Predictive biomarkers for personalised anti-cancer drug use: Discovery to clinical implementation

2010 ◽  
Vol 46 (5) ◽  
pp. 869-879 ◽  
Author(s):  
Nayef A. Alymani ◽  
Murray D. Smith ◽  
David J. Williams ◽  
Russell D. Petty
2020 ◽  
Vol 11 (1) ◽  
Author(s):  
JungHo Kong ◽  
Heetak Lee ◽  
Donghyo Kim ◽  
Seong Kyu Han ◽  
Doyeon Ha ◽  
...  

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.


2010 ◽  
Vol 150 ◽  
pp. 106-106
Author(s):  
S. Angelini ◽  
E. Turrini ◽  
G. Ravegnini ◽  
S. Soverini ◽  
G. Martinelli ◽  
...  
Keyword(s):  
Drug Use ◽  

2015 ◽  
Vol 61 (12) ◽  
pp. 1457-1465 ◽  
Author(s):  
Michelle C Lowry ◽  
William M Gallagher ◽  
Lorraine O'Driscoll

Abstract BACKGROUND Although it has been long realized that eukaryotic cells release complex vesicular structures into their environment, only in recent years has it been established that these entities are not merely junk or debris, but that they are tailor-made specialized minimaps of their cell of origin and of both physiological and pathological relevance. These exosomes and microvesicles (ectosomes), collectively termed extracellular vesicles (EVs), are often defined and subgrouped first and foremost according to size and proposed origin (exosomes approximately 30–120 nm, endosomal origin; microvesicles 120–1000 nm, from the cell membrane). There is growing interest in elucidating the relevance and roles of EVs in cancer. CONTENT Much of the pioneering work on EVs in cancer has focused on breast cancer, possibly because breast cancer is a leading cause of cancer-related deaths worldwide. This review provides an in-depth summary of such studies, supporting key roles for exosomes and other EVs in breast cancer cell invasion and metastasis, stem cell stimulation, apoptosis, immune system modulation, and anti–cancer drug resistance. Exosomes as diagnostic, prognostic, and/or predictive biomarkers and their potential use in the development of therapeutics are discussed. SUMMARY Although not fully elucidated, the involvement of exosomes in breast cancer development, progression, and resistance is becoming increasingly apparent from preclinical and clinical studies, with mounting interest in the potential exploitation of these vesicles for breast cancer biomarkers, as drug delivery systems, and in the development of future novel breast cancer therapies.


2010 ◽  
Vol 4 (5-6) ◽  
Author(s):  
M Asghar ◽  
G Murtaza ◽  
M Ashraf ◽  
Y Luqman ◽  
M Asgher ◽  
...  

1994 ◽  
Vol 161 (10) ◽  
pp. 638-638 ◽  
Author(s):  
Michael Friedlander ◽  
Craig Lewis ◽  
David Goldstein
Keyword(s):  
Drug Use ◽  

Xenobiotica ◽  
2009 ◽  
Vol 00 (00) ◽  
pp. 090901052053001-8
Author(s):  
K. Murai ◽  
H. Yamazaki ◽  
K. Nakagawa ◽  
R. Kawai ◽  
T. Kamataki

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