Andrew D. Trister

The mPower study, Parkinson disease mobile data collected using ResearchKit

Current measures of health and disease are often insensitive, episodic, and subjective. Further, these measures generally are not designed to provide meaningful feedback to individuals. The impact of high-resolution activity data collected from mobile phones is only beginning to be explored. Here we present data from mPower, a clinical observational study about Parkinson disease conducted purely through an iPhone app interface. The study interrogated aspects of this movement disorder through surveys and frequent sensor-based recordings from participants with and without Parkinson disease. Benefitting from large enrollment and repeated measurements on many individuals, these data may help establish baseline variability of real-world activity measurement collected via mobile phones, and ultimately may lead to quantification of the ebbs-and-flows of Parkinson symptoms. App source code for these data collection modules are available through an open source license for use in studies of other conditions. We hope that releasing data contributed by engaged research participants will seed a new community of analysts working collaboratively on understanding mobile health data to advance human health.

Toward Patient-Specific, Biologically Optimized Radiation Therapy Plans for the Treatment of Glioblastoma

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.

A patient-specific computational model of hypoxia-modulated radiation resistance in glioblastoma using 18F-FMISO-PET

Glioblastoma multiforme (GBM) is a highly invasive primary brain tumour that has poor prognosis despite aggressive treatment. A hallmark of these tumours is diffuse invasion into the surrounding brain, necessitating a multi-modal treatment approach, including surgery, radiation and chemotherapy. We have previously demonstrated the ability of our model to predict radiographic response immediately following radiation therapy in individual GBM patients using a simplified geometry of the brain and theoretical radiation dose. Using only two pre-treatment magnetic resonance imaging scans, we calculate net rates of proliferation and invasion as well as radiation sensitivity for a patients disease. Here, we present the application of our clinically targeted modelling approach to a single glioblastoma patient as a demonstration of our method. We apply our model in the full three-dimensional architecture of the brain to quantify the effects of regional resistance to radiation owing to hypoxia in vivo determined by [18F]-fluoromisonidazole positron emission tomography (FMISO-PET) and the patient-specific three-dimensional radiation treatment plan. Incorporation of hypoxia into our model with FMISO-PET increases the model–data agreement by an order of magnitude. This improvement was robust to our definition of hypoxia or the degree of radiation resistance quantified with the FMISO-PET image and our computational model, respectively. This work demonstrates a useful application of patient-specific modelling in personalized medicine and how mathematical modelling has the potential to unify multi-modality imaging and radiation treatment planning.

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.

Periostin is a novel therapeutic target that predicts and regulates glioma malignancy.

BACKGROUND Periostin is a secreted matricellular protein critical for epithelial-mesenchymal transition and carcinoma metastasis. In glioblastoma, it is highly upregulated compared with normal brain, and existing reports indicate potential prognostic and functional importance in glioma. However, the clinical implications of periostin expression and function related to its therapeutic potential have not been fully explored. METHODS Periostin expression levels and patterns were examined in human glioma cells and tissues by quantitative real-time PCR and immunohistochemistry and correlated with glioma grade, type, recurrence, and survival. Functional assays determined the impact of altering periostin expression and function on cell invasion, migration, adhesion, and glioma stem cell activity and tumorigenicity. The prognostic and functional relevance of periostin and its associated genes were analyzed using the TCGA and REMBRANDT databases and paired recurrent glioma samples. RESULTS Periostin expression levels correlated directly with tumor grade and recurrence, and inversely with survival, in all grades of adult human glioma. Stromal deposition of periostin was detected only in grade IV gliomas. Secreted periostin promoted glioma cell invasion and adhesion, and periostin knockdown markedly impaired survival of xenografted glioma stem cells. Interactions with αvβ3 and αvβ5 integrins promoted adhesion and migration, and periostin abrogated cytotoxicity of the αvβ3/β5 specific inhibitor cilengitide. Periostin-associated gene signatures, predominated by matrix and secreted proteins, corresponded to patient prognosis and functional motifs related to increased malignancy. CONCLUSION Periostin is a robust marker of glioma malignancy and potential tumor recurrence. Abrogation of glioma stem cell tumorigenicity after periostin inhibition provides support for exploring the therapeutic impact of targeting periostin.

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.

The Mole Mapper Study, mobile phone skin imaging and melanoma risk data collected using ResearchKit

Sensor-embedded phones are an emerging facilitator for participant-driven research studies. Skin cancer research is particularly amenable to this approach, as phone cameras enable self-examination and documentation of mole abnormalities that may signal a progression towards melanoma. Aggregation and open sharing of this participant-collected data can be foundational for research and the development of early cancer detection tools. Here we describe data from Mole Mapper, an iPhone-based observational study built using the Apple ResearchKit framework. The Mole Mapper app was designed to collect participant-provided images and measurements of moles, together with demographic and behavioral information relating to melanoma risk. The study cohort includes 2,069 participants who contributed 1,920 demographic surveys, 3,274 mole measurements, and 2,422 curated mole images. Survey data recapitulates associations between melanoma and known demographic risks, with red hair as the most significant factor in this cohort. Participant-provided mole measurements indicate an average mole size of 3.95 mm. These data have been made available to engage researchers in a collaborative, multidisciplinary effort to better understand and prevent melanoma.

Prognostic value of primary tumor FDG uptake for occult mediastinal lymph node involvement in clinically N2/N3 node-negative non-small cell lung cancer

Objectives:The objective of this study was to identify predictive factors of occult mediastinal nodal involvement on staging positron emission tomography with 18F-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 18F-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.