Periodic Reporting for period 1 - CanDoIt (Intelligent Breast Cancer DiagnOsis and MonItoring Therapeutic Response Training Network)
Reporting period: 2024-01-01 to 2025-12-31
A breakthrough in precision diagnostics and treatement for breast cancer
Breast cancer diagnosis and treatment face persistent challenges, with current methods often lacking the precision and ease of use required for optimal patient care. Existing techniques may lead to misdiagnosis, delayed intervention or non-adapted treatment. With the support of the Marie Skłodowska-Curie Actions programme, the CanDoIt project aims to integrate breakthrough technology – a multimodal, multi-physical biosensor array for the identification and quantification of peripheral biomarkers in liquid biopsies. By collaborating with industry and academia, the project will develop breast cancer diagnostic technologies and establish a high-impact structure for extensive dissemination. The overall goal is to revolutionise breast cancer research and treatment, bringing forth a new era of accuracy and efficiency in diagnostics and therapeutic monitoring.
Context and Objectives of the CanDoIt Project
Cancer remains a major global health challenge, responsible for nearly 10 million deaths in 2020, with projections estimating a rise in new cases to over 30 million by 2040. Among all cancer types, breast cancer (BC) stands out as the most commonly diagnosed and one of the deadliest, particularly affecting women across Europe. Early and accurate detection, combined with effective monitoring of treatment response, is essential to improving patient outcomes. Yet, current diagnostic methods often rely on invasive procedures and fall short in sensitivity, specificity, and real-time responsiveness.
In this context, the CanDoIt Doctoral Network (DN) addresses an urgent need: to revolutionize how we detect and monitor breast cancer by developing an innovative, smart, multimodal biosensing platform based on fluid biopsies. These non-invasive diagnostic techniques offer rich molecular insights into cancer progression and therapy response, positioning themselves as a transformative tool in personalized oncology.
________________________________________
The Strategic Need and Technological Opportunity
The rise of liquid biopsies is mirrored by a rapidly growing market—expected to reach over €5 billion by 2030—reflecting the pressing demand for accurate, minimally invasive diagnostic solutions. However, to fully realize their potential, there is a critical need for next-generation biosensors capable of detecting and interpreting complex biomarker profiles across multiple biological fluids. This calls for breakthroughs in material science, sensor design, data analytics, and clinical validation.
CanDoIt responds to this challenge by assembling a consortium of leading European universities, research institutions, healthcare providers, and high-tech SMEs with complementary expertise in micro/nano-engineering, advanced algorithms and signal processing, bio-nanomaterials, and oncology. Together, they will pioneer a new class of multi-physic, algorithm-empowered biosensors capable of ultra-sensitive, real-time detection of breast cancer biomarkers from blood, saliva, and urine.
________________________________________
Project Objectives and Pathway to Impact
CanDoIt will develop and clinically validate a smart sensor array integrating electrochemical, acoustic, magnetic, optical, and impedance-based detection, combined with advanced algorithms and signal processing methods for data fusion and interpretation. The project’s core scientific objectives include:
• Designing multimodal biosensors with clinically required accuracy and ultra-low limits of detection;
• Engineering advanced nanomaterials and bio-functional coatings for highly selective biomarker capture;
• Systematically evaluating biosensor performance in both preclinical and real-world clinical contexts;
• Identifying and validating breast cancer-specific extracellular vesicles (EVs) and RNA biomarkers;
• Developing diagnostic algorithms based on advanced signal processing and data analysis that correlate peripheral biomarker data with clinical standards.
________________________________________
Scale and Significance of Impact
CanDoIt will lay the foundation for a paradigm shift in cancer diagnostics—transitioning from invasive, delayed procedures to real-time, non-invasive monitoring tailored to individual patients. Beyond scientific advances, the project supports key European strategies in healthcare innovation, digital medicine, and academic-industry cooperation. It will significantly enhance Europe's competitiveness in the diagnostics sector while improving patient care and health system efficiency.
Moreover, the integrated training program will deliver highly skilled, employable researchers equipped to navigate and lead in complex, cross-sectoral environments. The long-term impact extends to the broader adoption of biosensing technologies empowered by advanced algorithms and signal processing methods in oncology and beyond, setting a precedent for how future diagnostic platforms are conceived, developed, and brought to clinical practice
Find out more on the Project website:
https://candoit-dn.eu/(opens in new window)
Breast cancer diagnosis and treatment face persistent challenges, with current methods often lacking the precision and ease of use required for optimal patient care. Existing techniques may lead to misdiagnosis, delayed intervention or non-adapted treatment. With the support of the Marie Skłodowska-Curie Actions programme, the CanDoIt project aims to integrate breakthrough technology – a multimodal, multi-physical biosensor array for the identification and quantification of peripheral biomarkers in liquid biopsies. By collaborating with industry and academia, the project will develop breast cancer diagnostic technologies and establish a high-impact structure for extensive dissemination. The overall goal is to revolutionise breast cancer research and treatment, bringing forth a new era of accuracy and efficiency in diagnostics and therapeutic monitoring.
Context and Objectives of the CanDoIt Project
Cancer remains a major global health challenge, responsible for nearly 10 million deaths in 2020, with projections estimating a rise in new cases to over 30 million by 2040. Among all cancer types, breast cancer (BC) stands out as the most commonly diagnosed and one of the deadliest, particularly affecting women across Europe. Early and accurate detection, combined with effective monitoring of treatment response, is essential to improving patient outcomes. Yet, current diagnostic methods often rely on invasive procedures and fall short in sensitivity, specificity, and real-time responsiveness.
In this context, the CanDoIt Doctoral Network (DN) addresses an urgent need: to revolutionize how we detect and monitor breast cancer by developing an innovative, smart, multimodal biosensing platform based on fluid biopsies. These non-invasive diagnostic techniques offer rich molecular insights into cancer progression and therapy response, positioning themselves as a transformative tool in personalized oncology.
________________________________________
The Strategic Need and Technological Opportunity
The rise of liquid biopsies is mirrored by a rapidly growing market—expected to reach over €5 billion by 2030—reflecting the pressing demand for accurate, minimally invasive diagnostic solutions. However, to fully realize their potential, there is a critical need for next-generation biosensors capable of detecting and interpreting complex biomarker profiles across multiple biological fluids. This calls for breakthroughs in material science, sensor design, data analytics, and clinical validation.
CanDoIt responds to this challenge by assembling a consortium of leading European universities, research institutions, healthcare providers, and high-tech SMEs with complementary expertise in micro/nano-engineering, advanced algorithms and signal processing, bio-nanomaterials, and oncology. Together, they will pioneer a new class of multi-physic, algorithm-empowered biosensors capable of ultra-sensitive, real-time detection of breast cancer biomarkers from blood, saliva, and urine.
________________________________________
Project Objectives and Pathway to Impact
CanDoIt will develop and clinically validate a smart sensor array integrating electrochemical, acoustic, magnetic, optical, and impedance-based detection, combined with advanced algorithms and signal processing methods for data fusion and interpretation. The project’s core scientific objectives include:
• Designing multimodal biosensors with clinically required accuracy and ultra-low limits of detection;
• Engineering advanced nanomaterials and bio-functional coatings for highly selective biomarker capture;
• Systematically evaluating biosensor performance in both preclinical and real-world clinical contexts;
• Identifying and validating breast cancer-specific extracellular vesicles (EVs) and RNA biomarkers;
• Developing diagnostic algorithms based on advanced signal processing and data analysis that correlate peripheral biomarker data with clinical standards.
________________________________________
Scale and Significance of Impact
CanDoIt will lay the foundation for a paradigm shift in cancer diagnostics—transitioning from invasive, delayed procedures to real-time, non-invasive monitoring tailored to individual patients. Beyond scientific advances, the project supports key European strategies in healthcare innovation, digital medicine, and academic-industry cooperation. It will significantly enhance Europe's competitiveness in the diagnostics sector while improving patient care and health system efficiency.
Moreover, the integrated training program will deliver highly skilled, employable researchers equipped to navigate and lead in complex, cross-sectoral environments. The long-term impact extends to the broader adoption of biosensing technologies empowered by advanced algorithms and signal processing methods in oncology and beyond, setting a precedent for how future diagnostic platforms are conceived, developed, and brought to clinical practice
Find out more on the Project website:
https://candoit-dn.eu/(opens in new window)
WP3:
- Synthesis of gold nanoparticles (Turkevich method) and magnetic nanoparticles.
- Development of novel immobilisation strategies to enhance sensor surface area and sensitivity.
- Optimisation of gold electrode cleaning, electrochemical characterisation (CV, EIS), and photochemical antibody immobilisation.
- Identification and functional characterisation of the aptamer ex55.T and its molecular target.
- Isolation and characterisation of extracellular vesicles (EVs) using NTA and mass spectrometry.
WP4:
- Design and validation of electrochemical biosensors (ELISA-based, aptamer- and antibody-functionalised).
- Development of fluidic-compatible electrochemical platforms and microfluidic systems.
- Characterisation and comparison of advanced electrodes (screen-printed, gold, graphene foam).
- Validation of QCM sensors for miRNA detection and initiation of multimodal impedance-based sensing.
- Development of magnetic sensing technologies (MPS prototypes, GMR/TMR sensor analysis).
- Creation of biodegradable and 3D-printed microfluidic platforms compatible with magnetic sensors.
- Establishment of surface functionalisation and fluorescence-assisted analysis of EV protein distribution.
WP5:
- Initiation of an in silico pipeline for EV surface protein prediction using deep learning.
- Development of aptamer–protein molecular docking workflows.
- Preliminary exploration of multimodal data integration (magnetic spectroscopy, QCM).
- Adaptation of strategy due to limited EV datasets; shift to whole-cell data with EV validation.
- Deployment of computational infrastructure using Docker and Kubernetes.
WP6:
- Establishment of EV isolation protocols and quality assessment methods.
- Generation of chemoresistant breast cancer cell lines (doxorubicin, tamoxifen).
- Development of 3D in vitro breast cancer models to study EV-mediated effects.
- Adaptation and testing of spiral inertial microfluidics for EV separation.
- Initiation of active EV sorting techniques using surface acoustic wave sensors.
- Synthesis of gold nanoparticles (Turkevich method) and magnetic nanoparticles.
- Development of novel immobilisation strategies to enhance sensor surface area and sensitivity.
- Optimisation of gold electrode cleaning, electrochemical characterisation (CV, EIS), and photochemical antibody immobilisation.
- Identification and functional characterisation of the aptamer ex55.T and its molecular target.
- Isolation and characterisation of extracellular vesicles (EVs) using NTA and mass spectrometry.
WP4:
- Design and validation of electrochemical biosensors (ELISA-based, aptamer- and antibody-functionalised).
- Development of fluidic-compatible electrochemical platforms and microfluidic systems.
- Characterisation and comparison of advanced electrodes (screen-printed, gold, graphene foam).
- Validation of QCM sensors for miRNA detection and initiation of multimodal impedance-based sensing.
- Development of magnetic sensing technologies (MPS prototypes, GMR/TMR sensor analysis).
- Creation of biodegradable and 3D-printed microfluidic platforms compatible with magnetic sensors.
- Establishment of surface functionalisation and fluorescence-assisted analysis of EV protein distribution.
WP5:
- Initiation of an in silico pipeline for EV surface protein prediction using deep learning.
- Development of aptamer–protein molecular docking workflows.
- Preliminary exploration of multimodal data integration (magnetic spectroscopy, QCM).
- Adaptation of strategy due to limited EV datasets; shift to whole-cell data with EV validation.
- Deployment of computational infrastructure using Docker and Kubernetes.
WP6:
- Establishment of EV isolation protocols and quality assessment methods.
- Generation of chemoresistant breast cancer cell lines (doxorubicin, tamoxifen).
- Development of 3D in vitro breast cancer models to study EV-mediated effects.
- Adaptation and testing of spiral inertial microfluidics for EV separation.
- Initiation of active EV sorting techniques using surface acoustic wave sensors.