival, of thrombocytopenia induced by lomustine in recurrent glioblastoma. METHODS We performed a retrospective analysis of the associations of thrombocytopenia with treatment delivery and outcome in EORTC 26101, a randomised trial designed to define the role of lomustine versus bevacizumab versus their combination in recurrent glioblastoma. RESULTS A total of 225 patients were treated with lomustine alone (median 1 cycle) (group 1) and 283 patients were treated with lomustine plus bevacizumab (median 3 lomustine cycles) (group 2). Among cycle delays and dose reductions of lomustine for toxicity, thrombocytopenia was the leading cause. Among 129 patients (57%) of group 1 and 187 patients (66%) of group 2 experiencing at least one episode of thrombocytopenia, 36 patients (16%) in group 1 and 93 (33%) in group 2 had their treatment modified because of thrombocytopenia. Lomustine was discontinued for thrombocytopenia in 16 patients (7.1%) in group 1 and in 38 patients (13.4%) in group 2. On adjusted analysis accounting for major prognostic factors, dose modification induced by thrombocytopenia was associated with inferior progression-free survival in patients with MGMT promoter-methylated tumours in groups 1 and 2. This effect was noted for overall survival, too, but only for group 2 patients. CONCLUSION Drug-induced thrombocytopenia is a major limitation to adequate exposure to lomustine chemotherapy in recurrent glioblastoma. Mitigating thrombocytopenia to enhance lomustine exposure might improve outcome in patients with MGMT promoter-methylated tumours

Recurrent glioblastoma: From molecular landscape to new treatment perspectives

Glioblastoma is the most frequent and aggressive form among malignant central nervous system primary tumors in adults. Standard treatment for newly diagnosed glioblastoma consists in maximal safe resection, if feasible, followed by radiochemotherapy and adjuvant chemotherapy with temozolomide; despite this multimodal treatment, virtually all glioblastomas relapse. Once tumors progress after first-line therapy, treatment options are limited and management of recurrent glioblastoma remains challenging. Loco-regional therapy with re-surgery or re-irradiation may be evaluated in selected cases, while traditional systemic therapy with nitrosoureas and temozolomide rechallenge showed limited efficacy. In recent years, new clinical trials using, for example, regorafenib or a combination of tyrosine kinase inhibitors and immunotherapy were performed with promising results. In particular, molecular targeted therapy could show efficacy in selected patients with specific gene mutations. Nonetheless, some molecular characteristics and genetic alterations could change during tumor progression, thus affecting the efficacy of precision medicine. We therefore reviewed the molecular and genomic landscape of recurrent glioblastoma, the strategy for clinical management and the major phase I-III clinical trials analyzing recent drugs and combination regimens in these patients

Genomic aberrations associated with outcome in anaplastic oligodendroglial tumors treated within the EORTC phase III trial 26951

Despite similar morphological aspects, anaplastic oligodendroglial tumors (AOTs) form a heterogeneous clinical subgroup of gliomas. The chromosome arms 1p/19q codeletion has been shown to be a relevant biomarker in AOTs and to be perfectly exclusive from EGFR amplification in gliomas. To identify new genomic regions associated with prognosis, 60 AOTs from the EORTC trial 26951 were analyzed retrospectively using BAC-array-based comparative genomic hybridization. The data were processed using a binary tree method. Thirty-three BACs with prognostic value were identified distinguishing four genomic subgroups of AOTs with different prognosis (p < 0.0001). Type I tumors (25%) were characterized by: (1) an EGFR amplification, (2) a poor prognosis, (3) a higher rate of necrosis, and (4) an older age of patients. Type II tumors (21.7%) had: (1) loss of prognostic BACs located on 1p tightly associated with 19q deletion, (2) a longer survival, (3) an oligodendroglioma phenotype, and (4) a frontal location in brain. Type III AOTs (11.7%) exhibited: (1) a deletion of prognostic BACs located on 21q, and (2) a short survival. Finally, type IV tumors (41.7%) had different genomic patterns and prognosis than type I, II and III AOTs. Multivariate analysis showed that genomic type provides additional prognostic data to clinical, imaging and pathological features. Similar results were obtained in the cohort of 45 centrally reviewed–validated cases of AOTs. Whole genome analysis appears useful to screen the numerous genomic abnormalities observed in AOTs and to propose new biomarkers particularly in the non-1p/19q codeleted AOTs

Drug sensitivity of single cancer cells is predicted by changes in mass accumulation rate

Assays that can determine the response of tumor cells to cancer therapeutics could greatly aid the selection of drug regimens for individual patients. However, the utility of current functional assays is limited, and predictive genetic biomarkers are available for only a small fraction of cancer therapies. We found that the single-cell mass accumulation rate (MAR), profiled over many hours with a suspended microchannel resonator, accurately defined the drug sensitivity or resistance of glioblastoma and B-cell acute lymphocytic leukemia cells. MAR revealed heterogeneity in drug sensitivity not only between different tumors, but also within individual tumors and tumor-derived cell lines. MAR measurement predicted drug response using samples as small as 25 μl of peripheral blood while maintaining cell viability and compatibility with downstream characterization. MAR measurement is a promising approach for directly assaying single-cell therapeutic responses and for identifying cellular subpopulations with phenotypic resistance in heterogeneous tumors.United States. National Institutes of Health (R01 CA170592)United States. National Institutes of Health (R33 CA191143)National Cancer Institute (U.S.) (U54 CA143874)United States. National Institutes of Health (NIH/NIGMS T32 GM008334

Prognostic Markers of DNA Methylation and Next-Generation Sequencing in Progressive Glioblastoma from the EORTC-26101 Trial

PURPOSE: The EORTC-26101 study was a randomized phase II and III clinical trial of bevacizumab in combination with lomustine versus lomustine alone in progressive glioblastoma. Other than for progression-free survival (PFS), there was no benefit from addition of bevacizumab for overall survival (OS). However, molecular data allow for the rare opportunity to assess prognostic biomarkers from primary surgery for their impact in progressive glioblastoma. EXPERIMENTAL DESIGN: We analyzed DNA methylation array data and panel sequencing from 170 genes of 380 tumor samples of the EORTC-26101 study. These patients were comparable with the overall study cohort in regard to baseline characteristics, study treatment, and survival.RESULTS: Of patients' samples, 295/380 (78%) were classified into one of the main glioblastoma groups, receptor tyrosine kinase (RTK)1, RTK2 and mesenchymal. There were 10 patients (2.6%) with isocitrate dehydrogenase mutant tumors in the biomarker cohort. Patients with RTK1 and RTK2 classified tumors had lower median OS compared with mesenchymal (7.6 vs. 9.2 vs. 10.5 months). O6-methylguanine DNA-methyltransferase (MGMT) promoter methylation was prognostic for PFS and OS. Neurofibromin (NF)1 mutations were predictive of response to bevacizumab treatment.CONCLUSIONS: Thorough molecular classification is important for brain tumor clinical trial inclusion and evaluation. MGMT promoter methylation and RTK1 classifier assignment were prognostic in progressive glioblastoma. NF1 mutation may be a predictive biomarker for bevacizumab treatment.</p

Deep-learning-based reconstruction of undersampled MRI to reduce scan times: a multicentre, retrospective, cohort study

Background: The extended acquisition times required for MRI limit its availability in resource-constrained settings. Consequently, accelerating MRI by undersampling k-space data, which is necessary to reconstruct an image, has been a long-standing but important challenge. We aimed to develop a deep convolutional neural network (dCNN) optimisation method for MRI reconstruction and to reduce scan times and evaluate its effect on image quality and accuracy of oncological imaging biomarkers. Methods: In this multicentre, retrospective, cohort study, MRI data from patients with glioblastoma treated at Heidelberg University Hospital (775 patients and 775 examinations) and from the phase 2 CORE trial (260 patients, 1083 examinations, and 58 institutions) and the phase 3 CENTRIC trial (505 patients, 3147 examinations, and 139 institutions) were used to develop, train, and test dCNN for reconstructing MRI from highly undersampled single-coil k-space data with various acceleration rates (R=2, 4, 6, 8, 10, and 15). Independent testing was performed with MRIs from the phase 2/3 EORTC-26101 trial (528 patients with glioblastoma, 1974 examinations, and 32 institutions). The similarity between undersampled dCNN-reconstructed and original MRIs was quantified with various image quality metrics, including structural similarity index measure (SSIM) and the accuracy of undersampled dCNN-reconstructed MRI on downstream radiological assessment of imaging biomarkers in oncology (automated artificial intelligence-based quantification of tumour burden and treatment response) was performed in the EORTC-26101 test dataset. The public NYU Langone Health fastMRI brain test dataset (558 patients and 558 examinations) was used to validate the generalisability and robustness of the dCNN for reconstructing MRIs from available multi-coil (parallel imaging) k-space data. Findings: In the EORTC-26101 test dataset, the median SSIM of undersampled dCNN-reconstructed MRI ranged from 0·88 to 0·99 across different acceleration rates, with 0·92 (95% CI 0·92-0·93) for 10-times acceleration (R=10). The 10-times undersampled dCNN-reconstructed MRI yielded excellent agreement with original MRI when assessing volumes of contrast-enhancing tumour (median DICE for spatial agreement of 0·89 [95% CI 0·88 to 0·89]; median volume difference of 0·01 cm3 [95% CI 0·00 to 0·03] equalling 0·21%; p=0·0036 for equivalence) or non-enhancing tumour or oedema (median DICE of 0·94 [95% CI 0·94 to 0·95]; median volume difference of -0·79 cm3 [95% CI -0·87 to -0·72] equalling -1·77%; p=0·023 for equivalence) in the EORTC-26101 test dataset. Automated volumetric tumour response assessment in the EORTC-26101 test dataset yielded an identical median time to progression of 4·27 months (95% CI 4·14 to 4·57) when using 10-times-undersampled dCNN-reconstructed or original MRI (log-rank p=0·80) and agreement in the time to progression in 374 (95·2%) of 393 patients with data. The dCNN generalised well to the fastMRI brain dataset, with significant improvements in the median SSIM when using multi-coil compared with single-coil k-space data (p<0·0001). Interpretation: Deep-learning-based reconstruction of undersampled MRI allows for a substantial reduction of scan times, with a 10-times acceleration demonstrating excellent image quality while preserving the accuracy of derived imaging biomarkers for the assessment of oncological treatment response. Our developments are available as open source software and hold considerable promise for increasing the accessibility to MRI, pending further prospective validation

Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology:a multicentre, retrospective cohort study

International audienceBackground Gadolinium-based contrast agents (GBCAs) are widely used to enhance tissue contrast during MRI scans and play a crucial role in the management of patients with cancer. However, studies have shown gadolinium deposition in the brain after repeated GBCA administration with yet unknown clinical significance. We aimed to assess the feasibility and diagnostic value of synthetic post-contrast T1-weighted MRI generated from pre-contrast MRI sequences through deep convolutional neural networks (dCNN) for tumour response assessment in neuro-oncology. Methods In this multicentre, retrospective cohort study, we used MRI examinations to train and validate a dCNN for synthesising post-contrast T1-weighted sequences from pre-contrast T1-weighted, T2-weighted, and fluid-attenuated inversion recovery sequences. We used MRI scans with availability of these sequences from 775 patients with glioblastoma treated at Heidelberg University Hospital, Heidelberg, Germany (775 MRI examinations); 260 patients who participated in the phase 2 CORE trial (1083 MRI examinations, 59 institutions); and 505 patients who participated in the phase 3 CENTRIC trial (3147 MRI examinations, 149 institutions). Separate training runs to rank the importance of individual sequences and (for a subset) diffusion-weighted imaging were conducted. Independent testing was performed on MRI data from the phase 2 and phase 3 EORTC-26101 trial (521 patients, 1924 MRI examinations, 32 institutions). The similarity between synthetic and true contrast enhancement on post-contrast T1-weighted MRI was quantified using the structural similarity index measure (SSIM). Automated tumour segmentation and volumetric tumour response assessment based on synthetic versus true post-contrast T1-weighted sequences was performed in the EORTC-26101 trial and agreement was assessed with Kaplan-Meier plots. Interpretation Generating synthetic post-contrast T1-weighted MRI from pre-contrast MRI using dCNN is feasible and quantification of the contrast-enhancing tumour burden from synthetic post-contrast T1-weighted MRI allows assessment of the patient's response to treatment with no significant difference by comparison with true post-contrast T1-weighted sequences with administration of GBCAs. This finding could guide the application of dCNN in radiology to potentially reduce the necessity of GBCA administration

Marizomib for patients with newly diagnosed glioblastoma: a randomized phase 3 trial

Background: Standard treatment for patients with newly diagnosed glioblastoma includes surgery, radiotherapy (RT) and temozolomide (TMZ) chemotherapy (TMZ/RT→TMZ). The proteasome has long been considered a promising therapeutic target because of its role as a central biological hub in tumor cells. Marizomib is a novel pan-proteasome inhibitor that crosses the blood brain barrier. Methods: EORTC 1709/CCTG CE.8 was a multicenter, randomized, controlled, open label phase 3 superiority trial. Key eligibility criteria included newly diagnosed glioblastoma, age > 18 years and Karnofsky performance status > 70. Patients were randomized in a 1:1 ratio. The primary objective was to compare overall survival (OS) in patients receiving marizomib in addition to TMZ/RT→TMZ with patients receiving only standard treatment in the whole population, and in the subgroup of patients with MGMT promoter-unmethylated tumors. Results: The trial was opened at 82 institutions in Europe, Canada and the US. A total of 749 patients (99.9% of planned 750) were randomized. OS was not different between the standard and the marizomib arm (median 17 vs 16.5 months; HR=1.04; p=0.64). PFS was not statistically different either (median 6.0 vs. 6.3 months; HR=0.97; p=0.67). In patients with MGMT promoter-unmethylated tumors, OS was also not different between standard therapy and marizomib (median 14.5 vs 15.1 months, HR=1.13; p=0.27). More CTCAE grade 3/4 treatment-emergent adverse events were observed in the marizomib arm than in the standard arm. Conclusions: Adding marizomib to standard temozolomide-based radiochemotherapy resulted in more toxicity, but did not improve OS or PFS in patients with newly diagnosed glioblastoma