Periodic Reporting for period 1 - HD-BRECA (Integrating longitudinal multi-modal profiling of metastatic breast cancer patients for high-definition oncology)
Période du rapport: 2024-06-01 au 2026-05-31
Résumé du contexte et des objectifs généraux du projet
The current paradigm in cancer care, known as precision medicine, tailors treatments based on the genetic profile of a patient's tumor. While this has been a significant step forward from the 'one-size-fits-all' approach, its success is limited. A considerable number of patients do not respond to therapies that should, in theory, be effective for them. This indicates that factors beyond genomics play a crucial role in a patient's journey with cancer.
The HD-BRECA project is founded on the understanding that a more holistic view is necessary to truly personalize cancer treatment. The overall objective is to move beyond precision medicine into 'high-definition medicine'. This new approach considers not just the tumor's genetics, but a wide array of other data dimensions that influence health and disease. These include the patient's gut microbiome, diet, physical activity, routine medications, environmental exposures, and even physiological patterns like heart rate and sleep rhythms.
To achieve this, HD-BRECA will pioneer the largest study of its kind in advanced breast cancer. We will create a comprehensive, multi-dimensional dataset by following a group of 100 patients with metastatic breast cancer. By integrating and analyzing this wealth of information, the project aims to build a much more accurate predictive model of the disease's progression.
The expected impact is transformative. By understanding the complex interplay of factors that determine treatment success, HD-BRECA will 'set the scene' for a new chapter in oncology. The project will provide clinicians with better tools to predict patient outcomes, paving the way for more effective, truly personalized interventions. Ultimately, this will help to overcome the current limitations in breast cancer therapy and improve the quality of life for patients. The methodologies developed will also serve as a blueprint for applying the high-definition medicine paradigm to other types of cancer.
The HD-BRECA project is founded on the understanding that a more holistic view is necessary to truly personalize cancer treatment. The overall objective is to move beyond precision medicine into 'high-definition medicine'. This new approach considers not just the tumor's genetics, but a wide array of other data dimensions that influence health and disease. These include the patient's gut microbiome, diet, physical activity, routine medications, environmental exposures, and even physiological patterns like heart rate and sleep rhythms.
To achieve this, HD-BRECA will pioneer the largest study of its kind in advanced breast cancer. We will create a comprehensive, multi-dimensional dataset by following a group of 100 patients with metastatic breast cancer. By integrating and analyzing this wealth of information, the project aims to build a much more accurate predictive model of the disease's progression.
The expected impact is transformative. By understanding the complex interplay of factors that determine treatment success, HD-BRECA will 'set the scene' for a new chapter in oncology. The project will provide clinicians with better tools to predict patient outcomes, paving the way for more effective, truly personalized interventions. Ultimately, this will help to overcome the current limitations in breast cancer therapy and improve the quality of life for patients. The methodologies developed will also serve as a blueprint for applying the high-definition medicine paradigm to other types of cancer.
Travail effectué depuis le début du projet jusqu’à la fin de la période considérée dans le rapport et principaux résultats atteints jusqu’à présent
The core of the project has been the execution of the High-Definition Oncology (HDO) study, a prospective, multicenter observational study designed to deeply profile patients with metastatic cancer. The protocol and its feasibility have been established and are detailed in the manuscript "Longitudinal clinical, physiological and molecular profiling of female metastatic cancer patients: protocol and feasibility of a multicenter high-definition oncology study," currently in its second revision at JCO Precision Oncology. This foundational work outlines the methodology for longitudinally collecting data across eleven distinct modalities, including genomics, epigenomics, microbiome, metabolome, proteome, continuous physiological monitoring via wearables, and patient-reported outcomes (PROs). Feasibility data from the initial cohort of patients demonstrated high compliance and data quality, with patient engagement in clinical visits, sample submission, and digital monitoring exceeding expectations. This success was communicated at the ESMO congress in the work "Constructing a high-definition patient-digital twin (PDT) in treatment-naïve women with advanced cancer" (2024), highlighting the viability of constructing high-definition 'patient-digital twins'.
A significant achievement has been the development of an explainable AI model for cancer prognosis that leverages PROs, presented at the ESMO congress in the communication "Explainable AI model for cancer prognosis using weighted analysis of patient-reported outcomes (PROs)" (2025). By applying a sparse, interpretable neural network to item-level data from the EORTC QLQ-C30 and GHQ-28 questionnaires, we can predict progressive disease within 12 months with high accuracy (AUC ≈ 0.85). This model surpasses traditional scoring methods by identifying and weighting the most predictive symptoms and functional items, demonstrating the prognostic power of longitudinally collected PROs.
Another key technical advance, communicated at the ASCO meeting in "Remote physiologic and behavioral monitoring to predict early treatment response in metastatic cancer: High-Definition Oncology study (HDOs) preliminary results" (2026), involves the use of remote monitoring data to predict treatment response. Analysis of passively collected data from smartwatches (e.g. heart rate, sleep, activity) and actively reported behavioral data (e.g. emotions) revealed strong associations with clinical benefit at the first tumor assessment. We identified specific 'Response-Associated Behavioral Patterns' (RABPs) that can flag patients at high risk of early disease progression. This work has culminated in a submitted manuscript describing a novel alert system based on this behavioral data, designed to detect disease progression earlier.
Finally, the project has driven significant technical innovations in the analysis of epigenetic data. The publication "Applicability of epigenetic age models to next-generation methylation arrays" (Garma & Quintela-Fandino, 2024) in Genome Medicine addresses the critical issue of cross-platform compatibility for epigenetic clocks. We developed and validated a new epigenetic age model that is compatible with both legacy and next-generation DNA methylation arrays (EPICv2), ensuring the continued utility of established biomarkers. Furthermore, the pre-print manuscript "A breast tissue-specific epigenetic clock provides accurate chronological age predictions and reveals de-correlation of age and DNA methylation in tumor-adjacent and tumor samples" describes the creation of a novel Breast Tissue-specific Epigenetic Clock (BTEC). This clock provides far more accurate age predictions in breast tissue than general pan-tissue models and has revealed a de-correlation between chronological age and DNA methylation in tumor and tumor-adjacent tissues, with tumors appearing epigenetically 'younger', particularly in more aggressive subtypes.
A significant achievement has been the development of an explainable AI model for cancer prognosis that leverages PROs, presented at the ESMO congress in the communication "Explainable AI model for cancer prognosis using weighted analysis of patient-reported outcomes (PROs)" (2025). By applying a sparse, interpretable neural network to item-level data from the EORTC QLQ-C30 and GHQ-28 questionnaires, we can predict progressive disease within 12 months with high accuracy (AUC ≈ 0.85). This model surpasses traditional scoring methods by identifying and weighting the most predictive symptoms and functional items, demonstrating the prognostic power of longitudinally collected PROs.
Another key technical advance, communicated at the ASCO meeting in "Remote physiologic and behavioral monitoring to predict early treatment response in metastatic cancer: High-Definition Oncology study (HDOs) preliminary results" (2026), involves the use of remote monitoring data to predict treatment response. Analysis of passively collected data from smartwatches (e.g. heart rate, sleep, activity) and actively reported behavioral data (e.g. emotions) revealed strong associations with clinical benefit at the first tumor assessment. We identified specific 'Response-Associated Behavioral Patterns' (RABPs) that can flag patients at high risk of early disease progression. This work has culminated in a submitted manuscript describing a novel alert system based on this behavioral data, designed to detect disease progression earlier.
Finally, the project has driven significant technical innovations in the analysis of epigenetic data. The publication "Applicability of epigenetic age models to next-generation methylation arrays" (Garma & Quintela-Fandino, 2024) in Genome Medicine addresses the critical issue of cross-platform compatibility for epigenetic clocks. We developed and validated a new epigenetic age model that is compatible with both legacy and next-generation DNA methylation arrays (EPICv2), ensuring the continued utility of established biomarkers. Furthermore, the pre-print manuscript "A breast tissue-specific epigenetic clock provides accurate chronological age predictions and reveals de-correlation of age and DNA methylation in tumor-adjacent and tumor samples" describes the creation of a novel Breast Tissue-specific Epigenetic Clock (BTEC). This clock provides far more accurate age predictions in breast tissue than general pan-tissue models and has revealed a de-correlation between chronological age and DNA methylation in tumor and tumor-adjacent tissues, with tumors appearing epigenetically 'younger', particularly in more aggressive subtypes.
Progrès au-delà de l’état des connaissances et impact potentiel prévu (y compris l’impact socio-économique et les conséquences sociétales plus larges du projet jusqu’à présent)
The HD-BRECA project has produced several results that push the boundaries of current cancer research and care, opening new avenues for truly personalized medicine.
1. A Feasible Framework for High-Definition Medicine and Patient Digital Twins:
- Beyond the State of the Art: The project moves beyond the current paradigm of collecting sparse, episodic clinical data. We have established a feasible protocol for creating a continuous, high-resolution, multi-modal view of a cancer patient's journey. This lays the practical groundwork for constructing 'patient digital twins'—a concept at the frontier of personalized medicine that has been largely theoretical until now.
- Potential Impact: This framework can shift cancer care from being reactive to proactive and predictive. Digital twins could be used to simulate individual responses to different therapies, optimize treatment sequencing, and provide far more accurate, personalized prognoses, ultimately improving patient outcomes and quality of life.
- Future Needs: Further research is required to build and prospectively validate the predictive power of these digital twin models. This will necessitate access to significant computational resources, international collaborations to increase cohort size and diversity, and the development of standardized data collection and integration protocols for clinical adoption.
2. Advanced Prognostic Models from Patient-Reported Outcomes (PROs) and Behavioral Data:
- Beyond the State of the Art: We have transformed the use of PROs and wearable data from simple wellness tracking into powerful clinical tools. Our AI model extracts deep prognostic signals from raw questionnaire data, outperforming traditional scoring methods. Similarly, our analysis of passive data from smartwatches has led to a novel alert system that identifies behavioral patterns associated with disease progression.
- Potential Impact: These tools represent a cost-effective, non-invasive method for continuous patient monitoring and risk stratification. The PRO-based model can be integrated into clinical apps to flag patients at high risk, while the behavioral alert system can provide clinicians with an early warning of patient deterioration, enabling timely intervention.
- Future Needs: The clinical utility of the alert system must be validated in a prospective trial to demonstrate that it improves patient outcomes. For broader uptake, these tools require integration with existing Electronic Health Record (EHR) systems and a clear pathway for regulatory approval (e.g. as Software as a Medical Device). IPR protection for the underlying algorithms will be crucial for commercialization.
3. Novel Insights into the Biology of Aging in Breast Cancer:
- Beyond the State of the Art: Our work in epigenetics has solved a key technical challenge with the creation of an epigenetic clock compatible across different microarray platforms, ensuring research continuity. More profoundly, our Breast Tissue-specific Epigenetic Clock (BTEC) challenges the established dogma that cancer is a disease of accelerated aging. It reveals that breast tumors, particularly aggressive subtypes, are epigenetically 'younger' than healthy tissue, a finding that links this rejuvenation to de-differentiation and replicative potential.
- Potential Impact: This discovery opens entirely new lines of inquiry into the fundamental biology of breast cancer. The link between a tumor's epigenetic age and its prognosis, especially in TNBC, suggests that BTEC could become a novel biomarker for risk stratification. It also points towards new therapeutic strategies aimed at the molecular pathways that control this 'rejuvenation' process.
- Future Needs: Further research is needed to dissect the biological mechanisms driving the observed epigenetic rejuvenation in tumors. The prognostic value of BTEC needs to be validated in larger, independent patient cohorts. Success in these areas would warrant commercialization of BTEC as a licensed diagnostic test, requiring IPR support and partnership with diagnostic companies.
1. A Feasible Framework for High-Definition Medicine and Patient Digital Twins:
- Beyond the State of the Art: The project moves beyond the current paradigm of collecting sparse, episodic clinical data. We have established a feasible protocol for creating a continuous, high-resolution, multi-modal view of a cancer patient's journey. This lays the practical groundwork for constructing 'patient digital twins'—a concept at the frontier of personalized medicine that has been largely theoretical until now.
- Potential Impact: This framework can shift cancer care from being reactive to proactive and predictive. Digital twins could be used to simulate individual responses to different therapies, optimize treatment sequencing, and provide far more accurate, personalized prognoses, ultimately improving patient outcomes and quality of life.
- Future Needs: Further research is required to build and prospectively validate the predictive power of these digital twin models. This will necessitate access to significant computational resources, international collaborations to increase cohort size and diversity, and the development of standardized data collection and integration protocols for clinical adoption.
2. Advanced Prognostic Models from Patient-Reported Outcomes (PROs) and Behavioral Data:
- Beyond the State of the Art: We have transformed the use of PROs and wearable data from simple wellness tracking into powerful clinical tools. Our AI model extracts deep prognostic signals from raw questionnaire data, outperforming traditional scoring methods. Similarly, our analysis of passive data from smartwatches has led to a novel alert system that identifies behavioral patterns associated with disease progression.
- Potential Impact: These tools represent a cost-effective, non-invasive method for continuous patient monitoring and risk stratification. The PRO-based model can be integrated into clinical apps to flag patients at high risk, while the behavioral alert system can provide clinicians with an early warning of patient deterioration, enabling timely intervention.
- Future Needs: The clinical utility of the alert system must be validated in a prospective trial to demonstrate that it improves patient outcomes. For broader uptake, these tools require integration with existing Electronic Health Record (EHR) systems and a clear pathway for regulatory approval (e.g. as Software as a Medical Device). IPR protection for the underlying algorithms will be crucial for commercialization.
3. Novel Insights into the Biology of Aging in Breast Cancer:
- Beyond the State of the Art: Our work in epigenetics has solved a key technical challenge with the creation of an epigenetic clock compatible across different microarray platforms, ensuring research continuity. More profoundly, our Breast Tissue-specific Epigenetic Clock (BTEC) challenges the established dogma that cancer is a disease of accelerated aging. It reveals that breast tumors, particularly aggressive subtypes, are epigenetically 'younger' than healthy tissue, a finding that links this rejuvenation to de-differentiation and replicative potential.
- Potential Impact: This discovery opens entirely new lines of inquiry into the fundamental biology of breast cancer. The link between a tumor's epigenetic age and its prognosis, especially in TNBC, suggests that BTEC could become a novel biomarker for risk stratification. It also points towards new therapeutic strategies aimed at the molecular pathways that control this 'rejuvenation' process.
- Future Needs: Further research is needed to dissect the biological mechanisms driving the observed epigenetic rejuvenation in tumors. The prognostic value of BTEC needs to be validated in larger, independent patient cohorts. Success in these areas would warrant commercialization of BTEC as a licensed diagnostic test, requiring IPR support and partnership with diagnostic companies.