The vision of ITFoC is a radical new approach to a true personalisation of drug therapy in many areas of medicine and prevention, and its demonstration in subarea of oncology (breast cancer), based on a deep molecular characterisation of individual tumours and patients and the establishment and use of digital medicine approaches to model effects and side effects of all therapy options. Such a vision represents a transformation in current health care practice across Europe, necessitating engagement of a wide variety of stakeholders from different European countries, from patients, clinicians, scientists, regulators and politicians and the breaking down of silos that prevent progress and adoption of such new approaches.

To move forward, we need to provide a systematic route towards the deployment of such computational models as clinical decisions helpers (to make what are ultimately life and death decisions) based on validated probabilistic, mechanistic and systemic models to predict patient responses to drugs on an individual basis. This will entail the optimisation, validation and standardisation of the computational approaches used to predict drug responses based on as many omics and clinical data as currently obtainable.

Benchmark tests will be conducted to further develop models and compare their performance in terms of sensitivity and specificity to determine the best simulation approaches. The wider community (including SMEs and industry) will be invited to test modelling methodologies within a joint platform. Evaluation also requires analysis of ethical, regulatory, acceptance and economical issues, highlighting risks/benefits of this digital medicine approach for cancer and set in place a pathway towards the policy and regulations that will ultimately enable deployment of these models as clinical decision helpers in Europe’s health care systems.

Stakeholders (e.g. politicians, citizens, patient associations, health authorities, industry, ethicists, health economists, oncologists) will be consulted to start to address the difficult challenges of regulation, cost-effectiveness, public/oncologist acceptance, and reimbursement in a diverse environment as Europe. This approach will rely on the sharing of information and the co-operation of researchers.

The Flag-ERA proof-of-concept model provides an opportunity to integrate efforts on a pan European level, not only providing access to a wide range of relevant scientific and technical expertise and resources, but also to country-specific practices and perspectives that are relevant to the acceptance and implementation of the virtual patient modelling approach in oncology. The key innovative factors of ITFoC range from the beyond state-of-the-art computational modelling technologies developed by project partners to predict patient drug response, to the systemic approach to data standardisation and validation of model-generated predictions of responder/non-responders.

Through its tightly aligned federated activities, ITFoC will aim to propose two advanced (TRFL 5-6) demonstrators in digital medicine that will set the scene for future larger-scale initiatives.

ITFoC will establish a standardised and well-validated approach for virtual patient modelling in oncology, through comparative analysis of computational model approaches for predicting patient response to treatment based on molecular data (e.g. exome/transcriptome/metabolome) from individual patients and tumours. Existing and newly generated large scale molecular and clinical datasets on (triple negative) breast cancer patients from across Europe, as well as ongoing data standard initiatives, will be leveraged to provide standardised and validated datasets, accessible via a common database (for medical data we will integrate data from distant databases).

A secure access portal will be created to enable multiple users to access and share omics data for comparative analysis of computational modelling approaches (e.g. machine learning and mechanistic modelling methodologies) to predict the response of patients (singly or as cohorts) to targeted drugs. Computer generated predictions will be validated within pre-clinical/clinical studies and the reliability of the predictions quantified and compared to effectiveness of current European-wide standards for treatment of breast cancer. This combination of standardised datasets (existing and new) and independent comparative analysis alongside preclinical/clinical validation, provides the opportunity to quantify the effectiveness of different approaches.

The results of these proof-of-principle investigations will provide nascent benchmarks for further development in larger scale initiatives. Although ITFoC places emphasis on defining the accuracy and validity of the predictive modelling approach, efforts will also be focussed on bringing the ideas and vision of a sustainable and personalised ICT-driven approach to oncology to key stakeholders. This will done by establishing a strong ITFoC network, aligned with ongoing initiatives, that will feed results generated into the wider community soliciting feedback and helping to further optimise and refine, working towards building a standardised and well-validated approach for virtual patient modelling in oncology. Each element is regarded as essential to the future implementation of virtual patient models in oncology within the health care system and will provide a baseline for future development.