Network Analysis for Formative Evaluation of Collaborative, Team Science Research Partnerships.

Cancer health disparities persist across the cancer care continuum despite decades of effort to eliminate them. Among the strategies currently used to address these disparities are multi-institution research initiatives that engage multiple stakeholders and change efforts. Endemic to the theory of change of such programs is the idea that collaboration-across institutions, research disciplines, and academic ranks-is necessary to improve outcomes. Despite this emphasis on collaboration, however, it is not often a focus of evaluation for these programs and others like them. In this paper we describe a method for evaluating collaboration within the Meharry-Vanderbilt-Tennessee State University Cancer Partnership using network analysis. Specifically, we used network analysis of co-authorship on academic publications to visualize the growth and patterns of scientific collaboration across partnership institutions, research disciplines, and academic ranks over time. We presented the results of the network analysis to internal and external advisory groups, creating the opportunity to discuss partnership collaboration, celebrate successes, and identify opportunities for improvement. We propose that basic network analysis of existing data along with network visualizations can foster conversation and feedback and are simple and effective ways to evaluate collaboration initiatives.

Modelling kidney disease using ontology: insights from the Kidney Precision Medicine Project.

An important need exists to better understand and stratify kidney disease according to its underlying pathophysiology in order to develop more precise and effective therapeutic agents. National collaborative efforts such as the Kidney Precision Medicine Project are working towards this goal through the collection and integration of large, disparate clinical, biological and imaging data from patients with kidney disease. Ontologies are powerful tools that facilitate these efforts by enabling researchers to organize and make sense of different data elements and the relationships between them. Ontologies are critical to support the types of big data analysis necessary for kidney precision medicine, where heterogeneous clinical, imaging and biopsy data from diverse sources must be combined to define a patient's phenotype. The development of two new ontologies - the Kidney Tissue Atlas Ontology and the Ontology of Precision Medicine and Investigation - will support the creation of the Kidney Tissue Atlas, which aims to provide a comprehensive molecular, cellular and anatomical map of the kidney. These ontologies will improve the annotation of kidney-relevant data, and eventually lead to new definitions of kidney disease in support of precision medicine.

Improved Prognosis and Increased Tumor-Infiltrating Lymphocytes in Patients Who Have SCLC With Neurologic Paraneoplastic Syndromes.

BACKGROUND: Approximately 10% of patients with SCLC develop a paraneoplastic syndrome (PNS). Neurologic PNS are thought to improve prognosis, which we hypothesized is related to increased tumor-infiltrating lymphocytes and immune recognition. METHODS: We queried 2,512,042 medical records from a single institution to identify patients who have SCLC with and without PNS and performed manual, retrospective chart review. We then performed multiplexed fluorescence immunohistochemistry and automated quantitative analysis (AQUA Technology) on tumors to assess CD3, CD4, and CD8 T cell infiltrates and programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1) interactions. T cell infiltrates and PD-1/PD-L1 interaction scores were compared among patients with neurologic PNS, endocrinologic PNS, and a control group without PNS. Clinical outcomes were analyzed using the Kaplan-Meier method and Cox proportional hazards models. RESULTS: We evaluated 145 SCLC patients: 55 with PNS (25 neurologic and 30 endocrinologic) and 90 controls. Patients with neurologic PNS experienced improved overall survival compared to patients with endocrinologic PNS and controls (median overall survival of 24 months versus 12 months versus 13 months, respectively). Of the 145 patients, we identified tumor tissue from 34 patients that was adequate for AQUA analysis. Among 37 specimens from these 34 patients, patients with neurologic PNS had increased T cell infiltrates (p = 0.033) and PD-1/PD-L1 interaction (p = 0.014) compared to tumors from patients with endocrinologic PNS or controls. CONCLUSIONS: Tumor tissue from patients with SCLC with neurologic PNS showed increased tumor-infiltrating lymphocytes and PD-1/PD-L1 interaction consistent with an inflamed tumor microenvironment.

A Multikinase and DNA-PK Inhibitor Combination Immunomodulates Melanomas, Suppresses Tumor Progression, and Enhances Immunotherapies.

Combination therapies have the potential to improve outcomes in melanoma patients but have not yet been clinically efficacious. Here, we used high-throughput flow cytometry-based screening to identify and characterize candidate therapies that might synergize with and augment T-cell immunotherapy efficacy. Two lead therapies, regorafenib (Reg) and NU7441, were selected based on their ability to alter a variety of immunomodulatory proteins, including CD55, CD73, CD155, programmed death-ligand 1 (PD-L1), nerve growth factor receptor (NGFR), and HLA class I in a heterogeneous panel of melanomas. The therapies also upregulated several melanoma antigens, inhibited proliferation, and perturbed activation of oncogenic signaling pathways in melanomas. T cells treated with the therapies proliferated normally and exhibited a favorably altered phenotype, including increased CD25, CD28, inducible T-cell costimulator (ICOS), and reduced expression of coinhibitory receptors. Cytokine production was also increased in treated T cells. When administered in mice, REg suppressed melanoma progression in a CD8+ T cell-dependent manner when used alone and with various immunotherapies. Additionally, Reg altered the number, phenotype, and function of various T-cell subsets in the tumor microenvironment. These studies reveal that Reg and NU7441 influence the immunobiology of both tumor cells and T cells and enhance the efficacy of various immunotherapies. Cancer Immunol Res; 5(9); 790-803. ©2017 AACR.

Automating the Determination of Prostate Cancer Risk Strata From Electronic Medical Records.

Purpose: Risk stratification underlies system-wide efforts to promote the delivery of appropriate prostate cancer care. Although the elements of risk stratum are available in the electronic medical record, manual data collection is resource intensive. Therefore, we investigated the feasibility and accuracy of an automated data extraction method using natural language processing (NLP) to determine prostate cancer risk stratum. Methods: Manually collected clinical stage, biopsy Gleason score, and preoperative prostate-specific antigen (PSA) values from our prospective prostatectomy database were used to categorize patients as low, intermediate, or high risk by D'Amico risk classification. NLP algorithms were developed to automate the extraction of the same data points from the electronic medical record, and risk strata were recalculated. The ability of NLP to identify elements sufficient to calculate risk (recall) was calculated, and the accuracy of NLP was compared with that of manually collected data using the weighted Cohen's κ statistic. Results: Of the 2,352 patients with available data who underwent prostatectomy from 2010 to 2014, NLP identified sufficient elements to calculate risk for 1,833 (recall, 78%). NLP had a 91% raw agreement with manual risk stratification (κ = 0.92; 95% CI, 0.90 to 0.93). The κ statistics for PSA, Gleason score, and clinical stage extraction by NLP were 0.86, 0.91, and 0.89, respectively; 91.9% of extracted PSA values were within ± 1.0 ng/mL of the manually collected PSA levels. Conclusion: NLP can achieve more than 90% accuracy on D'Amico risk stratification of localized prostate cancer, with adequate recall. This figure is comparable to other NLP tasks and illustrates the known trade off between recall and accuracy. Automating the collection of risk characteristics could be used to power real-time decision support tools and scale up quality measurement in cancer care.

Fluctuation Analysis of Peak Expiratory Flow and Its Association with Treatment Failure in Asthma.

RATIONALE: Temporal fluctuations have been demonstrated in lung function and asthma control, but the effect of controller therapy on these fluctuations is unknown. OBJECTIVES: To determine if fluctuations in peak expiratory flow (PEF) are predictive of subsequent treatment failure and may be modified by controller therapy. METHODS: We applied detrended fluctuation analysis to once-daily PEF data from 493 participants in the LOCCS (Leukotriene Modifier Corticosteroid or Corticosteroid-Salmeterol) trial of the American Lung Association Airways Clinical Research Centers. We evaluated the coefficient of variation of PEF (CVpef) and the scaling exponent α, reflecting self-similarity of PEF, in relation to treatment failure from the run-in period of open-label inhaled fluticasone, and the treatment periods for subjects randomized to (1) continued twice daily fluticasone (F), (2) once daily fluticasone plus salmeterol (F + S), or (3) once daily oral montelukast (M). MEASUREMENTS AND MAIN RESULTS: The CVpef was higher in those with treatment failure in the F and F + S groups in the run-in phase, and all three groups in the treatment phase. α was similar between those with and without treatment failure in all three groups during the run-in phase but was higher among those with treatment failure in the F and F + S groups during the treatment phase. Participants in all three groups showed variable patterns of change in α leading up to treatment failure. CONCLUSIONS: We conclude that increased temporal self-similarity (α) of more variable lung function (CVpef) is associated with treatment failure, but the pattern of change in self-similarity leading up to treatment failure is variable across individuals.

Development of a Novel Markov Chain Model for the Prediction of Head and Neck Squamous Cell Carcinoma Dissemination.

Prediction of microscopic tumor spread to regional lymph nodes can assist in radiation planning for cancer treatment. However, it is still challenging to predict tumor spread. In this paper, we present a unique approach to modeling how tumor cells disseminate to form regional metastases. This involves leveraging well established knowledge resources and commonly held notions of how cancer spreads. Using patient data, we utilized our approach to create a model of metastasis for the subset of head and neck squamous cell carcinoma that arises in the mucosa of the lateral tongue. The model was created using a training set extracted from the clinical records of 50 patients with tumors of this type who presented to the University of Washington head and tumor board over a three and half year period. The test sets consist of four case series drawn from the literature.

Electrical impedance myography in Duchenne muscular dystrophy and healthy controls: A multicenter study of reliability and validity.

INTRODUCTION: Electrical impedance myography (EIM) is a non-invasive, painless, objective technique to quantify muscle pathology. METHODS: We measured EIM in 8 arm and leg muscles in 61 boys with Duchenne muscular dystrophy (DMD) and 31 healthy boys, ages 3-12 years, at 5 centers. We determined the reliability of EIM and compared results in boys with DMD to controls and to 6-minute walk distance (6MWD), North Star Ambulatory Assessment (NSAA), timed functional tests (TFTs), and strength (hand-held dynamometry). RESULTS: EIM was well tolerated and had good inter- and intrarater reliability (intraclass correlation coefficient 0.81-0.96). The averaged EIM phase value from all muscles was higher (P < 0.001) in controls (10.45 ± 2.29) than boys with DMD (7.31 ± 2.23), and correlated (P ≤ 0.001) with 6MWD (r = 0.55), NSAA (r = 0.66), TFTs (r = -0.56), and strength (r = 0.44). CONCLUSION: EIM is a reliable and valid measure of disease severity in DMD. Longitudinal studies comparing EIM with other assessments over time in DMD are warranted.

Biological Model Development as an Opportunity to Provide Content Auditing for the Foundational Model of Anatomy Ontology.

Constructing a biological model using an established ontology provides a unique opportunity to perform content auditing on the ontology. We built a Markov chain model to study tumor metastasis in the regional lymphatics of patients with head and neck squamous cell carcinoma (HNSCC). The model attempts to determine regions with high likelihood for metastasis, which guides surgeons and radiation oncologists in selecting the boundaries of treatment. To achieve consistent anatomical relationships, the nodes in our model are populated using lymphatic objects extracted from the Foundational Model of Anatomy (FMA) ontology. During this process, we discovered several classes of inconsistencies in the lymphatic representations within the FMA. We were able to use this model building opportunity to audit the entities and connections in this region of interest (ROI). We found five subclasses of errors that are computationally detectable and resolvable, one subclass of errors that is computationally detectable but unresolvable, requiring the assistance of a content expert, and also errors of content, which cannot be detected through computational means. Mathematical descriptions of detectable errors along with expert review were used to discover inconsistencies and suggest concepts for addition and removal. Out of 106 organ and organ parts in the ROI, 8 unique entities were affected, leading to the suggestion of 30 concepts for addition and 4 for removal. Out of 27 lymphatic chain instances, 23 were found to have errors, with a total of 32 concepts suggested for addition and 15 concepts for removal. These content corrections are necessary for the accurate functioning of the FMA and provide benefits for future research and educational uses.

Assessment of alterations in the electrical impedance of muscle after experimental nerve injury via finite-element analysis.

The surface measurement of electrical impedance of muscle, incorporated as the technique of electrical impedance myography (EIM), provides a noninvasive approach for evaluating neuromuscular diseases, including amyotrophic lateral sclerosis. However, the relationship between alterations in surface impedance and the electrical properties of muscle remains uncertain. In order to investigate this further, a group of healthy adult rats, a group of rats two weeks postsciatic crush, and a group of animals six months postcrush underwent EIM of the gastrocnemius-soleus complex. The animals were then killed and the conductivity and permittivity of the extracted muscle measured. Finite-element models based on MRI data were then constructed for each group. The characteristic EIM parameter, 50 kHz phase (±standard error), obtained with surface impedance measurements was 17.3° ± 0.3° for normal animals, 13.8° ± 0.7° for acutely injured animals, and 16.1° ± 0.5° for chronically injured animals. The models predicted parallel changes with phase values of 24.3°, 18.8°, and 21.2° for the normal, acute, and chronic groups, respectively. Other multifrequency impedance parameters showed similar alterations. These results confirm that surface impedance measurements taken in conjunction with anatomical data and finite-element models may offer a noninvasive approach for assessing biophysical alterations in muscle in neuromuscular disease states.