# Microfluidic and organ-on-a-chip approaches to model the tumor microenvironment

Valentin Bonnet, Emmanouil Angelidakis, Sébastien Sart, Charles Baroud

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Valentin Bonnet, Emmanouil Angelidakis, Sébastien Sart, Charles Baroud. Microfluidic and organ-on-a-chip approaches to model the tumor microenvironment. Current Opinion in Biomedical Engineering, 2025, 35, pp.100606. 10.1016/j.cobme.2025.100606. pasteur-05191830

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Current Opinion in
Biomedical Engineering

# Microfluidic and organ-on-a-chip approaches to model the tumor microenvironment

Valentin Bonnet<fnref n="1" />,<fnref n="2" />,ᵃ, Emmanouil Angelidakis<fnref n="1" />,<fnref n="2" />,ᵃ,
Sébastien Sart<fnref n="1" /> and Charles N. Baroud<fnref n="1" />,<fnref n="2" />

The tumor microenvironment (TME) is a complex ecosystem that involves cancer cells, immune and stromal cells, in addition to extracellular matrix and secreted factors. The interactions within this complex ecosystem regulate tumor cell phenotypes and direct cancer progression, making their understanding essential for advancing our knowledge of cancer biology and developing innovative treatments. Since standard culture conditions cannot account for the complexity of the TME, organ-on-a-chip (OOC) technologies have been developed to fill this need. Here, we describe the recent advances in OOCs designed to improve *in vitro* models of the TME by controlling the physical, chemical, geometrical, and biological environment of tumor cells. We begin with studies that leverage OOCs to understand cancer biology, followed by a description of works that test drug effects within the TME. Finally, we discuss future avenues for development that will enhance the interest of OOCs for diverse applications, including clinical testing.

## Addresses

Corresponding author: Baroud, Charles N. (charles.baroud@pasteur.fr)

**Current Opinion in Biomedical Engineering 2025, 35:100606**

This review comes from a themed issue on **Micro-environments for organoids and multi organ systems**

Edited by Matthias Lutolf and Saba Rezakhani

Received 10 January 2025, revised 15 May 2025, accepted 28 May 2025

Available online xxx

https://doi.org/10.1016/j.cobme.2025.100606

2468-4511/© 2025 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

## Introduction

The tumor microenvironment (TME) is a complex and dynamic ecosystem, with many key players including different cell types, extracellular components, and physical, chemical, and mechanical stimuli. During

cancer progression, reciprocal interactions between tumor cells and healthy cells lead to reprogramming of the resident cells, such as fibroblasts and immune cells, as well as recruitment of circulating immune cells. These interactions are mediated by secreted molecules or direct contact. They can also be due to mechanical interactions between cells or with the extracellular matrix (ECM). This aberrant alteration of the host tissue determines tumor cell phenotypes and modulates tumor growth and response to treatments [1]. For example, the conversion of fibroblasts into cancer-associated fibroblasts (CAFs) can promote ECM remodeling, modulate immune cell composition, and favor neo-angiogenesis [2]. In turn, the metabolic rewiring of cancer cells and tumor growth can lead to nutrient exhaustion and the generation of a hypoxic environment, affecting both immune and CAF phenotypes [1,3]. The dynamics of the TME therefore emerge from all of these interacting elements and play a crucial role in determining cancer progression.

Therefore, incorporating different components of the TME is crucial to understand cancer biology and predict tumor response to treatments. Many models are available to study cancer biology, such as 2D- and 3D-cultured cell lines, stem cell- or patient-derived organoids, as well as different animal models [4]. Amongst these, organoids have gained increasing popularity as they recapitulate crucial features of the parental tumor. However, their relevance is limited by the lack of many native TME components [5].

Organ-on-a-chip (OOC) technologies have been developed to integrate biological models into complex structures, allowing control of geometrical, biological, physical, and chemical aspects (Figure 1). Some of these OOC methods also enable high-throughput measurements [6] and monitoring of the system's time evolution. The incorporation of cancer models into OOC (sometimes called cancer/tumor-on-a-chip) has been motivated (1) by a quest for improvement of the tumor models to better understand the complexity of cancer biology at different stages of tumor development and (2) by the use of these *in vitro* devices as avatars to predict patient response to treatment. Below, we review some of the latest OOC models focusing on their use to understand the role of the TME as well as their use to model response to treatment. We end by discussing how these systems can be further developed to enhance their

<footnote n="1">¹ Institut Pasteur, Department of Genomes and Genetics, Université Paris Cité, Physical Microfluidics and Bioengineering, Paris F-75015, France</footnote>

<footnote n="2">² LadHyX, CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France</footnote>

<footnote>ᵃ These authors contributed equally.</footnote>
<figure><img src="image_3.png" /><figcaption>Figure 1</figcaption></figure>

Organ-on-a-chip platforms aim to capture different elements of the TME. These include the following: (a) Geometric features of the TME across a wide range of length scales and integrating the geometry of tissues, interfaces, and vascularization. (b) Biological complexity, which accounts for the source of the cellular material and the mix of cell types within the TME. (c) Physical parameters, such as active and passive mechanical forces within the tissues, as well as extracellular matrix (ECM) parameters. (d) Chemical parameters, including the presence of cell-secreted molecules and externally imposed chemical conditions. Created using BioRender.com. TME, tumor microenvironment.

potential for preclinical research and clinical
applications.

Using OOC devices to understand cancer biology

Microfluidic systems have been extensively developed
to model different aspects of the TME with control-
lable parameters. Recent publications highlight the
maturation of this field, with an increasing number of
works that combine the now established microfluidic
platforms with patient-derived cells, thus strongly
improving the relevance of the *in vitro* results to
patients.

Modeling the cellular composition and geometry of
the TME using microfluidics

The primary use of microfluidics has been to control the
timing and positioning of the introduction of different
cell populations into the TME, in order to study their
interactions. Combining these cocultures with the
design of complex shapes, microfluidics has been
instrumental in dissecting the impact of the cellular
composition and geometry of the TME on cancer biol-
ogy (Figure 1a–b).

By introducing cytotoxic T lymphocytes (CTLs) into
hydrogel droplets that contain an individual cancer
spheroid, Ronteix et al. were able to track thousands of
individual T cells within the anchored droplets [7].
Probabilistic modeling revealed a positive feedback loop
of T-cell accumulation on the spheroids, as well as
cooperative killing. More recently, Bonnet et al. used
a similar system to show reduced cytotoxicity of CTLs
mutated for adenomatous polyposis coli (*Apc*) due to
reduced engagement with cancer spheroids compared
with wild-type T cells [8].

In addition to integrating immune cells, microfluidics is
advantageous for performing patterned cancer cell
coculture with stromal cells and understanding their
interactions. Truong et al. cultured breast cancer cells
with cancer-associated fibroblasts (CAFs) in communi-
cating compartments. Coupled with live imaging and
RNA-sequencing analysis, this platform identified a role
of CAFs in promoting cancer cell migration through
upregulation of GPNMB [9]. Interestingly, this system
was extended to coculture glioma stem cells (GSCs),
astrocytes, and endothelial cells and identified, using
single-cell RNA-sequencing, ligand-receptor pairs
driving chemotactic invasion of GSCs [10].
Another key advantage of microfluidic systems is the ability to create customized 3D geometries to recapitulate the architecture of the TME, which is particularly useful for modeling vasculature. Most approaches rely on the formation of a hollow tube coated with endothelial cells to mimic the endothelium. Virumbrales-Muñoz et al. showed increased angiogenic sprouting and reduced vessel permeability of tumor-associated renal endothelial cells compared with healthy endothelial cells [11]. By coculturing head and neck cancer cells with CAFs around a lymphatic microvessel, Yada et al. identified that the secretion of macrophage migration inhibitory factor by lymphatic endothelial cells modifies the metabolism of cancer cells. Interestingly, researchers combined this chip with optical metabolic imaging, illustrating the compatibility of microfluidic systems with different quantitative assays [12]. Advanced vascular models were obtained by creating self-organized perfusable vascular networks by mixing endothelial cells with fibroblasts. Using this system, Angelidakis et al. showed that the extravasation rate of ICAM-1⁺ breast cancer cells increases in the presence of fibrinogen [13].

Going further, microfluidics allows the generation of complex geometries recapitulating organ morphologies. Using laser ablation of a hydrogel (mixture of type I collagen and Matrigel) coupled with optogenetics, researchers reproduced cancer initiation in a system mimicking colon geometry [14]. The morphology of the tumor differed according to the initiation site, while tumor development could be modified by the delivery of metabolites secreted by the colon microbiome through the perfusable luminal side of the model. These experiments highlight the new insights that are only available through OOC platforms and that cannot be obtained with conventional organoid culture.

## Controlling the physicochemical properties of the TME using microfluidics

Accurate modeling of the TME requires control over its physical and chemical components in a time-resolved manner (Figure 1c–d). This effort has greatly benefited from the advent of microfluidics.

The unprecedented spatiotemporal control of microfluidic systems has allowed the study of mechanical properties in the TME. Focusing on stiffness, Azadi et al. cultured breast cancer cells on polydimethylsiloxane (PDMS) substrates of increasing stiffness and found that stiffer substrates result in increased invasion, extravasation, and MMP9 secretion [15]. Additionally, using a microfluidic device with an endothelialized lumen, Uroz et al. demonstrated that the difference in stiffness between brain vasculature and parenchyma promotes vessel co-option by migrating cells and therefore metastatic infiltration [16].

In addition to modulating the mechanical properties of the TME, microfluidic devices can be designed to actively apply mechanical stretching to the cells. This was found to increase the invasiveness, intravasation capabilities, and degree of epithelial–mesenchymal transition (EMT) of the cancer cells, effects that are mediated by mechanical regulation of the GABAergic-related pathways [17]. Using a human lung-on-a-chip model including mechanical actuation to mimic breathing, Hassell et al. demonstrated that lung cancer cells become resistant to tyrosine kinase inhibitors under cyclic strain, likely due to mechanical down-regulation of epidermal growth factor receptor (EGFR) [18]. Microfluidics further enables the incorporation of flows to mimic shear, such as applying interstitial flow on MCF7 spheroids embedded within Matrigel that leads to upregulation of cell cycle and DNA repair genes, and downregulation of hypoxic signaling [19].

Microfluidic systems are also excellent tools to recapitulate biochemical gradients that are observed in real tumors. Auxillos et al. exposed cells to an extracellular pH gradient using a microfluidic chip [20] and found that acidosis reduces the migration speed of cancer cells, linked with the regulation of cytoskeletal organization, apoptosis, and hypoxic signaling. Microfluidics has also been used to show how oxygen stress acts on the stromal and immune compartments, indirectly modifying cancer cell behavior. For instance, a hypoxic environment was found to modify nestin⁺ endothelial cells in glioblastoma, promoting chemoresistance [21]. Ayuso et al. used a microvessel system enabling the formation of a nutrient gradient. Natural killer cells seeded alongside the gradient displayed gradual signs of exhaustion and increased degree of starvation and response to hypoxic signaling [22].

## Using OOC devices to model the response to treatments

The previous section focused on approaches allowing relevant modeling of the TME and gaining insights into the underlying cancer biology. In parallel, OOC systems have been used to test the response of tumor models to treatments. The goal is to better understand the role of the TME in the response to treatments and to eventually develop functional precision medicine assays [23].

### OOC models for therapeutic testing

OOC systems have enabled investigations of how TME architecture influences drug delivery and therapeutic response. For instance, specific organ structures, such as the blood–brain barrier (BBB), can impair tumor treatment. Straehla et al. used self-assembled microvessels to mimic the BBB [24]. By incorporating AP2 peptide moieties on nanoparticles, they improved the delivery of cisplatin to eliminate glioblastoma cells on-chip, which they further validated *in vivo*. Recently,
Lorenzo-Martín et al. used a microfluidic system to recapitulate the colon geometry and cellular diversity, which allowed them to reproduce tumor and healthy tissue responses to drug exposure, for up to one month [25]. Interestingly, the geometry of the system improved its predictive power compared with conventional organoids that were missing a perfusable lumen.

An OOC could also be used to test a range of conditions, for example through the creation of controlled gradients. Park et al. measured the effect of hypoxia and hyper- tonic solutions on the permeability of the BBB to drugs, by incorporating electrodes on a chip [26]. Recently, Baek et al. cultured human hepatocellular carcinoma cells in a microfluidic chip generating orthogonal oxygen and drug gradients, thus allowing them to recreate the heterogeneity of oxygen levels found within a tumor or between patients [27]. Their results suggested that cells from hypoxic regions of the tumor have higher drug resistance in hypoxic conditions compared with normoxia, which they further validated in vivo .

Beyond the response to small molecules, microfluidic devices are well-suited for testing immunotherapies while accounting for the different TME components. In a recent publication, Veith et al. combined lung tumor cells with CAFs and CTLs and showed a negative impact of CAFs on anti-PD-1 response. They used a small cohort of six patients and measured differences in response to anti-PD-1 that did not correlate with PD-L1 expression, suggesting that PD-L1 levels may not be a suitable marker to predict response to immune checkpoint blockade (ICB) [28]. Cui et al. performed a coculture of glioblastoma cells with CTLs and tumor- associated macrophages (TAMs) embedded in ECM near a model microvessel. They showed that the mesenchymal glioblastoma subtype promotes immuno- suppressive M2-like TAMs leading to decreased CTL recruitment and activation. They provide evidence that blocking CSF-1R improves anti-PD-1 ICB by inhibiting the immunosuppressive TAM phenotype [29].

To increase translatability to a medical setting, these approaches need to be compatible with the limited number of cells from each patient. For this reason, different groups have proposed microfluidic droplets as a platform to perform a large number of screens while using minimal biological samples. For instance, Jiang et al. formed homogeneous organoids from different tumors and encapsulated them within Matrigel droplets [30]. They demonstrated that droplet encapsulation had negligible effects on gene expression compared with the parental tumors. Then, they challenged organoids from 21 patients with a screen of 31 molecules, and showed that for 17 out of the 21, the organoid drug response was concordant with the clinical response to a specific drug, highlighting the promising potential of this platform. A similar approach was followed by Ding et al. and Wang

et al., who encapsulated patient cells in hydrogel drop- lets that were then collected and maintained in cell culture medium [31,32]. They demonstrated the growth of several types of tumor cells from patient biopsies, with high preservation of molecular features of the parental tumors. Benchmarking this platform against conven- tional organoid culture revealed that the droplet format allowed better nutrient accessibility to enhance culture homogeneity [32]. This system was used to screen a panel of 119 drugs in 11 days on one patient. Tests on a cohort of eight colorectal cancer patients suggested a potential predictive power of the system to assess response to oxaliplatin [31]. Finally, this technology could also capture responses to different types of im- munotherapies (anti-PD-1, T-cell bispecific antibody (TCB), or adoptive cell therapy) [31].

More recently, Zhang et al. cocultured lung cancer organoids with CAFs and tumor-infiltrating lymphocytes (TILs) in droplets, generating lung cancer assembloids. They highlighted the importance of the TME as the organoids and assembloids could present different re- sponses to drugs. Interestingly, assembloids responded differently to EGFR-targeted drugs, consistent with their mutation status of EGFR. These results also suggested a potential to predict patients' responses to chemotherapies and immunotherapies in a small cohort with clinical comparison [33]. A different droplet-based platform to test combinations of drugs was also proposed by Fèvre et al., based on stationary microfluidic droplets used to culture cancer spheroids [34]. Drug droplet li- braries can be generated and fluorescently barcoded to test different drug concentrations on the same chip.

Finally, recent work has started addressing the need to study radiotherapy in vitro in physiologically relevant cancer models. Chermat et al. developed a microfluidic chip to study brachytherapy on-chip, demonstrating reliable dose calculation by clinically used standards, as well as recapitulating key clinical observations for the first time in vitro [35].

OOCs to evaluate therapeutic toxicities

To develop a novel treatment, it is always important to consider toxic side-effects on the organism, often revealed at the system level. Some recent work has focused on cointegrating several organs on the same microfluidic device, thus capturing toxicity effects on the healthy organs [36–38]. Ronaldson-Bouchard et al. developed a multi-organ microfluidic system linking human heart, liver, bone, and skin tissues with a vascular compartment [38]. Studying miRNA biomarkers of cardiotoxicity, the coupled system increased the pre- dictive values of clinically observed miRNA responses to doxorubicin compared with OOCs in isolation.

Microfluidic platforms have also been used to test the safety of immunotherapy. For example, Kroll et al.
proposed a device to test the impact of TCB on kidney organoids [39]. Maulana et al. used a system to mimic cytokine release syndrome resulting from chimeric antigen receptor (CAR) T-cell therapies on cell line models and breast cancer patient-derived organoids (PDOs) expressing different levels of ROR1. They tested the effect of dasatinib, a molecule that decreases T-cell activation, by leveraging the continuous perfusion capabilities of their devices to try several time-dependent protocols. They showed that only the administration of dasatinib after one day of coculture reduces cytokine production without impairing tumor infiltration by CAR-T cells [40].

## Looking ahead

The previous sections highlight advances in OOC studies toward better modeling the TME and toward applications for therapy testing. As both efforts progress, future developments are likely to diverge, owing to different needs and requirements of the different applications. On one hand, a deep understanding of cancer biology will require models offering long culture periods to monitor the dynamic evolution of the TME, in addition to better capturing the contribution of intra- and inter-tumor heterogeneity to a high degree [25]. These models are likely to incorporate an increasing number of components of the native TME to recapitulate their dynamic interplay during cancer progression. On the other hand, therapy testing will require simpler and higher throughput systems, compatible with low numbers of cells from patient samples, and a rapid turnover time to yield results within a clinically actionable time frame. This may require multiplexing and automated workflows, in order to improve the reliability of preclinical drug testing or to pave the way for functional precision medicine.

In both directions, key challenges persist. As increasingly sophisticated microfluidic models of the TME are being developed, the set of markers used to evaluate their relevance is also expanding. There is a need to define parameters that will ensure reproducibility, allow comparability between different studies, and facilitate translation into the clinics [41]. This effort has been undertaken by several initiatives by experts (Innovation and Quality consortium Microphysiological Systems (IQ MPS) Affiliate, European Union Reference Laboratory for alternatives to animal testing (EURL ECVAM), Japan Agency for Medical Research and Development), aiming to define functional parameters and needed model capabilities for specific organotypic models to accelerate drug discovery and development [42].

Additionally, standardization and commercialization of microfluidic systems will extend their use in settings that might lack technical expertise. This will enable the

incorporation of microfluidics in diverse workflows as a complementary tool. Using mouse- and patient-derived organotypic tumor spheroids cultured in microfluidic devices in parallel with animal experiments, Sun et al. recently demonstrated that TBK1 inhibition can resensitize cancer cells to T cell-mediated cytotoxicity [43]. Using both microfluidic and *in vivo* assays, Park et al. showed that isolinderalactone suppresses growth and angiogenesis in glioblastoma [44]. These studies exemplify the use of microfluidics as a complementary assay to different *in vitro* and *in vivo* setups. Increasing accessibility of microfluidic models will allow more researchers to use them, to test hypotheses that cannot be tested in other formats.

Finally, these efforts to improve OOCs' reliability and accessibility can pave the way to implementing such assays for therapeutic testing in clinical settings to perform functional precision medicine assays. This is further supported by regulatory authorities (such as through the US Food and Drug Administration Modernization Act 2.0 [45]). However, while many previously discussed systems allow therapeutic agent testing, most remain at the proof-of-concept stage. An increasing number of studies use patient samples, but limited cohort sizes often prevent clinical validation. The clinical relevance of PDOs is currently being tested in clinical trials that showed high sensitivity and specificity of the miniaturized *in vitro* models [46–48]. Clinical validation of the different OOC platforms will be instrumental to justifying the adoption of these techniques to complement or replace animal testing, or to guide the treatment of patients in the clinic.

## Conclusion

Altogether, the publications presented here reveal a maturation of OOC technologies toward enabling relevant modeling of the TME. An increasing number of studies incorporate patient samples, gaining significant insights into tumor biology and allowing therapy testing. These technologies present decisive advantages for functional therapy testing compared with conventional culture systems or even *in vivo* models. In addition to the cost, ethical considerations, difficulty, and time to establish, mouse models often lack elements specific to human biology and have very limited throughput for personalized therapy testing. The examples highlighted here propose solutions to address these shortcomings.

As the field evolves, we expect to see increasingly complex *in vitro* systems that better capture the dynamic evolution of cancer. Additionally, there is a strong need to define minimal standards to benchmark the relevance of *in vitro* models, a step that will increase study reproducibility and comparability and make those systems widely available. This will further help
implement these systems in new scientific research settings, as well as clinical practice. For the latter, further clinical demonstration of benefits to patient outcomes is needed to incentivize large-scale efforts and investments. This will amplify their potential in drug discovery and development and will allow their incorporation into the delivery of functional precision medicine.

## Declaration of Competing Interest

The authors declare the following financial interests/ personal relationships which may be considered as potential competing interests: Charles Baroud reports financial support was provided by the French National Research Agency. Charles Baroud reports a relationship with the French National Research Agency that includes funding grants. Charles Baroud is inventor of a patent owned by Ecole Polytechnique. Sebastien Sart and Charles Baroud have patent pending with Ecole Polytechnique. Charles Baroud is the cofounder and scientific advisor to OKOMERA, a spin-off company from the lab. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

This work has benefited from state financial aid, managed by the Agence Nationale de Recherche under the investment program integrated into France 2030, project reference ANR-21- RHUS-0003.

## List of abbreviations

<table><tr><td>AP2</td><td>angiopep-2</td></tr><tr><td>BBB</td><td>blood-brain barrier</td></tr><tr><td>CAF</td><td>cancer-associated fibroblast</td></tr><tr><td>CAR</td><td>chimeric antigen receptor</td></tr><tr><td>CSF-1R</td><td>colony-stimulating factor-1 receptor</td></tr><tr><td>CTL</td><td>cytotoxic T lymphocyte</td></tr><tr><td>ECM</td><td>extracellular matrix</td></tr><tr><td>EGFR</td><td>epidermal growth factor receptor</td></tr><tr><td>EMT</td><td>epithelial-mesenchymal transition</td></tr><tr><td>GABA</td><td>γ-aminobutyric acid</td></tr><tr><td>GPNMB</td><td>glycoprotein nonmetastatic B</td></tr><tr><td>GSC</td><td>glioma stem cell</td></tr><tr><td>ICAM-1</td><td>intercellular adhesion molecule 1</td></tr><tr><td>ICB</td><td>immune checkpoint blockade</td></tr><tr><td>LCA</td><td>lung cancer assembloid</td></tr><tr><td>LCO</td><td>lung cancer organoid</td></tr><tr><td>MIF</td><td>macrophage migration inhibitory factor</td></tr><tr><td>MMP9</td><td>matrix metalloproteinase-9</td></tr><tr><td>OOC</td><td>organ-on-a-chip</td></tr><tr><td>PD-1</td><td>programmed cell death receptor-1</td></tr><tr><td>PD-L1</td><td>programmed death-ligand 1</td></tr><tr><td>PDO</td><td>patient-derived organoid</td></tr><tr><td>ROR1</td><td>receptor tyrosine kinase-like orphan receptor 1</td></tr><tr><td>TAM</td><td>tumor-associated macrophage</td></tr><tr><td>TBK1</td><td>TANK-binding kinase 1</td></tr></table>

<table><tr><td>TCB</td><td>T cell bispecific antibody</td></tr><tr><td>TIL</td><td>tumor-infiltrating lymphocyte</td></tr><tr><td>TME</td><td>tumor microenvironment</td></tr></table>

## Data availability

No data was used for the research described in the article.

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20. Auxillos Jamie, Crouignneau Roxane, Li Yan-Fang, Dai Yifan, Stigliani Arnaud, Tavernaro Isabella, Resch-Genger Ute, Sandelin Albin, Marie Rodolphe, Pedersen Stine F: *Spatially resolved analysis of microenvironmental gradient impact on cancer cell phenotypes.* *Sci Adv* May 2024, 10, eadn3448 [Publisher: American Association for the Advancement of Science].

21. Zheng Zong-Qing, Chen Jin-Tao, Zheng Ming-Cheng, Yang Li-Juan, Wang Jun-Ming, Liu Quan-Li, Chen Lu-Fei, Ye Zu-Cheng, Lin Jin-Ming, Lin Zhi-Xiong: *Nestin+/CD31+ cells in the hypoxic perivascular niche regulate glioblastoma chemoresistance by upregulating JAG1 and DLL4.* *Neuro Oncol* June 2021, 23: 905–919 [Place: England].

22. Ayuso Jose M, Rehman Shujah, Virumbrales-Munoz Maria, McMinn Patrick H, Geiger Peter, Fitzgerald Cate, Heaster Tiffany, Skala Melissa C, Beebe David J: *Microfluidic tumor-on-a-chip model to evaluate the role of tumor environmental stress on NK cell exhaustion.* *Sci Adv* February 2021, 7, eabc2331.

23. van Renterghem Allard WJ, van de Haar Joris, Voest Emile E: *Functional precision oncology using patient-derived assays: bridging genotype and phenotype.* *Nat Rev Clin Oncol* 2023, 20:305–317.

24. Straehla Joelle P, Hajal Cynthia, Safford Hannah C,

* Offeddu Giovanni S, Boehnke Natalie, Dacoba Tamara G, Wyckoff Jeffrey, Kamm Roger D, Hammond Paula T:

A predictive microfluidic model of human glioblastoma to assess trafficking of blood-brain barrier-penetrant nanoparticles. *Proc Natl Acad Sci* June 2022, 119, e2118697119 [Publisher: Proceedings of the National Academy of Sciences].

Development of a glioblastoma model including a microvasculature around cancer spheroids. The authors demonstrated that the spheroid upregulates LRP1 locally and design nanoparticles targeting it to deliver cisplatin to the spheroid while minimizing cytotoxic effects to distant cells. They further validated the results in vivo.

25. Francisco Lorenzo-Martín L, Broguiere Nicolas, Langer Jakob, Tillard Lucie, Nikolaev Mikhail, Coukos George, Homicsko Krisztian, Lutolf Matthias P: *Patient-derived mini-colons enable long-term modeling of tumor–microenvironment complexity.* *Nat Biotechnol* July 2024:1–10 [Publisher: Nature Publishing Group].

Researchers used gut-shaped human colon epithelia to study the interaction of patient-derived tumor cells with their environment composed of healthy tissue, CAFs, and TILs. They demonstrated the ability to perform long-term culture in this system and measured drug efficacy and toxicity. They revealed roles of CAFs in promoting EMT and modulating the immune response.

26. Park Tae-Eun, Mustafaoglu Nur, Herland Anna, Hasselkus Ryan, Mannix Robert, FitzGerald Edward A, Prantil-Baun Rachelle, Watters Alexander, Henry Olivier, Benz Maximilian, Sanchez Henry, McCrea Heather J, Goumnerova Liliana Christova, Song Hannah W, Palecek Sean P, Shusta Eric, Ingber Donald E: *Hypoxia-enhanced Blood-Brain Barrier Chip recapitulates human barrier function and shuttling of drugs and antibodies.* *Nat Commun* June 2019, 10:2621 [Publisher: Nature Publishing Group].

27. Baek Sewoom, Ha Hyun-Su, Park Jeong Su, Cho Min Jeong, Kim Hye-Seon, Yu Seung Eun, Chung Seyong, Kim Chansik, Kim Jueun, Lee Ji Youn, Lee Yerin, Kim Hyunjae, Nam Yujin, Cho Sungwoo, Lee Kyubae, Yoon Ja Kyung, Jin Sub Choi, Han Dai Hoon, Sung Hak-Joon: *Chip collection of hepatocellular carcinoma based on O2 heterogeneity from patient tissue.* *Nat Commun* June 2024, 15:5117 [Publisher: Nature Publishing Group].

28. Veith Irina, Nurmik Martin, Mencattini Arianna, Damei Isabelle, Lansche Christine, Brosseau Solenn, Gropplero Giacomo, Cognac Stéphanie, Filippi Joanna, Poté Nicolas, Guenzi Edouard, Chassac Anaïs, Mordant Pierre, Tosello Jimena, Sedlik Christine, Piaggio Eliane, Girard Nicolas, Camonis Jacques, Shirvani Hamassris, Mami-Chouaib Fathia, Mechta-Grigoriou Fatima, Descroix Stéphanie, Martinelli Eugenio, Zalcman Gérard, Parrini Maria Carla: *Assessing personalized responses to anti-PD-1 treatment using patient-derived lung tumor-on-chip.* *Cell Rep Med* May 2024, 5, 101549.

29. Cui Xin, Ma Chao, Vasudevaraja Varshini, Serrano Jonathan, Tong Jie, Peng Yansong, Delorenzo Michael, Shen Guomiao, Frenster Joshua, Tan Morales Renee-Tyler, Qian Weiyi, Tsrigos Aristotelis, Chi Andrew S, Jain Rajan, Kurz Sylvia C, Sulman Erik P, Placantonakis Dimitris G, Snuderl Matija, Chen Weiqiang: *Dissecting the immunosuppressive tumor microenvironments in Glioblastoma-on-a-Chip for optimized PD-1 immunotherapy.* *eLife* September 2020, 9, e52253 [Publisher: eLife Sciences Publications, Ltd].

30. Jiang Shengwei, Zhao Haoran, Zhang Weijie, Wang Jiaqi, Liu Yuhong, Cao Yuanxiong, Zheng Honghui, Hu Zhiwei, Wang Shubin, Zhu Yu, Wang Wei, Cui Shuzhong, Lobie Peter E, Huang Laiqiang, Ma Shaohua: *An automated organoid platform with inter-organoid homogeneity and inter-patient heterogeneity.* *Cell Rep Med* December 2020, 1, 100161.

31. Ding Shengli, Hsu Carolyn, Wang Zhaohui, Natesh Naveen R, Millen Rosemary, Negrete Marcos, Giroux Nicholas, Rivera Grecia O, Dohlman Anders, Bose Shree, Rotstein Tomer, Spiller Kassandra, Yeung Athena, Sun Zhiguo, Jiang Chongming, Xi Rui, Wilkin Benjamin, Randon Peggy M, Williamson Ian, Nelson Daniel A, Delubac Daniel, Oh Sehwa, Rupprecht Gabrielle, Isaacs James, Jia Jingquan, Chen Chao, Shen John Paul, Scott Kopetz, Shannon McPaul, Smith Amber, Gjorevski Nikolche, Walz Antje-Christine, Antonia Scott, Marrer-Berger Estelle, Clevers Hans, Hsu David, Shen Xiling: *Patient-derived micro-organospheres enable clinical precision oncology.* *Cell Stem Cell* June 2022, 29:905–917.e6 [Publisher: Elsevier].
The authors encapsulated patients' tumor cells into droplets that grow into organoids preserving stromal and immune cells. They tested both chemotherapies and immunotherapies in a high-throughput manner.

32. Wang Zhaohui, Boretto Matteo, Millen Rosemary, Natesh Naveen, Reckzeh Elena S, Hsu Carolyn, Negrete Marcos, Yao Haipei, Quayle William, Heaton Brook E, Harding Alfred T, Bose Shree, Driehuis Else, Beumer Joep, Rivera Grecia O, van Ineveld Ravian L, Gex Donald, DeVilla Jessica, Wang Daisong, Puschhof Jens, Geurts Maarten H, Yeung Athena, Hamele Cait, Smith Amber, Bankaitis Eric, Xiang Kun, Ding Shengli, Nelson Daniel, Delubac Daniel, Rios Anne, Abi-Hachem Ralph, Jang David, Goldstein Bradley J, Glass Carolyn, Heaton Nicholas S, Hsu David, Clevers Hans, Shen Xiling: Rapid tissue prototyping with micro-organospheres. Stem Cell Rep September 2022, 17:1959-1975 [Publisher: Elsevier].

33. Zhang Yanmei, Hu Qifan, Pei Yuquan, Luo Hao, Wang Zixuan, Xu Xinxin, Zhang Qing, Dai Jianli, Wang Qianqian, Fan Zilian, Fang Yongcong, Ye Min, Li Binhan, Chen Mailin, Qi Xue, Zheng Qingfeng, Zhang Shulin, Huang Miao, Zhang Ting, Gu Jin, Xiong Zhuo: A patient-specific lung cancer assembloid model with heterogeneous tumor microenvironments. Nat Commun April 2024, 15:3382 [Place: England].

34. Fevre Romain, Mary Gaëtan, Vertti-Quintero Nadia, Durand Aude, Tomasi Raphaël F-X, Nery Elaine Del, Baroud Charles N: Combinatorial drug screening on 3d ewing sarcoma spheroids using droplet-based microfluidics. iScience 2023, 26.

35. Chermat Rodin, Refet-Mollof Elena, Kamio Yuji, Carrier Jean-François, Wong Philip, Gervais Thomas: Brachytherapy on-a-chip: a clinically-relevant approach for radiotherapy testing in 3d biology. Lab Chip 2024, 24:2335-2346.

36. Chramiec Alan, Teles Diogo, Yeager Keith, Marturano-Kruik Alessandro, Pak Joseph, Chen Timothy, Luke Hao, Wang Miranda, Lock Roberta, Tavakol Daniel Naveed, Lee Marcus Busub, Kim Jinho, Ronaldson-Bouchard Kacey, Vunjak-Novakovic Gordana: Integrated human organ-on-a-chip model for predictive studies of anti-tumor drug efficacy and cardiac safety. Lab Chip 2020, 20:4357-4372 [Publisher: Royal Society of Chemistry].

37. Lee Junmin, Mehrotra Shreya, Zare-Eelanjegh Elaheh, Rodrigues Raquel O, Akbarinejad Alireza, Ge David, Amato Luca, Kiaee Kiavash, Fang YongCong, Rosenkranz Aliza, Keung Wendy, Mandal Biman B, Li Ronald A, Zhang Ting, Lee HeaYeon, Dokmeci Mehmet Remzi, Zhang Yu Shrike, Ali Khademhosseini, Shin Su Ryon: A heart-breast cancer-on-a-chip platform for disease modeling and monitoring of cardiotoxicity induced by cancer chemotherapy. Small (Weinh) 2021, 17, 2004258. eprint, https://onlinelibrary.wiley.com/doi/pdf/10.1002/smll.202004258.

38. Ronaldson-Bouchard Kacey, Teles Diogo, Yeager Keith, Tavakol Daniel Naveed, Zhao Yimu, Chramiec Alan, Tagore Somnath, Summers Max, Stylianos Sophia, Tamargo Manuel, Lee Busub Marcus, Halligan Susan P, Abaci Erbil Hasan, Guo Zongyou, Jacków Joanna, Pappalardo Alberto, Shih Jerry, Soni Rajesh K, Sonar Shivam, German Carrie, Christiano Angela M, Califano Andrea, Hirschi Karen K, Chen Christopher S, Przekwas Andrzej, Vunjak-Novakovic Gordana: A multi-organ chip with matured tissue niches linked by vascular flow. Nat Biomed Eng April 2022, 6: 351-371 [Publisher: Nature Publishing Group].

Development of a multiplexed OOC system that combines human heart, liver, bone, and skin tissue niches, linked by a vascular compartment. The authors showed that the combined OOC increases the predictive values of clinically observed miRNA responses relative to non-multiplexed culture systems.

39. Kroll Katharina T, Mata Mariana M, Homan Kimberly A, Micallef Virginie, Carpy Alejandro, Hiratsuka Ken, Morizane Ryuji, Moisan Annie, Gubler Marcel, Walz Antje-Christine, Marrer-Berger Estelle, Lewis Jennifer A: Immune-infiltrated kidney organoid-on-chip model for assessing T cell bispecific antibodies. Proc Natl Acad Sci August 2023, 120, e2305322120 [Publisher: Proceedings of the National Academy of Sciences].

40. Ibrahim Maulana Tengku, Teufel Claudia, Cipriano Madalena, Roosz Julia, Lazarevski Lisa, van den Hil Francijna E, Scheller Lukas, Orlova Valeria, Koch André, Hudecek Michael, Alb Miriam, Loskill Peter: Breast cancer-on-chip for patient-specific efficacy and safety testing of CAR-T cells. Cell Stem Cell May 2024, S1934-590900145-0 [Place: United States].

The authors cocultured CAR-T cells with cancer spheroids and measured cytokine secretions modeling cytokine release syndrome. They proposed different time administrations of dasatinib, a drug inhibiting T cell activation, to preserve T cell infiltration while reducing undesired cytokine release.

41. Nahon Dennis M, Moerkens Renée, Aydogmus Hande, Lendeijmer Bas, Martínez-Silgado Adriana, Stein Jeroen M, Dostanić Milica, Frimat Jean-Philippe, Gontan Cristina, de Graaf Mees NS, Hu Michel, Kasi Dhanesh G, Koch Lena S, Le Kieu TT, Lim Sangho, Middelkamp Heleen HT, Mooiweer Joram, Motreuil-Ragot Paul, Niggl Eva, Pleguezuelos-Manzano Cayetano, Puschhof Jens, Revyn Nele, Rivera-Arbelaez José M, Slager Jelle, Windt Laura M, Zakharova Mariia, van Meer Berend J, Orlova Valeria V, de Vrij Femke MS, Withoff Sebo, Mastrangeli Massimo, van der Meer Andries D, Mummery Christine L: Standardizing designed and emergent quantitative features in microphysiological systems: Nature biomedical engineering. August 2024:1-22 [Publisher: Nature Publishing Group].

42. Tomlinson Lindsay, Ramsden Diane, Batista Leite Sofia, Beken Sonja, Bonzo Jessica A, Brown Paul, Candarlioglu Pelin L, Chan Tom S, Chen Eugene, Choi Colin K, et al.: Considerations from an international regulatory and pharmaceutical industry (iq mps affiliate) workshop on the standardization of complex in vitro models in drug development. Advanced Biology 2024, 8, 2300131.

43. Sun Yi, Revach Or-yam, Anderson Seth, Kessler Emily A, Wolfe Clara H, Jenney Anne, Mills Caitlin E, Robitschek Emily J, Davis Thomas GR, Kim Sarah, Fu Amina, Xiang Ma, Jia Gwee, Tiwari Payal, Du Peter P, Sindurakar Princy, Tian Jun, Mehta Arnav, Schneider Alexis M, Yizhak Keren, Sade-Feldman Moshe, LaSalle Thomas, Sharova Tatyana, Xie Hongyan, Liu Shuming, Michaud William A, Saad-Beretta Rodrigo, Yates Kathleen B, Iracheta-Vellve Arvin, Spetz Johan KE, Qin Xingping, Sarosiek Kristopher A, Zhang Gao, Kim Jong Woook, Su Mack Y, Cicerchia Angelina M, Rasmussen Martin Q, Klempner Samuel J, Juric Dejan, Pai Sara I, Miller David M, Giobbie-Hurder Anita, Chen Jonathan H, Pelka Karin, Frederick Dennie T, Stinon Susanna, Ivanova Elena, Aref Amir R, Paweletz Cloud P, Barbie David A, Sen Debattama R, Fisher David E, Corcoran Ryan B, Hacohen Nir, Sorger Peter K, Flaherty Keith T, Boland Genevieve M, Manguso Robert T, Jenkins Russell W: Targeting TBK1 to overcome resistance to cancer immunotherapy. Nature March 2023, 615:158-167 [Publisher: Nature Publishing Group].

44. Park Jung Hwa, Kim Min Jae, Kim Woo Jean, Kwon Ki-Dong, Ha Ki-Tae, Choi Byung Tae, Lee Seo-Yeon, Shin Hwa Kyoung: Isolinderalactone suppresses human glioblastoma growth and angiogenic activity in 3D microfluidic chip and in vivo mouse models. Cancer Lett May 2020, 478:71-81.

45. Stewart Alastair, Denoyer Delphine, Gao Xumei, Toh Yi-Chin: The FDA modernisation act 2.0: bringing non-animal technologies to the regulatory table. Drug Discov Today April 2023, 28, 103496.

46. Kornauth Christoph, Pemovska Tea, Vladimer Gregory I, Bayer Günther, Bergmann Michael, Eder Sandra, Eichner Ruth, Erl Martin, Esterbauer Harald, Exner Ruth, Felsleitner-Hauer Verena, Forte Maurizio, Gaiger Alexander, Geissler Klaus, Greinix Hildegard T, Gstöttner Wolfgang, Hacker Marcus, Hartmann Bernd Lorenz, Hauswirth Alexander W, Heinemann Tim, Heintel Daniel, Hoda Mir Alireza, Hopfinger Georg, Jaeger Ulrich, Kazianka Lukas, Kenner Lukas, Kiesewetter Barbara, Krall Nikolaus, Krajnik Gerhard, Kubicek Stefan, Le Trang, Lubowitzki Simone, Mayerhoefer Marius E, Menschel Elisabeth, Merkel Olaf, Miura Katsuhiro, Müllauer Leonhard, Neumeister Peter, Noesslinger Thomas, Ocko Katharina, Öhler Leopold, Panny Michael, Pichler Alexander, Porpaczy Edit, Prager Gerald W, Raderer Markus, Ristl Robin, Ruckser Reinhard, Salamon Julius, Schiefer Ana-Iris, Schmolke Ann-Sofie, Schwarzinger Ilse, Selzer Edgar, Sillaber Christian, Skrabs Cathrin, Sperr Wolfgang R, Srndic Ismet, Thalhammer Renate, Valent Peter, Kouwe Emiel van der, Vanura Katrina, Vogt Stefan, Waldstein Cora, Wolf Dominik, Zielinski Christoph C, Zojer Niklas, Simonitsch-Klupp Ingrid, Superti-Furga Giulio, Snijder Berend, Staber Philipp B:
**Functional precision medicine provides clinical benefit in advanced aggressive hematologic cancers and identifies exceptional responders. Cancer Discov February 2022, 12: 372–387.**

47. Cartry Jérôme, Bedja Sabrina, Boilève Alice, Mathieu Jacques RR, Gontran Emilie, Annereau Maxime, Job Bastien, Mouawia Ali, Mathias Pierre, De Baère Thierry, Italiano Antoine, Besse Benjamin, Sourrouille Isabelle, Gelli Maximiliano, Bani Mohamed-Amine, Dartigues Peggy, Hollebecque Antoine, Smolenschi Cristina, Ducreux Michel, Malka David, Jaulin Fanny: Implementing patient derived organoids in functional precision medicine for patients with advanced colorectal cancer. *J Exp Clin Cancer Res* October 2023, **42**:281.

48. Boilève Alice, Cartry Jérôme, Goudarzi Negaar, Bedja Sabrina, Mathieu Jacques RR, Bani Mohamed-Amine, Nicolle Rémy, Ali Mouawia, Bouyakoub Ryme, Nicotra Claudio, Ngo-Camus Maud, Job Bastien, Lipson Karélia, Boige Valérie, Marine Valéry, Tarabay Anthony, Dartigues Peggy, Tselikas Lambros, de Baere Thierry, Italiano Antoine, Cosconea Simona, Gelli Maximiliano, Fernandez-de Sevilla Elena, Annereau Maxime, Malka David, Smolenschi Cristina, Ducreux Michel, Hollebecque Antoine, Jaulin Fanny: **Organoids for functional precision medicine in advanced pancreatic cancer.** *Gastroenterology* (New York, N Y, 1943) June 2024.