A heart-breast cancer-on-a-chip platform for disease modeling and monitoring of cardiotoxicity induced by cancer chemotherapy

Autor: Mehmet R. Dokmeci, Elaheh Zare-Eelanjegh, Su Ryon Shin, Alireza Akbarinejad, David Ge, Ronald A. Li, Yu Shrike Zhang, Aliza Rosenkranz, Raquel O. Rodrigues, Yongcong Fang, Ting Zhang, HeaYeon Lee, Shreya Mehrotra, Junmin Lee, Wendy Keung, Kiavash Kiaee, Ali Khademhosseini, Biman B. Mandal, Luca Amato
Přispěvatelé: Universidade do Minho
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Small
Repositório Científico de Acesso Aberto de Portugal
Repositório Científico de Acesso Aberto de Portugal (RCAAP)
instacron:RCAAP
Popis: Cardiotoxicity is one of the most serious side effects of cancer chemotherapy. Current approaches to monitoring of chemotherapy-induced cardiotoxicity (CIC) as well as model systems that develop in vivo or in vitro CIC platforms fail to notice early signs of CIC. Moreover, breast cancer (BC) patients with preexisting cardiac dysfunctions may lead to different incident levels of CIC. Here, a model is presented for investigating CIC where not only induced pluripotent stem cell (iPSC)-derived cardiac tissues are interacted with BC tissues on a dual-organ platform, but electrochemical immuno-aptasensors can also monitor cell-secreted multiple biomarkers. Fibrotic stages of iPSC-derived cardiac tissues are promoted with a supplement of transforming growth factor-beta 1 to assess the differential functionality in healthy and fibrotic cardiac tissues after treatment with doxorubicin (DOX). The production trend of biomarkers evaluated by using the immuno-aptasensors well-matches the outcomes from conventional enzyme-linked immunosorbent assay, demonstrating the accuracy of the authors' sensing platform with much higher sensitivity and lower detection limits for early monitoring of CIC and BC progression. Furthermore, the versatility of this platform is demonstrated by applying a nanoparticle-based DOX-delivery system. The proposed platform would potentially help allow early detection and prediction of CIC in individual patients in the future.
J.L., S.M., and E.Z. contributed equally to this work. This paper was sponsored by the Office of the Secretary of Defense and was accomplished under Agreement Number W911NF-17-3-003. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Office of the Secretary of Defense or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein. This research was funded in part by the Advanced Regenerative Manufacturing Institute, Inc. ("ARMI") through the above referenced agreement. The views and conclusions contained in this document are those of the authors and should not be interpreted as the views of ARMI. The authors gratefully acknowledge funding by the Center for Nanoscale systems (CNS) at Harvard university. S.M. acknowledges funding and support from Fulbright Nehru Doctoral Research Fellowship (FNDR), MHRD (India) and IIE (U.S.A.) to carry out the research work. E.Z. acknowledges Vahabzadeh scholarship in Switzerland. B.B.M. acknowledges generous funding from Department of Biotechnology (DBT) and Department of Science and Technology (DST), Government of India. Y.S.Z. acknowledges support by the National Institutes of Health (K99CA201603, R00CA201603, R21EB025270, R21EB026175, R01EB028143, R03EB027984, R21EB030257) and National Science Foundation (1936105). The authors also acknowledge BWH Neuroscience Department for Confocal Microscope Facility and thank Dr. Kiho Im and Kamyar Mehrabi for providing advices on the MATLAB code for analyzing the beating behaviors.
Databáze: OpenAIRE
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