Big data driven co-occurring evidence discovery in chronic obstructive pulmonary disease patients

© 2017, The Author(s). Background: Chronic Obstructive Pulmonary Disease (COPD) is a chronic lung disease that affects airflow to the lungs. Discovering the co-occurrence of COPD with other diseases, symptoms, and medications is invaluable to medical staff. Building co-occurrence indexes and finding causal relationships with COPD can be difficult because often times disease prevalence within a population influences results. A method which can better separate occurrence within COPD patients from population prevalence would be desirable. Large hospital systems may potentially have tens of millions of patient records spanning decades of collection and a big data approach that is scalable is desirable. The presented method, Co-Occurring Evidence Discovery (COED), presents a methodology and framework to address these issues. Methods: Natural Language Processing methods are used to examine 64,371 deidentified clinical notes and discover associations between COPD and medical terms. Apache cTAKES is leveraged to annotate and structure clinical notes. Several extensions to cTAKES have been written to parallelize the annotation of large sets of clinical notes. A co-occurrence score is presented which can penalize scores based on term prevalence, as well as a baseline method traditionally used for finding co-occurrence. These scoring systems are implemented using Apache Spark. Dictionaries of ground truth terms for diseases, medications, and symptoms have been created using clinical domain knowledge. COED and baseline methods are compared using precision, recall, and F1 score. Results: The highest scoring diseases using COED are lung and respiratory diseases. In contrast, baseline methods for co-occurrence rank diseases with high population prevalence highest. Medications and symptoms evaluated with COED share similar results. When evaluated against ground truth dictionaries, the maximum improvements in recall for symptoms, diseases, and medications were 0.212, 0.130, and 0.174. The maximum improvements in precision for symptoms, diseases, and medications were 0.303, 0.333, and 0.180. Median increase in F1 score for symptoms, diseases, and medications were 38.1%, 23.0%, and 17.1%. A paired t-test was performed and F1 score increases were found to be statistically significant, where p < 0.01. Conclusion: Penalizing terms which are highly frequent in the corpus results in better precision and recall performance. Penalizing frequently occurring terms gives a better picture of the diseases, symptoms, and medications co-occurring with COPD. Using a mathematical and computational approach rather than purely expert driven approach, large dictionaries of COPD related terms can be assembled in a short amount of time

A Systems Medicine approach to multimorbidity. Towards personalised care for patients with Chronic Obstructive Pulmonary Disease

[eng] BACKGROUND: Multimorbidity (i.e. the presence of more than one chronic disease in the same patient) and comorbidity (i.e. the presence of more than one chronic disease in the presence of an index disease) are main sources of dysfunction in chronic patients and avoidable costs in conventional health systems worldwide. By affecting a majority of elderly population worldwide, multimorbidity prompts the need for revisiting the single disease approach followed by contemporary clinical practice and elaborate strategies that target shared mechanisms of associated diseases with the potential of preventing, decelerating or even halting multimorbid disease progression. However, our current understanding on disease interactions is rather limited, and although many disorders have been associated based on their shared molecular traits and their observed co-occurrence in different populations, no comprehensive approach has been outlined to translate this knowledge into clinical practice. The advent of novel measurement technologies (e.g. omics) and recent initiatives on digital health (e.g. registries, electronic health records) are facilitating access to an enormous amount of patient-related information from whole populations to molecular levels. State-of-the art computational models and machine learning tools demonstrate high potential for health prediction and together with systems biology are shaping the practicalities of systems medicine. Given the extremely long and expensive bench to clinics cycles of the biomedical sector, systems medicine promises a fast track approach where scientific evidence support clinical care, while simultaneously collected insights from daily clinical practice promote new scientific discoveries and optimize healthcare. The PhD thesis aims to explore multimorbidity from a systems medicine perspective on the concrete and practical use case of chronic obstructive pulmonary disease (COPD). COPD constitutes an ideal use case due to several factors, including: i) its high impact on healthcare and its ever-increasing burden; ii) its heterogeneous disease manifestations, and progress, often involving extra-pulmonary effects, including highly prevalent comorbidities (e.g. type 2 diabetes mellitus, cardiovascular disorders, anxiety-depression and lung cancer); and, iii) its well described systemic effects that are suggested associations with comorbidities in terms of underlying mechanisms. HYPOTHESIS: The central hypothesis of the PhD thesis builds on the emerging biological evidence that clustering of comorbid conditions, a phenomenon seen in complex chronic patients, could be due to shared abnormalities in relevant biological pathways (i.e. bioenergetics, inflammation and tissue remodelling). It is assumed that a systems understanding of the patient conditions may help to uncover the molecular mechanisms and lead to the design of preventive and targeted therapeutic strategies aiming at modulating patient prognosis. The PhD thesis focuses on non-pulmonary phenomena of COPD; that is, systemic effects and comorbidities, often observed in patients with COPD as a paradigm of complex chronic disease. OBJECTIVES: The general objective of the PhD thesis is threefold: i) to investigate molecular disturbances at body systems level that may lead to a better understanding of characteristic systemic effects and comorbidities of patients with COPD; ii) to analyse population level patterns of COPD comorbidities and investigate their role in the health risk of patients with COPD; and, iii) to explore technological strategies and tools that facilitate the transfer of the collected knowledge on comorbidity into clinical practice. MAIN FINDINGS: Firstly, the PhD thesis introduced a novel knowledge management tool for targeted molecular analysis of underlying disease mechanisms of skeletal muscle dysfunction in patients with COPD. Second, a network analysis approach was outlined to further study this systemic effect, as well as the causes of abnormal adaptation of COPD muscle to exercise training. Furthermore, this work together with three other studies also aimed to reveal the general underlying causes of comorbidity clustering in COPD, using different modelling approaches. Overarching outcome of these studies indicates abnormalities in the complex co-regulation of core biological pathways (i.e. bioenergetics, inflammation, oxidative stress and tissue remodelling) both on muscle and body systems level (blood, lung), which paves the way for the development of novel pharmacological and non-pharmacological preventive interventions on non- pulmonary phenomena in patients with COPD. Furthermore, results indicated strong relation of muscle related dysregulations to aerobic capacity, in opposed to pulmonary severity of COPD. These findings have far reaching potential in COPD care, starting from defining the need for better characterization of exercise performance in the clinic practice and the promotion of physical activity from early stages of the disease. This PhD thesis also generated outcomes with respect to the risk of multimorbidity in patients with COPD using a population health approach. The thesis validated that patients with COPD are in increased risk to co-occur with other diseases compared to non-COPD patients, regardless of the population and healthcare system specificities of different regions (i.e. Catalonia, US). These findings indicated the potential role of multimorbidity as a risk factor for COPD, that was evaluated in the PhD thesis by constructing health risk assessment models to predict unexpected medical events in patients with COPD. The promising performance of the models and the prominent role of multimorbidity in these models presented a powerful argument for its role in clinical staging of the disease and their potential in clinical decision support. CONCLUSIONS: The PhD thesis achieved main points of the general objectives, namely: i) to perform a systems analysis of patients with COPD by investigating molecular disturbances at body systems level leading to a better understanding of characteristic systemic effects and comorbidities of patient with COPD; ii) to analyse population level patterns of COPD comorbidities and investigate their role in the health risk of patients with COPD; and iii) to explore technological strategies and tools that facilitate the transfer of the collected knowledge on comorbidity into clinical practice. Accordingly, the following conclusions arise: 1. Non-pulmonary manifestations in patients with Chronic Obstructive Pulmonary Disease (COPD) have a major negative impact on: highly relevant clinical events, use of healthcare resources and prognosis. Accordingly, the following indications were made: a. Actionable insights on non-pulmonary phenomena should be included in the clinical staging of these patients in an operational manner. b. Management of patients with COPD should be revisited to incorporate an integrative approach to non-pulmonary phenomena. c. Innovative cost-effective interventions, and pharmacological and non- pharmacological treatments targeting prevention of non-pulmonary manifestations in patients with COPD should be developed, and properly assessed. 2. Abnormal co-regulation of core biological pathways (i.e. bioenergetics, inflammation, tissue remodelling and oxidative stress), both in skeletal muscle and at body systems level, are common characteristics of patients with COPD, which potentially play a major role in comorbidity clustering. 3. Consistent relationships between cardiovascular health, skeletal muscle dysfunction and clinical outcomes in patients with COPD was identified, which makes it a priority to characterize patient exercise performance and physical activity in the clinic, and to adopt early cardiopulmonary rehabilitation strategies to modulate prognosis and prevent comorbidity clustering in these patients. 4. Multimorbidity is a strong predictor of unplanned medical events in patients with COPD and shows high potential to be used for personalized health risk prediction and service workflow selection. 5. Personalized health risk prediction was identified as a high potential tool for the integration and transfer of scientific evidence on multimorbidity to daily clinical practice. Limiting factors of its present applicability were explored and implementation strategies based on cloud computing solutions were proposed.[cat] INTRODUCCIÓ: Tant la multimorbiditat (la presència de més d'una malaltia crònica en el mateix pacient), com la comorbiditat (la presència de més d'una malaltia crònica quan hi ha una malaltia de referència) són una font important de disfuncions en l’atenció sanitària dels pacients crònics i generen importants despeses evitables en sistemes de salut arreu del món. La multimorbiditat/comorbiditat afecta la majoria de població de més de 65 anys. El seu gran impacte sanitari i social fa necessària la revisió d’aspectes essencials de la pràctica mèdica convencional, molt enfocada al tractament de cada malaltia de forma aïllada. En aquest sentit, cal elaborar estratègies que considerin els mecanismes biològics comuns entre patologies, per tal de prevenir, retardar o fins i tot aturar la progressió del fenomen. Malauradament, el poc coneixement dels mecanismes biològics que modulen les interaccions entre malalties és un factor limitant important. Hi ha estudis sobre els mecanismes moleculars comuns entre malalties i s’han realitzat anàlisis poblacionals de la multimorbiditat, però no existeix encara una aproximació holística per tal de traduir aquest coneixement a la pràctica clínica. L’aparició de noves tecnologies òmiques, així com iniciatives recents en l’àmbit de la salut digital, han facilitat l'accés a una quantitat enorme d'informació dels pacients, tant a nivell poblacional com a nivell molecular. A més, les eines computacionals i d'aprenentatge automàtic existents estan demostrant un gran potencial predictiu que, conjuntament amb les metodologies de la biologia de sistemes, estan conformant els aspectes pràctics del desplegament de la medicina de sistemes. De forma progressiva, aquesta última esdevé una via efectiva per accelerar el rol de l’evidència científica com a suport a la atenció clínica. De forma recíproca, la digitalització sistemàtica de la pràctica clínica diària, permet la generació de noves descobertes científiques i la optimització de l’assistència sanitària. Aquesta tesis doctoral pretén explorar la multimorbiditat des d’una perspectiva de medicina de sistemes, considerant com a cas d'ús concret i pràctic la malaltia pulmonar obstructiva crònica (MPOC). La MPOC constitueix un cas d'ús ideal a causa de diversos factors: i) el seu alt impacte a nivell sanitari; ii) la heterogeneïtat en quant a manifestacions i progrés, sovint amb efectes extra-pulmonars, incloent de forma freqüent comorbiditats com la diabetis mellitus tipus 2, trastorns cardiovasculars, l'ansietat-depressió i el càncer de pulmó; i, iii) els efectes sistèmics de la malaltia pulmonar, que podrien presentar mecanismes biològics comuns a algunes comorbiditats. HIPÒTESIS: La hipòtesi central d’aquesta tesis doctoral considera que la multimorbiditat podria explicar-se per alteracions en les xarxes de regulació de mecanismes biològics rellevants com la bioenergètica, inflamació i remodelació de teixits. En aquest sentit, l’anàlisi holística del problema podria millorar la comprensió dels mecanismes moleculars que modulen les associacions entre malalties i, per tant, facilitar el disseny d'estratègies terapèutiques preventives i dirigides a modular el pronòstic dels pacients. Aquesta tesis doctoral estudia els fenòmens extra-pulmonars de la MPOC; és a dir, efectes sistèmics (disfunció del múscul esquelètic) i comorbiditats, com a paradigma de malalties cròniques complexes. OBJECTIUS: L'objectiu general d’aquesta tesis doctoral és triple: i) l’anàlisi holístic de pacients amb MPOC amb focus en la disfunció muscular i les comorbiditats; ii) avaluar el paper de les comorbiditats en el risc de salut dels pacients amb MPOC, tant a nivell poblacional com individual; i, iii) explorar estratègies tecnològiques i eines de salut digital que facilitin la transferència de coneixement a la pràctica clínica diària. RESULTATS: El primer manuscrit de la tesi descriu una nova eina de gestió del coneixement per l’anàlisi molecular dels mecanismes de disfunció del múscul esquelètic en pacients amb MPOC. També dins el primer objectiu de la tesi, s’efectua un anàlisi de xarxes orientat a la identificació de mòduls biològics explicatius de la disfunció muscular i de l’adaptació anòmala d’aquests malalts a l’entrenament físic, tal com es descriu en el segon manuscrit. Els tres articles següents exploren, des de diferents perspectives, l’impacte i mecanismes de les comorbiditats en els pacients amb MPOC. Els principals resultats d'aquests estudis indiquen una complexa i anormal regulació de vies biològiques principals, com es el cas de la bioenergètica, inflamació, estrès oxidatiu i remodelació de teixits, tant a nivell del múscul com a nivell sistèmic (sang, pulmó). Aquests resultats obren noves vies per a intervencions preventives, tant farmacològiques com no farmacològiques, sobre els fenòmens no pulmonars que presenten els pacients amb MPOC. Els resultats indiquen una associació de les alteracions musculars amb la capacitat aeròbica, i no pas amb la gravetat de la malaltia pulmonar. Aquestes troballes tenen un gran potencial en la millora de la gestió dels pacients amb MPOC, començant per la necessitat d’una millor caracterització de la capacitat aeròbica en la pràctica clínica i la promoció d'activitat física des de les primeres etapes de la malaltia. La tesi també ha generat resultats d’interès en relació amb el risc de multimorbiditat en pacients amb MPOC, mitjançant un enfocament de salut poblacional. Els resultats evidencien que els pacients amb MPOC presenten un risc mes elevat de comorbiditat que els pacients sense MPOC, independentment de les especificitats de la població i del sistema sanitari de les àrees analitzades (Catalunya, EUA). La tesi també demostra el paper de la multimorbiditat com a factor modulador del risc clínic dels pacients amb MPOC. Aquests resultats indiquen l’interès de l’ús de la multimobiditat en l’estadiatge dels pacients amb MPOC i en l’elaboració d’eines de suport al procés de decisió clínica. CONCLUSIONS: Aquesta tesi doctoral ha assolit els objectius generals plantejats i proposa les següents conclusions: 1. Les manifestacions no pulmonars en els pacients amb malaltia pulmonar obstructiva crònica (MPOC) tenen un impacte negatiu respecte a esdeveniments de gran rellevància clínica, ús de recursos sanitaris i pronòstic. En conseqüència, es fan les següents recomanacions: a. Els fenòmens no pulmonars de la MPOC s’haurien d’incloure de manera operativa en l’estadiatge d'aquests pacients. b. S’hauria de redefinir la gestió clínica dels pacients amb MPOC tot incorporant un enfocament holístic dels fenòmens no pulmonars. c. S’haurien de desenvolupar i avaluar correctament noves intervencions, farmacològiques i no farmacològiques, per a la prevenció de les manifestacions no pulmonars en pacients amb MPOC. 2. Les alteracions de la regulació de vies biològiques rellevants com la bioenergètica, inflamació, estrès oxidatiu i la remodelació de teixits a nivell del múscul esquelètic, i també a nivell sistèmic, s’observa en els pacients amb MPOC i pot tenir un paper important en les co-morbiditats. 3. Les relacions entre alteracions cardiovasculars, disfunció del múscul esquelètic i altres aspectes clínics dels pacients amb MPOC, indiquen la necessitat de caracteritzar la capacitat aeròbica i els nivells d'activitat física en la pràctica clínica, així com la implementació d’estratègies de rehabilitació cardiopulmonar en les primeres etapes de la malaltia, per tal de modular la prognosis dels malalts i prevenir l’aparició de comorbiditats. 4. La multimorbiditat és un bon predictor d’esdeveniments clínics rellevants en pacients amb MPOC i mostra un gran potencial per a personalitzar l’estimació de risc i la selecció de serveis. 5. La predicció de risc de forma personalitzada s’ha identificat com una eina amb molt potencial per a la gestió de la multimorbiditat en la pràctica clínica diària. S’han explorat els factors limitants de la seva aplicabilitat i s’han proposat estratègies d'implementació d’eines predictives adients, basades en solucions de computació en el núvol.[spa] INTRODUCCIÓN: Tanto la multimorbilidad (la presencia de más de una enfermedad crónica en un mismo paciente) como la comorbilidad (la presencia de más de una enfermedad crónica en presencia de una enfermedad de referencia) son una fuente importante de disfunciones en la atención sanitaria de los pacientes crónicos y generan importantes costes evitables en los sistemas de salud de todo el mundo. La multimorbilidad/comorbilidad afecta a la mayoría de la población de más de 65 años. Debido a su gran impacto sanitario y social, resulta necesaria la revisión de aspectos esenciales de la práctica médica convencional, muy enfocada en el tratamiento de cada enfermedad de forma aislada. En este sentido, es necesario elaborar estrategias que consideren mecanismos biológicos comunes entre patologías, con el fin de prevenir, retrasar o incluso detener la progresión del fenómeno. Desgraciadamente, el escaso conocimiento de los mecanismos biológicos que modulan las interacciones entre enfermedades es un factor limitante importante. Existen estudios sobre los mecanismos moleculares comunes entre enfermedades y se han realizados análisis poblaciones de la multimorbilidad, pero no existe aún una aproximación holística que permita traducir este conocimiento a la práctica clínica. La aparición de nuevas tecnologías ómicas, así como recientes iniciativas en el ámbito de la salud digital, han facilitado el acceso a una cantidad enorme de información sobre los pacientes, tanto a nivel poblacional como a nivel molecular. Además, las herramientas computacionales y de aprendizaje automático existentes demuestran un gran potencial predictivo que, conjuntamente con las metodologías de biología de sistemas, están conformando los aspectos prácticos de la medicina de sistemas. De manera progresiva esta última se está convirtiendo en una vía efectiva para acelerar el papel de la evidencia científica como soporte a la atención clínica. De forma recíproca, la digitalización sistemática de la práctica clínica diaria permite la generación de nuevos descubrimientos científicos y la optimización de la asistencia sanitaria. Esta tesis doctoral pretende explorar la multimorbilidad desde una perspectiva de medicina de sistemas, considerando como caso de uso concreto y práctico la enfermedad pulmonar obstructiva crónica (EPOC). La EPOC constituye un caso de uso ideal debido a diversos factores: i) su alto impacto a nivel sanitario; ii) la heterogeneidad en cuanto a manifestaciones y progreso, a menudo con efectos extra pulmonares, incluyendo de forma frecuente comorbilidades como la diabetes mellitus tipo 2, trastornos cardiovasculares, la ansiedad-depresión y el cáncer de pulmón; y, iii) los efectos sistémicos de la enfermedad pulmonar, que podrían presentar mecanismos biológicos comunes a algunas comorbilidades. HIPÓTESIS: La hipótesis central de esta tesis doctoral considera que la multimorbilidad podría explicarse por alteraciones en las redes de regulación de mecanismos biológicos relevantes como la bioenergética, inflamación y remodelación de tejidos. En este sentido, el análisis holístico del problema podría mejorar la comprensión de los mecanismos moleculares que modulan las asociaciones entre enfermedades y, por tanto, facilitar el diseño de estrategias terapéuticas preventivas y dirigidas a modular el pronóstico de los pacientes. Esta tesis doctoral estudia los fenómenos extra pulmonares de la EPOC; es decir, efectos sistémicos (disfunción del músculo esquelético) y comorbilidades, como paradigma de enfermedades crónicas complejas. OBJETIVOS: El objetivo general de esta tesis doctoral es triple: i) el análisis holístico de pacientes con EPOC focalizando en la disfunción muscular y la comorbilidades; ii) evaluar el papel de las comorbilidades en el riesgo de salud de los pacientes con EPOC, tanto a nivel poblacional como individual; y, iii) explorar estrategias tecnológicas y herramientas de salud digital que faciliten la transferencia de conocimiento a la práctica clínica diaria. RESULTADOS: El primer manuscrito de la tesis describe una nueva herramienta de gestión del conocimiento para el análisis molecular de los mecanismos de disfunción del músculo esquelético en pacientes con EPOC. Incluido en el primer objetivo de la tesis, se efectúa un análisis de redes orientado a la identificación de módulos biológicos que explican la disfunción muscular y la adaptación anómala de estos pacientes al entrenamiento físico, tal y cómo se describe en el segundo manuscrito. Los tres artículos siguientes exploran, desde perspectivas diferentes, el impacto y mecanismos de las comorbilidades en los pacientes con EPOC. Los principales resultados de estos estudios indican una compleja y anormal regulación de vías biológicas principales, como es el caso de la bioenergética, inflamación, estrés oxidativo y remodelación de tejidos, tanto a nivel del músculo como a nivel sistémico (sangre, pulmón). Estos resultados abren nuevas vías para intervenciones preventivas, tanto farmacológicas como no farmacológicas, sobre los fenómenos no pulmonares que presentan los pacientes con E

Multi-Level Integrated Analysis of Chronic Obstructive Pulmonary Disease (COPD) heterogeneity

[eng] Non-Communicable Diseases (NCDs), including cancer, cardiovascular (heart diseases or stroke), respiratory (COPD or asthma) and metabolic diseases (diabetes) are chronic conditions that represent a major global health problem of the 21st century. All of them, however, are the end-result of a complex set of gene-environment interactions that develop over years and often lead to several NCDs co-existing in the same individual (multi-morbidity). Multi-level integrated analysis has the potential to uncover the heterogeneity of NCDs by conceptualizing them as emergent properties of a complex, non-linear, dynamic and multilevel biological system, or network of biological and environmental interactions. Chronic Obstructive Pulmonary Disease (COPD) is a NCD of increasing prevalence worldwide that is projected to be by 2020 the third leading cause of death worldwide. It is currently viewed as a broad diagnostic term that encompass a continuum of subtypes each characterized by distinct functional or pathobiological mechanisms (endotypes) and is characterized by persistent respiratory symptoms and airflow limitation. The underlying hypothesis of this PhD Thesis is that multi-level integrated analysis can help us understand highly heterogeneous respiratory diseases such as COPD. Specifically, the following two aspects of COPD heterogeneity will be addressed: 1) Exacerbations of COPD (ECOPD): ECOPD are episodes of worsening of the symptoms whose pathogenesis and biology are not entirely understood. They are heterogeneous events of non-specific diagnosis. Biomarkers analysis and networks medicine were used to uncover novel pathobiological information from the comparison of the multi-level (i.e., clinical, physiological, biological, imaging and microbiological) correlation networks determined during ECOPD and clinical recover. We concluded that ECOPD are characterised by disruption of network homeokinesis that exists during convalescence and can be identified objectively by using a panel of three biomarkers (dyspnoea, circulating neutrophils and CRP levels) frequently determined in clinical practice. 2) Early low lung function and health in later life: In 2015 Lange P. et al. showed that low peak lung function in early adulthood is associated with the diagnosis of COPD later in life. We assessed in three general population cohorts the prevalence of low peak lung function and its association with other clinical or biological parameters - specifically respiratory, cardiovascular, and metabolic abnormalities – as well as incidence of comorbid diseases during follow-up. We concluded that low peak lung function in early adulthood is common in the general population and could identify a group of individuals at risk of early (cardiovascular, metabolic and systemic) comorbidities and premature death

Integrative transcriptomics in smoking related lung diseases

Chronic lung diseases including Chronic Obstructive Pulmonary Disease (COPD), Idiopathic Pulmonary Fibrosis (IPF) and lung cancer are major causes of morbidity and mortality in the United States due to high incidence and limited therapeutic options. In order to address this critical issue, I have leveraged RNA sequencing and integrative genomics to define disease-associated transcriptomic changes which could be potentially targeted to lead to new therapeutics. We sequenced the lung transcriptome of subjects with IPF (n=19), emphysema (n=19, a subtype of COPD), or neither (n=20). The expression levels of 1770 genes differed between IPF and control lung, and 220 genes differed between emphysema and control lung (p<0.001). Upregulated genes in both emphysema and IPF were enriched for the p53/hypoxia pathway. These results were validated by immunohistochemistry of select p53/hypoxia proteins and by GSEA analysis of independent expression microarray experiments. To identify regulatory events, I constructed an integrative miRNA target prediction and anticorrelation miRNA-mRNA network, which highlighted several miRNA whose expression levels were the opposite of genes differentially expressed in both IPF and emphysema. MiR-96 was a highly connected hub in this network and was subsequently overexpressed in cell lines to validate several potential regulatory connections. Building upon these successful experiments, I next sought to define gene expression changes and the miRNA-mRNA regulatory network in never smoker lung cancer. Large and small RNA was sequenced from matched lung adenocarcinoma tumor and adjacent normal lung tissue obtained from 22 subjects (8 never, 14 current and former smokers). I identified 120 genes whose expression was modified uniquely in never smoker lung tumors. Using a repository of gene-expression profiles associated with small bioactive molecules, several compounds which counter the never smoker tumor signature were identified in silico. Leveraging differential expression information, I again constructed an mRNA-miRNA regulatory network, and subsequently identified a potential never smoker oncomir has-mir-424 and its transcription factor target FOXP2. In this thesis, I have identified genes, pathways and the miRNA-mRNA regulatory network that is altered in COPD, IPF, and lung adenocarcinoma among never smokers. My findings may ultimately lead to improved treatment options by identifying targetable pathways, regulators, and therapeutic drug candidates.2017-02-01T00:00:00

The role of the P2X7 receptor in cigarette smoke driven-inflammation associated with COPD

Chronic Obstructive Pulmonary Disease (COPD) is a cigarette smoke (CS)-driven inflammatory airway disease with an increasing global prevalence. Currently, there are no effective therapies to stop the relentless progression of this disease. In the inflammatory milieu present in the lungs of animal models of, and human patients with COPD are increased levels of cytokines (IL-1β/IL-18) linked to activation of the NLRP3-Inflammasome. It has been postulated that exposure to CS leads to the release of endogenous danger signals (e.g. ATP) activating the NLRP3-Inflammasome via the P2X7 receptors driving the maturation and release of IL-1β and IL-18. The literature suggests that these cytokines are central to the chronic inflammation in the airway which drives the pathological changes seen in COPD. My hypothesis is that blockade of the P2X7 - NLRP3-Inflammasome pathway will attenuate the inflammation present in CS-induced airway inflammation. I developed an acute (3-day) model of COPD-like inflammation to investigate the role of the P2X7 receptor in this pathway. I demonstrated that CS-induced neutrophilia in a pre-clinical model is temporally associated with markers of Inflammasome activation (increased caspase 1 activity and release of IL-1β/IL-18) in the lungs. I used genetically modified mice lacking functional P2X7 receptors to show attenuation in caspase-1 activity, IL-1β release and airway neutrophilia in response to acute CS exposure but not LPS-induced airway inflammation. These findings were validated using a specific P2X7 receptor antagonist. Furthermore, I confirmed that the role of this pathway was not restricted to early stages of disease development by showing increased caspase-1 activity in lungs from a more chronic exposure to CS (28-day) and patients with COPD. This translational data suggests the P2X7-Inflammasome pathway plays an on-going role in disease pathogenesis. These results advocate the crucial role of the P2X7 – caspase-1 axis in CS-induced inflammation, highlighting this as a possible therapeutic target in combating COPD

The use of clinical, behavioral, and social determinants of health to improve identification of patients in need of advanced care for depression

Indiana University-Purdue University Indianapolis (IUPUI)Depression is the most commonly occurring mental illness the world over. It poses a significant health and economic burden across the individual and community. Not all occurrences of depression require the same level of treatment. However, identifying patients in need of advanced care has been challenging and presents a significant bottleneck in providing care. We developed a knowledge-driven depression taxonomy comprised of features representing clinical, behavioral, and social determinants of health (SDH) that inform the onset, progression, and outcome of depression. We leveraged the depression taxonomy to build decision models that predicted need for referrals across: (a) the overall patient population and (b) various high-risk populations. Decision models were built using longitudinal, clinical, and behavioral data extracted from a population of 84,317 patients seeking care at Eskenazi Health of Indianapolis, Indiana. Each decision model yielded significantly high predictive performance. However, models predicting need of treatment across high-risk populations (ROC’s of 86.31% to 94.42%) outperformed models representing the overall patient population (ROC of 78.87%). Next, we assessed the value of adding SDH into each model. For each patient population under study, we built additional decision models that incorporated a wide range of patient and aggregate-level SDH and compared their performance against the original models. Models that incorporated SDH yielded high predictive performance. However, use of SDH did not yield statistically significant performance improvements. Our efforts present significant potential to identify patients in need of advanced care using a limited number of clinical and behavioral features. However, we found no benefit to incorporating additional SDH into these models. Our methods can also be applied across other datasets in response to a wide variety of healthcare challenges