Tumor-on-a-chip platforms to study cancer–immune system crosstalk in the era of immunotherapy

Parlato, Stefania; Grisanti, Giulia; Sinibaldi, Giorgia; Peruzzi, Giovanna; Casciola, Carlo Massimo; Gabriele, Lucia

doi:10.1039/d0lc00799d

Cited by 44 publications

(36 citation statements)

References 89 publications

(105 reference statements)

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“…The range for the parameter variations is chosen by varying the initial guess by a percentage of ±50%. Specifically, we searched γ in I 50% = [3.5 × 10 −3 , 6.5 × 10 −3 ], b 1 in I 50% = [11,33], b 2 in I 50% = [6,18] and b 4 in I 50% = [9,27]. We also choose as initial guess a perturbation of the model parameters by +30%.…”

Section: Results On Parameters Estimationmentioning

confidence: 99%

“…Timelapse recordings were performed in a microfluidic platform designed for oncoimmunology [1] research to assess physical and chemical contacts between malignant and immune populations. Some of the earliest applications of microfluidic cell culture technology focused on modeling specific steps in the cancer cascade, including tumor growth [9] and expansion [10], angiogenesis [11][12][13][14][15][16], progression from early to late stage lesions involving an epithelial-mesenchymal transition [17,18], tumor cell invasion [19] and metastasis [20]. Although Organs-on-Chip represent an increasing in importance field of research because it allows experimentalists to have major control on the objects of investigation, resulting in more accurate measurements, some aspects still remain difficult to understand.…”

Section: Introductionmentioning

confidence: 99%

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Estimation Algorithm for a Hybrid PDE–ODE Model Inspired by Immunocompetent Cancer-on-Chip Experiment

Bretti

Ninno

Natalini

et al. 2021

Axioms

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The present work is motivated by the development of a mathematical model mimicking the mechanisms observed in lab-on-chip experiments, made to reproduce on microfluidic chips the in vivo reality. Here we consider the Cancer-on-Chip experiment where tumor cells are treated with chemotherapy drug and secrete chemical signals in the environment attracting multiple immune cell species. The in silico model here proposed goes towards the construction of a “digital twin” of the experimental immune cells in the chip environment to better understand the complex mechanisms of immunosurveillance. To this aim, we develop a tumor-immune microfluidic hybrid PDE–ODE model to describe the concentration of chemicals in the Cancer-on-Chip environment and immune cells migration. The development of a trustable simulation algorithm, able to reproduce the immunocompetent dynamics observed in the chip, requires an efficient tool for the calibration of the model parameters. In this respect, the present paper represents a first methodological work to test the feasibility and the soundness of the calibration technique here proposed, based on a multidimensional spline interpolation technique for the time-varying velocity field surfaces obtained from cell trajectories.

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Section: Results On Parameters Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation Algorithm for a Hybrid PDE–ODE Model Inspired by Immunocompetent Cancer-on-Chip Experiment

Bretti

Ninno

Natalini

et al. 2021

Axioms

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“…These models should recreate the physiological and mechanical cues typical of cancer microenvironment, recapitulating the relationship between tumor cells and TME and possibly reproducing sexual dimorphism [39]. Recent advances in three-dimensional (3D) modeling, also using chips and microfluidic devices [170,171], provide us with the opportunity of studying the biology of TME. Understanding the cellular and molecular mechanisms that govern the complex interactions between tumor cells and microenvironments could contribute to the progress of therapeutic strategies that encompass the biological complexity of a tumor by reducing the gap between translational research and clinical practice.…”

Section: Discussionmentioning

confidence: 99%

The Sex-Related Interplay between TME and Cancer: On the Critical Role of Estrogen, MicroRNAs and Autophagy

Matarrese

Mattia

Pagano

et al. 2021

Cancers

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The interplay between cancer cells and the tumor microenvironment (TME) has a fundamental role in tumor progression and response to therapy. The plethora of components constituting the TME, such as stroma, fibroblasts, endothelial and immune cells, as well as macromolecules, e.g., hormones and cytokines, and epigenetic factors, such as microRNAs, can modulate the survival or death of cancer cells. Actually, the TME can stimulate the genetically regulated programs that the cell puts in place under stress: apoptosis or, of interest here, autophagy. However, the implication of autophagy in tumor growth appears still undefined. Autophagy mainly represents a cyto-protective mechanism that allows cell survival but, in certain circumstances, also leads to the blocking of cell cycle progression, possibly leading to cell death. Since significant sex/gender differences in the incidence, progression and response to cancer therapy have been widely described in the literature, in this review, we analyzed the roles played by key components of the TME, e.g., estrogen and microRNAs, on autophagy regulation from a sex/gender-based perspective. We focused our attention on four paradigmatic and different forms of cancers—colon cancer, melanoma, lymphoma, and lung cancer—concluding that sex-specific differences may exert a significant impact on TME/cancer interaction and, thus, tumor growth.

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“…One aspect of the immune system that is of significance in microfluidic device development and has a potential of clinical importance is that of cancer-immune interactions ( Parlato et al, 2021 ). Cancer cells exhibit immune-evasive behaviors that can limit the ability of the immune system to recognize and eliminate cancer cells.…”

Section: Examples Of Mpsmentioning

confidence: 99%

Critical Considerations for the Design of Multi-Organ Microphysiological Systems (MPS)

Malik

Yang

Fathi

et al. 2021

Front. Cell Dev. Biol.

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Identification and approval of new drugs for use in patients requires extensive preclinical studies and clinical trials. Preclinical studies rely on in vitro experiments and animal models of human diseases. The transferability of drug toxicity and efficacy estimates to humans from animal models is being called into question. Subsequent clinical studies often reveal lower than expected efficacy and higher drug toxicity in humans than that seen in animal models. Microphysiological systems (MPS), sometimes called organ or human-on-chip models, present a potential alternative to animal-based models used for drug toxicity screening. This review discusses multi-organ MPS that can be used to model diseases and test the efficacy and safety of drug candidates. The translation of an in vivo environment to an in vitro system requires physiologically relevant organ scaling, vascular dimensions, and appropriate flow rates. Even small changes in those parameters can alter the outcome of experiments conducted with MPS. With many MPS devices being developed, we have outlined some established standards for designing MPS devices and described techniques to validate the devices. A physiologically realistic mimic of the human body can help determine the dose response and toxicity effects of a new drug candidate with higher predictive power.

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Tumor-on-a-chip platforms to study cancer–immune system crosstalk in the era of immunotherapy

Abstract: Immunotherapy is a powerful therapeutic approach able to re-educate the immune system to fight cancer. A key player in this process is the tumor microenvironment (TME), which is a dynamic...

Cited by 44 publications

References 89 publications

Estimation Algorithm for a Hybrid PDE–ODE Model Inspired by Immunocompetent Cancer-on-Chip Experiment

Estimation Algorithm for a Hybrid PDE–ODE Model Inspired by Immunocompetent Cancer-on-Chip Experiment

The Sex-Related Interplay between TME and Cancer: On the Critical Role of Estrogen, MicroRNAs and Autophagy

Critical Considerations for the Design of Multi-Organ Microphysiological Systems (MPS)

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