scholarly journals The effect of 2D and 3D cell cultures on treatment response, EMT profile and stem cell features in head and neck cancer

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Styliani Melissaridou ◽  
Emilia Wiechec ◽  
Mustafa Magan ◽  
Mayur Vilas Jain ◽  
Man Ki Chung ◽  
...  
2017 ◽  
Vol 37 (5) ◽  
pp. 2201-2210 ◽  
Author(s):  
JAN HAGEMANN ◽  
CHRISTIAN JACOBI ◽  
MORITZ HAHN ◽  
VANESSA SCHMID ◽  
CHRISTIAN WELZ ◽  
...  

2014 ◽  
Vol 17 (6) ◽  
pp. 469-476 ◽  
Author(s):  
P. Pedicini ◽  
R. Caivano ◽  
A. Fiorentino ◽  
L. Strigari

Cells ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1707
Author(s):  
Larisa Goričan ◽  
Boris Gole ◽  
Uroš Potočnik

Cancer stem cells (CSCs), a rare cell population in tumors, are resistant to conventional chemotherapy and thus responsible for tumor recurrence. To screen for active compounds targeting CSCs, a good CSC-enriched model compatible with high-throughput screening (HTS) is needed. Here, we describe a new head and neck cancer stem cell-enriched spheroid model (SCESM) suitable for HTS analyses of anti-CSC compounds. We used FaDu cells, round-bottom ultra-low adherent (ULA) microplates, and stem medium. The formed spheroids displayed increased expression of all stem markers tested (qRT-PCR and protein analysis) in comparison to the FaDu cells grown in a standard adherent culture or in a well-known HTS-compatible multi-cellular tumor spheroid model (MCTS). Consistent with increased stemness of the cells in the spheroid, confocal microscopy detected fast proliferating cells only at the outer rim of the SCESM spheroids, with poorly/non-proliferating cells deeper in. To confirm the sensitivity of our model, we used ATRA treatment, which strongly reduced the expression of selected stem markers. Altogether, we developed a CSC-enriched spheroid model with a simple protocol, a microplate format compatible with multimodal detection systems, and a high detection signal, making it suitable for anti-CSC compounds’ HTS.


2020 ◽  
Vol 6 (1) ◽  
pp. FSO433 ◽  
Author(s):  
William T Tran ◽  
Harini Suraweera ◽  
Karina Quaioit ◽  
Daniel Cardenas ◽  
Kai X Leong ◽  
...  

Aim: We aimed to identify quantitative ultrasound (QUS)-radiomic markers to predict radiotherapy response in metastatic lymph nodes of head and neck cancer. Materials & methods: Node-positive head and neck cancer patients underwent pretreatment QUS imaging of their metastatic lymph nodes. Imaging features were extracted using the QUS spectral form, and second-order texture parameters. Machine-learning classifiers were used for predictive modeling, which included a logistic regression, naive Bayes, and k-nearest neighbor classifiers. Results: There was a statistically significant difference in the pretreatment QUS-radiomic parameters between radiological complete responders versus partial responders (p < 0.05). The univariable model that demonstrated the greatest classification accuracy included: spectral intercept (SI)-contrast (area under the curve = 0.741). Multivariable models were also computed and showed that the SI-contrast + SI-homogeneity demonstrated an area under the curve = 0.870. The three-feature model demonstrated that the spectral slope-correlation + SI-contrast + SI-homogeneity-predicted response with accuracy of 87.5%. Conclusion: Multivariable QUS-radiomic features of metastatic lymph nodes can predict treatment response a priori.


2016 ◽  
Vol 43 (5) ◽  
pp. 556-561 ◽  
Author(s):  
Young Min Park ◽  
Sei Young Lee ◽  
Suk Won Park ◽  
Se-Heon Kim

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