@article{Brammeld01042017,
author = {Brammeld, Jonathan S. and Petljak, Mia and Martincorena, Inigo and Williams, Steven P. and Alonso, Luz Garcia and Dalmases, Alba and Bellosillo, Beatriz and Robles-Espinoza, Carla Daniela and Price, Stacey and Barthorpe, Syd and Tarpey, Patrick and Alifrangis, Constantine and Bignell, Graham and Vidal, Joana and Young, Jamie and Stebbings, Lucy and Beal, Kathryn and Stratton, Michael R. and Saez-Rodriguez, Julio and Garnett, Mathew and Montagut, Clara and Iorio, Francesco and McDermott, Ultan}, 
title = {Genome-wide chemical mutagenesis screens allow unbiased saturation of the cancer genome and identification of drug resistance mutations},
volume = {27}, 
number = {4}, 
pages = {613-625}, 
year = {2017}, 
doi = {10.1101/gr.213546.116}, 
abstract ={Drug resistance is an almost inevitable consequence of cancer therapy and ultimately proves fatal for the majority of patients. In many cases, this is the consequence of specific gene mutations that have the potential to be targeted to resensitize the tumor. The ability to uniformly saturate the genome with point mutations without chromosome or nucleotide sequence context bias would open the door to identify all putative drug resistance mutations in cancer models. Here, we describe such a method for elucidating drug resistance mechanisms using genome-wide chemical mutagenesis allied to next-generation sequencing. We show that chemically mutagenizing the genome of cancer cells dramatically increases the number of drug-resistant clones and allows the detection of both known and novel drug resistance mutations. We used an efficient computational process that allows for the rapid identification of involved pathways and druggable targets. Such a priori knowledge would greatly empower serial monitoring strategies for drug resistance in the clinic as well as the development of trials for drug-resistant patients.}, 
URL = {http://genome.cshlp.org/content/27/4/613.abstract}, 
eprint = {http://genome.cshlp.org/content/27/4/613.full.pdf+html}, 
journal = {Genome Research} 
}