Tumour evolution inferred by single-cell sequencing
Nicholas Navin,Jude Kendall,Jennifer Troge,Peter Andrews,Linda Rodgers,Jeanne McIndoo,Kerry Cook,Asya Stepansky,Dan Levy,Diane Esposito,Lakshmi Muthuswamy,Alexander Krasnitz,W. Richard McCombie,James W. Hicks,Michael Wigler +14 more
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TLDR
It is shown that with flow-sorted nuclei, whole genome amplification and next generation sequencing the authors can accurately quantify genomic copy number within an individual nucleus and indicate that tumours grow by punctuated clonal expansions with few persistent intermediates.Abstract:
Genomic analysis provides insights into the role of copy number variation in disease, but most methods are not designed to resolve mixed populations of cells. In tumours, where genetic heterogeneity is common, very important information may be lost that would be useful for reconstructing evolutionary history. Here we show that with flow-sorted nuclei, whole genome amplification and next generation sequencing we can accurately quantify genomic copy number within an individual nucleus. We apply single-nucleus sequencing to investigate tumour population structure and evolution in two human breast cancer cases. Analysis of 100 single cells from a polygenomic tumour revealed three distinct clonal subpopulations that probably represent sequential clonal expansions. Additional analysis of 100 single cells from a monogenomic primary tumour and its liver metastasis indicated that a single clonal expansion formed the primary tumour and seeded the metastasis. In both primary tumours, we also identified an unexpectedly abundant subpopulation of genetically diverse 'pseudodiploid' cells that do not travel to the metastatic site. In contrast to gradual models of tumour progression, our data indicate that tumours grow by punctuated clonal expansions with few persistent intermediates.read more
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Ten years of next-generation sequencing technology.
TL;DR: An overview of the evolution of NGS is provided and the most significant improvements in sequencing technologies and library preparation protocols are discussed and the current landscape of N GS applications is explored to provide a perspective for future developments.
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The Life History of 21 Breast Cancers
Serena Nik-Zainal,Peter Van Loo,Peter Van Loo,Peter Van Loo,David C. Wedge,Ludmil B. Alexandrov,Christopher Greenman,Christopher Greenman,King Wai Lau,Keiran Raine,David T. Jones,John L. Marshall,Manasa Ramakrishna,Adam Shlien,Susanna L. Cooke,Jonathan Hinton,Andrew Menzies,Lucy Stebbings,Catherine Leroy,Mingming Jia,Richard Rance,Laura Mudie,Stephen J. Gamble,Philip J. Stephens,Stuart McLaren,Patrick S. Tarpey,Elli Papaemmanuil,Helen Davies,Ignacio Varela,David J. McBride,Graham R. Bignell,Kenric Leung,Adam Butler,Jon W. Teague,Sancha Martin,Göran Jönsson,Odette Mariani,Sandrine Boyault,Penelope Miron,Aquila Fatima,Anita Langerød,Samuel Aparicio,Andrew Tutt,Anieta M. Sieuwerts,Åke Borg,Gilles Thomas,Anne Vincent Salomon,Andrea L. Richardson,Anne Lise Børresen-Dale,Anne Lise Børresen-Dale,P. Andrew Futreal,Michael R. Stratton,Peter J. Campbell,Peter J. Campbell,Peter J. Campbell +54 more
TL;DR: Algorithms were developed to decipher this narrative and applied them to 21 breast cancers, finding that expansion of the dominant subclone to an appreciable mass may represent the final rate-limiting step in a breast cancer's development, triggering diagnosis.
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Evolution and Impact of Subclonal Mutations in Chronic Lymphocytic Leukemia
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TL;DR: Analysis of intratumoral heterogeneity in chronic lymphocytic leukemia cases uncovers patterns of clonal evolution in CLL, providing insights into its stepwise transformation, and links the presence of subclones with adverse clinical outcomes.
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Mapping the Mouse Cell Atlas by Microwell-Seq
Xiaoping Han,Renying Wang,Yincong Zhou,Lijiang Fei,Huiyu Sun,Shujing Lai,Assieh Saadatpour,Ziming Zhou,Haide Chen,Fang Ye,Daosheng Huang,Yang Xu,Wentao Huang,Mengmeng Jiang,Xinyi Jiang,Jie Mao,Yao Chen,Chenyu Lu,Jin Xie,Qun Fang,Yibin Wang,Rui Yue,Tiefeng Li,He Huang,Stuart H. Orkin,Guo-Cheng Yuan,Ming Chen,Guoji Guo +27 more
TL;DR: This study developed Microwell-seq, a high-throughput and low-cost scRNA-seq platform using simple, inexpensive devices, and built a web-based "single-cell MCA analysis" pipeline that accurately defines cell types based on single-cell digital expression.
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Lessons from the cancer genome.
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