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      Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance

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          Abstract

          Therapies targeting signaling molecules mutated in cancers can often have striking short-term effects, but the emergence of resistant cancer cells is a major barrier to full cures 1, 2 . Resistance can result from a secondary mutations 3, 4 , but other times there is no clear genetic cause, raising the possibility of non-genetic rare cell variability 511 . Here, we show that melanoma cells can display profound transcriptional variability at the single cell level that predicts which cells will ultimately resist drug treatment. This variability involves infrequent, semi-coordinated transcription of a number of resistance markers at high levels in a very small percentage of cells. The addition of drug then induces epigenetic reprogramming in these cells, converting the transient transcriptional state to a stably resistant state. This reprogramming begins with a loss of SOX10-mediated differentiation followed by activation of new signaling pathways, partially mediated by activity of Jun-AP-1 and TEAD. Our work reveals the multistage nature of the acquisition of drug resistance and provides a framework for understanding resistance dynamics in single cells. We find that other cell types also exhibit sporadic expression of many of these same marker genes, suggesting the existence of a general rare-cell expression program.

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          Most cited references 34

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          Is Open Access

          Fast and accurate short read alignment with Burrows–Wheeler transform

          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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            An integrated map of genetic variation from 1,092 human genomes

            Summary Through characterising the geographic and functional spectrum of human genetic variation, the 1000 Genomes Project aims to build a resource to help understand the genetic contribution to disease. We describe the genomes of 1,092 individuals from 14 populations, constructed using a combination of low-coverage whole-genome and exome sequencing. By developing methodologies to integrate information across multiple algorithms and diverse data sources we provide a validated haplotype map of 38 million SNPs, 1.4 million indels and over 14 thousand larger deletions. We show that individuals from different populations carry different profiles of rare and common variants and that low-frequency variants show substantial geographic differentiation, which is further increased by the action of purifying selection. We show that evolutionary conservation and coding consequence are key determinants of the strength of purifying selection, that rare-variant load varies substantially across biological pathways and that each individual harbours hundreds of rare non-coding variants at conserved sites, such as transcription-factor-motif disrupting changes. This resource, which captures up to 98% of accessible SNPs at a frequency of 1% in populations of medical genetics focus, enables analysis of common and low-frequency variants in individuals from diverse, including admixed, populations.
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              Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples

              Detection of somatic point substitutions is a key step in characterizing the cancer genome. Mutations in cancer are rare (0.1–100/Mb) and often occur only in a subset of the sequenced cells, either due to contamination by normal cells or due to tumor heterogeneity. Consequently, mutation calling methods need to be both specific, avoiding false positives, and sensitive to detect clonal and sub-clonal mutations. The decreased sensitivity of existing methods for low allelic fraction mutations highlights the pressing need for improved and systematically evaluated mutation detection methods. Here we present MuTect, a method based on a Bayesian classifier designed to detect somatic mutations with very low allele-fractions, requiring only a few supporting reads, followed by a set of carefully tuned filters that ensure high specificity. We also describe novel benchmarking approaches, which use real sequencing data to evaluate the sensitivity and specificity as a function of sequencing depth, base quality and allelic fraction. Compared with other methods, MuTect has higher sensitivity with similar specificity, especially for mutations with allelic fractions as low as 0.1 and below, making MuTect particularly useful for studying cancer subclones and their evolution in standard exome and genome sequencing data.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                16 May 2017
                07 June 2017
                15 June 2017
                07 December 2017
                : 546
                : 7658
                : 431-435
                Affiliations
                [1 ]Department of Bioengineering, University of Pennsylvania, Melanoma Research Center, Philadelphia, PA
                [2 ]Department of Biochemistry, University of Pennsylvania, Melanoma Research Center, Philadelphia, PA
                [3 ]The Wistar Institute, Molecular and Cellular Oncogenesis Program, Melanoma Research Center, Philadelphia, PA
                [4 ]Perelman School of Medicine, University of Pennsylvania, Newark, DE, USA
                [5 ]Biomedical Engineering, University of Delaware, Newark, DE, USA
                [6 ]Electrical and Computer Engineering, University of Delaware, Newark, DE, USA
                [7 ]Department of Genetics, University of Pennsylvania, University of Pennsylvania
                [8 ]Genomics and Computational Biology Group, University of Pennsylvania
                Article
                NIHMS871454
                10.1038/nature22794
                5542814
                28607484

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