A platform for artificial intelligence based identification of the extravasation potential of cancer cells into the brain metastatic niche

Oliver, C. Ryan; Altemus, Margaret; Westerhof, Trisha M.; Cheriyan, Hannah; Cheng, Xu; Dziubinski, Michelle; Wu, Zhifen; Yates, Joel A.; Morikawa, Aki; Heth, Jason; Castro, Maria G.; Leung, Brendan M.; Takayama, Shuichi; Merajver, Sofía D.

doi:10.1039/c8lc01387j

Cited by 38 publications

(36 citation statements)

References 37 publications

(41 reference statements)

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“…Thus, this metastatic tumor progression model was used for long-term cell co-culture at an increasing ratio from 1:9 to 9:1 in a liver-specific ECM, and partial functions of hepatocytes were maintained. Although metastasis-on-a-chip models were used to study the migration of cancer cells and offer a feasible platform for drug testing 25, 27, 40, the progression of post-metastasis tumor within an organ-specific ECM has not been studied. Therefore, we mimic the post-metastasis progression through co-cultures of various ratios of HepLL and Caki-1 cells in a liver-specific ECM.…”

Section: Discussionmentioning

confidence: 99%

Metastasis-on-a-chip mimicking the progression of kidney cancer in the liver for predicting treatment efficacy

Wang¹,

Wu²,

Wu³

et al. 2020

Theranostics

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Metastasis is one of the most important factors that lead to poor prognosis in cancer patients, and effective suppression of the growth of primary cancer cells in a metastatic site is paramount in averting cancer progression. However, there is a lack of biomimetic three-dimensional (3D) in vitro models that can closely mimic the continuous growth of metastatic cancer cells in an organ-specific extracellular microenvironment (ECM) for assessing effective therapeutic strategies.Methods: In this metastatic tumor progression model, kidney cancer cells (Caki-1) and hepatocytes (i.e., HepLL cells) were co-cultured at an increasing ratio from 1:9 to 9:1 in a decellularized liver matrix (DLM)/gelatin methacryloyl (GelMA)-based biomimetic liver microtissue in a microfluidic device.Results: Via this model, we successfully demonstrated a linear anti-cancer relationship between the concentration of anti-cancer drug 5-Fluorouracil (5-FU) and the percentage of Caki-1 cells in the co-culture system (R2 = 0.89). Furthermore, the Poly(lactide-co-glycolide) (PLGA)-poly(ethylene glycol) (PEG)-based delivery system showed superior efficacy to free 5-FU in killing Caki-1 cells.Conclusions: In this study, we present a novel 3D metastasis-on-a-chip model mimicking the progression of kidney cancer cells metastasized to the liver for predicting treatment efficacy. Taken together, our study proved that the tumor progression model based on metastasis-on-a-chip with organ-specific ECM would provide a valuable tool for rapidly assessing treatment regimens and developing new chemotherapeutic agents.

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Section: Discussionmentioning

confidence: 99%

Metastasis-on-a-chip mimicking the progression of kidney cancer in the liver for predicting treatment efficacy

Wang¹,

Wu²,

Wu³

et al. 2020

Theranostics

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“…slow-release painkillers and contraceptive injections or implants), or compounds produced by non-traditional methods such as synthetic biology or genetic engineering, could also be extensively assayed for unexpected side effects. Coupling these types of new molecular technologies with powerful computational modeling tools, including quantitative systems pharmacology (QSP) 131 , machine learning 13 , and artificial intelligence (AI) 132 , could offer novel and helpful insights for current toxicological assessment. For example, capecitabine and tegafur (anticancer prodrugs) have been shown to be effective in a multi-organ pneumatic pressure-driven platform 133 , and recently Boos et al 134 used a hanging-drop organoid system to test how products metabolized by human liver microtissues affect embryoid bodies.…”

Section: Readouts Of Vascular-related Toxicity May Be Critical For Therapeutics and Vascular Network Onmentioning

confidence: 99%

Organs-on-chips: into the next decade

Low

Mummery

Berridge

et al. 2020

Nat Rev Drug Discov

516

484

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Organs-on-chips (OoCs), also known as microphysiological systems or "tissue chips" (the terms are synonymous), have garnered substantial interest in recent years owing to their potential to be informative at multiple stages of the drug discovery and development process. These innovative devices could provide insights into normal human organ function and disease pathophysiology, as well as more accurately predict the safety and efficacy of investigational drugs in humans. Therefore, they are likely to become useful additions to traditional preclinical cell culture methods and in vivo animal studies in the near term, and in some cases, replacements for them in the longer term. In the last decade, the OoC field has seen dramatic advances in the sophistication of biology and engineering, in the demonstration of physiological relevance, and in the range of applications. These advances have also revealed new challenges and opportunities, and expertise from multiple biomedical and engineering fields will be needed to fully realize the promise of OoCs for fundamental and translational applications. This Review provides a snapshot of this fast-evolving technology, discusses current applications and caveats for their implementation, and offers suggestions for directions in the next decade.

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“…As illustrated by these representative studies, the microfluidics-based metastasis-on-chip approach could expand our fundamental knowledge of breast TME and enable a more accurate in vitro representation of breast cancer metastasis processes. In recent years, a myriad of studies has used similar microengineering principles to investigate the molecular mechanisms of breast cancer cell invasion ( Gioiella et al, 2016 ; Blaha et al, 2017 ; Truong et al, 2019 ; Yankaskas et al, 2019 ), intravasation ( Cui et al, 2017 ; Nagaraju et al, 2018 ; Shirure et al, 2018 ), extravasation ( Chen M. B. et al, 2016 ; Chen et al, 2017 ; Song et al, 2018 ; Boussommier-Calleja et al, 2019 ), breast cancer metastasis organotropism and metastasis niche formation ( Bersini et al, 2014 ; Wheeler et al, 2014 ; Jeon et al, 2015 ; Clark et al, 2016b ; Xu et al, 2016 ; Narkhede et al, 2017 ; Shumakovich et al, 2017 ; Marturano-Kruik et al, 2018 ; Mei et al, 2019 ; Oliver et al, 2019 ; Kim et al, 2020 ; Ribeiro et al, 2020 ; Tian et al, 2020 ).…”

Section: The Breast Tumor Microenvironmentmentioning

confidence: 99%

“…Due to the presence of BBB, targeting brain metastasis has been challenging. In a study conducted by Oliver et al (2019) , machine learning algorithms were trained to predict the metastatic potential of aggressive TNBC cell lines and patient-derived xenografts (PDX) across the BBB ( Oliver et al, 2019 ). This was attempted using a microfluidic blood-brain niche (μBBN) and confocal tomography for live-cell 3D imaging.…”

Section: The Brain Nichementioning

confidence: 99%

“…Furthermore, Microfluidic devices enable features to be extracted from dynamic biomarkers present in live cells as opposed to being limited to static biomarkers found in fixed cells ( Manak et al, 2018 ). For example, the previously mentioned study conducted by Oliver et al (2019) utilized a variety of common classification algorithms such as Neural Networks, Adaboost, and Random Forests to predict the potential of cells from normal and cancerous breast cell lines and PDX to migrate across the BBB ( Oliver et al, 2019 ). Currently, there is a lack of other such studies that apply AI techniques to make predictions based on microfluidic platforms mimicking the breast cancer TME.…”

Section: The Brain Nichementioning

confidence: 99%

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Biomimetic Microfluidic Platforms for the Assessment of Breast Cancer Metastasis

Sigdel

Gupta

Faizee

et al. 2021

Front. Bioeng. Biotechnol.

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Of around half a million women dying of breast cancer each year, more than 90% die due to metastasis. Models necessary to understand the metastatic process, particularly breast cancer cell extravasation and colonization, are currently limited and urgently needed to develop therapeutic interventions necessary to prevent breast cancer metastasis. Microfluidic approaches aim to reconstitute functional units of organs that cannot be modeled easily in traditional cell culture or animal studies by reproducing vascular networks and parenchyma on a chip in a three-dimensional, physiologically relevant in vitro system. In recent years, microfluidics models utilizing innovative biomaterials and micro-engineering technologies have shown great potential in our effort of mechanistic understanding of the breast cancer metastasis cascade by providing 3D constructs that can mimic in vivo cellular microenvironment and the ability to visualize and monitor cellular interactions in real-time. In this review, we will provide readers with a detailed discussion on the application of the most up-to-date, state-of-the-art microfluidics-based breast cancer models, with a special focus on their application in the engineering approaches to recapitulate the metastasis process, including invasion, intravasation, extravasation, breast cancer metastasis organotropism, and metastasis niche formation.

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A platform for artificial intelligence based identification of the extravasation potential of cancer cells into the brain metastatic niche

Abstract: Brain metastases are the most lethal complication of advanced cancer; therefore, it is critical to identify when a tumor has the potential to metastasize to the brain.

Cited by 38 publications

References 37 publications

Metastasis-on-a-chip mimicking the progression of kidney cancer in the liver for predicting treatment efficacy

Metastasis-on-a-chip mimicking the progression of kidney cancer in the liver for predicting treatment efficacy

Organs-on-chips: into the next decade

Biomimetic Microfluidic Platforms for the Assessment of Breast Cancer Metastasis

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