November marks Pancreatic Cancer Awareness Month. We likely will not see purple laces on NFL fields or large groups wearing purple in solidarity, yet pancreatic cancer is still one of the deadliest forms of cancer and accounts for 7% of all cancer deaths in the United States.
I once asked an audience to raise their hands if they knew someone diagnosed with pancreatic cancer. I then asked them to keep their hands up if they knew someone who had died from pancreatic cancer. Nearly every hand remained raised. The toll of pancreatic cancer on patients and families is devastating – but it doesn’t have to be this way. It’s time to push harder for early detection.
Unlike mammography for breast cancer or colonoscopy for colon cancer, there is no early detection tool for pancreatic cancer. Without early diagnosis and intervention, most patients are diagnosed when their cancer has progressed to a late stage, leaving patients and their loved ones feeling hopeless. We cannot allow this devastation to continue, and it is vital that we find a solution to the gap in early diagnosis for pancreatic cancer.
One critical piece to improving early detection will be better identification of individuals at risk. While there are several known risk factors for pancreatic cancer, including smoking, diabetes and obesity, it is clear that genetics plays a more significant role than was previously believed. Today, genetic testing is not yet fully integrated into pancreatic cancer care, but what if we changed that? Maximizing testing of patients with pancreatic cancer can help us make better recommendations for their family members who could benefit from participating in surveillance programs.
Solving a complex problem like pancreatic cancer requires the best and brightest minds equipped with cutting-edge resources. Thankfully, we have researchers, clinicians, and experts in artificial intelligence and data management on our side to help answer the call. To crack the code, we needed a large-scale organized effort with a novel model of data-sharing and infrastructure among pancreatic cancer centers worldwide to enable and encourage the level of collaboration this difficult problem requires. Thus, we created the Pancreatic Cancer Early Detection (PRECEDE) Consortium.
Together with REALM IDx and its subsidiary companies, Ambry Genetics and Invicro, PRECEDE will apply an integrated diagnostic approach to the early detection and prevention of pancreatic cancer. With the team’s expertise in risk modeling, machine learning, data management, genetic testing biomarkers, and imaging, we can determine who is at an elevated risk for developing pancreatic cancer, define that risk, and invite those at elevated risk into state-of-the-art screening programs. The analysis of this data is critical for disease detection, prevention, and treatment and will help to inform future early detection methods.
Our collective goal is to increase the 5-year survival rates of individuals diagnosed with pancreatic cancer from 10% to 50% within the next ten years. To achieve this goal, the PRECEDE Consortium, with NYU Langone as the academic coordinating center, has begun a study  to discover the earliest signals of pancreatic cancer, allowing for early detection and prevention. This work relies on blood samples and de-identified clinical data collected from patients. Using the data and blood samples, we can look for new genetic risk factors that may contribute to pancreatic cancer, and look for early signs that may predict the development of cancer.
A previous challenge of maintaining a study of this scale was data: how to compile massive amounts of data and optimize its organization for clinicians, researchers, and patients. To address this problem, REALM IDx is developing a platform called LATTICETM to enable us to examine large-scale data obtained through the PRECEDE study using state-of-the art expertise in machine learning and artificial intelligence.
This Pancreatic Cancer Awareness Month, we can make a real impact in early detection with the PRECEDE study. The PRECEDE study is the largest of its kind, and to have the most accurate data we need to engage individuals at elevated risk of pancreatic cancer to join us in this mission. If you, or someone you know, has a family history of pancreatic cancer, I encourage you to visit precedestudy.org and join us. You could help us find the critical link that will save countless lives.
 Stoffel EM, McKernin SE, Brand R, Canto M, Goggins M, Moravek C, Nagarajan A, Petersen GM, Simeone DM, Yurgelun M, Khorana AA. Evaluating Susceptibility to Pancreatic Cancer: ASCO Provisional Clinical Opinion. J Clin Oncol. 2019 Jan 10;37(2):153-164. doi: 10.1200/JCO.18.01489. Epub 2018 Nov 20. PMID: 30457921.
 Everett JN, Burgos G, Chun J, Baptiste A, Khanna LG, Oberstein PE, Simeone DM. Cancer surveillance awareness and practice among families at increased risk for pancreatic adenocarcinoma. Cancer. 2021 Jul 1;127(13):2271-2278. doi: 10.1002/cncr.33500. Epub 2021 Mar 15. PMID: 33721345.
 Gonda TA, Everett JN, Wallace M, Simeone DM; PRECEDE Consortium. Recommendations for a More Organized and Effective Approach to the Early Detection of Pancreatic Cancer From the PRECEDE (Pancreatic Cancer Early Detection) Consortium. Gastroenterology. 2021 Aug 27:S0016-5085(21)03416-8. doi: 10.1053/j.gastro.2021.08.036. Epub ahead of print. PMID: 34454916.
Featured image: Ambry Genetics SuperLab. Photo courtesy © 2020 Ambry Genetics. Used with permission.