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

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

doi:10.1039/c8lc01387j

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

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

Medicine

Producción científica: Contribución a una revista › Artículo › revisión exhaustiva

42 Citas (Scopus)

Resumen

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. There are currently no interventions that shed light on the potential of primary tumors to metastasize to the brain. We constructed and tested a platform to quantitatively profile the dynamic phenotypes of cancer cells from aggressive triple negative breast cancer cell lines and patient derived xenografts (PDXs), generated from a primary tumor and brain metastases from tumors of diverse organs of origin. Combining an advanced live cell imaging algorithm and artificial intelligence, we profile cancer cell extravasation within a microfluidic blood-brain niche (μBBN) chip, to detect the minute differences between cells with brain metastatic potential and those without with a PPV of 0.91 in the context of this study. The results show remarkably sharp and reproducible distinction between cells that do and those which do not metastasize inside of the device.

Idioma original	English
Páginas (desde-hasta)	1162-1173
Número de páginas	12
Publicación	Lab on a Chip
Volumen	19
N.º	7
DOI	https://doi.org/10.1039/c8lc01387j
Estado	Published - abr. 7 2019

Nota bibliográfica

Funding Information:
Work was supported by: NIH T32CA009676 (CRO, MA), the University of Michigan Rogel Cancer Center Nancy Newton Loeb Fund (MA), by NIH grants P30CA046592 (BL, SDM, MA, CRO), CA196018 (CRO, ST), AI116482 (CRO, ST), by the META-vivor Foundation (MA, SDM) and the Breast Cancer Research Foundation (CRO, SDM).

Publisher Copyright:
© 2019 The Royal Society of Chemistry.

ASJC Scopus Subject Areas

Bioengineering
Biochemistry
General Chemistry
Biomedical Engineering

PubMed: MeSH publication types

Journal Article
Research Support, N.I.H., Extramural

ODS de las Naciones Unidas

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Oliver, C. R., Altemus, M. A., Westerhof, T. M., Cheriyan, H., Cheng, X., Dziubinski, M., Wu, Z., Yates, J., Morikawa, A., Heth, J., Castro, M. G., Leung, B. M., Takayama, S., & Merajver, S. D. (2019). A platform for artificial intelligence based identification of the extravasation potential of cancer cells into the brain metastatic niche. Lab on a Chip, 19(7), 1162-1173. https://doi.org/10.1039/c8lc01387j

Oliver, CR, Altemus, MA, Westerhof, TM, Cheriyan, H, Cheng, X, Dziubinski, M, Wu, Z, Yates, J, Morikawa, A, Heth, J, Castro, MG, Leung, BM, Takayama, S & Merajver, SD 2019, 'A platform for artificial intelligence based identification of the extravasation potential of cancer cells into the brain metastatic niche', Lab on a Chip, vol. 19, n.º 7, pp. 1162-1173. https://doi.org/10.1039/c8lc01387j

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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. There are currently no interventions that shed light on the potential of primary tumors to metastasize to the brain. We constructed and tested a platform to quantitatively profile the dynamic phenotypes of cancer cells from aggressive triple negative breast cancer cell lines and patient derived xenografts (PDXs), generated from a primary tumor and brain metastases from tumors of diverse organs of origin. Combining an advanced live cell imaging algorithm and artificial intelligence, we profile cancer cell extravasation within a microfluidic blood-brain niche (μBBN) chip, to detect the minute differences between cells with brain metastatic potential and those without with a PPV of 0.91 in the context of this study. The results show remarkably sharp and reproducible distinction between cells that do and those which do not metastasize inside of the device.",

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AU - Cheng, Xu

AU - Dziubinski, Michelle

AU - Wu, Zhifen

AU - Yates, Joel

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AU - Heth, Jason

AU - Castro, Maria G.

AU - Leung, Brendan M.

AU - Takayama, Shuichi

AU - Merajver, Sofia D.

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N2 - 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. There are currently no interventions that shed light on the potential of primary tumors to metastasize to the brain. We constructed and tested a platform to quantitatively profile the dynamic phenotypes of cancer cells from aggressive triple negative breast cancer cell lines and patient derived xenografts (PDXs), generated from a primary tumor and brain metastases from tumors of diverse organs of origin. Combining an advanced live cell imaging algorithm and artificial intelligence, we profile cancer cell extravasation within a microfluidic blood-brain niche (μBBN) chip, to detect the minute differences between cells with brain metastatic potential and those without with a PPV of 0.91 in the context of this study. The results show remarkably sharp and reproducible distinction between cells that do and those which do not metastasize inside of the device.

AB - 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. There are currently no interventions that shed light on the potential of primary tumors to metastasize to the brain. We constructed and tested a platform to quantitatively profile the dynamic phenotypes of cancer cells from aggressive triple negative breast cancer cell lines and patient derived xenografts (PDXs), generated from a primary tumor and brain metastases from tumors of diverse organs of origin. Combining an advanced live cell imaging algorithm and artificial intelligence, we profile cancer cell extravasation within a microfluidic blood-brain niche (μBBN) chip, to detect the minute differences between cells with brain metastatic potential and those without with a PPV of 0.91 in the context of this study. The results show remarkably sharp and reproducible distinction between cells that do and those which do not metastasize inside of the device.

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

Resumen

Nota bibliográfica

ASJC Scopus Subject Areas

PubMed: MeSH publication types

ODS de las Naciones Unidas

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