Núria Malats. /A. Garrido. CNIO
The project aims to identify high-risk populations for pancreatic cancer, to include them in screening and early detection interventions so as to try to increase patients' survival rates
Pancreatic cancer is the third most common cause of cancer-related death in Spain, ahead of other types of more common tumors, like breast or prostate cancer; it is usually detected at an advanced stage when it has probably invaded other organs
Pancreatic Cancer Collective, an initiative of Lustgarten Foundation and Stand Up to Cancer, has given one million dollars to the project, which will explore genetic factors and the potential relationship of the microbiome and the immune system with this type of cancer
To date, CNIO is the only research center outside USA and the UK to be awarded funds from the Pancreatic Cancer Collective
The Pancreatic Cancer Collective (PCC), a joint initiative by Lustgarten Foundation and Stand Up to Cancer (SU2C), has granted one million dollars to a two-year project aimed at identifying high-risk populations for pancreatic cancer. The project is co-led by Núria Malats, Head of the Genetic and Molecular Epidemiology Group at the Spanish National Cancer Research Centre (CNIO), Spain, and Raúl Rabadán, Professor of Systems Biology and of Biomedical Informatics in the Institute for Cancer Genetics at Columbia University, USA. The team will study the microbiome and the immune and genetic factors to identify populations with an increased risk of developing pancreatic cancer, who could then be suggested for screening to early detect their cancer and, therefore, to improve their survival.
The early detection challenge
Although pancreatic cancer is not among the most common type of cancer, it is the third most common cause of cancer deaths in Spain, ahead of other much more common malignant neoplasms, such as breast or prostate cancer. The incidence of pancreatic cancer worldwide makes it the 13th most commonly occurring cancer, but its ranks 7th among the leading cause of cancer death. One of the reasons of such a high mortality rate is that early detection is almost absent in this type of cancer, which means that when it is effectively detected, it is usually at an advanced stage or it has grown and invaded other organs. Only a few risk factors directly linked to pancreatic cancer have been identified so far; therefore, both early stage tumor markers and risk predictor should be identified.
“The etiology of pancreatic cancer is really complex; there is no single cause for this type of cancer,” says Núria Malats. “Screening has been done for hereditary/family tumors, but they only make 10% of the total burden of the disease. For this reason, we want to have more patients taking part in screening programs so as to enable early detection of pancreatic cancer, mainly in stages with asymptomatic tumors.” The ultimate goal is to raise survival rates.
To attain this goal, within the large proportion of sporadic (non-hereditary) pancreatic tumors, researchers will try to identify genetic and immune factors in high-risk populations. “This is really a very interdisciplinary effort that involves physicians and basic science researchers,” says Raúl Rabadán. “We will get information from the genome and from the environment, in particular, with the microbial and immune components, and we will try to learn how these two systems interact.”
Genome, immune system, and microbiome
The team of researchers will join forces to try and locate rare genetic variants, as well as specific DNA regions and mutations in large sets of clinical and molecular data. They will have access to data from nearly 5,000 pancreatic cancer patients stored at the UK Biobank, The Cancer Genome Atlas, the International Cancer Genome Consortium, and the European Study into Digestive Illnesses and Genetics (PanGenEU Study). The PanGenEU Study, led by Núria Malats, is a large multi-centric case-control study that was initiated a decade ago in six European countries to identify multiple relevant risk factors for pancreatic cancer.
“We will define the high-risk population with data on non-genetic factors, but also with data on genetic factors, both the most common and rare variants,” adds Malats. “To do so, we will use the data our team has generated in the context of the PanGenEU Study. We will contrast these samples with those in the other data sets and try and find similarities, and apply and extend results.”
In addition, some studies reveal that there might be a direct relationship between pancreatic cancer and some bacterial and viral infections, a possibility that will also be explored by the teams that take part in the project co-led by CNIO and Columbia University. They will characterize the tumor microenvironment – specifically, the metagenome (genome sequences of the microbiome, i.e. the microbes present in the human body) and the expression of proteins that participate in the regulation of the immune system. “There might be a huge potential in tumor microenvironment and immune biomarkers for the identification of individuals with an increased risk of developing pancreatic cancer,” explains Malats.
“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 SU2C Scientific Advisory Committee and scientific co-leader of the Collective.
SU2C and Lustgarten Foundation are two of the largest private charitable organizations supporting state-of-the-art cancer research around the world. In 2012, they decided to join forces to study pancreatic cancer. These efforts led to the creation of the PCC in 2018.
This year, besides the project co-lead by CNIO and Columbia University, the Collective has also granted funds to a project run by Dana-Farber Cancer Institute and the Massachusetts Institute of Technology. This project will try to identify individuals at a high risk of developing pancreatic cancer, through machine learning analysis of clinical records and images.