Andrew Schrepf - US grants

Project Summary/Abstract The goal of this 3 year project is to train the applicant in novel research techniques as he conducts a series of experiments designed to establish the relationship between ovarian cancer-derived inflammation, social isolation, and depressive and anxious symptoms in an animal model of the disease. This training is motivated by the high rates of depression and anxiety in ovarian cancer patients, which affect their treatment decisions, compliance to medical regimens, and quality of life, and is not adequately addressed by current treatments. To achieve this goal, the applicant will be mentored at two different institutions by experts in the fields of behavioral neuroscience, clinical psychology and tumor biology, and will learn surgical techniques, pharmacologic manipulations, and the use of small interfering RNA (siRNA) in vivo. The anticipated outcome of this project is the identification of key molecular and psychosocial mediators of depressive and anxious symptoms in ovarian cancer, possibilities for new pharmacologic and behavioral interventions in ovarian cancer patients, and the establishment of an animal model of depressive and anxious symptoms in ovarian cancer. Further, this project will advance NCI's mission statement to train promising scientists in cancer related research. The proposed research has several strengths: it approaches the issue of symptoms of negative affect in ovarian cancer from the novel perspective of cytokine-induced sickness behavior, will use an immunocompetent animal model of ovarian cancer which closely mimics the trajectory of the disease trajectory, and considers both molecular and psychosocial mediators of symptoms of negative affect. The work will test the hypotheses that depressive and anxious symptoms in ovarian cancer patients are due, in part, to evolutionarily conserved patterns of sickness behavior, and that cancer-derived inflammation is exacerbated by social isolation. The applicant will learn to apply cytokine antagonists and siRNA to the periphery and central nervous system of animals with induced tumors of the ovary to determine if depressive and anxious symptoms can be attenuated. In the second phase of the project, the applicant will conduct experiments to determine if cancer-derived inflammation can be modulated by social isolation. This will be under the guidance of the sponsor and co-sponsors who have considerable expertise in all the techniques that will be applied, as well as successful track record of mentoring former trainees. The project will include a mentored research program at M.D. Anderson in which the applicant will learn to apply siRNA for gene modulation in animal models of ovarian cancer from an established expert, followed by execution of the above mentioned experiments at the University of Iowa.

Project Summary Fibromyalgia (FM) is a complex condition characterized by widespread pain and fatigue that is associated with sleep dysfunction and reduced function that affects 2-4% of the population (Heidari et al., 2017). Current 2016 diagnostic criteria are by symptomology only, as there are no validated chronic pain biomarkers to assist with diagnosis, or treatment evaluation endpoints (Wolfe et al., 2016). Diagnosing FM often takes years with patients seeing multiple physicians, which delays treatment (Choy, 2010). This delayed diagnosis and treatment initiation would be dramatically reduced with the identification of FM biomarkers. The long-term goal of this line of research is to identify unique biomarkers for FM to improve the diagnosis and/or develop therapeutic targets for individuals with widespread pain. Using a semi-targeted metabolomics approach, our preliminary data from women with FM (n=59), compared to healthy controls (n=38), show 18 potential candidates that differ significantly between cohorts with several metabolites showing good-excellent sensitivity (>90%) and specificity (>90%). The primary goal of this proposed research is to assess and validate candidate metabolic biomarkers in a new, larger cohort of individuals and compared to other chronic pain populations. The proposed study will use a multi-site, cross-sectional design to identify and characterize metabolic biomarkers, biosignatures, and their associations with multiple symptomology domains to address the following two specific aims: Aim 1: We will characterize diagnostic test metrics for candidate biomarkers using receiver operating curves (ROCs), i.e. sensitivity and specificity, and test-retest reliability, to correctly identify individuals with FM from healthy controls and other chronic pain conditions: osteoarthritis, carpal tunnel syndrome, and rheumatoid arthritis. Aim 2: We will determine associations between putative metabolic biomarkers and multiple self-reported symptom domains in those with FM: a) pain; b) fatigue; c) sleep; d) physical function; e) psychological factors, and f) disease impact/disability. We have identified several promising metabolic biomarkers that may serve as diagnostic or within-disease phenotype identifiers. Once completed, we will examine potential mechanistic and therapeutic targets for the candidate biomarkers in subsequent studies. These novel studies have the potential to identify a diagnostic, and potentially a therapeutic, biomarker of FM associated with cell metabolism. To accomplish this study, we have developed a strong multidisciplinary and multi-site team, leveraging blood samples and phenotype data collected as part of an on-going funded study, as well as additional data collection for repeatability analyses. The study team has the necessary expertise in human, basic science and metabolomics investigations to successfully complete these aims.

Urologic Chronic Pelvic Pain Syndrome (UCPPS) is a debilitating urologic condition characterized by bladder/pelvic pain and urinary symptoms such as urgency and frequency. Treating UCPPS remains a serious challenge for clinicians in part because the mechanisms of the disease are not well understood. This challenge is even greater when patients have pain outside the pelvis and/or comorbid pain syndromes, as the central nervous system (CNS) may play a significant role in their symptoms, a phenomenon we term ?centralized? pain. The Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Research Network was initiated in response to these challenges. In a site-specific study conducted during first phase of MAPP, we showed that when immune cells isolated from UCPPS patients are stimulated ex vivo, the resulting inflammatory response distinguishes patients with localized pain from those with a more widespread pain presentation. In the second phase of MAPP, samples of stimulated whole blood have been collected on the vast majority of patients in conjunction with functional connectivity magnetic resonance imaging (fcMRI), psychophysical sensory testing, and a comprehensive battery of patient reported outcomes. However, as the second phase of MAPP draws to a close, funding is not available to analyze these stimulated samples. In the current project, we propose to complete this critical work and elucidate how inflammation influences the CNS in UCPPS. We have assembled a team of MAPP investigators and added expertise in clustering and classification bioinformatics in order to address three scientific aims. AIM 1: Validate the inflammatory phenotype of UCPPS. (A) We will replicate and extend our previous cross-sectional findings indicating that stimulated inflammatory responses may be able to classify UCPPS patients with greater clinical evidence of centralized pain (test n = 200, validation n = 200). (B) We will determine if longitudinal changes in genitourinary symptoms and pain over 18 months are paralleled by changes in the stimulated inflammatory responses in a subset of these patients (n = 250). AIM 2: Identify a neurobiological signature of the inflammatory phenotype using fcMRI and psychophysical sensory testing. (A) We will conduct cross-sectional analyses to identify neurobiological correlates of the inflammatory phenotype (test n = 200, validation n = 200). (B) We will create longitudinal models to determine if identified fcMRI and psychophysical outcomes co-vary with inflammation over time (n = 250). AIM 3: Identify biotypes of UCPPS using combined inflammatory, neuroimaging, psychophysical, and clinical data. We will use bioinformatics techniques including canonical correlation analysis and hierarchical clustering to attempt to define unique, latent, biotypes of UCPPS. We hypothesize that these biotypes will reflect different clinical phenotypes and may be useful for predicting treatment responses. The impact of these studies is that they may lead to novel therapeutic targets and tailored treatments for UCPPS by elucidating an important and under-studied mechanism of UCPPS symptomology.