Periodic Reporting for period 1 - LUCIA (Understanding Lung Cancer related risk factors and their Impact)
Reporting period: 2023-01-01 to 2024-06-30
Lung cancer (LC) is the leading cause of cancer-related deaths worldwide, with a low five-year survival rate ranging from 5% to 25%. While tobacco smoking is widely recognized as the primary risk factor for lung cancer, there are cases, including non-smokers, where this explanation falls short. To address this gap, the LUCIA project aims to establish a novel toolbox for discovering and understanding new risk factors that contribute to the development of lung cancer.
The LUCIA toolbox focuses on analyzing three key aspects: 1) Personal risk factors, include a person's exposure to chemical pollutants, behavioral and lifestyle factors. 2) External risk factors, encompass urban, built and transport environments, social aspects, and climate. 3) Biological responses, refer to changes in genetics, epigenetics, and metabolism. To analyze personal and external risk factors, the LUCIA toolbox utilizes various tools such as retrospective and prospective cohort databases, AI models, wearable devices, novel non-invasive sensors, and multi-omics approaches. These tools enable the identification of a wide range of environmental, biological, demographic, community, and individual-level risk factors associated with the formation of LC. Once the risk factors are identified, molecular changes associated with them are validated using cell and molecular biology methods and in vivo analysis. The impact of the identified risk factors, and their biological responses, is then validated through three clinical use cases: general population risk assessment and screening, precision screening of high-risk populations, and digital diagnostics. The evidence generated by the LUCIA project will be translated into policymaking recommendations, with the ultimate goal of implementing a screening program for LC and to improve our understanding of LC risk factors contributing to better prevention and early detection strategies.
The LUCIA toolbox focuses on analyzing three key aspects: 1) Personal risk factors, include a person's exposure to chemical pollutants, behavioral and lifestyle factors. 2) External risk factors, encompass urban, built and transport environments, social aspects, and climate. 3) Biological responses, refer to changes in genetics, epigenetics, and metabolism. To analyze personal and external risk factors, the LUCIA toolbox utilizes various tools such as retrospective and prospective cohort databases, AI models, wearable devices, novel non-invasive sensors, and multi-omics approaches. These tools enable the identification of a wide range of environmental, biological, demographic, community, and individual-level risk factors associated with the formation of LC. Once the risk factors are identified, molecular changes associated with them are validated using cell and molecular biology methods and in vivo analysis. The impact of the identified risk factors, and their biological responses, is then validated through three clinical use cases: general population risk assessment and screening, precision screening of high-risk populations, and digital diagnostics. The evidence generated by the LUCIA project will be translated into policymaking recommendations, with the ultimate goal of implementing a screening program for LC and to improve our understanding of LC risk factors contributing to better prevention and early detection strategies.
The main achievements and activities performed are listed briefly:
1. Establishment requirements definition and specification documents that include topics such as: (a) the user personas and establish use cases, (b) high-level user requirements and (c) initial key performance indicators (KPIs) and associated Minimal Viable Target metrics for each of the main project activities.
2. Consortium analysed the applicable ethical frameworks, the technologies to be developed, mapped relevant stakeholders and conducted some workshops and focus groups to collectively defined the requirements.
3. Consortium focused on shaping the AI Impact Assessment (AIIA) across different life cycles stages and defining the stages of AIIA for the project. Core values to trustworthy AI in healthcare were identified.
4. To test the real-life implementation of LUCIA technologies, two potential use scenarios were built and evaluated also by the Social lab workshops.
5. Focused on characterising all different retrospective datasets for analysis. Defining, and implementing an initial version of the health data platform to support the collection of both retrospective and prospective data from different health and social registers, as well as exposure data into a virtual Data Lake. Including initiation of data ingestion, integration and harmonization, virtual research environment requirement drafting, including a data browser and Data Version Control that allows to keep track of changes in our datasets.
6. The development of the citizens and patients mHealth app, as a hub for collecting and storing health determinants. It will enable tracking of patients' biomarker readings from developed technologies
7. Started designing and developing a GIS-based tool that integrates georeferenced open data sources, completed the Census tract searcher
8. Developed and produced noninvasive technologies for identifying health determinants: breath system, WBSP (patch) and SPOC (blood measurement).
9. AI models for medical image analysis (CT, digital pathology) started based on Mock up data and open databases.
10. Established feasibility of a polygenic risk score (PRS) for LC, using the current state-of-the-art methods and large-scale datasets towards a multiomic PRS that includes epigenomic data.
11. Defined the clinical protocols, achieved ethical approvals for the clinical trials planned and started clinical enrollment in all sites.
12. Started molecular risk factor identification including environmental and sociodemographic GeoSpatial Data, risk factors AI modelling based on available datasets, and developing knowledge graphs.
13. Established the procedures for the DNA based nanopore analysis of risk factors. Started targeting of molecular risk factors that contribute to transformation mechanisms. Two potential genes were identified. Cell lines were evaluated for suitability of the plane animal model trials.
1. Establishment requirements definition and specification documents that include topics such as: (a) the user personas and establish use cases, (b) high-level user requirements and (c) initial key performance indicators (KPIs) and associated Minimal Viable Target metrics for each of the main project activities.
2. Consortium analysed the applicable ethical frameworks, the technologies to be developed, mapped relevant stakeholders and conducted some workshops and focus groups to collectively defined the requirements.
3. Consortium focused on shaping the AI Impact Assessment (AIIA) across different life cycles stages and defining the stages of AIIA for the project. Core values to trustworthy AI in healthcare were identified.
4. To test the real-life implementation of LUCIA technologies, two potential use scenarios were built and evaluated also by the Social lab workshops.
5. Focused on characterising all different retrospective datasets for analysis. Defining, and implementing an initial version of the health data platform to support the collection of both retrospective and prospective data from different health and social registers, as well as exposure data into a virtual Data Lake. Including initiation of data ingestion, integration and harmonization, virtual research environment requirement drafting, including a data browser and Data Version Control that allows to keep track of changes in our datasets.
6. The development of the citizens and patients mHealth app, as a hub for collecting and storing health determinants. It will enable tracking of patients' biomarker readings from developed technologies
7. Started designing and developing a GIS-based tool that integrates georeferenced open data sources, completed the Census tract searcher
8. Developed and produced noninvasive technologies for identifying health determinants: breath system, WBSP (patch) and SPOC (blood measurement).
9. AI models for medical image analysis (CT, digital pathology) started based on Mock up data and open databases.
10. Established feasibility of a polygenic risk score (PRS) for LC, using the current state-of-the-art methods and large-scale datasets towards a multiomic PRS that includes epigenomic data.
11. Defined the clinical protocols, achieved ethical approvals for the clinical trials planned and started clinical enrollment in all sites.
12. Started molecular risk factor identification including environmental and sociodemographic GeoSpatial Data, risk factors AI modelling based on available datasets, and developing knowledge graphs.
13. Established the procedures for the DNA based nanopore analysis of risk factors. Started targeting of molecular risk factors that contribute to transformation mechanisms. Two potential genes were identified. Cell lines were evaluated for suitability of the plane animal model trials.
The direct impacts of the LUCIA will be on the scientific, innovation, and health communities. During the first period of the project, we set up baseline work and started various aspects towards the implementation of the expected impact. Briefly:
For the Health Care professionals Improved understanding of risk factors and sensor technology eventually impacting (i) Reduced incidence of LC. (ii) Early detection of LC. (iii) Substitute CT in the long term for screening of at-risk population. To this end LUCIA has been working on innovative technology and AI models development during first period. Initiation of retrospective and prospective clinical trials started and their outcomes during second and third period should contribute to the impact of the project. For the research community data and computational models polygenic risk scores, data lake, biological pathway and molecular mechanism of action, sensor technology and novel biomarkers are expected to provide new data improved AI models and improved understanding of risk factors, at this stage LUCIA already identified several potential genes as well as PRS that could contribute to LC understanding. Models have been initiated based on databases and mock up data that could eventually be integrated also with other cancer platforms being established in the EU. On the technology side LUCIA has finalized the prototype developments on the non-invasive devices that can widely impact clinical practice, diagnostics and monitoring.
LUCIA will have impact in societal aspects as well, briefly: i) Project initiated, value-sensitive design, social impacts, SDG and legal and ethical recommendations, best practices, and AI system’ impact assessment. ii) Open innovative, LUCIA is developing a data health care platform using among other things, open innovative models that will be available for public use, such as a dedicated patient app, that was developed during first period, that allows direct interaction of patients with the toolbox.
For the Health Care professionals Improved understanding of risk factors and sensor technology eventually impacting (i) Reduced incidence of LC. (ii) Early detection of LC. (iii) Substitute CT in the long term for screening of at-risk population. To this end LUCIA has been working on innovative technology and AI models development during first period. Initiation of retrospective and prospective clinical trials started and their outcomes during second and third period should contribute to the impact of the project. For the research community data and computational models polygenic risk scores, data lake, biological pathway and molecular mechanism of action, sensor technology and novel biomarkers are expected to provide new data improved AI models and improved understanding of risk factors, at this stage LUCIA already identified several potential genes as well as PRS that could contribute to LC understanding. Models have been initiated based on databases and mock up data that could eventually be integrated also with other cancer platforms being established in the EU. On the technology side LUCIA has finalized the prototype developments on the non-invasive devices that can widely impact clinical practice, diagnostics and monitoring.
LUCIA will have impact in societal aspects as well, briefly: i) Project initiated, value-sensitive design, social impacts, SDG and legal and ethical recommendations, best practices, and AI system’ impact assessment. ii) Open innovative, LUCIA is developing a data health care platform using among other things, open innovative models that will be available for public use, such as a dedicated patient app, that was developed during first period, that allows direct interaction of patients with the toolbox.