Predictive models for diffuse low-grade glioma patients under chemotherapy

International audienceDiffuse low-grade gliomas are rare primitive cerebral tumours of adults. These tumors progress continuously over time and then turn to a higher grade of malignancy associated with neurological disability, leading ultimately to death. Tumour size is one of the most important prognostic factors. Thus, it is of great importance to be able to assess the volume of the tumor during the patients’ monitoring.MRI is nowadays the recommended modality to achieve this. Furthermore, if surgery remains the first option for diffuse low-grade gliomas, chemotherapy is increasingly used (before or after a possible surgery). However, crucial and difficult questions remain to be answered: identifying subgroups ofpatients who could benefit from chemotherapy, determining the best time to initiate chemotherapy, defining the duration of chemotherapy and evaluating the optimal time to perform surgery, or otherwise radiotherapy. In this study, we propose to help clinicians in decision-making, by designing new predictivemodels dedicated to the evolution of the diameter of the tumor. Two proposed statistical models (linear and exponential) have been validated on a database of 16 patients whose temozolomide-based chemotherapy lasted between 14 and 32 months, with an average duration of 22.8 months. The selection of the most appropriate model has been achieved with the corrected Akaike’s Information Criterion. The results are very promising, with coefficients of determination varying from 0.79 to 0.97 with an average value of 0.90 for the linear model. This shows it is possible to alert the clinician to a change in the tumor diameter’s dynamics

INTEGRATIVE ANALYSIS OF OMICS DATA IN ADULT GLIOMA AND OTHER TCGA CANCERS TO GUIDE PRECISION MEDICINE

Transcriptomic profiling and gene expression signatures have been widely applied as effective approaches for enhancing the molecular classification, diagnosis, prognosis or prediction of therapeutic response towards personalized therapy for cancer patients. Thanks to modern genome-wide profiling technology, scientists are able to build engines leveraging massive genomic variations and integrating with clinical data to identify “at risk” individuals for the sake of prevention, diagnosis and therapeutic interventions. In my graduate work for my Ph.D. thesis, I have investigated genomic sequencing data mining to comprehensively characterise molecular classifications and aberrant genomic events associated with clinical prognosis and treatment response, through applying high-dimensional omics genomic data to promote the understanding of gene signatures and somatic molecular alterations contributing to cancer progression and clinical outcomes. Following this motivation, my dissertation has been focused on the following three topics in translational genomics. 1) Characterization of transcriptomic plasticity and its association with the tumor microenvironment in glioblastoma (GBM). I have integrated transcriptomic, genomic, protein and clinical data to increase the accuracy of GBM classification, and identify the association between the GBM mesenchymal subtype and reduced tumorpurity, accompanied with increased presence of tumor-associated microglia. Then I have tackled the sole source of microglial as intrinsic tumor bulk but not their corresponding neurosphere cells through both transcriptional and protein level analysis using a panel of sphere-forming glioma cultures and their parent GBM samples.FurthermoreI have demonstrated my hypothesis through longitudinal analysis of paired primary and recurrent GBM samples that the phenotypic alterations of GBM subtypes are not due to intrinsic proneural-to-mesenchymal transition in tumor cells, rather it is intertwined with increased level of microglia upon disease recurrence. Collectively I have elucidated the critical role of tumor microenvironment (Microglia and macrophages from central nervous system) contributing to the intra-tumor heterogeneity and accurate classification of GBM patients based on transcriptomic profiling, which will not only significantly impact on clinical perspective but also pave the way for preclinical cancer research. 2) Identification of prognostic gene signatures that stratify adult diffuse glioma patientsharboring1p/19q co-deletions. I have compared multiple statistical methods and derived a gene signature significantly associated with survival by applying a machine learning algorithm. Then I have identified inflammatory response and acetylation activity that associated with malignant progression of 1p/19q co-deleted glioma. In addition, I showed this signature translates to other types of adult diffuse glioma, suggesting its universality in the pathobiology of other subset gliomas. My efforts on integrative data analysis of this highly curated data set usingoptimizedstatistical models will reflect the pending update to WHO classification system oftumorsin the central nervous system (CNS). 3) Comprehensive characterization of somatic fusion transcripts in Pan-Cancers. I have identified a panel of novel fusion transcripts across all of TCGA cancer types through transcriptomic profiling. Then I have predicted fusion proteins with kinase activity and hub function of pathway network based on the annotation of genetically mobile domains and functional domain architectures. I have evaluated a panel of in -frame gene fusions as potential driver mutations based on network fusion centrality hypothesis. I have also characterised the emerging complexity of genetic architecture in fusion transcripts through integrating genomic structure and somatic variants and delineating the distinct genomic patterns of fusion events across different cancer types. Overall my exploration of the pathogenetic impact and clinical relevance of candidate gene fusions have provided fundamental insights into the management of a subset of cancer patients by predicting the oncogenic signalling and specific drug targets encoded by these fusion genes. Taken together, the translational genomic research I have conducted during my Ph.D. study will shed new light on precision medicine and contribute to the cancer research community. The novel classification concept, gene signature and fusion transcripts I have identified will address several hotly debated issues in translational genomics, such as complex interactions between tumor bulks and their adjacent microenvironments, prognostic markers for clinical diagnostics and personalized therapy, distinct patterns of genomic structure alterations and oncogenic events in different cancer types, therefore facilitating our understanding of genomic alterations and moving us towards the development of precision medicine

Irradiation to Improve the Response to Immunotherapeutic Agents in Glioblastomas.

PurposeGlioblastoma (GBM) remains an incurable disease despite extensive treatment with surgical resection, irradiation, and temozolomide. In line with many other forms of aggressive cancers, GBM is currently under consideration as a target for immunotherapy. However, GBM tends to be nonimmunogenic and exhibits a microenvironment with few or no effector T cells, a relatively low nonsynonymous somatic mutational load, and a low predicted neoantigen burden. GBM also exploits a multitude of immunosuppressive strategies.Methods and materialsA number of immunotherapeutic approaches have been tested with disappointing results. A rationale exists to combine immunotherapy and radiation therapy, which can induce an immunogenic form of cell death with T-cell activation and tumor infiltration.ResultsVarious immunotherapy agents, including immune checkpoint modulators, transforming growth factor beta receptor inhibitors, and indoleamine-2,3-dioxygenase inhibitors, have been evaluated with irradiation in preclinical GBM models, with promising results, and are being further tested in clinical trials.ConclusionsThis review aims to present the basic rationale behind this emerging complementary therapeutic approach in GBM, appraise the current preclinical and clinical data, and discuss the future challenges in improving the antitumor immune response

The Systemic Treatment of Glioma

Gliomas have been treated by a specialized team including neurosurgery, radiation therapy, and neuro-oncology, as well as depending on integrated sophisticated facilities and multi-professional team. Despite these huge efforts to glioma treatment, glioblastoma, one of the most frequent gliomas, has median life expectance for just 15 months, so these results are still an unmet need. Related to the systemic treatment, some cancer approaches have been revolutionized with new strategies, such as immunotherapy, although in neuro-oncology, this alternative still has challenges to overcome. Throughout this chapter, relevant information and key points will be discussed to the best way to manage systemic treatment and improve glioma overall survival

Temozolomide chemotherapy versus radiotherapy in high-risk low-grade glioma (EORTC 22033-26033): a randomised, open-label, phase 3 intergroup study.

BACKGROUND: Outcome of low-grade glioma (WHO grade II) is highly variable, reflecting molecular heterogeneity of the disease. We compared two different, single-modality treatment strategies of standard radiotherapy versus primary temozolomide chemotherapy in patients with low-grade glioma, and assessed progression-free survival outcomes and identified predictive molecular factors. METHODS: For this randomised, open-label, phase 3 intergroup study (EORTC 22033-26033), undertaken in 78 clinical centres in 19 countries, we included patients aged 18 years or older who had a low-grade (WHO grade II) glioma (astrocytoma, oligoastrocytoma, or oligodendroglioma) with at least one high-risk feature (aged >40 years, progressive disease, tumour size >5 cm, tumour crossing the midline, or neurological symptoms), and without known HIV infection, chronic hepatitis B or C virus infection, or any condition that could interfere with oral drug administration. Eligible patients were randomly assigned (1:1) to receive either conformal radiotherapy (up to 50·4 Gy; 28 doses of 1·8 Gy once daily, 5 days per week for up to 6·5 weeks) or dose-dense oral temozolomide (75 mg/m(2) once daily for 21 days, repeated every 28 days [one cycle], for a maximum of 12 cycles). Random treatment allocation was done online by a minimisation technique with prospective stratification by institution, 1p deletion (absent vs present vs undetermined), contrast enhancement (yes vs no), age (<40 vs ≥40 years), and WHO performance status (0 vs ≥1). Patients, treating physicians, and researchers were aware of the assigned intervention. A planned analysis was done after 216 progression events occurred. Our primary clinical endpoint was progression-free survival, analysed by intention-to-treat; secondary outcomes were overall survival, adverse events, neurocognitive function (will be reported separately), health-related quality of life and neurological function (reported separately), and correlative analyses of progression-free survival by molecular markers (1p/19q co-deletion, MGMT promoter methylation status, and IDH1/IDH2 mutations). This trial is closed to accrual but continuing for follow-up, and is registered at the European Trials Registry, EudraCT 2004-002714-11, and at ClinicalTrials.gov, NCT00182819. FINDINGS: Between Sept 23, 2005, and March 26, 2010, 707 patients were registered for the study. Between Dec 6, 2005, and Dec 21, 2012, we randomly assigned 477 patients to receive either radiotherapy (n=240) or temozolomide chemotherapy (n=237). At a median follow-up of 48 months (IQR 31-56), median progression-free survival was 39 months (95% CI 35-44) in the temozolomide group and 46 months (40-56) in the radiotherapy group (unadjusted hazard ratio [HR] 1·16, 95% CI 0·9-1·5, p=0·22). Median overall survival has not been reached. Exploratory analyses in 318 molecularly-defined patients confirmed the significantly different prognosis for progression-free survival in the three recently defined molecular low-grade glioma subgroups (IDHmt, with or without 1p/19q co-deletion [IDHmt/codel], or IDH wild type [IDHwt]; p=0·013). Patients with IDHmt/non-codel tumours treated with radiotherapy had a longer progression-free survival than those treated with temozolomide (HR 1·86 [95% CI 1·21-2·87], log-rank p=0·0043), whereas there were no significant treatment-dependent differences in progression-free survival for patients with IDHmt/codel and IDHwt tumours. Grade 3-4 haematological adverse events occurred in 32 (14%) of 236 patients treated with temozolomide and in one (<1%) of 228 patients treated with radiotherapy, and grade 3-4 infections occurred in eight (3%) of 236 patients treated with temozolomide and in two (1%) of 228 patients treated with radiotherapy. Moderate to severe fatigue was recorded in eight (3%) patients in the radiotherapy group (grade 2) and 16 (7%) in the temozolomide group. 119 (25%) of all 477 patients had died at database lock. Four patients died due to treatment-related causes: two in the temozolomide group and two in the radiotherapy group. INTERPRETATION: Overall, there was no significant difference in progression-free survival in patients with low-grade glioma when treated with either radiotherapy alone or temozolomide chemotherapy alone. Further data maturation is needed for overall survival analyses and evaluation of the full predictive effects of different molecular subtypes for future individualised treatment choices. FUNDING: Merck Sharpe & Dohme-Merck & Co, Canadian Cancer Society, Swiss Cancer League, UK National Institutes of Health, Australian National Health and Medical Research Council, US National Cancer Institute, European Organisation for Research and Treatment of Cancer Cancer Research Fund

Prediction of Response to Temozolomide in Low-Grade Glioma Patients Based on Tumor Size Dynamics and Genetic Characteristics

International audienceBoth molecular profiling of tumors and longitudinal tumor size data modeling are relevant strategies to predict cancer patients' response to treatment. Herein we propose a model of tumor growth inhibition integrating a tumor's genetic characteristics (p53 mutation and 1p/19q codeletion) that successfully describes the time course of tumor size in patients with low-grade gliomas treated with first-line temozolomide chemotherapy. The model captures potential tumor progression under chemotherapy by accounting for the emergence of tissue resistance to treatment following prolonged exposure to temozolomide. Using information on individual tumors' genetic characteristics, in addition to early tumor size measurements, the model was able to predict the duration and magnitude of response, especially in those patients in whom repeated assessment of tumor response was obtained during the first 3 months of treatment. Combining longitudinal tumor size quantitative modeling with a tumor''s genetic characterization appears as a promising strategy to personalize treatments in patients with low-grade gliomas. WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC? þ First-line temozolomide is frequently used to treat low-grade gliomas (LGG), which are slow-growing brain tumors. The duration of response depends on genetic characteristics such as 1p/19q chromosomal codeletion, p53 mutation, and IDH mutations. However, up to now there are no means of predicting, at the individual level, the duration of the response to TMZ and its potential benefit for a given patient. • WHAT QUESTION DID THIS STUDY ADDRESS? þ The present study assessed whether combining longitudinal tumor size quantitative modeling with a tumor's genetic characterization could be an effective means of predicting the response to temozolomide at the individual level in LGG patients. • WHAT THIS STUDY ADDS TO OUR KNOWLEDGE þ For the first time, we developed a model of tumor growth inhibition integrating a tumor's genetic characteristics which successfully describes the time course of tumor size and captures potential tumor progression under chemotherapy in LGG patients treated with first-line temozolomide. The present study shows that using information on individual tumors' genetic characteristics, in addition to early tumor size measurements, it is possible to predict the duration and magnitude of response to temozolomide. • HOW THIS MIGHT CHANGE CLINICAL PHARMACOLOGY AND THERAPEUTICS þ Our model constitutes a rational tool to identify patients most likely to benefit from temozolomide and to optimize in these patients the duration of temozolomide therapy in order to ensure the longest duration of response to treatment. Response evaluation criteria such as RECIST—or RANO for brain tumors—are commonly used to assess response to anticancer treatments in clinical trials. 1,2 They assign a patient's response to one of four categories, ranging from " complete response " to " disease progression. " Yet, criticisms have been raised regarding the use of such categorical criteria in the drug development process, 3,4 and regulatory agencies have promoted the additional analysis of longitudinal tumor size measurements through the use of quantitative modeling. 5 Several mathematical models of tumor growth and response to treatment have been developed for this purpose. 6,7 These analyses have led to th

Computer simulation of glioma growth and morphology

Despite major advances in the study of glioma, the quantitative links between intra-tumor molecular/cellular properties, clinically observable properties such as morphology, and critical tumor behaviors such as growth and invasiveness remain unclear, hampering more effective coupling of tumor physical characteristics with implications for prognosis and therapy. Although molecular biology, histopathology, and radiological imaging are employed in this endeavor, studies are severely challenged by the multitude of different physical scales involved in tumor growth, i.e., from molecular nanoscale to cell microscale and finally to tissue centimeter scale. Consequently, it is often difficult to determine the underlying dynamics across dimensions. New techniques are needed to tackle these issues. Here, we address this multi-scalar problem by employing a novel predictive three-dimensional mathematical and computational model based on first-principle equations (conservation laws of physics) that describe mathematically the diffusion of cell substrates and other processes determining tumor mass growth and invasion. The model uses conserved variables to represent known determinants of glioma behavior, e.g., cell density and oxygen concentration, as well as biological functional relationships and parameters linking phenomena at different scales whose specific forms and values are hypothesized and calculated based on in vitro and in vivo experiments and from histopathology of tissue specimens from human gliomas. This model enables correlation of glioma morphology to tumor growth by quantifying interdependence of tumor mass on the microenvironment (e.g., hypoxia, tissue disruption) and on the cellular phenotypes (e.g., mitosis and apoptosis rates, cell adhesion strength). Once functional relationships between variables and associated parameter values have been informed, e.g., from histopathology or intra-operative analysis, this model can be used for disease diagnosis/prognosis, hypothesis testing, and to guide surgery and therapy. In particular, this tool identifies and quantifies the effects of vascularization and other cell-scale glioma morphological characteristics as predictors of tumor-scale growth and invasion

Protein kinase a distribution differentiates human glioblastoma from brain tissue

Brain tumor glioblastoma has no clear molecular signature and there is no effective therapy. In rodents, the intracellular distribution of the cyclic AMP (cAMP)-dependent protein kinase (Protein kinase A, PKA) R2Alpha subunit was previously shown to differentiate tumor cells from healthy brain cells. Now, we aim to validate this observation in human tumors. The distribution of regulatory (R1 and R2) and catalytic subunits of PKA was examined via immunohistochemistry and Western blot in primary cell cultures and biopsies from 11 glioblastoma patients. Data were compared with information obtained from 17 other different tumor samples. The R1 subunit was clearly detectable only in some samples. The catalytic subunit was variably distributed in the different tumors. Similar to rodent tumors, all human glioblastoma specimens showed perinuclear R2 distribution in the Golgi area, while it was undetectable outside the tumor. To test the effect of targeting PKA as a therapeutic strategy, the intracellular cyclic AMP concentration was modulated with different agents in four human glioblastoma cell lines. A significant increase in cell death was detected after increasing cAMP levels or modulating PKA activity. These data raise the possibility of targeting the PKA intracellular pathway for the development of diagnostic and/or therapeutic tools for human glioblastoma