Application of a Microfluidic-Based Three-Dimensional Culture Device for Testing Anti-Cancer Drug Sensitivity

2015 ◽  
Vol 119 (3) ◽  
pp. e211-e212
Author(s):  
Dong Jin ◽  
Yong Luo ◽  
Bingchen Lin ◽  
Tingjiao Liu
Keyword(s):  
Drug Sensitivity ◽  
Three Dimensional ◽  
Cancer Drug ◽  
Anti Cancer ◽  
Three Dimensional Culture

scholarly journals In Situ Vitrification of Lung Cancer Organoids on a Microwell Array

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 624
Author(s):  
Qiang Liu ◽  
Tian Zhao ◽  
Xianning Wang ◽  
Zhongyao Chen ◽  
Yawei Hu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Drug Sensitivity ◽  
Simple Procedure ◽  
Three Dimensional ◽  
Cancer Drug ◽  
Microwell Array ◽  
Sensitivity Tests ◽  
Anti Cancer

Three-dimensional cultured patient-derived cancer organoids (PDOs) represent a powerful tool for anti-cancer drug development due to their similarity to the in vivo tumor tissues. However, the culture and manipulation of PDOs is more difficult than 2D cultured cell lines due to the presence of the culture matrix and the 3D feature of the organoids. In our other study, we established a method for lung cancer organoid (LCO)-based drug sensitivity tests on the superhydrophobic microwell array chip (SMAR-chip). Here, we describe a novel in situ cryopreservation technology on the SMAR-chip to preserve the viability of the organoids for future drug sensitivity tests. We compared two cryopreservation approaches (slow freezing and vitrification) and demonstrated that vitrification performed better at preserving the viability of LCOs. Next, we developed a simple procedure for in situ cryopreservation and thawing of the LCOs on the SMAR-chip. We proved that the on-chip cryopreserved organoids can be recovered successfully and, more importantly, showing similar responses to anti-cancer drugs as the unfrozen controls. This in situ vitrification technology eliminated the harvesting and centrifugation steps in conventional cryopreservation, making the whole freeze–thaw process easier to perform and the preserved LCOs ready to be used for the subsequent drug sensitivity test.

Prevascularized, Co-Culture Model for Breast Cancer Drug Development

2012 ◽  
Author(s):  
Lauren Marshall ◽  
Isabel Löwstedt ◽  
Paul Gatenholm ◽  
Joel Berry
Keyword(s):  
Breast Cancer ◽  
Drug Development ◽  
Three Dimensional ◽  
Human Tumor ◽  
3D Models ◽  
Cancer Drug ◽  
Cancer Therapies ◽  
Anti Cancer

The objective of this study was to create 3D engineered tissue models to accelerate identification of safe and efficacious breast cancer drug therapies. It is expected that this platform will dramatically reduce the time and costs associated with development and regulatory approval of anti-cancer therapies, currently a multi-billion dollar endeavor [1]. Existing two-dimensional (2D) in vitro and in vivo animal studies required for identification of effective cancer therapies account for much of the high costs of anti-cancer medications and health insurance premiums borne by patients, many of whom cannot afford it. An emerging paradigm in pharmaceutical drug development is the use of three-dimensional (3D) cell/biomaterial models that will accurately screen novel therapeutic compounds, repurpose existing compounds and terminate ineffective ones. In particular, identification of effective chemotherapies for breast cancer are anticipated to occur more quickly in 3D in vitro models than 2D in vitro environments and in vivo animal models, neither of which accurately mimic natural human tumor environments [2]. Moreover, these 3D models can be multi-cellular and designed with extracellular matrix (ECM) function and mechanical properties similar to that of natural in vivo cancer environments [3].

Abstract 5138: Inhibition of kPNB1 and NUPR1 binding increase the anti-cancer drug sensitivity

2019 ◽  
Author(s):  
Chan Hee Park ◽  
Seung Wook Ham ◽  
HyeKyoung Shin ◽  
Kyung Soo Oh
Keyword(s):  
Drug Sensitivity ◽  
Cancer Drug ◽  
Anti Cancer

scholarly journals Development of a High-Throughput Three-Dimensional Invasion Assay for Anti-Cancer Drug Discovery

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e82811 ◽  
Author(s):  
Nikki A. Evensen ◽  
Jian Li ◽  
Jie Yang ◽  
Xiaojun Yu ◽  
Nicole S. Sampson ◽  
...  
Keyword(s):  
Drug Discovery ◽  
High Throughput ◽  
Three Dimensional ◽  
Cancer Drug ◽  
Invasion Assay ◽  
Cancer Drug Discovery ◽  
Anti Cancer

scholarly journals Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
JungHo Kong ◽  
Heetak Lee ◽  
Donghyo Kim ◽  
Seong Kyu Han ◽  
Doyeon Ha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cancer Patient ◽  
Cancer Patients ◽  
Three Dimensional ◽  
Predictive Biomarkers ◽  
Drug Efficacy ◽  
Cancer Drug ◽  
Drug Responses ◽  
Anti Cancer ◽  
Culture Models

Abstract Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational biomarkers from preclinical models. Here, we present a machine-learning framework to identify robust drug biomarkers by taking advantage of network-based analyses using pharmacogenomic data derived from three-dimensional organoid culture models. The biomarkers identified by our approach accurately predict the drug responses of 114 colorectal cancer patients treated with 5-fluorouracil and 77 bladder cancer patients treated with cisplatin. We further confirm our biomarkers using external transcriptomic datasets of drug-sensitive and -resistant isogenic cancer cell lines. Finally, concordance analysis between the transcriptomic biomarkers and independent somatic mutation-based biomarkers further validate our method. This work presents a method to predict cancer patient drug responses using pharmacogenomic data derived from organoid models by combining the application of gene modules and network-based approaches.

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