Review. Published 3 June 2025.
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Tumour-associated angiogenesis plays a key role at all stages of cancer development and progression by providing a nutrient supply, promoting the creation of protective niches for therapy-resistant cancer stem cells, and supporting the metastatic cascade. Therapeutic strategies aimed at vascular targeting, including vessel disruption and/or normalisation, have yielded promising but inconsistent results, pointing to the need to set up reliable models dissecting the steps of the angiogenic process, as well as the ways to interfere with them, to improve patients’ outcomes while limiting side effects. Murine models have successfully contributed to both translational and pre-clinical cancer research, but they are time-consuming, expensive, and cannot recapitulate the genetic heterogeneity of cancer inside its native microenvironment. Non-animal technologies (NATs) are rapidly emerging as invaluable human-centric tools to reproduce the complex and dynamic tumour ecosystem, particularly the tumour-associated vasculature. In the present review, we summarise the currently available NATs able to mimic the vascular structure and functions with progressively increasing complexity, starting from two-dimensional static cultures to the more sophisticated tri-dimensional dynamic ones, patient-derived cultures, the perfused engineered microvasculature, and in silico models. We emphasise the added value of a “one health” approach to cancer research, including studies on spontaneously occurring tumours in companion animals devoid of the ethical concerns associated with traditional animal studies. The limitations of the present tools regarding broader use in pre-clinical oncology, and their translational potential in terms of new target identification, drug development, and personalised therapy, are also discussed.
Cancer emerges when mutated cells grow uncontrollably within a permissive tissue environment [1]. Initially, natural safeguards inhibit neoplastic formation; however, prolonged exposure to carcinogenic factors favours the accumulation of further mutations in cancer cells until they become able to evade these defences. Concurrently, growing tumours alter the local and systemic environments, enabling cancer progression and spread [2].
Cancers are complex dynamic ecosystems composed of tumour cells and different non-cancerous cells, all embedded in an extracellular matrix (ECM), exhibiting distinctive and unique physical, biochemical, and mechanical properties [3].
The tumour microenvironment (TME) includes immune cells, cancer-associated fibroblasts, endothelial cells, pericytes, and tissue-resident cell types, all playing critical roles in each stage of cancer progression [3,4]. Tumour–TME crosstalk, mediated by cellular interactions, soluble factors, and metabolite availability [5], affects cancer cell survival, proliferation, responses to anti-cancer drugs, and immune evasion, and is emerging as an attractive therapeutic target.
Typically, all cancers elicit a certain degree of local tissue inflammation. Inflammation initially combats cancer development, but, at the same time, it exercises selective pressure on cancer cells [6,7,8]. Those cells that survive become tolerant to inflammation and ultimately shape the immune landscape both in the TME [2] and systemically. In solid tumours, these initial malignant lesions are called carcinoma in situ, whose prognosis is favourable if they are promptly detected [9,10].
Tumour-associated angiogenesis (TAA) and vascular alterations are hallmarks of the TME, and, following the work of J. Folkman [11,12], microvascular involvement is included among the criteria defining invasive versus non-invasive lesions. A hypovascular, hypoxic TME can limit the efficacy of chemo-/radiotherapy by hampering the delivery of oxygen and drugs [13,14]. On the other hand, uneven intratumour angiogenesis and angiocrine signalling further promote cancer cells’ phenotypic heterogeneity [2].
Overall, the TME behaves like a chronic, never-healing wound [15], because the sources of tissue damage and inflammation are not removed. The cellular and molecular dynamics in such systems are complex to predict as they depend on many contextual signals that vary widely over time, across different genetic backgrounds, across different organs, and even within the same tissue. Moreover, by means of systemic signalling, mediated by soluble factors like cyto-/chemokines and extracellular vesicles [16], cancer may induce changes in distant tissue types, including vascular leakiness, angiogenesis, and immunosuppression. All of this forms a pre-metastatic niche [17]. Pre-metastatic niches promote the seeding of metastases and are tumour-specific (both within the same cancer type and across different types), dictating preferences for specific target organs (seed and soil theory [18]).
Notably, the tumour-associated microvasculature (TAMV) plays a key role at all stages of cancer development and progression, including the “angiogenic switch”, the creation of protective niches for therapy-resistant cancer stem cells (CSCs), the creation of pre-metastatic niches, and metastasis.
Malignant cancer cells have a high metabolic demand to sustain their abnormal growth, and rapid cell growth promotes intratumoural hypoxia and chronic inflammation [19]. All these features contribute to the “angiogenic switch”, i.e., the sustained and uncontrolled induction of pathological angiogenesis, leading to the formation of an aberrant TAMV. Hypoxia activates the cellular oxygen-sensing machinery (hypoxia-inducible factor (HIF) pathway) [20] in endothelial, immune, stromal, and cancer cells, directly regulating the transcription of pro-angiogenic genes like vascular endothelial growth factor (VEGF-A), stromal-derived factor (SDF-1), and angiopoietin-2 [19]. However, due to the unbalanced signalling in the TME, the TAMV does not fully mature, is morphologically and functionally abnormal, and exhibits the suboptimal diffusion of oxygen and nutrients, contributing to a self-sustaining hypoxic–inflammatory loop. In such environments, cancer cells can acquire distinct phenotypes, including fast-growing clones, slow growing CSCs with self-renewal potential, and migratory clones with high metastatic potential.
Endothelial cells (ECs) are the main cellular components of the microvasculature. ECs are physiologically heterogeneous across different organs and within the same organ, reflecting a variety of different functions [21]. ECs composing the TAMV are phenotypically distinct from their healthy counterparts [22] and have distinct metabolism [23] and angiocrine signalling [24].
Dysregulated angiocrine signalling to immune cells contributes to an immunosuppressive TME, which hampers natural anti-tumour responses [25,26,27]. Concurrently, a leaky TAMV favours the intravasation and dissemination of metastasis-initiating cells, which travel as circulating tumour cells to distant organs along the blood or lymphatic streams [28].
In breast cancer, ECs, tumour-associated perivascular macrophages (TAMs), and specific cancer cells form a functional triad, named the tumour microenvironment of metastasis (TMEM) doorway. The number of TMEM doorways in the primary tumour has been found to correlate with the propensity for metastasis [29]. It is tempting to assume that similar doorways might exist within other cancer types, possibly with distinct molecular signatures; however, this has not been demonstrated so far.
Upon seeding in multiple organs, metastasis-initiating cells may generate overt, clinically evident metastases via the co-option of permissive microenvironments, mimicking the native niches, including a primed microvasculature [30,31,32,33].
Given the central role of the TAMV in tumour development, vascular targeting has been explored as a therapeutic option in the past 20 years.
Anti-angiogenic therapy (AAT), i.e., attempting to prune the abnormal TAMV via interference with pro-angiogenic signalling, especially the VEGF pathway, has been widely studied and tested experimentally and clinically [32]. Despite the initial promise and some clinical success, AAT has yielded inconsistent clinical results. Not all cancer types are sensitive to AAT, and, in those sensitive types, AATs like the monoclonal antibody against VEGF-A, Bevacizumab, cause a host of escape mechanisms, like the “angiogenic rebound”, i.e., the compensatory upregulation of VEGF or other angiogenic molecules. Additionally, interference with the VEGF axis causes vessel disruption locally and systemically, producing severe side effects and increasing the propensity for metastasis [22,32].
More recently, vascular normalisation therapy (VNT), i.e., attempting to modulate rather than prune the TMV via the fine-tuning of AATs, has been proposed as a promising new route. Animal studies have shown that VNT could alleviate intratumour hypoxia, facilitate the delivery of drugs, increase the oxygen-dependent effects of radiotherapy, and favourably modulate the immune TME [13,34,35,36].
The clinical testing of VNT is currently ongoing [36]; however, previous studies have already highlighted that the clinical success of VNT will depend on precisely identifying and measuring the timeframe of its therapeutic activity (TAMV normalisation window).
In summary, it is now clear that VNTs are potent tools to modulate the TAMV, with huge therapeutic potential. However, their clinical efficacy is currently limited by our capacity to measure and understand the dynamic crosstalk between cancer, the TME, and therapeutic agents.
For example, we do not know how to timely and precisely deliver therapeutic agents to maximise their beneficial effects while minimising their side effects.
Thanks to intensive research and powerful technologies like omics, we are rapidly learning which cell types and molecules are involved in these processes. However, we do not fully understand how these elements crosstalk before, during, and after therapy in different tissue types and organs.
Characterising the cellular and molecular dynamics driving TAMV functions is thus a central and unmet clinical need, as TAMV-associated features, like metastases, still represent the leading cause of cancer-associated death [30,33].
Deciphering the dynamic interplay between cancer cells and the TAMV may offer the opportunity to identify and target pathways common to most cancers. For this purpose, reliable models of the TAMV are urgently needed.
Murine models are a cornerstone of biological research and have been successfully used in both translational and pre-clinical research [37]. However, pre-clinical animal models are typically complex, time-consuming, expensive, and difficult to standardise [38]. More importantly, they do not fully capture the complexity and genetic heterogeneity of human cancers, and they cannot recapitulate the molecular signalling and cancer–TME cellular crosstalk, limiting their translation potential. Indeed, US Food and Drug Administration (FDA) data show that over 90% of drugs that are successful in pre-clinical animal models fail to demonstrate safety or effectiveness in humans [39].
We need to develop affordable research tools that are able to increase our mechanistic understanding of the TME, to facilitate the development of new therapeutic strategies and to customise treatment to individual patients. Non-animal technologies (NATs) able to reproduce selected aspects of human biology are rapidly emerging to fill these gaps.
In the following sections of this review, we discuss the advances in modelling the TME, the TAMV, and their dynamic crosstalk in vitro, ex vivo, and in silico, focusing on those that are promising to advance cancer research, therapy, and care.
Cancer research and drug development have long relied upon experiments performed using in vitro cultures of cell lines and primary tumour cells grown in two-dimensional (2D) static cultures [40,41,42]. While these culture systems have allowed us to elucidate the basic molecular signatures of cancer and still are the gold standard in primary drug screenings [43], they do not adequately reproduce the spatial and functional complexity of the TME; hence, they do not enable us to predict the imp
