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      Tumor cells can follow distinct evolutionary paths to become resistant to epidermal growth factor receptor inhibition

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          Abstract

          Although mechanisms of acquired resistance of EGFR mutant non-small cell lung cancers to EGFR inhibitors have been identified, little is known about how resistant clones evolve during drug therapy. Here, we observe that acquired resistance caused by the T790M gatekeeper mutation can occur either by selection of pre-existing T790M clones or via genetic evolution of initially T790M-negative drug tolerant cells. The path to resistance impacts the biology of the resistant clone, as those that evolved from drug tolerant cells had a diminished apoptotic response to third generation EGFR inhibitors that target T790M EGFR; treatment with navitoclax, an inhibitor of BCL-XL and BCL-2 restored sensitivity. We corroborated these findings using cultures derived directly from EGFR inhibitor-resistant patient tumors. These findings provide evidence that clinically relevant drug resistant cancer cells can both pre-exist and evolve from drug tolerant cells, and point to therapeutic opportunities to prevent or overcome resistance in the clinic.

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          Most cited references 57

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            Is Open Access

            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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              STAR: ultrafast universal RNA-seq aligner.

              Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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                Author and article information

                Journal
                9502015
                8791
                Nat Med
                Nat. Med.
                Nature medicine
                1078-8956
                1546-170X
                7 January 2016
                01 February 2016
                March 2016
                01 August 2016
                : 22
                : 3
                : 262-269
                Affiliations
                [1 ]Massachusetts General Hospital Cancer Center, Charlestown, MA, USA
                [2 ]Department of Medicine, Harvard Medical School, Boston, MA, USA
                [3 ]Broad Institute of MIT and Harvard, Cambridge, MA, USA
                [4 ]Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
                [5 ]Oncology Disease Area, Novartis Institutes for Biomedical Research, Cambridge, MA, USA
                [6 ]University of Torino, Department of Oncology, Torino, Italy
                [7 ]Candiolo Cancer Institute - Fondazione Piemontese per l’Oncologia (FPO), Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Candiolo, Torino, Italy
                [8 ]Translational Clinical Oncology, Novartis Institutes for Biomedical Research, Cambridge, MA, USA
                [9 ]Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
                [10 ]Department of Pathology, Harvard Medical School, Boston, MA, USA
                [11 ]Virginia Commonwealth University Philips Institute for Oral Health Research, School of Dentistry and Massey Cancer Center, Richmond, VA, USA
                Author notes
                Address correspondence to: Jeffrey A. Engelman, jengelman@ 123456partners.org
                [*]

                These authors contributed equally to this work

                Article
                NIHMS748245
                10.1038/nm.4040
                4900892
                26828195

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