Biotechnological advances in 3D modeling of cancer initiation. Examples from pancreatic cancer research and beyond

C Handschin, H Shalhoub, A Mazet, C Guyon, N Dusserre, E Boutet-Robinet, H Oliveira, Julie Guillermet-Guibert

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C Handschin, H Shalhoub, A Mazet, C Guyon, N Dusserre, et al. Biotechnological advances in 3D modeling of cancer initiation. Examples from pancreatic cancer research and beyond. Biofabrication, 2025, 17, 10.1088/1758-5090/adb51c hal-04993299

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Biotechnological advances in 3D modeling of
cancer initiation. Examples from pancreatic cancer
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TOPICAL REVIEW

# Biotechnological advances in 3D modeling of cancer initiation. Examples from pancreatic cancer research and beyond

C Handschin<sup>1,2 ID</sup>, H Shalhoub<sup>3,4 ID</sup>, A Mazet<sup>1,2</sup>, C Guyon<sup>3,4,5 ID</sup>, N Dusserre<sup>1,2 ID</sup>, E Boutet-Robinet<sup>5 ID</sup>, H Oliveira<sup>1,2 ID</sup> and J Guillermet-Guibert<sup>3,4,5,* ID</sup>

E-mail: julie.guillermet@inserm.fr

Keywords: biofabrication, cancer initiation, early cancer lesions, 3D bioprinting, 3D precancer models, cancer inception

Abstract

In recent years, biofabrication technologies have garnered significant attention within the scientific community for their potential to create advanced *in vitro* cancer models. While these technologies have been predominantly applied to model advanced stages of cancer, there exists a pressing need to develop pertinent, reproducible, and sensitive 3D models that mimic cancer initiation lesions within their native tissue microenvironment. Such models hold profound relevance for comprehending the intricacies of cancer initiation, to devise novel strategies for early intervention, and/or to conduct sophisticated toxicology assessments of putative carcinogens. Here, we will explain the pivotal factors that must be faithfully recapitulated when constructing these models, with a specific focus on early pancreatic cancer lesions. By synthesizing the current state of research in this field, we will provide insights into recent advances and breakthroughs. Additionally, we will delineate the key technological and biological challenges that necessitate resolution in future endeavors, thereby paving the way for more accurate and insightful *in vitro* cancer initiation models.

<footnote n="1">1 Université de Bordeaux, Tissue Bioengineering - BioTis, INSERM U1026, Bordeaux, F-33000, France</footnote>

<footnote n="2">2 INSERM U1026, ART BioPrint, F-33000 Bordeaux, France</footnote>

<footnote n="3">3 CRCT, Université de Toulouse, Inserm, CNRS, Centre de Recherches en Cancérologie de Toulouse, 2 av Hubert Curien, Toulouse, France</footnote>

<footnote n="4">4 Labex Toucan, 2 av Hubert Curien, Toulouse, France</footnote>

<footnote n="5">5 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UT3, Toulouse, France</footnote>

<footnote>* Author to whom any correspondence should be addressed.</footnote>
## Contents

1. Role of *in vitro* models in deciphering the processes driving cancer initiation

1.1. General introduction on cancer initiation

1.2. Modeling the earliest stages of cancer development

1.3. Targeting xenobiotics with in-vitro models focused on cancer initiation

1.4. Pancreatic cancer—why modeling the earliest stages of cancer onset?

1.4.1. Social and clinical impact

1.4.2. Selective mechanisms of pancreatic cancer initiation: genetic and epigenetic factors, cellular origin

2. Exploring the cell-to-cell and cell-matrix interactions during cancer initiation and progression

2.1. Impact of cell-to-cell communication on the initiation and progression of precancerous lesions in cancer development

2.1.1. The role of the stromal cells on cancer initiation

2.1.2. Fibroblasts

2.1.3. Mesenchymal stem cells (MSCs)

2.1.4. Tumor-associated endothelial cells (TECs)

2.1.5. Immune cells

2.2. Impact of the ECM on cancer initiation and progression

2.3. In-vivo cancer models as a source of relevant cancer initiating cells

2.4. Use of human embryonic pluripotent stem cells

3. From *in vitro* models to biofabrication technologies

3.1. From 2D in vitro models to 3D cultures

3.2. Spheroids culture & the combination with microfluidic devices

3.3. Matrices and scaffolds

3.4. 3D bioprinting

3.4.1. Microextrusion bioprinting

3.4.2. Inkjet bioprinting

3.4.3. LAB

3.4.4. Multimodal biofabrication approaches:

4. Discussion

4.1. Analysis of the existing work

4.1.1. Culture

4.1.2. Hydrogel and ECM model

4.1.3. Technology and analysis

4.2. Key considerations to take into account when developing in vitro models of cancer initiation

4.2.1. Deciphering the complexity of the initiation niche—case study with pancreatic cancer

4.2.2. Expanding the scope: mimicking the heterogeneity of the cell population surrounding the cancerous niche

4.2.3. Modeling the ECM in cancer initiation models while dealing with high throughput. How to overcome the constraints?

4.2.4. Spatial organization of the cells

5. Synthesis 28

Data availability statement 29

References 29

# 1. Role of *in vitro* models in deciphering the processes driving cancer initiation

## 1.1. General introduction on cancer initiation

Despite decades of dedicated research into the intricacies of tumor biology, the challenge of controlling cancer development persists. Deciphering the processes that drives the pivotal shift from a normal cell to a malignant state, commonly known as cancer initiation (figure 1(A)), remains a crucial step for comprehending and impeding the transformation of precancerous cells into fully developed tumors. This review highlights the critical role of the tissue microenvironment in these processes, with a particular focus on the potential of sophisticated 3D models to dissect the complexities of cancer initiation.

The multistep development of carcinogenesis is characterized by dynamic interactions between oncogenic signaling and the cellular microenvironment (Puisieux et al 2018). Initiation, promotion, and progression (figure 1(A)) emerge as pivotal phases in the malignant transformation of tumor cells, during which genetic and environmental factors intricately mold cell plasticity (Nickels et al 2013, Vicente-Dueñas et al 2018, Zhang et al 2023). Oncogene expression appears to be a crucial factor in cancer development and progression, suggesting a molecular continuum across the spectrum from tumor origin to expansion. While the clonal nature of human cancers implied a uniformity in initiating oncogenic lesions, the observed cellular heterogeneity in fully developed cancers calls for better understanding of these processes. Examining pancreatic cancer (figure 1(B)) provides a compelling example, shedding light on the profound impact of genetic factors and microenvironmental signaling on the persistence of a variety of differentiated states starting from acinar-to-ductal metaplasia (ADM), culminating in precancerous lesions pancreatic intraepithelial neoplasia (panIN) and adenocarcinoma pancreatic
<figure><img src="image_6.png" /><figcaption>**Figure 1.** The multi-step process of carcinogenesis.</figcaption></figure>

ductal adenocarcinoma (PDAC) eventually associated with metastasis.

## 1.2. Modeling the earliest stages of cancer development

*In vitro* models and *in vivo* genetically engineered mice models (GEMMs) suggest that the tissular context and environmental complexity propel tumor development. This underscores the multifactorial approach needed to replicate both initiation and progression phases (Karlsson *et al* 2023, Subtil *et al* 2023).

The use of cultures derived from primary cells extracted from biologically relevant mouse models adds a new layer of sophistication to cancer research. These models, engineered to mimic human-like conditions, offer a unique opportunity to study the intricacies of cancer initiation with enhanced physiological relevance. By utilizing cultures derived from these models, researchers can dissect the specific genetic and environmental factors that contribute to cancerogenesis, providing valuable insights that are translatable to human cancer scenarios. Primary cell cultures, in initiation study, surpass established cell lines by
providing richer biological insights due to their more
native susceptibility. The advent of 3D culture mod-
els revolutionizes our ability to cultivate primary cells,
providing a more physiologically relevant environ-
ment for these cells to thrive in (Batenburg et al 2023).
Unlike traditional 2D cultures, 3D models mimic the
spatial and biochemical cues present in vivo, needed
to sustain survival and growth of many primary cells.
This advancement is particularly significant in the
context of cancer initiation, where the reliance on
primary cells enhances the biological relevance of
experimental outcomes, fostering a deeper under-
standing of the early events triggering the commit-
ment to a new cancer lineage.

To bridge gaps in our comprehension of cancer initiation, 3D models emerge as invaluable tools to finely tune the tumor niche and extracellular components surrounding the tumor (Shukla et al 2022). Bioprinting technologies increase our ability to replicate the nuanced structures of the tumor niche by expanding our capacity to specifically modify the various components of the tumor microenvironment, while retaining control over its spatial organization. Indeed, the high tunability of 3D bioprinted models enables researchers to investigate, right down to the cellular level, the interactions of the different cell types composing the tumor niche. These models also offer a rare opportunity to assess the impact of the tumor stroma physico-chemical properties on the tumor initiation process. The tumor microenvironment, a dynamic player in this intricate process, is more accurately represented in these models, providing a platform for studying the multidirectional interactions between tumor cells and their surroundings. The application of these models to the study of pancreatic cancer holds promise for uncovering novel insights into the prevention and treatment of this challenging malignancy.

(A) **Schematic representation of the tumoral niche transformation.** In a healthy context, tissue homeostasis is assured by the recognition and replacement of altered cells through different repair mechanisms, such as DNA repair in lesion-bearing cells. Tumorigenesis is a multi-factorial process relying on dynamic interplay between oncogenic signaling and the cellular microenvironment. It is characterized by different phases occurring in the malignant transformation of tumor cells such as: initiation, promotion and progression. These phases are based on the acquisition of an invasive phenotype, initially through the immune escape of mutated cells and the appearance of proliferative, invasive and metastatic properties. These phenotypic changes are strongly influenced by genetic and environmental factors due to the interaction of cells with their niche. The

tumor microenvironment therefore influences
cell plasticity through a direct influence of the
tumor on its surroundings, but also reciprocally
via signals from the various players of the niche
(stroma structure, fibroblasts, endothelial cells,
immune cells, etc) on the tumor.

(B) **Pancreatic cancer as an example of the evolution from differentiated secretory acinar cells to metastasis.** Pancreatic cancer arises from the functional exocrine part of the organ. In a physiological context, secretory cells (acinar cells organized in normal acini), can transdifferentiate into duct-like cells in response to injury. Under inflammatory and stress conditions, acinar cells undergo reversible acinar-to-ductal metaplasia ADM to regenerate or repair the pancreatic injury. This mechanism is known as ADM and highlights the epithelial cell plasticity in this tissue. Cancer arises when strong genetic factors (such KRAS or p53 mutations), but also microenvironment signaling, with unresolved inflammation, lead to the persistence of the transdifferentiated state. The accumulation of dysregulations and competitive pressure induced by the microenvironment promote the appearance of precancerous lesions, or PanIN, that are notably characterized by cell hyperproliferation. Low-grades PanIns present minimal cytological and architectural atypia. High-grade PanIns are advanced lesions, also considered as *in situ* carcinomas, exhibiting severe nuclear atypia, luminal necrosis, epithelial cell budding and multiple genetic alterations. Further stimuli and or genetic alterations are needed to drive carcinogenesis beyond the PanIN stage. PDAC tumors range between well differentiated to poorly differentiated with glandular structures embedded in desmoplastic stroma (extended microenvironment). Under the influence of pro-tumoral signals, these lesions can form PDAC, which subsequently progress to metastasis.

The future of cancer research lies in the con-
tinued development and application of these soph-
isticated models, driving advancements that may
eventually transform our capacity to control and
manipulate the earliest stages of cancer develop-
ment. Within the framework of this review, we will
elucidate the essential factors that must be replic-
ated concerning cancer initiation, with a specific
emphasis on questions related to early pancreatic can-
cer lesions. We will provide a comprehensive overview
of the current advancements in this field, primarily
drawn from studies in other solid cancers, and assess
their applicability and relevance to pancreatic cancer.
Furthermore, we will identify pivotal technological
and biological challenges that demand resolution in the future.

### 1.3. Targeting xenobiotics with in-vitro models focused on cancer initiation

Xenobiotics, represent external substances (mainly chemicals substances) typically acquired through exposure to environmental pollutants, dietary contaminants, or occupational hazards. The presence of these ubiquitous environmental chemicals, often encountered at low doses and in various combinations, can heighten the risk of cancer in individuals exposed to them (Maele-Fabry et al 2019, Rebouillat et al 2021).

Many xenobiotics possess genotoxic properties, meaning they can directly damage DNA (Fernandez-Vidal et al 2019), ultimately resulting in the accumulation of mutations and genetic alterations that are closely linked to the initiation of cancer (Holme et al 2023). Furthermore, certain xenobiotics have the potential to disrupt essential cellular signaling pathways, including those regulating cell growth and differentiation (Arnaud et al 2021). This disruption can lead to uncontrolled cell proliferation, a hallmark characteristic of cancer (Hanahan 2022). Xenobiotics can also impede the body's natural DNA repair mechanisms, rendering cells more vulnerable to accumulating genetic abnormalities over time (Liamin et al 2017).

It is noteworthy that xenobiotics may induce inflammation upon contact with bodily tissues. Subsequently, the inflamed tissue becomes more susceptible to the carcinogenic effects of these toxins (Kay et al 2019). This inflammatory microenvironment creates an ideal setting for the initiation, progression, and dissemination of cancer (Multhoff et al 2012, Thompson et al 2015).

The assessment of the carcinogenic potential of chemical compounds has historically relied on *in vitro* genotoxicity assays and the evaluation of tumor formation in rodent models. This approach emphasizes the 'tumor initiation' properties of individual compounds and a one-at-a-time testing paradigm. While this method is experimentally robust, it has certain limitations as it oversimplifies the intricate and multifaceted nature of tumorigenesis. Consequently, there is a growing interest in developing more relevant 3D models to study the carcinogenic properties of compounds in toxicology research. Over the past decade, the field of toxicology has witnessed a significant shift towards the adoption of 3D models that better replicate various organ systems. This transformation has been facilitated by the integration of advanced technologies such as spheroids, organoids, microfluidics, and bioprinting. Notably, models simulating skin and liver have gained prominence in toxicology research, finding increasing application, particularly within the cosmetic and pharmaceutical industries, as valuable complements to regulatory testing

(Hou et al 2018, Nuciforo and Heim 2021, Costa et al 2023). In response to these regulations, the development of 3D skin models has gained momentum, with some models currently undergoing validation for the assessment of genotoxicity, a hallmark of cancer (Reus et al 2013, Reisinger et al 2018). Moreover, the Scientific Committee on Consumer Safety has recognized that assays relying on human reconstructed skin tissues can serve as a 'good alternative to bridge the gap between *in vitro* and *in vivo* testing in terms of final hazard assessment' (Scientific Committee on Consumer Safety (SCCS) 2016).

Furthermore, during drug development, predicting hepatotoxicity is challenging and is a significant reason for post-approval drug withdrawals. There is a growing interest in developing 3D liver models for early toxicology assessments during the preclinical phases, with the aim of identifying hepatotoxic compounds at an earlier stage of drug development. To mimic the extracellular matrix (ECM), researchers have experimented with both synthetic and natural scaffolds (Yang et al 2023). Of particular interest, these models exhibit an elevated expression of hepatocyte-metabolic-related genes, such as Cytochromes P450 (CYP450), which are crucial for the biotransformation of xenobiotics in the liver, potentially generating either toxic or non-toxic metabolites through enzymes like CYP450. Additionally, 3D models, whether used in monoculture or coculture configurations, enable extended exposure to xenobiotics compared to 2D models with primary cells. These 3D models closely replicate *in vivo* conditions, making them valuable tools for hepatotoxicity assessment.

When it comes to studying the effects on the pancreas, toxicological studies employing 3D *in vitro* models remain relatively scarce. However, there are noteworthy efforts in this direction. For instance, Hou and collaborators (Hou et al 2018) have reported the development and validation of 3D bioprinting technology for high-throughput screening of pancreatic anticancer drugs against solid tumor primary cells.

In conclusion, the field of bioprinting and biofabrication of 3D toxicological models is poised for significant growth. These advanced models hold great promise in replicating the physiological impact of more complex tissues. When compared to other existing 3D technologies, such as self-assembled spheroids and cell encapsulation, bioprinted tissues offer the unique ability to closely replicate the structural architecture of native tissue and achieve clinically relevant histological features, such as bridging fibrosis. However, it is important to note that these complex models currently have lower throughput, limiting their suitability for screening large compound collections. Therefore, ongoing research is focused on finding a balance between the complexity of biofabricated tissues and the capacity to increase
production throughput while maintaining reproduc-
cibility and efficient readout analysis.

To the best of our knowledge, biofabricated 3D
models specifically designed for evaluating the impact
of xenobiotics on cancer initiation have remained
largely unexplored, but they hold substantial poten-
tial for advancement in the fields of toxicology, cancer
research, and biofabrication. Looking ahead, we anti-
cipate that the development and utilization of 3D tox-
icological models will continue to gain momentum,
ultimately revolutionizing our approach to assess-
ing the safety and carcinogenic potential of chem-
ical compounds. The ability to replicate the intric-
ate microenvironments of various organs and tissues
*in vitro* holds the promise of providing more accurate,
predictive, and ethically conform tools to assess the
effects of xenobiotics on cancer initiation and other
health-related concerns.

## 1.4. Pancreatic cancer—why modeling the earliest stages of cancer onset?

1.4.1. *Social and clinical impact*

In the last 20 years, cancer treatment has improved,
leading to a clear decrease in cancer-related deaths for
the most frequent cancer-related pathologies such as
breast and lung cancer. However, some cancers do not
follow this trend. PDAC, in particular, presents signi-
ficant challenges for affected individuals, their fam-
ilies, society at large, and the scientific community.
Within the realm of cancer incidence, PDAC stands
out prominently, with the European Union experien-
cing the highest rates in the world (Lucas et al 2016).
Alarmingly, the cumulative risk of developing PDAC
before 65 years is also increasing (Bouvier et al 2017).
This disease is projected from 2030–2040 to be the
second highest cause of death due to cancer (Ma and
Jemal 2013, Rahib et al 2021). Key players in the phar-
maceutical industry highlight PDAC patients as a cru-
cial population for strategic targeting (Immunovia
2015, Grand View Research, Inc 2017). However, in
stark contrast to other solid cancers where innovat-
ive targeted and immuno-modulatory therapies have
achieved noteworthy success, clinical trials employ-
ing these treatments for PDAC have regrettably failed
to yield substantial improvements in its lethality rate
(Cintas et al 2018).

Furthermore, PDAC is characterized by unspe-
cific symptoms, lack of early detection and late
diagnosis which hinders opportunities for effective
treatment. Over the past fifteen years, GEMMs of
pancreatic cancer have significantly advanced our
comprehension of this tumor's biology (Ying et al
2016). Furthermore, the convergence of technolo-
gical advancements and the use of Big Data has
enabled the comprehensive characterization of the
molecular identity of pancreatic cancer (Oldfield
et al 2017). However, researchers are still working to
identify simple therapeutic strategies based on patient
stratification. What has clearly emerged from these

studies is that PDAC presents a formidable challenge
for treatment, even when diagnosed at its early stages,
such as Grade 1 or 2.

As such, efforts should be made to detect this can-
cer at earlier stages. A better understanding of its ini-
tiation is therefore essential to prevent the observed
recent rise. To this end, novel approaches for early
detection and interventions are crucial. In particu-
lar, *ex vivo* models of cancer initiation play an indis-
pensable role in complementing and expediting the
research conducted with GEMMs. While biofabrica-
tion techniques offer exquisite potential to fulfill this
need, the current focus predominantly centers on
modeling established cancers (Tomás-Bort *et al* 2020,
Osuna de la Peña *et al* 2021).

1.4.2. *Selective mechanisms of pancreatic cancer initiation: genetic and epigenetic factors, cellular origin*
In the vast majority of PDAC cases (90%), the onco-
genic protein KRAS stands as the primary mutation
driver. In this case, cell transformation is depend-
ent on the activity of the downstream signaling
molecule phosphatidylinositol-3-kinase PI3Kα (Baer
et al 2014). However, PDAC is inherently complex
and heterogeneous, shaped by a multitude of macro-
and micro-environmental factors (modifiers) that
dynamically influence its development, progression,
and treatment (figure 1(B)). Beyond the influence of
oncogenic KRAS, PDAC is characterized by an array
of genetic and epigenetic alterations. Several well-
known factors such as alcohol, tobacco, obesity, and
diabetes elevate the risk of acquiring these genetic
mutations and epigenetic changes. Moreover, after
the establishment of cancer, each patient's unique
treatment history and the lack of standardization in
care across clinical centers further contribute to the
diversification in PDAC's course.

Furthermore, plasticity of each pancreatic cell lin-
eage and of the stromal compartment contributes
to both inter- and intra-individual heterogeneity, as
demonstrated by GEMMs of PDAC (Smigiel *et al*
2018). PDAC develops from the exocrine part of the
pancreas. However, cancer initiating cells can arise
from different cell lineages (acinar cells, duct cells or
progenitor cells) leading to different types of pancre-
atic pre-cancer lesions: PanIN, intraductal papillary
mucinous neoplasm or mucinous cystic neoplasm,
ultimately leading to distinct cancer lesions.

The origin of these cancer cells can be traced
through epigenetic traits, and it significantly influ-
ences the aggressiveness of the resulting cancer
(Ferreira et al 2017, Espinet et al 2021, Flowers et al
2021). When modeling cancer initiation, the origin
of the primary cells is thus of primordial importance.
Following the example of established cancer models,
organoid models derived from early human lesions
should also be considered. However, while the hetero-
geneity of cancer cells is now well modeled, the avail-
ability of biopsies or early cancer lesions is limited,
in particular in pancreatic pathologies. This aspect is
particularly crucial and represents a major constraint
in the modeling approach. Hence, alternative meth-
odologies need to be developed.

## 2. Exploring the cell-to-cell and cell-matrix interactions during cancer initiation and progression

In general, the process of tumor initiation and pro-
gression is tightly interconnected with the surround-
ing tissue. This complex microenvironment, com-
posed of cells and ECM is highly dynamic and con-
ducive to a bidirectional interplay, including cell-to-
cell contact, cell to ECM contact and mediators that
enable/suppress this contact.

**2.1. Impact of cell-to-cell communication on the initiation and progression of precancerous lesions in cancer development**

**2.1.1. *The role of the stromal cells on cancer initiation*

Cell-to-cell communication is essential both on a healthy and on the tumor environment. For a long time, it was assumed that cancer cells operated in isolation from the surrounding tissue and that the lack of cell-to-cell communication contributed to a compromised tissue homeostasis and promoted tumor development and growth. However, it is now well accepted that cell communication with surrounding cells is essential to shape the nature of the microenvironment and to support or control the tumoral niche (Hanahan and Coussens 2012, Brücher and Jamall 2014). In the context of pancreas, it has been shown that genetic alterations alone are not sufficient to support cancer initiation and progression. Instead, the inflammatory environment fostered by the interaction between cancer-initiating cells and stromal has shown a significant role in supporting the progression of early lesions (Guerra et al 2007).

Cell-to-cell communication is a fundamental
process that involves several mechanisms operating
simultaneously. Communication occurs at different
scales, including direct autoregulation, direct inter-
cellular communication and indirect communica-
tion. In the direct autoregulation, cells regulate them-
selves or communicate with nearby cells. It includes
intracrine or autocrine signaling. Direct intercellular
communication involves interactions between neigh-
boring cells, often referred to as juxtacrine signal-
ing. It includes cell-to-cell contact via direct electrical
coupling, direct gap junctions, direct receptor-to-
ligand binding, or a mechanism relying on nanotu-
bular cell-to-cell connection called membrane nan-
otubes (recently reviewed in (Dominiak et al 2020)).
Another mechanism considers the use of signaling
molecules and mediators or the transfer of biological
information via extracellular vesicles (Dominiak et al
2020). At last, indirect communication can occur at

both short and long distances. Short-distance com-
munication involves paracrine and/or synaptic sig-
naling, while long-distance communication occurs
through endocrine signaling.

Considering the tumor initiation niche, and apart
for the malignant cancer initiating cells, there is a
myriad of supporting and regulating cells, includ-
ing fibroblast, pericytes, endothelial cells, adipocytes,
mesenchymal stromal cells and immune cells, that
participate in the onset and progression of the tumor.
To highlight the complexity of reconstituting initiat-
ing niche, we will briefly describe the known interplay
mechanisms drawn by these cells in solid cancer and
describe some examples (table 1). Here we will focus
in particular on cells participating in the cancer onset
instead of its progression.

**2.1.2. Fibroblasts**

Crucial cell components of the tumor microenviron-
ment are stromal fibroblasts, which, upon activation,
are commonly referred to as cancer-associated fibro-
blasts (CAFs). CAFs have been extensively demon-
strated to promote tumor growth, survival, and meta-
stasis, through alterations in their phenotype and
secretome (Bhowmick et al 2004). However, when
talking about cancer initiation and all processes
upstream from cancer progression, it makes sense to
keep the original name of "fibroblasts" and not CAFs.
In the specific case of pancreatic cancer, pancreatic
stellate cells (PSCs) which are quiescent fibroblast-
like cells, were shown to be involved in PDAC initi-
ation and progression (Pandol et al 2009).

**2.1.3. Mesenchymal stem cells (MSCs)**

MSCs are multipotent stromal cells, with the capa-
city to differentiate into osteoblasts, adipocytes and
chondrocytes. They are implicated in multiple mech-
anisms promoting cancer cell proliferation and
angiogenesis from initiation through metastasis.
They also play a role on the cancer establishment
by generating an immunosuppressive microenvir-
onment (Ridge et al 2017, Zheng et al 2022). MSCs
support the tumor niche with signals that favor can-
cer proliferation and self-renewal (Davis et al 2015).

**2.1.4. Tumor-associated endothelial cells (TECs)**

TECs have shown heightened rates of proliferation
and migration compared to normal endothelial cells
(Hida et al 2013). Additionally, they exhibit an upreg-
ulation of several secreted molecules that play a role in
supporting the growth, self-renewal, and migratory
capacity of cancer stem cells. These molecules include
Bmi-1, Jagged-1, VEGF, IL6, IL8, and EGF (Neiva et al
2009, Krishnamurthy et al 2010, Beck et al 2011, Zhu
et al 2011, Lu et al 2013). Pancreatic tumors, however,
remain poorly vascularized (Nguyen et al 2019) and
blood supply seems to be assured via vascular mim-
icry by epithelial cells (Fouladzadeh et al 2021).
<table><caption>**Table 1.** Preponderant cells involved in cancer initiation and their main secretions having an impact on the triggering and the progression of the disease. The specific case of pancreatic cancer is highlighted as typical example (right column).</caption><thead><tr><th>Cells</th><th>Secretions/receptors in cancer initiation</th><th>Role in cancer (initiation or progression)</th><th>Role in pancreatic cancer (initiation or progression)</th></tr></thead><tbody><tr><td>Fibroblasts</td><td>Fibroblast growth factor (FGF), Hepatocyte growth factor (HGF) coupled with Met receptor, TGF-β, (Bhowmick et al 2004; Michieli et al 2004).</td><td>Influence epithelial transformation via production of paracrine factors (Bhowmick et al 2004)</td><td>- Pancreatic stellate cells (PSCs) which are quiescent fibroblast-like cells induce the predominance of M2-macrophages<br>- Participate to the progression of pancreatic fibrosis (Hu et al 2020)<br>- The major producer of ECM, cytokines, chemokines and GFs during the development and progression of PDAC (Pandol et al 2009)</td></tr><tr><td>MSCs</td><td>IL6, and IL8, CXCL12. (Cabarcas et al 2011)</td><td>Support epithelial to mesenchymal transition (EMT) (Zheng et al 2022).</td><td>- Promote progression in pancreatic cancer through specific secretory molecules (Xiang et al 2022)</td></tr><tr><td>TECs</td><td>Bmi-1, VEGF, IL6, IL8 and EGF (Beck et al 2011; Krishnamurthy et al 2010; Neiva et al 2009).</td><td>- Support the integration of a persistent niche of cancer stem cells through different mechanisms of cell survival (Krishnamurthy et al 2010).<br>- Enhance migration and inhibit anoikis (Neiva et al 2009).</td><td>- Poorly vascularized<br>- Blood supply seems to be assured via vascular mimicry by epithelial cells. (Fouladzadeh et al 2021; Nguyen et al 2019)</td></tr><tr><td>M1-phenotype macrophages</td><td>IL6, IL12, IL23 and TNF-α (Basak et al 2023)</td><td>Produce cytokines propitious to inflammation and source of cancer initiation (Undi et al 2022).</td><td>- Recruited by acinar cells with G12D KRAS mutation that highly express ICAM-1 (Liou et al 2015)<br>- Secret RANTES and TNFα, activating nuclear factor κB, which are involved in the acinar to ductal metaplasia via the dysregulation of matrix metalloproteinases (Liou et al 2013)</td></tr><tr><td>M2-phenotype macrophages</td><td>EGF, PDGF and VEGF (Pan et al 2020).</td><td>Participate to the tumor initiation and progression by altering the immune mechanisms and interacting with the neo-angiogenesis (Gambardella et al 2020).</td><td>- Pre-cancer lesions cells induce shift into the M2-phenotype by secreting IL-13<br>- Through secretion of CCL2 and IL-ra, M2 macrophages induce inflammation suppression, lesion growth and fibrogenesis (Liou et al 2017)<br>- CD206-positive macrophages promote migration of pancreatic early cancer lesions to metastatic sites (Thibault et al 2021)</td></tr><tr><td>TANs</td><td>Typically overexpresses CXCR2 (Jamieson et al 2012).</td><td>Drive cancer initiation as a crucial component of the inflammation process (Jamieson et al 2012).</td><td>- Involved in acute and chronic pancreatitis (Habtezion 2015)</td></tr></tbody></table>
### 2.1.5. *Immune cells*

Another important aspect of cellular communication within the tumor niche considers the participation of the immune cells. In spite of the huge complexity associated with the immune system, we have identified and discussed key immune cells that have shown a significant impact on tumor initiation and progression.

The tumor microenvironment is characterized by a chronic inflammation state that sustains the malignancy onset, tumor cell proliferation and metastasis (Cabarcas et al 2011). In the context of pancreatic cancer, inflammation is needed to overcome mutated KRAS-induced pancreatic exocrine cell senescence (Guerra et al 2007) and it is now accepted that immunosuppression is also needed to allow the progression of cancer initiating lesions.

Several cell types, recruited in response to the secretion of chemokines and cytokines by cancer cells, actively contribute to the establishment of immunosuppression within the tumor microenvironment. These include tumor-associated macrophages (TAMs) and tumor-associated neutrophils (TANs). TAMs and TANs are derived from polarized macrophages and neutrophils, respectively. Both display pro-tumor phenotypes. During tumor development from precancer lesions, the tumor microenvironment elicits a modification in the polarization of anti-tumor macrophages (classically designated as M1 type) towards an anti-inflammatory M2 phenotype, that is pro-tumorigenic (Mantovani et al 2017).

Neutrophils, the most abundant immune cells in circulation, are gaining recognition as relevant players in different steps of cancer progression. Inflammation is now recognized as one of the drivers in cancer initiation, and neutrophils are crucial component of this process. TANs, typically overexpressing CXCR2, are attracted to cancer-prone tissues via the secretion of the cytokine IL-8 and chemokine ligands CXCL1, CXCL2 and CXCL5 by both cancer and stromal cells (Jamieson et al 2012, Katoh et al 2013).

Regarding tumor progression, it was shown that neutrophil-derived elastase can degrade insulin receptor substrate-1 within tumor cells, resulting in an increased interaction between PI3K and PDGFR and consequently augmenting tumor cell proliferation (Houghton et al 2010). The proteolytic action of neutrophils can also lead to increased tumor progression (Houghton et al 2010, Deryugina et al 2014).

In summary, neutrophils recruited to the tumor niche can promote cancer initiation and progression, although the precise mechanisms are not yet fully elucidated.

Moreover, other inflammatory and immune cells, such as natural killer cells, T and B lymphocytes, and dendritic cells, have been shown to play significant roles in tumor progression, collectively forming an intricate network of cell communication (Gonzalez et al 2018).

## 2.2. Impact of the ECM on cancer initiation and progression

It is widely accepted that cell phenotypes and associated molecular functions are tightly dependent on signals arising from outside the cell. That includes the cell interactions with the ECM.

The ECM is a highly dynamic and continuously remodeling network, with its components being consistently deposited, degraded, and transformed. It is composed of more than 300 unique matrix macromolecules that can be grouped into collagens, proteoglycans (e.g. heparan sulphate proteoglycans, versican and hyaluronic acid) and glycoproteins (e.g. laminins, elastin, fibronectin and tenascin) (Naba et al 2016). Additionally, the ECM has shown to serve as a reservoir for soluble factors, such as growth factors and other ECM-associated bioactive proteins (Gattazzo et al 2014). A wide range of cellular processes, including proliferation, differentiation, migration, and apoptosis, can be regulated via the interaction of cell surface receptors with the ECM (Hastings et al 2019). Furthermore, the ECM can modulate cell phenotype and responses through mechanosignaling pathways, either through mechanical cues or topographical stimuli (Humphrey et al 2014, Schmitter et al 2023).

Structurally, the ECM assumes two basic forms: (1) the basement membrane that provides crucial support to both epithelial and stromal layers, and (2) the interstitial matrix that forms a 3D lattice surrounding cells in the stroma and connects to the basement membrane. The interstitial matrix not only ensures the structural integrity of tissues and organs but also exerts influence over a wide range of cellular processes. Its composition varies between tissue types and within different niches of the same tissue. It can also be modified in response to trauma or pathological conditions. In cancer, the remodeling of the interstitial ECM has far-reaching effects on various cell signaling pathways, ECM stiffness, cell migration, and tumor progression (Egeblad et al 2010). On the other hand, the basal membrane, a more stable and dense basal structural support for epithelial and endothelial cells, is essential to establish the polarity of epithelial cells and crucial for a wide range of tissue processes. In the scope of cancer, the remodeling of this membrane is necessary to enable cancer cells to invade the stromal tissue and become malignant (Winkler et al 2020).

There are several mechanisms that contribute to the modification of the ECM composition and participate in supporting cancer establishment, namely: (a) ECM deposition, (b) post-transcriptional biochemical transformation, (c) proteolytic degradation, and (d) force-driven physical remodeling.

Tumors often display desmoplasia, an increased deposition and altered organization of ECM creating a fibrotic state (Lu et al 2012). This fibrosis is not only a feature of the cancer tissue but it also actively
contributes to its inherent development. It has been shown that, in the case of pancreatic cancer, a stromal desmoplasia is associated with increased tumor progression and can be correlated with poor disease outcome (Pandol et al 2009). In pancreatic cancer, the level of tumor cellularity, which refers to the relative proportion of tumor cells to normal cells, appears to be a key indicator of poor prognosis (Heid et al 2017).

Changes in matrix stiffness can lead to the activation of intracellular mechanism pathways that modulate cell response. Cancer cells can recognize the variation in ECM stiffness and activate increased traction forces on their surroundings, through actomyosin and cytoskeleton contractility (Butcher et al 2009). Furthermore, these changes can be transmitted intracellularly through mechanosensors, such as p130 CRK-associated proteins, growth factor receptors, or integrin-ECM adhesion complexes, that can recruit focal adhesion molecules, such as FAK, SRC, paxillin, RAC, RHO/RAS GTPases, and Rho-associated kinases, to trigger signaling cascades and cytoskeleton organization (Northey et al 2017). In the case of pancreatic cancer, PI3K activation triggers acinar to ductal metaplasia and subsequent tumor initiation (Costamagna et al 2022). The downstream signaling events resulting from these mechanosensitive responses lead to alterations in cancer cell morphology, enhanced survival, increased migration, and more invasive behavior.

While it is widely acknowledged that the tumor genotype plays a central role in initiating malignancy and driving tumor progression, mounting evidence suggests that mechanical heterogeneity within the cancer microenvironment can collaborate with other cancer hallmarks, influencing tumor diversity and contributing to its initiation and progression (Therville et al 2019). Additionally, the reorganization and densification of the ECM has also been shown to impact the migration of both cancer and immune cells. During tumor onset, densified collagen fibers become straightened and aligned perpendicular to the tumor boundaries (Provenzano et al 2006). This linear collagen fibers, oriented perpendicular to the tumor borders, have been shown to facilitate the migration of breast cancer cells and paired macrophages, thereby promoting metastasis to distant organs (Roussos et al 2011).

## 2.3. In-vivo cancer models as a source of relevant cancer initiating cells

In cancer initiation studies, the source of cancer-initiating cells is crucial. While cancer cell lines are commonly used in cancer models, they are irrelevant for cancer initiation studies, where cells should be free of phenotypic disease during the early stages of the model. Furthermore, as genetic alterations are known to play a considerable role in many cancers, such as PDAC or breast cancer, it makes sense to select cells with a genetically cancerous background.

In this context, GEMMs are of great interest. GEMMs typically exploit an organ-specific gene promoter that induces the localized and organ specific expression of mutant oncogenes or enable localized induced mutations (Westphalen and Olive 2012). Although GEMMs generally exploit the expression of a single oncogene, which represents only a fraction of the complete genotype that can be found in human cancers, they enable the expression of this gene to be isolated, making it easier to interpret its effects. In the context of cancer initiation study, GEMMs are a relevant source of primary cells that can reconstitute each of the oncogenic steps. This is of particular interest for the study of pancreatic cancer, as the late detection of pancreatic cancer in humans prevents the development of cellular models of early lesions and the study of early stages. In the case of PDAC, GEMMs have been successfully used in *in vivo* studies to realistically reproduce the different phases of cancer progression, from precancerous lesions or primary tumors to the development of metastases (Thibault et al 2021).

Because *in vivo* studies are time consuming and challenging to conduct over the long term, biofabrication technologies offer the opportunity to bridge the gap between *in vivo* work and the development of physiologically relevant *ex vivo* cell models, overcoming the limitations of *in vitro* systems. Indeed, the use of GEMMs as a source of cells for *in vitro* cancer initiation models provides a valuable resource of healthy cells with specific cancerous genetic alterations. This approach requires the extraction of cells from the animal before the actual cancer initiation process occurs.

A clear example is provided by a study conducted by Alladin and colleagues focused on the clonal selection of cancer-initiating cells in the early stages of breast cancer (Alladin et al 2020). In this study, researchers utilized GEMMs to stochastically activate the expression of two potent oncogenes, MYC and Neu (the rodent homolog for the human ERBB2 gene was used in the mouse model). The rationale behind this elegant study was based on establishing a model that would enable the authors to understand how, within a healthy epithelium, a few malignant cells can lead to the initiation of a tumor. Their work demonstrated the impact of the cancer niche on cancer initiation. Briefly, the authors extracted primary mammary epithelial cells from a transgenic mouse model bearing two oncogenes (MYC and Neu), under the control of a Tet-O promoter that can only be activated in the presence of both the reverse tetracycline-controlled transactivator (rtTA) protein and doxycycline. These cells were used to create primary acini in 3D, in Matrigel, that were then stochastically transduced with lentivirus bearing the rtTA inducer gene, and placed again in 3D culture (figure 2(A)). This model made possible the establishment of two cell populations within one acini, a tumorigenic and a non-tumorigenic population.
<figure><img src="image_7.png" /></figure>

The acini displayed a heterogeneous expression of the aforementioned oncogenes, and oncogenes induction was monitored by means of concomitant GFP expression (figure 2(B)).

This simple model, associated with rapid, non-cytotoxic, light-sheet imaging, was used to study the dynamics of early tumorigenesis in an otherwise normal epithelium. As observed in figure 2(C) the authors showed that, with their integrated imaging and data processing approach, they could identify isolated or clustered oncogene-activated epithelial cells within the acini, and track them over time.

Following 3D analysis, the capacity of each population to proliferate was determined and categorized (figure 2(C) in the center panel) as proliferative (orange) or non-proliferative (blue).

In this specific model, this approach demonstrated that not all cell expressing the MYC and Neu oncogenes can automatically lead to a tumorigenic/proliferative state and that an initial stochastic cluster creation can support the tumor formation. The authors, and others, hypothesize that the normal epithelial microenvironment can suppress the transformed cells, and that a minimum threshold of tumor
<figure><img src="image_8.png" /></figure>

cells may be needed to initiate their proliferation process. In the future, such an approach could make it possible to study and identify the underlying mechanisms that promote tumor cell initiation within a controlled 3D niche, as well as further clarify their molecular basis.

## 2.4. Use of human embryonic pluripotent stem cells

Other approaches have provided relevant cell models for cancer initiation studies. Huang and colleagues induced the differentiation of human embryonic pluripotent stem cells into the pancreatic exocrine
lineage, resulting in the development of exocrine organoids in a 3D Matrigel culture environment (Huang *et al* 2015). The organoids were shown to generate a polarized epithelial layer, and the cells to secrete a basal membrane (i.e. collagen IV and laminin 5 secretion) (figures 3(A) and (B)). As the most frequent mutations linked to PDAC are KRAS and TP53 (Casey *et al* 1993, Bryant *et al* 2014), the authors used lentiviral vectors to infect pancreatic lineage-committed cells with: mCherry (control), KRAS<sup>G12V</sup>, or the dominant-negative mutant TP53<sup>R175H</sup> (figure 3(C)). Organoids transduced with KRAS and TP53 were shown to express the correspondent transgenes and to display larger sizes than the mCherry control (figure 3(D)). Also, the KRAS-transduced organoids showed a significant increase of proliferation in relation to the two other conditions (figures 3(C) and (E)).

The authors also investigated the expression of the differentiation marker SOX9 in organoids transduced with KRAS and TP53. SOX9 cytoplasmic expression is described as a risk factor in for instance breast cancer (Chakravarty *et al* 2011) or oral squamous cell carcinoma (Sumita *et al* 2018). Interestingly, the authors demonstrated that SOX9 was localized in the cytoplasm in TP53 organoids, but not in mCherry control or KRAS organoids (figures 3(F) and (G)). Following this observation, the authors evaluated SOX9 localization in sections of normal human pancreas and PDAC. In normal pancreas sections, SOX9 was localized in the nucleus, whereas in p53-expressing PDAC human samples (indicating accumulation of mutant p53) SOX9 was localized in the cytoplasm (figure 3(H)).

This study showed that, by using progenitor organoids, changes in localization of the transcription factor SOX9 could be identified. It also established a clinically relevant phenotype readout associated with a mutant p53. Similar approaches could be used to further decrypt the initiation steps leading to PDAC.

# 3. From *in vitro* models to biofabrication technologies

## 3.1. From 2D *in vitro* models to 3D cultures

*In vitro* models have intensively been exploited during the twentieth century to study biochemical mechanisms implicated in cancer. The traditional 2D culture approach was first adopted due to its inherent simplicity and reduced cost compared to *in vivo* models (figure 4).

However, these monolayer cell cultures suffer from critical limitations compared to *in vivo* situations. In particular, the 2D rigid surfaces as plastic or glass force the cells to deform the way how they adhere to it, leading to a distorted elongated shape, to different patterns of cell proliferation, as well as overexposure to cell medium. The non-permeable surface on which the cells adhere in 2D avoids the cells

to exchange with the microenvironment via paracrine signaling, reducing the intercellular communication to only the nearby neighbors, the released signaling molecules finishing diluted in the cell culture medium. As a result, gene or protein expression profiles are deeply impacted, as for example the focal adhesion kinase and the capacity to the cells to differentiate is limited compared to an *in vivo* situation (Roskelley *et al* 1995, Cukierman *et al* 2002, Walpita and Hay 2002). In the context of cancer treatment, many studies have shown little resistance to the drugs when cells are cultured in 2D, which do not reflect a real situation (Imamura *et al* 2015, Fontoura *et al* 2020).

The apparition of the 3D cell culture enables finally to mimic more closely the *in vivo* 3D microenvironment and to overcome some of these constraints (Elsdale and Bard 1972, Jensen and Teng 2020). Embedded in hydrogels mimicking the ECM, cells can adhere naturally in a 3D way to the matrix via integrins, and exchange directly with it, in a bidirectional way, various signaling molecules as growth factors, cytokines, or other protein kinases (figure 4). Hydrogels are dynamic in molecular composition and stiffness, and act as local reservoir to allow the releasing and the diffusion of signaling molecules between the cells. Thanks to its viscous nature, hydrogels allow cell motility as matrix remolding, which is not conceivable in 2D surfaces. Finally, 3D culture with hydrogels allows to reproduce with more fidelity many features of *in vivo* cell microenvironment, by regulating proliferation, differentiation, migration, survival as well as gene expression (Cukierman *et al* 2001).

In this sense, advanced technological methods have been optimized to produce 3D *in vitro* models that mimic the natural spatial organization of cells, and provide an appropriate cell microenvironment (figure 5). Whereas *in vivo* models have to deal with ethical consideration while considering effective cost and time to develop and expand the model, 3D *in vitro* models can be produced in numerous replicates, thanks to the technology. In the context of cancer modeling, such kind of models finds their interest to explore the interactions between cancerous cell and their microenvironment, while reproducing specific characteristics as a necrotic core, hypoxia, angiogenesis, cell proliferation or metaplasia. Interestingly human primary cells can be exploited, in order to add more relevance to the model, or even to develop patient specific models.

Three main approaches can be exploited to grow cells in a 3D environment: one consists in the creation of cell spheroids using various strategies (Lin and Chang 2008, Breslin and O'Driscoll 2013, Fennema *et al* 2013), a second one uses cell encapsulation in biocompatible hydrogels designed to mimic the rheological properties of the ECM (Sato *et al* 2009, Wang *et al* 2011, Hospodiuk *et al* 2017), and a third
<table><thead><tr><th></th><th>2D culture</th><th>3D culture</th><th>In-vivo models</th></tr></thead><tbody><tr><th>Cell culture vs cancer models</th><td>Over exposure to cell medium<br>Poor cell differentiation<br>Lack of ECM<br>Non-physiological stiffness of adherent surface</td><td>ECM tunable in stiffness<br>Reservoir of molecular secretions<br>Cell-ECM interaction<br>Gene expression</td><td>Human mimetics<br>High<br>Cancer model in a native environment<br>High mimetics<br>Societal impact</td></tr><tr><th>Extracellular matrix</th><td><ul><li>Lack or largely reduced.</li><li>Do not support cells in 3D.</li><li>No molecular exchange with cells.</li><li>Non-physiological stiffness.</li></ul></td><td><ul><li>Natural or synthetic hydrogels.</li><li>Surround the cells in 3D.</li><li>Adaptable in stiffness and in molecular composition.</li><li>Bidirectional molecular exchange with cells.</li><li>Reservoir of signaling molecules and growth factors.</li></ul></td><td><ul><li>Native.</li><li>The most relevant in terms of mimicking properties.</li><li>Presence of active remolding.</li></ul></td></tr><tr><th>Intercellular communication</th><td><ul><li>Poor and reduced to nearby neighbors.</li><li>Released signaling molecules diluted in cell medium.</li></ul></td><td><ul><li>More realistic.</li><li>Communication privileged by the presence of the ECM and by the 3D cell arrangement.</li></ul></td><td>Realistic.</td></tr><tr><th>Gene expression, cell morphology</th><td><ul><li>Not representative.</li><li>Poor cell differentiation.</li><li>Excessive proliferation on the surface.</li></ul></td><td><ul><li>Better mimetics.</li><li>Gene and protein expression near an in vivo situation.</li><li>Cells can efficiently differentiate.</li><li>Proliferation is more realistic and dependent of the microenvironment.</li></ul></td><td>Recapitulate the full complexity of genetic cell heterogeneity.</td></tr><tr><th>Complex features</th><td>Lack.</td><td>Replicate some cancer features as pro-angiogenesis, hypoxia or necrotic cores.</td><td>Model cell dysfunctions/metabolism in a native in-vivo environment.</td></tr><tr><th>Research and societal investments</th><td><ul><li>Easy amplification.</li><li>Cheaper.</li><li>Reduced negative impact.</li></ul></td><td>Compromise between cost and representativeness.</td><td><ul><li>Cost effective.</li><li>Time consuming.</li><li>Ethical issues.</li></ul></td></tr></tbody></table>

**Figure 4.** The place of 3D culture in the modeling of diseases for cancer research. 3D culture, in combination with biofabrication techniques, allows to develop 3D *in vitro* models recapitulating many complex features characteristics from tumors as pro-angiogenesis, necrotic cores with hypoxia, or metaplasia. Thanks to a mimicking extracellular matrix, reproducing the rheological and molecular microenvironment of the tumor, cells can communicate each other by exchanging molecular signaling through integrins bound to the matrix, which is not the case in 2D culture where the cells are attached to a rigid and non-permeable surface like glass or polystyrene. Depending the application and the type of cells, various hydrogels can be employed to model the extracellular matrix, allowing to tune the stiffness of the matrix as well as the molecular nature and proteins involved in the microenvironment. The porous and hydrophilic nature of the hydrogels are source of diffusion of the nutrients and signaling molecules while playing on the same time a function of local reservoir around the microenvironment of the tumor. With such kind of 3D engineered extracellular matrix, 3D *in vitro* models compared to 2D cultures allow to better recapitulate the *in vivo* cell behavior, leading to an active cell differentiation, generally quite poor in 2D models. In drug screening assays, 3D culture exhibits also higher resistance to the treatment than 2D culture, which is more representative of the *in vivo* studies or a real situation on patient. While *in vivo* models represent still a powerful tool to mimic in a realistic way cell dysfunction in a native *in vivo* and systemic environment, they are also source of important ethical considerations, non-negligible cost, and development time challenges. Considering these elements, 3D culture represents an interesting alternative or additional to model certain features of cancer initiation.

approach involves the integration of scaffolds, cells, and bioprinters to fabricate 3D constructs, which can guide cell growth/maturation/organization along a 3D-designed pattern (Ozbolat et al 2017, Daly et al 2021) (figure 5). These different approaches will be discussed in the next sections.

## 3.2. Spheroids culture & the combination with microfluidic devices

Cell spheroids have emerged as a potent tool in cancer research for drug screening due to their ability to mimic *in vivo* tumors (Santini and Rainaldi 1999). Their 3D spherical structure, composed of densely packed cells, closely replicates the low drug

permeability observed *in vivo*, setting them apart from monolayer cultures (Nederman et al 1981, Nederman 1984). Spheroid cultures also exhibit crucial features such as strong cell-to-cell interactions and the presence of hypoxic and necrotic cores, factors contributing to tumor resistance to radiotherapy (Durand and Sutherland 1972, Kerbel et al 1994, Olive and Durand 1994).

Over recent years, technological advances have standardized and optimized the long-term culture of well-characterized and uniform spheroids with high throughput (Mulholland et al 2018, Shao et al 2020). The hanging drop method (Keller 1995, Kelm et al 2003) is probably one of the most simple
<table><caption>**Figure 5.** Different approaches to cultivate cells in 3D. In hydrogel cell embedding, the cells are directly seeded within the hydrogel. It allows the culture of cells in 3D at a reduced cost and with a simplified protocol, but prevents any control over cell organization. Moreover, the resulting cell density is generally far from those of living tissues, except if cells grow naturally as spheroids or organoids. In contrast, cell spheroids culture overcomes this limitation by producing systematically dense clusters of cells, but with a relatively low degree of complexity in terms of model and with a poor mimicking of the cell microenvironment. Finally, the biofabrication of 3D cell constructs exploits instruments such as bioprinters to position the cells according to a specific pattern, enabling the production of more complex microtissues. Thanks to the automation of the process, relatively high throughput can be reached, depending the degree of complexity of the 3D construct, the complexity increased being at the cost of a reduced throughput. In the same way cell densities near a real tissue become challenging to attain the more the construct is volumetric.</caption><thead><tr><th></th><th>Hydrogel cell embedding</th><th>Cell spheroids culture</th><th>3D cell constructs</th></tr></thead><tbody><tr><th>3D culture approaches</th><td></td><td></td><td>Complexity</td></tr><tr><th>Implementation</th><td>Really easy to implement.</td><td>Can be easy to implement. Need of special procedures and CAD design when dealing with microfluidics.</td><td>Time can be required to optimize the setup.</td></tr><tr><th>Tools</th><td>Free of instrumentation.</td><td>Can be free of instrumentation when no microfluidics device is implemented.</td><td>Need of special instrumentation as robots or bioprinters.</td></tr><tr><th>Throughput</th><td>Limited throughput in terms of samples. Can be high considering the cell structures of interest.</td><td>Relatively high throughput.</td><td>Medium to high depending the complexity of the construct.</td></tr><tr><th>Extracellular matrix (ECM)</th><td>Large choice of ECMs.</td><td>ECM produced by cells. When present, ECM surrounding the spheroids is generally inert.</td><td>ECM has to be compatible with the instrumentation.</td></tr><tr><th>Cell density</th><td>Far away from a native tissue. Need growing and maturation to get closer from a native tissue.</td><td>Cell density near of a native bulk tissue.</td><td>Cell density less than a native tissue. Need growing and maturation to get closer from a native tissue.</td></tr><tr><th>Cell organization</th><td>No control on cell organization.</td><td>No control on cell organization.</td><td>Control of the cell organization.</td></tr></tbody></table>

approaches to generate spheroids and can be transposed to high-throughput production (Tung et al 2011). Other popular methods use agitation, centrifugation or non adherent surfaces coating like polyHEMA or agarose on the bottom of 96 well plates (Ingram et al 1997, Ivascu and Kubbies 2006). While all the mentioned techniques for spheroid production offer cost-effective, high-yield, and relatively straightforward approaches, they come with their own set of challenges. A primary drawback is the time-consuming process of coating plates or wells with various substances to create a suitable environment for spheroid formation. Additionally, difficulties arise when attempting to change the culture medium, and achieving long-term culture with the hanging drop method can be problematic due to its limited culture volume.

In the context of cancer research, where the goal is to replicate specific features like hypoxia and necrotic cores found in tumors, the duration of spheroid maturation becomes crucial. Santini and coworkers (Santini and Rainaldi 1999) have highlighted the significance of the long culture time required

for spheroids to grow and attain dimensions sufficient to replicate these critical characteristics. Long-term culture can be achieved with non-adherent or agitation-based methods, but these approaches may result in variations in spheroid shape and cell numbers. Different manufactured products have been developed by companies and can be found on the market to facilitate the procedures or overcome some limitations described above but often come at a price (Breslin and O'Driscoll 2013).

More recently, microfluidic-based approaches have emerged as a methodology to produce spheroids with precise control and with high throughput (Guo et al 2012, Lee and Cha 2018, Shao et al 2020). The main advantage of microfluidics compared to the other previously described techniques relies on the automation of the process. In particular, spheroids can be easily manipulated by tuning the microchannel geometry, controlling the fluid properties, or modifying the flow applied to the microfluidic chambers (Teh et al 2008). Also, cultures can be kept for longer periods (Aeby et al 2018). As a key example, Aeby et al designed a microfluidic chip engineered for the
culture of microtissues in combination with a perfu-
sion system (see figure 6(A)). Such approach enabled
a control over drug delivery and the application of
staining agents via the perfusion system. In the same
way, culture media were collected in order to analyze
the released products. In this study, the authors were
able to keep the culture during more than 9 d while
following up its evolution by time-lapse microscopy.
Other interesting methods include the engineering of
microfluidic chips designed for drug screening and
capable of delivering drug concentration gradients
(see figure 6(B)) (Lim and Park 2018, Mulholland et al
2018).

For a more detailed description, recent reviews
summarize all the advantages and developments
achieved in this area (Breslin and O'Driscoll 2013,
Fennema et al 2013, Shao et al 2020).

In conclusion, spheroids culture indisputably
offers significant advantages for cancer research com-
pared to traditional *in-vitro* monolayer cultures.
Nonetheless, despite all the advantages cited above,
single cell type spheroid cultures have inherent lim-
itations in particular for cancer initiation studies.
These cultures do not provide precise control over the
intricate chemical and spatial interactions between
various types of cells or with the surrounding matrix.
Additionally, as researchers seek to exert even greater
control over the tumor microenvironment, aiming
for increased complexity involving factors such as
diverse cell types, ECM components, microvascu-
lature, and environmental cues, complementary tech-
niques like 3D bioprinting emerge as promising aven-
ues for future developments.

**3.3. Matrices and scaffolds**

The use of biofabricated matrices and scaffolds con-
stitute an interesting approach to engineer 3D cul-
tures with control over the microenvironment. This
control extends to molecular interactions with cells,
the structural and mechanical properties of the sur-
rounding biomaterials, and the incorporation of
ECM substitutes. One of the simplest methods for
conducting 3D cultures involves embedding cells
in hydrogels designed to mimic the biological and
physico-chemical properties of the ECM (figure 5).
This approach capitalizes on cells ability to self-
assemble and provides them with a suitable mat-
rix, potentially enriched with growth factors, which
enables cells to grow and mature in three dimen-
sions within the matrix (Clevers 2016, Du et al
2017). A relevant example is the one from the Clevers
group who succeeded in obtaining small intestinal
organoids through the culture of crypt intestinal stem
cells, within Matrigel, in presence of specific growth
factors (Sato et al 2009). Many proofs of concept have
been conducted on other types of organoids, exploit-
ing the self-organization of cells within compatible
ECMs (Huch et al 2013, Takebe et al 2013). In par-
ticular, in the context of PDAC, Boj and coworkers

demonstrated the production of pancreatic organoids
recapitulating the different phases of pancreatic can-
cer development (Boj et al 2015).

Hydrogels are frequently used in biofabrica-
tion and numerous varieties have been developed
and optimized depending on the applications
(Hospodiuk et al 2017). Hydrogels sourced from
vertebrate (e.g. collagen, gelatin, Matrigel or fib-
rin) have inherent signaling molecules allowing for
cell adhesion and growth, and have been shown to
more closely mimic the ECM. They are exploited for
applications requiring cell growth, maturation, or
differentiation (Olegovich Osidak et al 2020, Naomi
et al 2021). For instance, Matrigel, a commercial
product derived from the basement membrane pro-
teins of mice tumors, is widely employed for cul-
turing organoids or stem cells due to its ability to
promote outgrowth, differentiation, and vasculariza-
tion. It effectively mimics the mechanical and chem-
ical properties of the ECM, while providing essential
cell adhesion ligands (Lang et al 2001, Kleinman and
Martin 2005, Sato et al 2011). However, despite all
its virtues, Matrigel is also criticized for its lack of
reproducibility and batch to batch variability. Many
recent works propose some alternatives (Kaur et al
2021). Interestingly, while Matrigel has been a staple
for 3D culture since the late 1970s, the use of collagen
for 3D culture only gained traction in the early 2000s.
Furthermore, synthetic polymers for 3D culture have
been developed more recently, approximately fifteen
years after the adoption of collagen-based systems.

In recent years, 3D scaffolds produced from tar-
geted decellularized tissues have gained huge interest,
mainly for the capacity of such approaches to tailor
to the specific culture needs of the cells of interest
(Wang et al 2011, Tian et al 2018, Giobbe et al 2019). A
great advantage of these ECM hydrogels (also named
biomatrix scaffolds) lies in their own proteomic sig-
nature, specific of the tissue from which they are
extracted and suitable to the cells to be cultured.
Another example of usage of decellularized ECMs and
in the field of cancer model engineering, is the work
from Tian and coworkers who produced decellular-
ized scaffolds from lung and liver in order to eval-
uate their pertinence on a model of colorectal can-
cer metastasis (liver and lung representing common
sites of metastasis for colorectal cancer) (Tian et al
2018). In their study, they demonstrated that the
metastases obtained from these decellularized scaf-
folds were more similar to in vivo liver metastases in
terms of pathological features and gene expression
than colorectal cancerous cells grown on collagen and
Matrigel.

A second category of natural hydrogels to con-
sider comprises alginate and agarose, which are
derived from algae extraction. These hydrogels are
generally biocompatible with most cells, but are
mainly inert for animal cells. This characteristic is
often leveraged to encapsulate cells within hydrogel
<figure><img src="image_9.png" /><figcaption>**Figure 6.** Example of microfluidics chips engineered for cancer model applications. (A1)–(A3): the system developed by Aeby *et al* The system allows the growth of 3D microtissues in hanging inert hydrogel drops (PEG based hydrogels, agarose or alginate). Perfusion of drugs and changing of medium can be operated thanks to a microfluidic engineered system. This kind of chip can be reproduced in many replicates in order to produce statistically relevant data. (A1) Schematic representation of the chip. (A2) Confocal imaging of colon carcinoma cells (HCT-116) cultured in 3D inside the chip and induced with eGFP. Maximal projection of z-stacks with overlap of GFP, Hoechst nuclear stain and dead stain. (A3) Primary human liver cells with (down) or without (upper) cytochalasin, a toxin inhibiting the polymerization of actin and affecting the morphology of liver cells. Green stain: CellTracker green 5-chloromethylfluo- rescein diacetate. Cells with treatment show changes in their morphology, with a lower expression of bile canaliculi marker and a higher expression of SiR-actin (purple) that reveals dead cells. Aeby *et al* (2018) John Wiley & Sons. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. B1–B5: The system engineered by Mulholland & al (B1–(B3): design of the chip. The chip is organized in an array of 8 × 30 microfluidics compartments. Gradients of drug delivery are obtained on a row (B2) whereas continuous variation in size is present along the lines (B4). (B5): Live/Dead assay on UVW glioma spheroids treated with a gradient of cisplatin during 12 h (scale bar: 500 mm). Reproduced from Mulholland *et al* (2018). CC BY 4.0.</figcaption></figure>
microbeads, particularly with alginate, which can be crosslinked ionically using divalent cations like Ca²⁺ or Ba²⁺ (Tan and Takeuchi 2007, Vos et al 2014, Simó et al 2017). In particular, in the context of cancer models and drug screening, many studies have used this process to generate 3D cell aggregates or organoids in quantity and confined in a core-shell structure (Lan and Starly 2011, Yu et al 2015, Fang et al 2021, Zhu et al 2021). Additionally, with regard to cancer initiation models, it is worth mentioning the work of King and colleagues, who developed a method for embedding pieces of mice ovarian and oviductal organs into alginate. This allowed them to establish a primary culture of ovarian organoids for investigating the cell types involved in cancer initiation (King et al 2011).

In general, and particularly in the context of biofabrication, hydrogels are typically chemically modified to facilitate gel crosslinking, improve rheological properties, and streamline the production protocol. Among the most widespread chemically modified hydrogels, one can consider methacrylated gelatin (GelMA) which, despite being based on simple gelatin, can provide a biological suitable environment (e.g. by containing RGD motifs for adhesion and allowing proteolytical degradation by cells) and can be quickly photo-crosslinked, to guarantee shape fidelity and stability in physiological conditions.

Here, the scope is not to provide an extensive review of all hydrogels used in 3D culture or bioprinting. For a more complete review the reader can refer to the following reviews (Tibbitt and Anseth 2009, Caliari and Burdick 2016).

In conclusion, several hydrogels allow the 3D culture of cells and their growth or differentiation into organoids or tumoroids, provided they are cell compatible and supply adequate growth factors. In this way, cell densities near a real tissue can be reached locally, where cells create spontaneously spheroids or organoids. In such condition, many replicates of organoids/spheroids specimens can be produced but at the cost of an unorganized culture. However, in the context of biofabricated models applied to cancer initiation, the ability to produce the model in many independent replicates with a spatial organization conducive to effective readouts is crucial. Beyond the choice of hydrogel, it is therefore important to consider the technological devices required for high-throughput production and proper organization of the cells.

### 3.4. 3D bioprinting

3D bioprinting has generated significant interest in the fields of tissue engineering and bioengineering, as a set of tools with the capacity to tackle new challenges and to attain further control over cellular deposition and tissue fabrication. 3D bioprinting enables both an automatized control of the cell organization (not the case with classical cell embedding or typical cell

spheroids productions), and a control of the microenvironment, in terms of cells and ECM.

Particular interest has been drawn to the biofabrication of cancer models, where the modeling of the microenvironment is indisputably of crucial importance (Datta et al 2020). The modeling of the ECM can be finely tuned by the capacity to adjust several key characteristics, as stiffness, structure and topography, porosity, and molecular cell signaling. Furthermore, one can take advantage of bioprinting ability to spatially arrange different cell types to mimic certain situations considered to play a role in cancer initiation, such as the development of cancer niches or the invasion of the microenvironment surrounding the niche by tumor-associated cells. Bioprinting technologies have been intensively described in recent years and numerous reviews can be found on the thematic (Salaris and Rosa 2019, Daly et al 2021, Ravanbakhsh et al 2021, Bosmans et al 2024).

Here, we provide a brief overview of existing bioprinting technologies and their various applications in the biofabrication of cancer models.

Bioprinting technologies can be separated into two types of approaches: scaffold free and scaffold-based approaches. In the scaffold free approach, cells are loaded on a mold of sacrificial biomaterial, which is then removed once the cells have produce their own ECM (Ozbolat 2015). In this approach, it is actually the mold which is 3D printed and not the cells.

In the scaffold-based method, cells are embedded in a biomaterial and then printed along the specified pattern. This approach is the most common way bioprinting is implemented in cancer research, and we will focus on it here to explain how it can be utilized for the biofabrication of cancer models.

Concerning the implementation tools, bioprinting technologies have historically been classified in 3 main families: microextrusion, inkjet or microvalve, and laser-assisted bioprinting (LAB) (Ozbolat et al 2017, Daly et al 2021) (figure 7). In some instances, inkjet and microvalve technologies are also named droplet-based or jet-based technologies due to the ejection or droplet creation during the process (Gudapati et al 2016). When such kind of terminology is employed, LAB, also based on the ejection of a thin jet, should also be classified in the same family. Recently, stereography and lithography are more and more exploited and are now described as a fourth type of bioprinting technologies, (Grigoryan et al 2019, Yu et al 2020).

#### 3.4.1. Microextrusion bioprinting

Microextrusion bioprinting considers the extrusion of a bioink, through a syringe, following a computer defined pattern (Ozbolat and Hospodiuk 2016). In this sense, the viscosity of the bioink needs to be tightly controlled to sustain the shape of the specified bioprinted pattern. This technology is one of the most efficient to attain a fast printing of large
<figure><img src="image_10.png" /></figure>

volumes of bioink, and it is generally employed to print macroscopic constructs, but at a cost of cell density constructs quite far away from native tissues, due both to limitations on the quantity of cell required for such volume of constructs, as limitations of shear stress leading to cell damage. To reach higher cell densities, constructs need to be cultured and matured, using eventually a bioreactor. Nonetheless, some proofs of concept for microscopic structures have also been achieved using advanced instruments. Due to its simplicity, cost and great versatility, it is currently the most widely used technology in bioprinting (Pedroza-González et al 2021, Santoni et al 2022).

Microextrusion bioprinting has widely been used for the biofabrication of cancer models, with a particular focus on modeling stromal interactions with the cancerous cells and surrounding ECM (figure 7). Many proofs of concept have been achieved in the field of breast cancer or glioblastoma (Wang et al 2018, Langer et al 2019, Reid et al 2019). As an example, Reid et al demonstrated than the formation

of chimeric breast organoids was much more efficient when using 3D bioprinting patterning instead of manual deposit. Also, in the field of breast cancer, Grolman et al took advantage of coaxial microextrusion to fabricate core-shell structures in order to model the interactions between the cancerous cells and the microenvironment (Grolman et al 2015). In their model, breast cancerous cells (MDA-MB231) embedded in an alginate bioink were co-printed as a shell structure around a macrophage (RAW 264.7) core. The authors analyzed the impact of the printing geometry (i.e. straight, serpentine or helicoidal) on the cell-to-cell signaling and cell migration processes.

Another key example was reported by Yi and coworkers who developed human-glioblastoma patient-specific models using isolated cells from patients (Yi et al 2019). In the study, the authors exploited the microextrusion technology to enable the creation of a central glioblastoma deposition surrounded by a circular geometry containing vascular cells. In order to further mimic the glioblastoma native ECM, the authors established a bioink composed
<figure><img src="image_11.png" /><figcaption>**Figure 8.** Implementation of a 3D cancer model by inkjet bioprinting. Clusters of cancerous neuroblastoma cells are bioprinted in a microenvironment constituted of MSC as stromal cells and microbranches of endothelial cells. Reproduced from Duarte Campos *et al* (2019). CC BY 4.0.</figcaption></figure>

by decellularized porcine brain. Interestingly, the authors were able to generate an oxygen gradient with a hypoxic region in the central part of the model, and could show that the model could replicate a distinct profile of resistance to chemotherapy/radiotherapy, as the donor patients from whom glioblastoma cells were obtained.

### 3.4.2. Inkjet bioprinting

Inkjet and microvalve technologies function by creating microdroplets through the application of pressure to the bioink, which is then forced through a small orifice or nozzle that is opened and controlled by an operator (Daly *et al* 2015, Gudapati *et al* 2016, Zhou *et al* 2019). In general, inkjet and microvalve technologies offer the capability to print constructs with higher resolution when compared to microextraction bioprinting. The ejected droplets typically have volumes in the range of a few hundred picoliters, resulting in droplet diameters that can vary between 50 µm and 500 µm. The exact droplet size depends on the specificities of the technology and the characteristics of the receiving substrate (Knowlton *et al* 2015, Gudapati *et al* 2016). The high throughput and non-contact characteristics of inkjet and microvalve technologies make them particularly convenient for creating bioink microdroplet arrays. These arrays are well-suited for studies that demand high levels of statistical data or for precisely depositing biological materials onto various constructs or tissues (figure 7).

Nonetheless, some drawbacks can be associated with these technologies. One issue is the potential clogging of the output orifice caused by cell aggregation, in particular when high cell concentrations are used. To mitigate this, lower viscosity bioinks are often used, in combination with medium cell concentrations (lower than 30 million cells mL⁻¹). Additionally, there can be concerns about cell viability, particularly when working with high viscosity bioinks, due to the shear-stress applied to cells in the nozzle. These challenges should be carefully

considered when choosing a bioprinting method for specific applications.

In the field of cancer models biofabrication, these technologies have been used to create arrays of spheroids on-a-chip or to fabricate miniaturized tumors models surrounded by their microenvironment (Rizvi *et al* 2011, Ling *et al* 2015, Campbell *et al* 2019, Duarte Campos *et al* 2019). As a key example, Duarte Campos and coworkers established a 3D neuroblastoma model, using the human bone marrow-derived epithelial-neuroblastoma cell line (SH-SY5Y), co-printed with MSC and human umbilical vein endothelial cells (HUVECs) in bioinks based on type 1 collagen (Duarte Campos *et al* 2019). The model was designed with a HUVECs pattern mimicking vasculature branches, surrounded by stromal cells (MSC) (see figure 8). In addition, several cancerous cell clusters were present in the model, printed drop by drop in order to form cancer rosettes. The authors observed that cancer cells formed characteristic features, such as Homer Wright rosettes (halo of tumorous cells), associated to a proliferative behavior and to the production of a vimentin rich matrix, a component highly express in neuroblastoma.

In another example, Rizvi and coworkers implemented a 3D bioprinted model of ovarian cancer cells in coculture with fibroblasts and endothelial cells (Rizvi *et al* 2011). Using this model, they were able to assess the interactions between cancerous cells and stromal cells based on their proximity. Their findings revealed that the proximity of fibroblasts directly enhanced tumor proliferation.

### 3.4.3. LAB

LAB is the third most commonly used bioprinting technology, even if its usage represents less than 10% of the reported studies (Pedroza-González *et al* 2021). As inkjet or microvalve systems, LAB is based on the ejection of small bioink droplets. However, it differs in that it does not require a nozzle and relies on the interaction between a laser pulse and the bioink cartridge
(Barron et al 2004, Duocastella et al 2009, Guillemot et al 2010; Koch et al 2013, Ali et al 2014). Generally, the cartridge is composed by a glass slide coated with an absorbing layer, often sputtered gold or other metallic coating, on which a thin layer of bioink is spread. Due to the interaction between the focused laser pulse and the sacrificial layer, an ultrashort plasma is created, resulting in a formation of a cavitation bubble, whose expansion and collapse induces the ejection of an ultrasonic thin bioink jet. Thanks to the micrometric dimensions of the laser focus (a few tens of micrometers in diameter, depending on the configuration) and the ultrashort duration of the laser pulse (generally in the nanosecond range), the volume of the ejected droplet is in the picoliter range. This technology is therefore recognized as one of the most accurate in terms of droplet volume deposited. Since LAB is nozzle-free, it does not subject the cells to shear stress, allowing for the use of high cell concentrations (typically up to several tens of millions per milliliter and even until one hundred). This makes LAB particularly suitable for printing highly concentrated cell droplets in a small volume, which, in the context of this review, is advantageous for modeling cancer clusters (figure 7).

Recently, our group demonstrated the capability of LAB to create cell spheroids directly within a GelMA matrix, following a precise and predefined geometry (figures 9(A) and (B)) (Hakobyan et al 2020). In this study, we investigated the initiation of PDAC by following the metaplasia of acinar cells into ductal cells. To assess the impact of the two cell populations, acinar and ductal, on cell proliferation, we evaluated the behavior of bioprinted spheroids in 3 configurations: acinar spheroids, ductal spheroids and coculture spheroids. We monitored their maturation up to 14 d after printing using confocal imaging. Using image analysis, we quantified the evolution of each cell population in monoculture and coculture and assessed their respective proliferation profiles. The observed results contrast with the proliferative profile of ductal cells in 2D, suggesting that cellular communication has been established between acinar and ductal cells and subsequently influences cancer initiation and progression.

### 3.4.4. Multimodal biofabrication approaches:

Generally, extrusion bioprinting technologies are more suitable for depositing materials in a centimeter scale with a medium cell concentration, which makes it quite convenient for modeling ECM and stromal interactions, as an example (figure 7). On the other hand, inkjet or LAB are optimized for high precision deposition instead of large-scale printing. As a result, these technologies are implemented generally for the modeling of cell clusters (figure 7). In particular, in the context of cancer cluster modeling, high cell concentration printing can be reached with LAB (Hakobyan et al 2020) (figure 9).

Taking into account these considerations, multimodal bioprinting/biofabrication approaches represent an interesting way to take advantage of the different features of those technologies (Lee et al 2021, Ning et al 2022). In these multimodal strategies, different bioprinting technologies can be combined together or coupled with a microfluidic approach, where several studies demonstrate how to take advantage of microfluidic to perfuse bioprinted models (Zhang et al 2016, Fritschen et al 2024). For example, Zhang et al demonstrates an elegant proof of concept on the implementation of a microfluidic device where cells have been previously loaded using inkjet bioprinting (2016) (figure 10). In their study, human hepatoma cells (HepG2) and human glioma cells (U251) were injected in the chip according to a specified pattern (figure 10(A)). A specific drug (Tegafur) was diffused in the model thanks to the microfluidic device. In presence of Tegafur, HepG2 cells were able to metabolize it into an active anti-cancer drug (5-fluorouracil). The authors showed that the viability of U251 cells could then be reduced in the presence of HepG2 cells and Tegafur (figure 10(B)). They could also create a gradient of drug delivery by varying the distance between HepG2 cells and U251 and observed that the viability correlated with the drug gradient. Another example is the one from Fritschen et al who developed a special robotic approach to combine drop jet bioprinting with a microfluidic engineered chip (figure 11). In their work, they performed the production of a multicellular liver carcinoma model of HepG2 cells, in culture with HUVECs and human dermal fibroblasts (HDFs). Thanks to their design of chip, they were able to create, after 14 d of culture, perfusable vascularized islets of HepG2 cell clusters (figures 11(C) and (D)). Although the number of models present in the chip is limited to three chambers, the full automatized approach implemented in their study represents an interesting proof of concept with a capacity of scaling up towards systems with a higher throughput. With this in mind, the authors developed and tested a process chain where all manual steps of experimentation concerning the chip (manipulation, lid closure, storage) where automatized.

## 4. Discussion

### 4.1. Analysis of the existing work

The use of biofabricated models in cancer research has garnered significant attention in recent years, and it has proven to be a powerful tool for recreating *in vivo* tumor environments (Rodrigues et al 2021). Biofabrication technologies have witnessed a growing interest over the past decade, as evidenced by the increasing number of studies dedicated to this field. Nevertheless, as we have already pointed out in this article, the vast majority of these studies focus on the
<figure><img src="image_12.png" /></figure>

advanced development of cancer rather than on the initiation of the process.

When considering the different studies related to *in vitro* modeling of cancer initiation, it becomes apparent that there is a scarcity of articles focused on this particular aspect. Among the studies we reviewed,

only two specifically investigated the impact of a triggering niche on cell proliferation (Alladin et al 2020, Hakobyan et al 2020). Alladin and colleagues highlighted the significance of a threshold effect of the number of oncogenes expressing cells on the formation of proliferative clusters within mammary
<figure><img src="image_13.png" /><figcaption>**Figure 10.** Implementation of an inkjet printing on a microfluidic chip. Proof of concept of anti-cancer drug delivery on U251 human glioma cells. (A) Illustration of the bimodal inkjet-microfluidic setup. Droplet of cells are deposited thanks to inkjet modality and covered by a PDMS layer with microchannels previously prepared using a standard soft-lithography procedure. (B) U251 cell viability as a function of its distance from a drop point of HepG2 cells. Reproduced from Zhang *et al* (2016) with permission from the Royal Society of Chemistry.</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>**Figure 11.** Robotic approach combining microvalve 3D bioprinting and a microfluidic chip. (A) Illustration of the implemented automatic setup from Fritschen and coworkers. A robotic arm enables the automation of the different steps of device fabrication. (B) Design of the 3D cell model. Clusters of HepG2 cells with an average diameter of 850 µm are deposited inside a round chamber with an intern diameter of 3 mm. These HepG2 cell droplets are then embedded in a fibrin gel containing HDFs in coculture with HUVECs. (C) Image of the gel fibrin surrounding the HepG2 clusters after 14 d of culture. (D) Confocal microscopy of a HepG2 cluster in presence of a tubular pre-microvascularization branch obtained after 14 d of culture. Fritschen *et al* (2024) John Wiley & Sons. © 2024 The Authors. Advanced Healthcare Materials published by Wiley-VCH GmbH.</figcaption></figure>

organoids. In contrast, Hakobyan and team observed that pancreatic ductal cells exhibited a high degree of proliferation in presence of pancreatic acinar cells. Also oriented on exocrine pancreatic cancer, the work from Huang and co-workers showed that the culture of progenitor organoids could facilitate the

identification of readouts associated with cancerous cell phenotypes (Huang *et al* 2015).

It is interesting to note that, despite the fact that these 3 studies were based on 3D cultures of relatively simple spheroids or pre-organoids (comprising only one or two cellular types), the authors deployed
advanced culture, bioprinting, and analysis methods
in order to be able to fully exploit the final complexity
of their models.

4.1.1. *Culture*

Considering the culture, we can remark that the
works from both Alladin et al and Huang et al were
based on the use of primary cultures (Huang et al
2015, Alladin et al 2020). In the first cited work, the
use of a GEMMs allowed the authors to stochastically
activate the expression of oncogenes. This type of pro-
cess, combining GEMMs and in vitro culture, enables
a close modeling of in vivo behavior, while making it
easier to control the complexity of the system.

In the second work, the authors induced human
pluripotent stem cells into pancreatic exocrine
organoids. This approach is a logical choice when
it comes to exploring the mechanisms of early cancer
initiation. Unlike a well-established cancer model
using immortalized cancer cell lines, the use of
primary culture is not trivial in the context of the
creation of a pre-initiation cancer model. Indeed, as
the mechanisms studied consider events that occur
before the development of the disease, it could be
wise to also establish models using non-cancerous
cells, in order to identify selective mechanisms linked
to the subsequent development of cancerous phen-
otypes (e.g. early modification of cell metabolism).
Besides, most of cancerous immortalized cell lines
are cultured and selected in 2D non-physiological
conditions, resulting in the loss of genetic heterogen-
eity typically found in primary tumors. This aspect
is of importance and has to be also considered in
a situation where organoids derived from human
pluripotent stem cells are exploited, after drastic cell
selection. Despite the fact that the cell origin could
be more realistic or closer to the case study, the final
genetic heterogeneity might be lost during the cell
culture and maturation. Finally, the use of human
cell progenitors instead of GEMMs as a model of can-
cer initiation cells represents a paradigm for the cell
origin and has to be carefully taken into account by
the researchers, depending the readouts of interest.

4.1.2. *Hydrogel and ECM model*

Another interesting aspect concerns the hydrogel
enabling the 3D cell culture and maturation. It is
noticeable than, except for the work from Hakobyan
& al where GelMA was employed to model the
ECM, the above studies relied on Matrigel (Huang
et al 2015, Alladin et al 2020, Hakobyan et al 2020).
Matrigel, a commercial product, is frequently used as
an hydrogel for 3D culture, in particular for culture
of organoids due to its favorable mixture of growth
factors associated to relatively suitable rheology prop-
erties, which favor the differentiation of many cell
progenitors (Kleinman and Martin 2005, Hospodiuk
et al 2017). However, there are several limitations
associated with Matrigel.

Firstly, it is challenging to finely tune its stiff-
ness and other physical properties without blending
it with other hydrogels, as is done for some bioprint-
ing applications (Flores-Torres et al 2021). This can
cause some difficulties when using it for bioprint-
ing, which requires a specific range of viscosity prop-
erties for optimum printability. To be compatible
with this hydrogel, extrusion bioprinters have to be
equipped with special cooling modules in order to
allow its extrusion. A heating support is also neces-
sary to trigger the gelation of the bioprinted product
(Hospodiuk et al 2017). Secondly, as already pointed
out in this article and in many other studies, the mod-
ulation of the ECM stiffness has been identified as a
key process driving cancer initiation. It could be relev-
ant to explore this aspect in cancer initiation models
by playing on the rheology of the hydrogel, which is
obviously not possible with pure Matrigel.

4.1.3. *Technology and analysis*

In addition to the technical challenges associated with
establishing an *in vitro* model, another critical aspect
involves the accurate extraction of information from
the model while avoiding potential artifacts.

Still taking the studies we have reviewed as
examples, one can note that Alladin *et al* had to imple-
ment a special data processing approach in order to be
able to analyze in detail the cell proliferation of the
various progenitors, within the complexity of their
3D model. (Alladin *et al* 2020). This type of ana-
lysis carried out using conventional imaging meth-
ods could quickly become time-consuming, given the
time required to follow the various cell divisions over
time. Moreover, the high magnification required to
identify each cell within the spheroid volume may also
be perceived as a serious time constraint when data
imaging is to be performed for large data sets. These
fastidious aspects were resolved in this work thanks to
the use of a light sheet microscopy with a 62.5x ima-
ging objective.

In a different approach, Hakobyan *et al* exploited
the precision of the LAB to deposit, in large quanty-
ity and with a high throughput, cell spheroids inside
a GelMA matrix (2020). With this approach, they
could automatically create well-defined patterns of
small spheroids, directly embedded in their ECM, and
with a spatial organization facilitating their imaging
through conventional confocal microscopy.

4.2. Key considerations to take into account when
developing in vitro models of cancer initiation

4.2.1. Deciphering the complexity of the initiation
niche—case study with pancreatic cancer

Focusing on PDAC as an example, it becomes evident
that the stromal component of the tumor microen-
vironment displays remarkable diversity in terms of
cellular composition. However, while modeling this
diversity of stromal cells may be highly relevant, it is
essential to carefully assess the increased complexity
this may represent in relation to the final objective. It
may be more pragmatic to create models that allow
for the selective separation of different cell contribu-
tions or at least to restrict them to a few key cell types.
One approach to achieve this could involve replicating
the foundational model in multiple iterations, each
representing various possible cell combinations.

Given the intricate nature of intercellular commu-
nication, as described earlier, an alternative method
for modeling the inherent complexity introduced by
stromal cells is to focus solely on their molecular
secretions. Stromal cells have been shown to signific-
antly influence tumorigenesis through cell signaling
and intercellular communications (see table 1). The
array of molecules potentially involved in this pro-
cess is extensive, with complex interconnections that
evolve over time. Therefore, designing a model that
replicates the intricate signaling network orchestrated
by stromal cells requires a thoughtful and simplified
strategy to test and discern the contributions of key
factors.

For example, advanced methods in micro-
fluidics have been implemented not only to produce
high throughput spheroids but, more interestingly,
spheroids-on-a-chip. Devices that allow the release of
gradients of drugs as proofs of concepts have been
engineered by Lim and Park or Mulholland et al
(Lim and Park 2018, Mulholland et al 2018). Such
devices could be optimized to test the impact of sig-
naling molecules as cytokines or interleukins on a
pre-cancerous niche.

Delving into the pre-cancerous niche of the
PDAC, the creation of an hypoxic environment has
also been shown to support the epithelial to mesen-
chymal transition (EMT) of pancreatic epithelial cells
to malignant neoplastic cells (Yuen and Díaz 2014).
Interestingly, EMT occurs very early in PDAC onset
(Rhim et al 2012), further stressing the importance
of this process in this pathology. As an example, this
aspect could be easily replicated by using 3D bioprint-
ing technologies to develop perfusion chambers in
which hypoxia levels could be controlled. In addition,
the inclusion of PSCs within the bioprinted model
could provide an interesting approach to investig-
ate ECM deposition and remodeling, as well as cell
communication, during PDAC onset and evolution.
Furthermore, based on observations that the pancre-
atic cancer initiation niche is associated with extra-
cellular accumulation of collagens and hyaluronic
acid, one could also explore the hypothesis that mat-
rix composition and its viscoelastic properties may
transmit signals to cells that promote cancer initi-
ation and progression. In light of these intricacies, we
believe that expanding the repertoire of cell composi-
tions within these 3D bioprinted models, by including
diverse stromal and other cell types, while controlling
the non-cellular components of the pre-cancer niche,
such as ECM composition, stiffness, hypoxia levels
and nutrient composition, could shed light on many

as yet unanswered questions regarding the processes
of pancreatic cancer initiation and progression.

4.2.2. Expanding the scope: mimicking the heterogeneity of the cell population surrounding the cancerous niche

When focusing on cancer initiation models, the main
difference compared to ‘mature’ cancer models lies in
the fact that the cells are not yet in a cancerous state,
rather, their transformation into a cancerous state
is induced by the model and is the central focus of
the study. One strategy for selecting appropriate cell
populations involves using primary culture cells. This
choice aims to better replicate the heterogeneous pop-
ulation typically found in the initiation niche (e.g. in
pancreas (Wollny et al 2016)), as opposed to immor-
talized cell lines, which often lose genetic diversity
due to clonal selection or multiple passages in culture.
Primary cells do not harbor the same potential for cell
transformation.

Furthermore, in the process of solid tumor devel-
opment, it becomes increasingly evident that dam-
aged (yet normal) epithelial cells in close proximity to
cancer-initiating cells play a significant role in mod-
ulating the initiation process. A striking example of
this phenomenon was described for liver carcinogen-
esis initiation process: the microenvironment (nor-
mal hepatocytes vs stressed hepatocytes) was shown
to epigenetically regulate lineage-commitment in the
liver carcinogenesis process (via regulatory transcrip-
tion factor networks) leading to the development of
either hepatocarcinoma or cholangiocarcinoma, in
association with a difference in cancer aggressivity
(Seehawer et al 2018).

In consequence, sourcing the heterogeneity of the
stromal cells is important. For example, it becomes
an evidence from *in vivo* studies exploiting GEMMs
that depletion of each type of fibroblast does not lead
to the same results in term of tumor development,
whether PSCs or CAFs are used (Özdemir *et al* 2014,
Garcia *et al* 2020).

In conclusion, to gain a deeper understanding of
early clonal selection in cancer initiation, it is imper-
ative to mimic the heterogeneity observed in cancer-
initiating cells (including stromal cells). Therefore,
biofabrication systems designed to replicate this het-
erogeneity can be deployed in advancing our com-
prehension of these complex processes. For instance,
researchers can leverage GEMMs to create *in vitro*
artificial 3D niches where key gene expression can
be modulated, or, as in the study of Alladin *et al*,
to generate local cancerous cluster niches (Alladin
*et al* 2020). These kinds of approaches can also be
exploited to enable easier readouts from the models.
Interestingly, many phenotypes can also be induced
from primary culture of stem cells, and then exploited
for the culture of organoids (Huang *et al* 2015). It is
worth noting that engineered strategies can be integ-
rated into the model’s design. In the same scope, we
have recently demonstrated, using 3D bioprinting of acinar cells with pancreatic acinar-to-ductal metal- plastic cells (considered as a model of pre-cancer cell state), that surrounding acinar cells promote meta- plastic cell proliferation (Hakobyan et al 2020).

Finally, such strategies are advantageous com- pared to in vivo studies since they allow an easy rep- lication of the model in numerous copies, leading to animal reduction, and provide a faster readout than animal models.

### 4.2.3. Modeling the ECM in cancer initiation models while dealing with high throughput. How to overcome the constraints?

When designing the microenvironment of an *in vitro* initiation cancer model, two aspects should be con- sidered: the composition of the stromal cells and the nature of the ECM. During cancer initiation, the ECM is highly dynamic and constantly changing.

As highlighted throughout this article, three primary features of the ECM have shown to be key: its molecular composition, its structural organization, and its stiffness.

**Molecular composition.** The molecular composition of the hydrogel assumes paramount significance. This parameter is crucial for facilitating various cellu- lar processes, including growth, migration, trans- differentiation, and maturation within biofabricated organoids. Given that the majority of cell models used in such applications typically involve primary cell cul- tures, the hydrogel's ability to closely mimic the ECM from which the cells were originally derived becomes of utmost importance. Achieving this level of mim- icry holds the potential to sustain and preserve the phenotype of these primary cells, ensuring that the model accurately represents the intricacies of cancer initiation. In this line, inert hydrogels like alginate or PEG-based polymers should not be selected as mim- icking ECM. Despite the practicability of these mater- ials, their molecular properties are generally poorly relevant in terms of interaction with cells and there- fore not the most suitable for use in cancer initiation models.

**Structural organization.** The structural organization of the ECM can exert a significant influence on cancer initiation. For example, the organization of the col- lagen fibers has been pointed out to impact the cell migration and thus the tumorigenesis (Provenzano et al 2006, Roussos et al 2011). If one would be interested to explore the modifications of the ECM structure in a cancer niche, different strategies have been already developed in biofabrication in order to align collagen fibers or nano-pre-pattern the bioma- terials based on collagen. These strategies have been optimized with various methods like 3D bioprint- ing (Schwab et al 2020), microfluidics (Han et al 2016, Gong et al 2020), or scaffolds use (Tenboll et al 2010, Qian et al 2019), allowing its implementation

in different contexts in term of biofabricated mod- els. Also interesting on the subject is the work from H. Liou and coworkers from M. Zenobi-Wong group who developed recently a novel approach exploit- ing defects occurring during volumetric printing to generate in the bulk of biomaterials 3D anisotropic aligned structures (microfilaments and microchan- nels with tunable diameters from 2 to 30 µm) (Liu et al 2022, Puiggalí-Jou et al 2024). The method named FLight (Filament Light) presents the advan- age to be extremely fast and cell compatible. Used in combination with cells as fibroblasts, tenocytes or endothelial cells, the authors demonstrated in many examples the possibility to enhance cell alignment, accompanied by the presence of highly directional collagen fibrils. Such approach allowing in a reduced time cell alignment and ECM deposition in a bio- material structured could be of great interest to put into practice with cancer initiation *in vitro* models in order to explore the impact of a fibrotic anis- tropic microenvironment on the triggering of the cancer.

**Stiffness.** Considering the stiffness of the ECM, interesting approaches have been developed using extrusion-based bioprinting to achieve viscosity gradients of hydrogels (Kuzucu et al 2021). Such systems could be harnessed for the construction of cancer initiation models, offering precise control over the rheology of the ECM as a triggering ele- ment, thus opening new avenues for investigating cancer initiation mechanisms. Control of the gel rhe- ology can be also performed in a systematic way using high throughput microfluidics devices that allow the encapsulation of cells in microgel droplets. However, the gel materials typically employed in these setups are often inert hydrogels like alginate or PEG-based polymers (Siltanen et al 2016, 2017, Mao et al 2020).

Finally, only a few studies exploit such high throughput microfluidics platforms in combination with relevant biomimetic ECM hydrogels, such as col- lagen or gelatin (Lee et al 2018, Prince et al 2022). As an example, a microfluidic device as the one from (Prince et al 2022) could be optimized for cancer initi- ating models, provided it could be adapted to relevant ECMs (see figure 12).

**Dealing with the multiparameter equation. Exploiting hybrid methods could be a solution?**

Interestingly, other advanced biofabrication meth- ods are emerging, like volumetric printing, allowing to 3D shape hydrogels in a bulk with precision and in a reduced time (Bernal et al 2019). Some proves of concepts as perfusable systems have been engi- eered with such methods, but generally on the mac- roscopic scale. However, exploring the ability to pro- duce microstructures to generate 3D microfluidics systems, allowing both the patterning of a relevant ECM, while giving the possibility to diffuse on a controlled way various concentration of drugs on
<figure><img src="image_15.png" /></figure>

the models, could be quite relevant in the context
of *in vitro* cancer modeling. Recently, multimodal
methods based on volumetric printing have also been
developed to increase the range of printing abilit-
ies. For example, Rizzo and coworkers developed
a new hybrid printing, mixing volumetric print-
ing and two-photon ablation to enable the produc-
tion of multiscale models based on modified gelatin
photoresin, and with structures/microchannels ran-
ging from micrometer scale to mesoscopic scale
(Rizzo *et al* 2023). It would be of interest to investigate
the capability of such systems, to produce on various
relevant ECMs, arrays on a chip of perfusable 3D
micro-models.

4.2.4. *Spatial organization of the cells*

While the spatial organization of cells may not be
an absolute necessity when considering the design
of a cancer initiation model, it can offer practical
advantages. Firstly, it simplifies the imaging of res-
ults, and secondly, it allows for the assessment of how
the proximity of a cancer niche to the cells in ques-
tion can influence the initiation of their cancerous
phenotype. This becomes particularly valuable in co-
culture scenarios, where researchers aim to investig-
ate interactions with stromal cells. Spatial parameters
like cell-to-cell distance or interconnectivity can be
crucial in evaluating cell migration or local changes
in the microenvironment. For such applications, 3D
bioprinting technologies are probably more appro-
priate. Furthermore, many biomaterials have been
developed to be used with the bioprinting processes
(Hospodiuk et al 2017). In this way, it is possible
to adjust the rheology of hydrogels and this repres-
ents another interesting point of this technology com-
pared to microfluidic systems.

5. Synthesis

Following the different issues discussed in this art-
icle, an ideal *in vitro* model of cancer initiation should
gather several critical points, essential both for its per-
tinence and its practicability.

Among these criteria, one should consider, first
of all, the nature of the cells that play a central role
in cancer initiation and progression, and, secondly,
how these cells could be incorporated in a 3D model
capable of supporting the initiation of the early stages
of cancer. The 3D organization of cells into the
model and the modeling of the microenvironment,
taking into account both the ECM and the stromal
cells, are also important. Finally, the design of the
model should consider its ease of use, reproducibility,
cost effectiveness and ethical relevance in relation to
*in vivo* experiments. In particular, it should be feas-
ible to replicate the model in numerous copies and
incorporate strategies that facilitate the collection of
relevant data.

When it comes to technological approaches for
obtaining such cancer initiation models, bioprinting
technologies and microfluidic systems can comple-
ment each other. In the current context, where a high
number of model replicas is essential to achieve stat-
istically relevant results, the ability to create high-
throughput, high-concentration 3D cellular models is
of the utmost importance. Each technology discussed
above allows this, but with some distinctions. In gen-
eral, microfluidics seems to be more suitable for high-
throughput than bioprinting, where highest rate of
throughput is generally limited on achieving printing
on 96/384 well plates. For example, microfluidic sys-
tems can be exploited to test many conditions, such as
the types of stromal cells in the model, their ratio in
the coculture, the possible addition of growth factors
or secreted proteins. On the other hand, bioprinting
allows further control over ECM properties and cell
organization than microfluidics.

Finally, a strategy that could combine a high pro-
duction rate of spheroids with an easy control over the
properties of the ECM would be highly advantageous.
Achieving this could involve integrating multiple

technological approaches to harness the benefits of
both high-throughput microfluidic platforms and
precise ECM modeling, while allowing for the manip-
ulation of drug gradients.

It is important to highlight that when consid-
ering these bioprinting strategies for the develop-
ment of cancer initiation models, compatibility with
an appropriate hydrogel in terms of both molecu-
lar composition and rheological properties is crucial.
The choice of hydrogel should align with the specific
requirements and objectives of the model to ensure its
effectiveness in accurately mimicking the cancer ini-
tiation process.

An alternative to using complex biofabrication
platforms is to employ conventional methods for
spheroid production and then culture these spher-
oids within hydrogels, as demonstrated in the work
by Alladin et al (2020). The advantage of such an
approach lies in its simplicity and the ability to cul-
ture spheroids in an appropriate ECM. However, it
comes at the cost of unorganized cultures, which
can pose serious challenges for automated post-
analysis. To address this, specialized computational
methods of analysis must be implemented to extract
the required information effectively. When combined
with advanced automated imaging techniques, this
approach can be highly relevant without the need for
more intricate biofabrication strategies.

Furthermore, it is crucial to emphasize the
importance of automated analysis methods, such
as time-lapse microscopy, 3D reconstructions, and
computed analysis, especially when high-throughput
technologies are employed. Given the substantial
volume of data generated in such experiments,
implementing automated analysis strategies becomes
essential. Failing to do so may result in research-
ers only harnessing a fraction of the valuable data
provided by their models.

In conclusion, the creation of effective *in vitro*
models for studying cancer initiation presents excit-
ing opportunities and challenges. Integrating diverse
technologies, optimizing cell selection strategies,
and tailoring the microenvironment are essential
steps in creating relevant and reproducible models
(figure 13). The key lies in striking a balance between
complexity and practicality, to enable the compre-
hensive exploration of cancer initiation, while ensur-
ing the accessibility and usability of these models for
the scientific community. Ultimately, these models
hold great promise for advancing our knowledge of
cancer biology and facilitating the development of
innovative therapeutic strategies.
<figure><img src="image_16.png" /><figcaption>**Figure 13.** A process chain for engineering a cancer initiation model. In accordance with the case study, healthy cells but with selective genetic alterations are 3D cultured in a cancer initiating microenvironment, engineered thanks to biofabrication tools. Many biofabrication technologies can be exploited depending the readouts of interest. Bioprinting technologies are well adapted when cell patterning is needed, whereas cell embedding can facilitate the experimental procedure when such patterning is not essential. Printing design of the models can be optimized in order to simplified the process of imaging and analysis of readouts. In this way, automatic imaging like time-lapse microscopy or large mosaic confocal microscopy can be easily performed. Alternatively, microfluidics are interesting when high replicates and drug delivery are essential. Computing methods are also alternatives to extract quantity of information from the models. When correctly designed, such kind of model enables the observation of specific readouts from cancer initiation, like cell plasticity/transdifferentiation, cell proliferation, matrix remodeling or marker of inflammation. Created in BioRender Guillermet-guibert (2024).</figcaption></figure>

## Acknowledgements

We apologise to the authors whose work we could not cite in this review due to limited space. This work was supported for salaries by Inserm core funding (ART BioPrint), Institut National du Cancer INCA (PLBIO24-034-2024-128), Fondation ARC (ARCPGA2022120005630_6362-3), Hôpitaux de Toulouse (CG) and the « Agence Nationale de la Recherche » via the project PACINIMO (Projet-ANR-21-CE19-0023).

## Data availability statement

No new data were created or analysed in this study.

## ORCID iDs

C Handschin https://orcid.org/0009-0004-8232-876X

H Shalhoub https://orcid.org/0009-0001-5549-4568

C Guyon https://orcid.org/0000-0002-6470-4555

N Dusserre https://orcid.org/0000-0002-4699-3365

E Boutet-Robinet https://orcid.org/0000-0002-5223-4568

H Oliveira https://orcid.org/0000-0002-9715-8052

J Guillermet-Guibert https://orcid.org/0000-0003-3173-4907
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