Translational research bridges the gap between scientific discoveries and practical healthcare, comprising four phases: T1 (basic research), T2 (preclinical studies), T3 (clinical trials), and T4 (implementation and dissemination). The goal of translational research is to bridge the gap between basic science and clinical practice, with the ultimate aim of improving patient outcomes [1]. This has been particularly relevant in cancer research because of recent advances in the understanding of the molecular characterization of tumors and the tumor microenvironment, enabling the development of novel treatments [2]. According to Dolgos et al. (2016), by integrating basic science research with clinical development, translational medicine has helped to improve target understanding, patient subpopulation selection, and organizational learning. It has also led to advances in areas such as disease model validation, human cell- or tissue-derived models, and molecular characterization of retrospective human materials through biobanks, bioinformatics, and translational pharmacokinetics/pharmacodynamics [3]. The complexity and multifaceted nature of cancer demands a multidisciplinary and collaborative approach that brings together experts from a broader network of stakeholders, including researchers, clinicians, research centers, universities, government agencies, pharmaceutical partners, patient advocacy groups, and philanthropic organizations, all of whom play a vital role in supporting and advancing cancer research.

Therefore, collaboration is the cornerstone of successful large-scale translational research projects in cancer treatment. By fostering collaboration, these projects gain access to a diverse pool of knowledge, skills, and resources, which are essential for tackling the multifaceted challenges of cancer research [4]. Collaboration enhances the speed and efficiency of research, promotes knowledge exchange, and fosters innovation [5] by joining together advanced technologies, methodologies, extensive infrastructure, expertise, and resources, making it possible to undertake ambitious research endeavors that would be challenging for individual institutions to achieve. Collaborating with international partners allows for the exchange of insights and data from diverse patient populations, which can lead to more robust and generalizable findings.

The significant role of digital platforms in translational research has been crucial in providing support for the storage and integration of big data, an analysis context, and additional information from external sources. They were created as a solution to the growing amount of omics data and use informatics methods to link molecular and clinical data [6]. The benefits of these platforms include the ability to identify biomarkers, develop personalized medicine, and improve patient outcomes [7]. It is understood that such platforms enable collaboration by sharing research data and tools, such as in the case of Cancer Core Europe [8] or REDCap [9]. However, their actual impact on collaboration has not received attention in the literature in a systematic manner.

Effectiveness in translational medicine projects rests on seamless collaboration between individuals, teams, and institutional and organizational stakeholders, ensuring that promising discoveries are efficiently tested, validated, and translated into clinical applications that ultimately save lives. However, collaboration is naturally “difficult to manage, and the likelihood of disappointing outputs is high” [10]. However, managing large collaborations proves challenging, often leading to less than satisfactory outcomes [11]. Therefore, assessing collaborative processes is a key activity to reach sustainable research ecosystems that go beyond the period of funding and transform activities in health solutions in the long term. Several evaluation models can be traced in the literature in three categories: indices, processes, and relatedness. Indices are related to bibliometric analysis [12,13,14], processes focus on the method of evaluation and the dimensions that must be analyzed [15,16,17], and relatedness is explored frequently through social network analysis (SNA) [18,19].

Assessing collaboration implies exploring several factors such as planning, infrastructure, relationships, complexity, boundaries, dialogue, and human behavior. Some evaluation models involving these collaboration principles can be traced in the literature, focused mainly on bibliometric indices [12,13,14], collaboration processes [15,16,20], and social network analysis [18,21]. Despite the efforts of these studies, researchers are cautioning that traditional approaches are difficult to operationalize in real contexts [15]. Nevertheless, more empirical research on actual translational projects is necessary, including the conditions and outcomes of collaborative initiatives [22].

2. Features, Topics, and Barriers in Scientific Collaboration