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      Therapy-Induced Evolution of Human Lung Cancer Revealed by Single-Cell RNA Sequencing

      research-article
      1 , 15 , 2 , 3 , 15 , 4 , 16 , 1 , 16 , 2 , 3 , 5 , 16 , 2 , 16 , 2 , 6 , 1 , 1 , 7 , 1 , 2 , 3 , 3 , 2 , 8 , 8 , 1 , 9 , 2 , 2 , 1 , 1 , 10 , 11 , 3 , 12 , 12 , 2 , 2 , 2 , 2 , 2 , 2 , 2 , 1 , 2 , 3 , 2 , 3 , 13 , 13 , 1 , 6 , 5 , 14 , 2 , 3 , , 1 , ∗∗ , 2 , 3 , 5 , 17 , ∗∗∗
      Cell
      Cell Press
      single-cell RNA sequencing, lung cancer, EGFR, ALK, targeted therapy

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          Summary

          Lung cancer, the leading cause of cancer mortality, exhibits heterogeneity that enables adaptability, limits therapeutic success, and remains incompletely understood. Single-cell RNA sequencing (scRNA-seq) of metastatic lung cancer was performed using 49 clinical biopsies obtained from 30 patients before and during targeted therapy. Over 20,000 cancer and tumor microenvironment (TME) single-cell profiles exposed a rich and dynamic tumor ecosystem. scRNA-seq of cancer cells illuminated targetable oncogenes beyond those detected clinically. Cancer cells surviving therapy as residual disease (RD) expressed an alveolar-regenerative cell signature suggesting a therapy-induced primitive cell-state transition, whereas those present at on-therapy progressive disease (PD) upregulated kynurenine, plasminogen, and gap-junction pathways. Active T-lymphocytes and decreased macrophages were present at RD and immunosuppressive cell states characterized PD. Biological features revealed by scRNA-seq were biomarkers of clinical outcomes in independent cohorts. This study highlights how therapy-induced adaptation of the multi-cellular ecosystem of metastatic cancer shapes clinical outcomes.

          Graphical Abstract

          Highlights

          • scRNA-seq is feasible in metastatic human NSCLCs and reveals a rich tumor ecosystem

          • Individual tumors and cancer cells exhibit substantial molecular diversity

          • Cancer and tumor microenvironment cells exhibit marked therapy-induced plasticity

          • scRNA-seq of metastatic NSCLCs unveils new opportunities to improve clinical outcomes

          Abstract

          Analysis of metastatic lung cancer biopsies before and after targeted therapy reveals molecular and immune adaptations that shape clinical outcomes.

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          Most cited references168

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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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            Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal.

            The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.
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              Comprehensive Integration of Single-Cell Data

              Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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                Author and article information

                Contributors
                Journal
                Cell
                Cell
                Cell
                Cell Press
                0092-8674
                1097-4172
                03 September 2020
                03 September 2020
                : 182
                : 5
                : 1232-1251.e22
                Affiliations
                [1 ]Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
                [2 ]Department of Medicine, University of California, San Francisco, San Francisco, CA 94158, USA
                [3 ]Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, USA
                [4 ]Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
                [5 ]Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
                [6 ]Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
                [7 ]Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
                [8 ]Department of Pathology University of California, San Francisco, San Francisco, CA 94143, USA
                [9 ]Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94143 USA
                [10 ]Denver Health Medical Center, Denver, CO 80204, USA
                [11 ]Department of Radiology, University of Colorado, Aurora, CO 80045, USA
                [12 ]Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA
                [13 ]Department of Surgery, University of California, San Francisco, CA 94143, USA
                [14 ]Howard Hughes Medical Institute, University of California, San Francisco, CA 94143, USA
                Author notes
                []Corresponding author collin.blakely@ 123456ucsf.edu
                [∗∗ ]Corresponding author spyros.darmanis@ 123456czbiohub.org
                [∗∗∗ ]Corresponding author trever.bivona@ 123456ucsf.edu
                [15]

                These authors contributed equally

                [16]

                These authors contributed equally

                [17]

                Lead Contact

                Article
                S0092-8674(20)30882-5
                10.1016/j.cell.2020.07.017
                7484178
                32822576
                909eaecb-da4d-4421-a0af-5d26e8e4cc9e
                © 2021 The Authors. Published by Elsevier Inc.

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 9 December 2019
                : 4 May 2020
                : 13 July 2020
                Categories
                Article

                Cell biology
                single-cell rna sequencing,lung cancer,egfr,alk,targeted therapy
                Cell biology
                single-cell rna sequencing, lung cancer, egfr, alk, targeted therapy

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