Principal Investigator: Mohammad Ali Abbass, MD, Assistant Professor of Surgery (Gastrointestinal), Northwestern University Feinberg School of Medicine Colorectal cancer is the second most common cause of cancer-related death in the United States, but it is also largely preventable with screening. The Abbass team is identifying a genetic tool based on blood samples that can accurately predict an individual’s lifetime risk for developing colorectal cancer. The researchers will use DNA extracted from blood specimens of Northwestern Medicine patients who have either been diagnosed or confirmed clear of colorectal cancer. They will then evaluate changes that occur in the DNA building blocks to validate a polygenic risk score established for European patients. The aim is to use this score to identify patients who lack a family history for colorectal cancer but who should potentially begin their colorectal cancer screening before age 45, the age currently recommended to begin screening. The project could lead to a larger scale study that would target a more expansive population to initiate earlier screening in selected patients and decrease colorectal cancer-related deaths in younger patients. As a result, many people could more accurately know their individual genetic risk, pursue earlier screening if indicated, and detect colorectal cancer earlier, improving their chances of...
Division of Gastroenterology and Hepatology Northwestern Medicine/Feinberg School of Medicine Center for Artificial Intelligence and Mathematics in Gastroenterology The Center for Artificial Intelligence and Mathematics in Gastroenterology (AIM-GI) is a first of its kind program developed in a division of Gastroenterology. Artificial Intelligence and Machine Learning have the potential to vastly improve our ability to accurately predict, diagnose and treat our patients living with digestive diseases. Through collaboration with engineers at the McCormick School of Engineering and physician scientists at the Feinberg School of Medicine at Northwestern University, our team has been incorporating mathematical modeling and advanced programming to study the mechanisms that lead to poor gastrointestinal function. This work led to the development of a more formalized center that focuses on three main initiatives. Development of virtual organs which can be used to study the effects of surgery and medications; Development of new hybrid diagnostic tools using AI and machine learning to enhance diagnosis; Using machine learning and neutral networks to predict disease outcome. Although this is a new program, we have already had success developing an NIH funded Center of Research Expertise (CORE) and we have also developed new AI prototypes that can improve diagnostic accuracy and reliability of motility tests. This work is supported by the generosity of the Digestive Health Foundation and these funds help provide the computational power and expertise required to continue to develop these innovative tools. Our goal is to continue invent and develop new approaches and our partnership with the Digestive Disease Foundation will continue to allow us to grow and evolve this...
Principal Investigator: Beatriz Sosa-Pineda, PhD Pancreatic cancer has one of the worst survival rates in the world, and patients have very limited therapeutic options to fight the disease. It is predicted that close to 47,000 Americans will succumb to pancreatic ductal adenocarcinoma (PDAC) in 2020. Meeting the dire need for new therapies and diagnostic methods requires a better understanding of the fundamental biology of PDAC. Learning more about the molecular mechanisms that govern tumor formation, progression, and spread (metastasis) is critical to improving outcomes. In this study, Dr. Sosa-Pineda intends to build on her lab’s previous findings to firmly establish the role of a new gene regulator, the transcription factor ONECUT2, in the development of pancreatic cancer. This project will use molecular approaches to abolish the expression of ONECUT2 and test its effect on pancreatic tumor function. The Sosa-Pineda team will employ additional approaches to identify specific pathways and functions regulated by ONECUT2 in pancreatic...
Principal Investigator: Sri Komanduri, MD | Co- Principal Investigator: Domenico Farina, MD Esophageal cancer is a deadly and increasingly common disease in the United States. Up to 15 percent of patients with gastro-esophageal reflux disease (GERD) will develop Barrett’s esophagus (BE), a pre-cancerous state that can develop into esophageal cancer. While BE is highly treatable, the progression to esophageal cancer still commonly occurs due to missed detection and diagnosis of Barrett’s esophagus, which often returns despite treatment. A new technology, Wide-Area Trans-Epithelial Sampling (WATS-3D), may offer improved detection of abnormal cells (dysplasia) in Barrett’s. WATS-3D uses brush sampling to examine larger areas of the esophagus than is achieved in conventional biopsies. Preliminary studies show improved detection of cancerous changes in Barrett’s esophagus surveillance. Dr. Komanduri’s team hopes to determine if the addition of WATS-3D increases the rate of detection of recurrent Barrett’s following treatment. If so, this could be a game changer for patients, allowing for earlier re-treatment of Barrett’s and, ultimately, for the prevention of esophageal...
Principal Investigator: Sri Komanduri, MD, MS, FASGE, AGAF; Co-Principal Investigator: Robert Schenck, MD The thought of potentially having cancer is stressful enough without undergoing an invasive biopsy to diagnose it and finding out that the procedure needs to be repeated due to inadequate sample collection. This project aims to optimize strategies for acquiring tissue samples to assist gastroenterologists (and pathologists) in diagnosing many diseases, including cancers. Relying on endoscopic ultrasound guidance, the two methods currently utilized are fine needle aspiration, which is the gold standard, and a new method called fine needle biopsy. Drs. Komanduri and Schenck are conducting the largest study to date to compare these different strategies. After collecting data from patients who have undergone one of these procedures over a five-year period, the investigator will perform advanced statistical modeling to determine the strategy that best optimizes patient outcomes (including obtaining accurate diagnoses and minimizing repeat invasive procedures) and costs. Once identified, this strategy will ultimately be implemented throughout the Northwestern Digestive Health Center to enhance patient...
