The Pancreatic Cancer Collective, the strategic partnership of Lustgarten Foundation and Stand Up To Cancer (SU2C), today announced two, million-dollar grants for computational approaches to identifying high-risk pancreatic cancer populations. The grant money will be used to develop novel tools to identify individuals who are at high risk for developing pancreatic cancer that will be based on their health records. The announcement was made at the Annual Meeting of the American Association for Cancer Research.
Artificial intelligence (AI) is a term that refers to machines that are programmed to mimic human reasoning. The goal of AI is to learn from failure and be able to provide the best recommendation for a specific subject. Whether it be solving equations, finding the best treatment options for cancer patients, or aiding in vehicle autonomy, AI is becoming a powerful resource across multiple industries.
“From diagnosing pancreatic cancer to determining which treatment approach may be best for each patient, we believe the field of AI holds great promise for patients and their families,” stated David A. Tuveson, MD, PhD, Lustgarten’s chief scientist, director of the Cancer Center at Cold Spring Harbor Laboratory and co-scientific leader of the Collective.
The two teams will each pursue a different approach to identifying individuals in the general population who are at high risk for pancreatic cancer. One will use molecular and genetic data taken from a variety of datasets to identify new and accessible ways to identify high-risk individuals. The other focuses on identification of high-risk individuals by applying machine learning analysis to real world data comprising radiological images, electronic medical records, and information collected by physicians. Each team will receive up to $1 million over two years.
The Pancreatic Cancer Collective’s New Computational Approaches to Identifying High-Risk Pancreatic Cancer Populations Research Teams are:
Identification of Genomic and Immune Factors in High-Risk Populations for Pancreatic Cancer: Team Leader: Raul Rabadan, PhD, Columbia University; Co-leader: Núria Malats, MD, PhD, MPH, Spanish National Cancer Research Centre (CNIO). The team will focus on rare gene variants, specific DNA regions and modifications, within large clinical and molecular datasets from multiple cohorts. The datasets include the UK Biobank, European Study on Digestive Illnesses and Genetics (PanGen-EU), The Cancer Genome Atlas, and International Cancer Genome Consortium. This team also plans to characterize the tumor microenvironment, specifically the microbiome and expression of proteins important for immune system regulation.
“If these efforts to comprehensively integrate clinical, genetic, and microenvironmental factors are successful, this team will revolutionize the screening and identification of individuals highly susceptible to pancreatic cancer,” stated Phillip A. Sharp, PhD, the Nobel laureate who is chair of the SU2C Scientific Advisory Committee (SAC) and scientific co-leader of the Collective.
Identifying Individuals at High Risk of Pancreatic Cancer through Machine Learning Analysis of Clinical Records and Images: Team Leader: Chris Sander, PhD, Dana-Farber Cancer Institute; Co-leader: Regina Barzilay, PhD, Massachusetts Institute of Technology. The goal of this team’s project is to develop risk assessment models by using machine learning analysis of clinical records and images to identify high‐risk individuals for pancreatic cancer in the general population. These risk models will lead to a practical tool that can identify patients who are at elevated risk for pancreatic cancer and should be enrolled in screening programs for disease prevention and early detection. This team builds on recent advances in machine learning technology and the availability of rich clinical records from large, diverse patient populations within three health systems: Henry Ford Health System, Partners HealthCare, and the Danish National Patient Registry.