Andrew Trister, MD PhD

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… Mehr zeigen

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. Weniger anzeigen

PURPOSE:
To demonstrate a method of generating patient-specific, biologically-guided radiotherapy dose plans and compare them to the standard-of-care protocol.
METHODS AND MATERIALS:
We integrated a patient-specific biomathematical model of glioma proliferation, invasion and radiotherapy with a multiobjective evolutionary algorithm for intensity-modulated radiation therapy optimization to construct individualized, biologically-guided plans for 11 glioblastoma patients… Mehr zeigen

PURPOSE:
To demonstrate a method of generating patient-specific, biologically-guided radiotherapy dose plans and compare them to the standard-of-care protocol.
METHODS AND MATERIALS:
We integrated a patient-specific biomathematical model of glioma proliferation, invasion and radiotherapy with a multiobjective evolutionary algorithm for intensity-modulated radiation therapy optimization to construct individualized, biologically-guided plans for 11 glioblastoma patients. Patient-individualized, spherically-symmetric simulations of the standard-of-care and optimized plans were compared in terms of several biological metrics.
RESULTS:
The integrated model generated spatially non-uniform doses that, when compared to the standard-of-care protocol, resulted in a 67% to 93% decrease in equivalent uniform dose to normal tissue, while the therapeutic ratio, the ratio of tumor equivalent uniform dose to that of normal tissue, increased between 50% to 265%. Applying a novel metric of treatment response (Days Gained) to the patient-individualized simulation results predicted that the optimized plans would have a significant impact on delaying tumor progression, with increases from 21% to 105% for 9 of 11 patients.
CONCLUSIONS:
Patient-individualized simulations using the combination of a biomathematical model with an optimization algorithm for radiation therapy generated biologically-guided doses that decreased normal tissue EUD and increased therapeutic ratio with the potential to improve survival outcomes for treatment of glioblastoma. Weniger anzeigen

The progressive introduction of high-throughput molecular techniques in the clinic allows for the extensive and systematic exploration of multiple biological layers of tumors. Molecular profiles and classifiers generated from these assays represent the foundation of what the National Academy describes as the future of 'precision medicine.' However, the analysis of such complex data requires the implementation of sophisticated bioinformatic and statistical procedures. It is critical that… Mehr zeigen

The progressive introduction of high-throughput molecular techniques in the clinic allows for the extensive and systematic exploration of multiple biological layers of tumors. Molecular profiles and classifiers generated from these assays represent the foundation of what the National Academy describes as the future of 'precision medicine.' However, the analysis of such complex data requires the implementation of sophisticated bioinformatic and statistical procedures. It is critical that oncology practitioners be aware of the advantages and limitations of the methods used to generate classifiers in order to usher them into the clinic. This article uses publicly available expression data from NSCLC patients to first illustrate the challenges of experimental design and pre-processing of data prior to clinical application highlights the challenges of high-dimensional statistical analysis. It provides a roadmap for the translation of such classifiers to clinical practice and make key recommendations for good practice. Weniger anzeigen

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… Mehr zeigen

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. Weniger anzeigen

The explosive growth of molecular diagnostic tests introduces the possibility of increasing our understanding of disease mechanisms in the clinic and promises to improve target identification for new therapies. A new paradigm is emerging in drug discovery that is driven by these clinically derived data. Unfortunately, the translation of novel insights into useful therapies remains costly and difficult. New clinical trial design and open validation of markers may reduce these barriers to… Mehr zeigen

The explosive growth of molecular diagnostic tests introduces the possibility of increasing our understanding of disease mechanisms in the clinic and promises to improve target identification for new therapies. A new paradigm is emerging in drug discovery that is driven by these clinically derived data. Unfortunately, the translation of novel insights into useful therapies remains costly and difficult. New clinical trial design and open validation of markers may reduce these barriers to clinical utility. Weniger anzeigen

Glioblastoma multiforme (GBM) is the most aggressive type of primary brain tumor. GBM 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 demonstrate in 63 newly diagnosed GBM patients that Days Gained scores from a simple GBM growth model computed at the time of the first post-radiation therapy MRI scan are… Mehr zeigen

Glioblastoma multiforme (GBM) is the most aggressive type of primary brain tumor. GBM 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 demonstrate in 63 newly diagnosed GBM patients that Days Gained scores from a simple GBM growth model computed at the time of the first post-radiation therapy 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. Since Days Gained scores can be easily computed with routinely available clinical imaging devices, it offers immediate potential to be used in ongoing prospective studies. Weniger anzeigen

Accurate clinical assessment of a patient's response to treatment for glioblastoma multiforme (GBM), the most malignant type of primary brain tumor, is undermined by the wide patient-to-patient variability in GBM dynamics and responsiveness to therapy. Using computational models that account for the unique geometry and kinetics of individual patients' tumors, we developed a method for assessing treatment response that discriminates progression-free and overall survival following therapy for… Mehr zeigen

Accurate clinical assessment of a patient's response to treatment for glioblastoma multiforme (GBM), the most malignant type of primary brain tumor, is undermined by the wide patient-to-patient variability in GBM dynamics and responsiveness to therapy. Using computational models that account for the unique geometry and kinetics of individual patients' tumors, we developed a method for assessing treatment response that discriminates progression-free and overall survival following therapy for GBM. Applying these models as untreated virtual controls, we generate a patient-specific "Days Gained" response metric that estimates the number of days a therapy delayed imageable tumor progression. We assessed treatment response in terms of Days Gained scores for 33 patients at the time of their first MRI scan following first-line radiation therapy. Based on Kaplan-Meier analyses, patients with Days Gained scores of 100 or more had improved progression-free survival, and patients with scores of 117 or more had improved overall survival. Our results demonstrate that the Days Gained response metric calculated at the routinely acquired first post-radiation treatment time point provides prognostic information regarding progression and survival outcomes. Applied prospectively, our model-based approach has the potential to improve GBM treatment by accounting for patient-to-patient heterogeneity in GBM dynamics and responses to therapy. Weniger anzeigen

We demonstrate a patient-specific method of adaptive IMRT treatment for glioblastoma using a multiobjective evolutionary algorithm (MOEA). The MOEA generates spatially optimized dose distributions using an iterative dialogue between the MOEA and a mathematical model of tumor cell proliferation, diffusion and response. Dose distributions optimized on a weekly basis using biological metrics have the potential to substantially improve and individualize treatment outcomes. Optimized dose… Mehr zeigen

We demonstrate a patient-specific method of adaptive IMRT treatment for glioblastoma using a multiobjective evolutionary algorithm (MOEA). The MOEA generates spatially optimized dose distributions using an iterative dialogue between the MOEA and a mathematical model of tumor cell proliferation, diffusion and response. Dose distributions optimized on a weekly basis using biological metrics have the potential to substantially improve and individualize treatment outcomes. Optimized dose distributions were generated using three different decision criteria for the tumor and compared with plans utilizing standard dose of 1.8 Gy/fraction to the CTV (T2-visible MRI region plus a 2.5 cm margin). The sets of optimal dose distributions generated using the MOEA approach the Pareto Front (the set of IMRT plans that delineate optimal tradeoffs amongst the clinical goals of tumor control and normal tissue sparing). MOEA optimized doses demonstrated superior performance as judged by three biological metrics according to simulated results. The predicted number of reproductively viable cells 12 weeks after treatment was found to be the best target objective for use in the MOEA. Weniger anzeigen

OBJECTIVES:: The objective of this study was to identify predictive factors of occult mediastinal nodal involvement on staging positron emission tomography with F-fluorodeoxyglucose in patients with non-small cell lung cancer. METHODS:: We performed a retrospective review of 665 patients with suspected non-small cell lung cancer who underwent staging positron emission tomography with F-fluorodeoxyglucose from January 1, 2000 through August 31, 2010 at the Hospital of the University of… Mehr zeigen

OBJECTIVES:: The objective of this study was to identify predictive factors of occult mediastinal nodal involvement on staging positron emission tomography with F-fluorodeoxyglucose in patients with non-small cell lung cancer. METHODS:: We performed a retrospective review of 665 patients with suspected non-small cell lung cancer who underwent staging positron emission tomography with F-fluorodeoxyglucose from January 1, 2000 through August 31, 2010 at the Hospital of the University of Pennsylvania with clinical stage I or II disease and no evidence of N2 or N3 involvement on staging positron emission tomography (PET). A total of 201 of these patients underwent invasive pathologic staging of the mediastinum at the Hospital of the University of Pennsylvania with pathology reports available at the time of review. RESULTS:: A total of 63 of the 201 patients were found to have N2 disease at the time of pathologic staging. The mean standardized uptake value (SUV) of the primary tumor for patients with occult N2 metastases was significantly higher than the node-negative patients (SUV 9.31 vs. 7.24, P=0.04). Histology, tumor location (central vs. peripheral), sex, and age were not predictive for occult N2 disease. A multivariate analysis was performed and identified primary tumor SUV>6 was the only significant predictor (P=0.02). An analysis by quartile identified a primary tumor SUV>10 to have an odds ratio of 1.72 compared with an SUV<4 of occult N2 involvement. CONCLUSIONS:: Increased primary tumor SUV predicted for increased risk of mediastinal nodal disease. Tumor location was not predictive of PET-occult mediastinal nodal involvement, in contrast to previous publications. Pathologic staging of the mediastinum should be strongly considered in these patients even with a negative mediastinum on PET. Weniger anzeigen

We simulated the docking of human immunodeficiency virus (HIV) with a cell membrane using Brownian adhesive dynamics. The main advance in the current version of Brownian adhesive dynamics is that we use a simple bead-spring model to coarsely approximate the role of gp120 trimerization on HIV docking. We used our simulations to elucidate the effect of env spike density on the rate and probability of HIV binding, as well as the probability that each individual gp120 trimer is fully engaged. We… Mehr zeigen

We simulated the docking of human immunodeficiency virus (HIV) with a cell membrane using Brownian adhesive dynamics. The main advance in the current version of Brownian adhesive dynamics is that we use a simple bead-spring model to coarsely approximate the role of gp120 trimerization on HIV docking. We used our simulations to elucidate the effect of env spike density on the rate and probability of HIV binding, as well as the probability that each individual gp120 trimer is fully engaged. We found that for typical CD4 surface densities, viruses expressing as few as 8 env spikes will dock with binding rate constants comparable to viruses expressing 72 spikes. We investigated the role of cellular receptor diffusion on the degree of binding achieved by the virus on both short timescales (where binding has reached steady state but before substantial receptor accumulation in the viral-cell contact zone has occurred) and long timescales (where the system has reached steady state). On short timescales, viruses with 10-23 env trimers most efficiently form fully engaged trimers. On long timescales, all gp120 in the contact area will become bound to CD4. We found that it takes seconds for engaged trimers to cluster CD4 molecules in the contact zone, which partially explains the deleay in viral entry. Weniger anzeigen