Mac4Me project kicks off with a meeting in Rotterdam and the first doctoral training session

The European doctoral network Mac4Me (Macrophage Targets for Metastatic Treatment) has officially commenced its action with a successful kick-off meeting held in Rotterdam on June 25-26, 2025. The event, organised by the Erasmus University Medical Centre (the project coordinator), brought together the project’s core partners to harmonise efforts and set the stage for the next four years of research.

Mac4Me goes beyond technical expertise, striving to ensure each doctoral candidate has the tools to flourish both professionally and personally. This commitment was evident in the first training, which covered clinical aspects and requirements related to the three metastatic cancer types Mac4Me is focusing on. Besides advanced scientific methodologies, including single-cell mechanics and organ-on-chip technology, the students gained insights into fundamental biological mechanisms such as tumour formation, immune evasion, and DNA repair deficiency in age-related diseases. The training also explored the ethics of cancer research and included an activity in which the communication team produced short introductory videos featuring each doctoral candidate on the website. A significant part of the training focused on Patient and Public Involvement in Research (PPI). This session, led by patient advocates from Dublin and the US, fostered an immediate connection with the doctoral candidates, emphasising the importance of collaboration and direct patient engagement at every step in the research process. A profound mutual interest in the project’s success was shared.

With nearly all doctoral candidates and principal investigators meeting in person for the very first time, the training and the meeting were marked by a palpable spirit of eagerness and enjoyment. This initial gathering fostered strong team-building among the doctoral candidates and across the various subprojects, laying a crucial foundation for their scientific and technical collaboration. The meeting proved to be a success in promoting the exchange of expertise and significantly strengthening networking opportunities, thereby setting a precedent for ongoing collaboration.

Mac4Me, Rotterdam, June 25-26, 2025

Mac4Me is a Horizon Europe MSCA (Marie Skłodowska-Curie Actions) Doctoral Network. The project is led by a core consortium of 14 partners and supported by an additional 11 associated partners. For more information about the consortium and the project, visit the Mac4Me website.

For media inquiries, please contact: mac4me@upf.edu.

Mac4Me MSCA Doctoral Network

We are delighted to provide training and contribute to neuroblastoma research through the Mac4Me Doctoral Network Programme. Mac4Me is a 48-month project that addresses both technical and social challenges in cancer metastasis. It focuses on three tumour types that show poor response to current immunotherapies: neuroblastoma, breast, and prostate cancer. These tumour types reflect cancer development across a person’s lifetime and share metastatic disease spreading to the brain, bone, and liver.

Working alongside researchers and patients, the network will train 18 Doctoral Candidates to study the tumour microenvironment at metastatic sites, with a particular focus on the macrophage immune cell population. It will combine organ-on-chip technology with microfluidic systems to investigate early cell-cell and cell-matrix interactions during tumour invasion. Mac4Me will move beyond traditional “thinking in boxes” approaches by integrating bioinformatics and Artificial Intelligence solutions with real-world clinical data. The project will focus on patient experiences and translate scientific advances into meaningful outcomes.

The kick-off meeting of Mac4Me partners, Feb 2025

We are very proud to train two out of 18 Doctoral Candidates, building upon the expertise of Drs Ian Woods, Adrian Dervan and Prof Fergal O’Brien in biomaterials and 3D bioprinting and Dr Olga Piskareva in neuroblastoma biology and 3D in vitro cancer models.