Working Group 1
Disease Risk Profiling
This WG will use low penetrance (high frequency) and medium penetrance (low frequency) germline genetic variants associated with PC risk, as well as epigenetics, transcriptomics and environmental factors to model disease risk and apply risk stratification scores to better select individuals eligible to be screened for PC or its precursors.
Objectives
- To identify novel common and rare germline genetic variants associated with PC susceptibility and survival.
- To genotype individuals for germline genetic variants for PC and epigenetic markers.
- To gather environmental data for individuals to be analyzed such as age, sex, family history of cancer, alcohol, diet, smoking, history of diabetes, weight, and exercise habits.
- To construct PC risk models using low-penetrance (high frequency) and medium penetrance (low frequency) germline genetic variants for PC, epigenetics, transcriptomics measured in blood and environmental factors. This multifactorial risk model will be tested as a predictor for PC and its precursors. In addition, another line of research will focus on molecular markers of IPMN to PDAC transition measured in cyst fluid, blood, and tissue if available.
- To stratify the population, or specific subgroups known to be at elevated risk (e.g., diabetics) by using the new PC risk model.
- To pilot the risk model in country-specific PC prevention programs, and to evaluate its performance and acceptability.
- To evaluate the incorporation of the risk model into large-scale PC prevention programs, with a focus on health economics, and propose possible screening programs based on risk score and novel biomarkers for early detection.
- To develop a consensus model and adopt consistent measures of PC screening performance across the consortium.
- To report to the organizations, bodies and individuals involved in planning, funding, and running PC prevention programs with the aim of introducing the risk score into clinical practice.
- To increase awareness of the importance and usefulness of PC screening programs for PC prevention by actively involving patient associations.
- New GWAS on risk and survival of PC.
- Epigenome-wide association study on risk and survival of PC.
- Modelling of non-genetic risk factors through Mendelian randomization.
- Assembly and evaluation of polygenic and multifactorial risk scores.
- Exploration of possible implementation of risk scores in screening and clinical practice.
- Implementation of AI approaches in the optimization of all the above tasks.
Tasks
Activities
Dedicated WG meetings will take place (1-2 per year) either in person or via videoconferencing platforms. Workshops on the field will be included during meetings. A training school will be committed to the topic of risk profiling. Inter-laboratory exchanges in the form of STSMs are also envisioned especially for young researchers. A tight monitoring of the WG activities will be ensured by a strong WG committee.
- Identification of new genetic variants involved in germline predisposition to PC
- Creation of an epigenomic profile linked to PC and its precursors.
- PC risk modelling.
- Estimations of PC absolute risk as a function of different classes of risk prediction models.
Milestones
Objectives
- To identify novel common and rare germline genetic variants associated with PC susceptibility and survival.
- To genotype individuals for germline genetic variants for PC and epigenetic markers.
- To gather environmental data for individuals to be analyzed such as age, sex, family history of cancer, alcohol, diet, smoking, history of diabetes, weight, and exercise habits.
- To construct PC risk models using low-penetrance (high frequency) and medium penetrance (low frequency) germline genetic variants for PC, epigenetics, transcriptomics measured in blood and environmental factors. This multifactorial risk model will be tested as a predictor for PC and its precursors. In addition, another line of research will focus on molecular markers of IPMN to PDAC transition measured in cyst fluid, blood, and tissue if available.
- To stratify the population, or specific subgroups known to be at elevated risk (e.g., diabetics) by using the new PC risk model.
- To pilot the risk model in country-specific PC prevention programs, and to evaluate its performance and acceptability.
- To evaluate the incorporation of the risk model into large-scale PC prevention programs, with a focus on health economics, and propose possible screening programs based on risk score and novel biomarkers for early detection.
- To develop a consensus model and adopt consistent measures of PC screening performance across the consortium.
- To report to the organizations, bodies and individuals involved in planning, funding, and running PC prevention programs with the aim of introducing the risk score into clinical practice.
- To increase awareness of the importance and usefulness of PC screening programs for PC prevention by actively involving patient associations.
Tasks
- New GWAS on risk and survival of PC.
- Epigenome-wide association study on risk and survival of PC.
- Modelling of non-genetic risk factors through Mendelian randomization.
- Assembly and evaluation of polygenic and multifactorial risk scores.
- Exploration of possible implementation of risk scores in screening and clinical practice.
- Implementation of AI approaches in the optimization of all the above tasks.
Activities
Dedicated WG meetings will take place (1-2 per year) either in person or via videoconferencing platforms. Workshops on the field will be included during meetings. A training school will be committed to the topic of risk profiling. Inter-laboratory exchanges in the form of STSMs are also envisioned especially for young researchers. A tight monitoring of the WG activities will be ensured by a strong WG committee.
Milestones
- Identification of new genetic variants involved in germline predisposition to PC
- Creation of an epigenomic profile linked to PC and its precursors.
- PC risk modelling.
- Estimations of PC absolute risk as a function of different classes of risk prediction models.