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Andrew A. Lane

Other affiliations: Broad Institute, Vanderbilt University, University of Washington  ...read more
Bio: Andrew A. Lane is an academic researcher from Harvard University. The author has contributed to research in topics: Leukemia & Myeloid leukemia. The author has an hindex of 29, co-authored 96 publications receiving 7433 citations. Previous affiliations of Andrew A. Lane include Broad Institute & Vanderbilt University.


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Journal ArticleDOI
08 May 2019-Nature
TL;DR: The original Cancer Cell Line Encyclopedia is expanded with deeper characterization of over 1,000 cell lines, including genomic, transcriptomic, and proteomic data, and integration with drug-sensitivity and gene-dependency data, which reveals potential targets for cancer drugs and associated biomarkers.
Abstract: Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous framework with which to study genetic variants, candidate targets, and small-molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes, including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from individuals of various lineages and ethnicities. Integration of these data with functional characterizations such as drug-sensitivity, short hairpin RNA knockdown and CRISPR-Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource for the acceleration of cancer research using model cancer cell lines.

1,801 citations

Journal ArticleDOI
21 Apr 2017-Science
TL;DR: This refined analysis has identified, among others, a previously unknown dendritic cell population that potently activates T cells and reclassify pDCs as the originally described “natural interferon-producing cells (IPCs)” with weaker T cell proliferation induction ability.
Abstract: INTRODUCTION Dendritic cells (DCs) and monocytes consist of multiple specialized subtypes that play a central role in pathogen sensing, phagocytosis, and antigen presentation. However, their identities and interrelationships are not fully understood, as these populations have historically been defined by a combination of morphology, physical properties, localization, functions, developmental origins, and expression of a restricted set of surface markers. RATIONALE To overcome this inherently biased strategy for cell identification, we performed single-cell RNA sequencing of ~2400 cells isolated from healthy blood donors and enriched for HLA-DR + lineage − cells. This single-cell profiling strategy and unbiased genomic classification, together with follow-up profiling and functional and phenotypic characterization of prospectively isolated subsets, led us to identify and validate six DC subtypes and four monocyte subtypes, and thus revise the taxonomy of these cells. RESULTS Our study reveals: 1) A new DC subset, representing 2 to 3% of the DC populations across all 10 donors tested, characterized by the expression of AXL , SIGLEC1 , and SIGLEC6 antigens, named AS DCs. The AS DC population further divides into two populations captured in the traditionally defined plasmacytoid DC (pDC) and CD1C + conventional DC (cDC) gates. This split is further reflected through AS DC gene expression signatures spanning a spectrum between cDC-like and pDC-like gene sets. Although AS DCs share properties with pDCs, they more potently activate T cells. This discovery led us to reclassify pDCs as the originally described “natural interferon-producing cells (IPCs)” with weaker T cell proliferation induction ability. 2) A new subdivision within the CD1C + DC subset: one defined by a major histocompatibility complex class II–like gene set and one by a CD14 + monocyte–like prominent gene set. These CD1C + DC subsets, which can be enriched by combining CD1C with CD32B, CD36, and CD163 antigens, can both potently induce T cell proliferation. 3) The existence of a circulating and dividing cDC progenitor giving rise to CD1C + and CLEC9A + DCs through in vitro differentiation assays. This blood precursor is defined by the expression of CD100 + CD34 int and observed at a frequency of ~0.02% of the LIN – HLA-DR + fraction. 4) Two additional monocyte populations: one expressing classical monocyte genes and cytotoxic genes, and the other with unknown functions. 5) Evidence for a relationship between blastic plasmacytoid DC neoplasia (BPDCN) cells and healthy DCs. CONCLUSION Our revised taxonomy will enable more accurate functional and developmental analyses as well as immune monitoring in health and disease. The discovery of AS DCs within the traditionally defined pDC population explains many of the cDC properties previously assigned to pDCs, highlighting the need to revisit the definition of pDCs. Furthermore, the discovery of blood cDC progenitors represents a new therapeutic target readily accessible in the bloodstream for manipulation, as well as a new source for better in vitro DC generation. Although the current results focus on DCs and monocytes, a similar strategy can be applied to build a comprehensive human immune cell atlas.

1,468 citations

01 Apr 2017
TL;DR: In this paper, the authors performed single-cell RNA sequencing of ~2400 cells isolated from healthy blood donors and enriched for HLA-DR + lineage − cells, which led them to identify and validate six Dendritic cells (DCs) and four monocyte subtypes.
Abstract: INTRODUCTION Dendritic cells (DCs) and monocytes consist of multiple specialized subtypes that play a central role in pathogen sensing, phagocytosis, and antigen presentation. However, their identities and interrelationships are not fully understood, as these populations have historically been defined by a combination of morphology, physical properties, localization, functions, developmental origins, and expression of a restricted set of surface markers. RATIONALE To overcome this inherently biased strategy for cell identification, we performed single-cell RNA sequencing of ~2400 cells isolated from healthy blood donors and enriched for HLA-DR + lineage − cells. This single-cell profiling strategy and unbiased genomic classification, together with follow-up profiling and functional and phenotypic characterization of prospectively isolated subsets, led us to identify and validate six DC subtypes and four monocyte subtypes, and thus revise the taxonomy of these cells. RESULTS Our study reveals: 1) A new DC subset, representing 2 to 3% of the DC populations across all 10 donors tested, characterized by the expression of AXL , SIGLEC1 , and SIGLEC6 antigens, named AS DCs. The AS DC population further divides into two populations captured in the traditionally defined plasmacytoid DC (pDC) and CD1C + conventional DC (cDC) gates. This split is further reflected through AS DC gene expression signatures spanning a spectrum between cDC-like and pDC-like gene sets. Although AS DCs share properties with pDCs, they more potently activate T cells. This discovery led us to reclassify pDCs as the originally described “natural interferon-producing cells (IPCs)” with weaker T cell proliferation induction ability. 2) A new subdivision within the CD1C + DC subset: one defined by a major histocompatibility complex class II–like gene set and one by a CD14 + monocyte–like prominent gene set. These CD1C + DC subsets, which can be enriched by combining CD1C with CD32B, CD36, and CD163 antigens, can both potently induce T cell proliferation. 3) The existence of a circulating and dividing cDC progenitor giving rise to CD1C + and CLEC9A + DCs through in vitro differentiation assays. This blood precursor is defined by the expression of CD100 + CD34 int and observed at a frequency of ~0.02% of the LIN – HLA-DR + fraction. 4) Two additional monocyte populations: one expressing classical monocyte genes and cytotoxic genes, and the other with unknown functions. 5) Evidence for a relationship between blastic plasmacytoid DC neoplasia (BPDCN) cells and healthy DCs. CONCLUSION Our revised taxonomy will enable more accurate functional and developmental analyses as well as immune monitoring in health and disease. The discovery of AS DCs within the traditionally defined pDC population explains many of the cDC properties previously assigned to pDCs, highlighting the need to revisit the definition of pDCs. Furthermore, the discovery of blood cDC progenitors represents a new therapeutic target readily accessible in the bloodstream for manipulation, as well as a new source for better in vitro DC generation. Although the current results focus on DCs and monocytes, a similar strategy can be applied to build a comprehensive human immune cell atlas.

1,306 citations

Journal ArticleDOI
TL;DR: Holder deacetylase inhibitors are an important emerging therapy with single-agent activity against multiple cancers, and have significant potential in combination use.
Abstract: Purpose Epigenetic processes are implicated in cancer causation and progression. The acetylation status of histones regulates access of transcription factors to DNA and influences levels of gene expression. Histone deacetylase (HDAC) activity diminishes acetylation of histones, causing compaction of the DNA/histone complex. This compaction blocks gene transcription and inhibits differentiation, providing a rationale for developing HDAC inhibitors. Methods In this review, we explore the biology of the HDAC enzymes, summarize the pharmacologic properties of HDAC inhibitors, and examine results of selected clinical trials. We consider the potential of these inhibitors in combination therapy with targeted drugs and with cytotoxic chemotherapy. Results HDAC inhibitors promote growth arrest, differentiation, and apoptosis of tumor cells, with minimal effects on normal tissue. In addition to decompaction of the histone/DNA complex, HDAC inhibition also affects acetylation status and function of nonhistone protei...

852 citations


Cited by
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Journal ArticleDOI
TL;DR: An analytical strategy for integrating scRNA-seq data sets based on common sources of variation is introduced, enabling the identification of shared populations across data sets and downstream comparative analysis.
Abstract: Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

7,741 citations

Journal ArticleDOI
Timothy J. Ley1, Christopher A. Miller1, Li Ding1, Benjamin J. Raphael2, Andrew J. Mungall3, Gordon Robertson3, Katherine A. Hoadley4, Timothy J. Triche5, Peter W. Laird5, Jack Baty1, Lucinda Fulton1, Robert S. Fulton1, Sharon Heath1, Joelle Kalicki-Veizer1, Cyriac Kandoth1, Jeffery M. Klco1, Daniel C. Koboldt1, Krishna L. Kanchi1, Shashikant Kulkarni1, Tamara Lamprecht1, David E. Larson1, G. Lin1, Charles Lu1, Michael D. McLellan1, Joshua F. McMichael1, Jacqueline E. Payton1, Heather Schmidt1, David H. Spencer1, Michael H. Tomasson1, John W. Wallis1, Lukas D. Wartman1, Mark A. Watson1, John S. Welch1, Michael C. Wendl1, Adrian Ally3, Miruna Balasundaram3, Inanc Birol3, Yaron S.N. Butterfield3, Readman Chiu3, Andy Chu3, Eric Chuah3, Hye Jung E. Chun3, Richard Corbett3, Noreen Dhalla3, Ranabir Guin3, An He3, Carrie Hirst3, Martin Hirst3, Robert A. Holt3, Steven J.M. Jones3, Aly Karsan3, Darlene Lee3, Haiyan I. Li3, Marco A. Marra3, Michael Mayo3, Richard A. Moore3, Karen Mungall3, Jeremy Parker3, Erin Pleasance3, Patrick Plettner3, Jacquie Schein3, Dominik Stoll3, Lucas Swanson3, Angela Tam3, Nina Thiessen3, Richard Varhol3, Natasja Wye3, Yongjun Zhao3, Stacey Gabriel6, Gad Getz6, Carrie Sougnez6, Lihua Zou6, Mark D.M. Leiserson2, Fabio Vandin2, Hsin-Ta Wu2, Frederick Applebaum7, Stephen B. Baylin8, Rehan Akbani9, Bradley M. Broom9, Ken Chen9, Thomas C. Motter9, Khanh Thi-Thuy Nguyen9, John N. Weinstein9, Nianziang Zhang9, Martin L. Ferguson, Christopher Adams10, Aaron D. Black10, Jay Bowen10, Julie M. Gastier-Foster10, Thomas Grossman10, Tara M. Lichtenberg10, Lisa Wise10, Tanja Davidsen11, John A. Demchok11, Kenna R. Mills Shaw11, Margi Sheth11, Heidi J. Sofia, Liming Yang11, James R. Downing, Greg Eley, Shelley Alonso12, Brenda Ayala12, Julien Baboud12, Mark Backus12, Sean P. Barletta12, Dominique L. Berton12, Anna L. Chu12, Stanley Girshik12, Mark A. Jensen12, Ari B. Kahn12, Prachi Kothiyal12, Matthew C. Nicholls12, Todd Pihl12, David Pot12, Rohini Raman12, Rashmi N. Sanbhadti12, Eric E. Snyder12, Deepak Srinivasan12, Jessica Walton12, Yunhu Wan12, Zhining Wang12, Jean Pierre J. Issa13, Michelle M. Le Beau14, Martin Carroll15, Hagop M. Kantarjian, Steven M. Kornblau, Moiz S. Bootwalla5, Phillip H. Lai5, Hui Shen5, David Van Den Berg5, Daniel J. Weisenberger5, Daniel C. Link1, Matthew J. Walter1, Bradley A. Ozenberger11, Elaine R. Mardis1, Peter Westervelt1, Timothy A. Graubert1, John F. DiPersio1, Richard K. Wilson1 
TL;DR: It is found that a complex interplay of genetic events contributes to AML pathogenesis in individual patients and the databases from this study are widely available to serve as a foundation for further investigations of AMl pathogenesis, classification, and risk stratification.
Abstract: BACKGROUND—Many mutations that contribute to the pathogenesis of acute myeloid leukemia (AML) are undefined The relationships between patterns of mutations and epigenetic phenotypes are not yet clear METHODS—We analyzed the genomes of 200 clinically annotated adult cases of de novo AML, using either whole-genome sequencing (50 cases) or whole-exome sequencing (150 cases), along with RNA and microRNA sequencing and DNA-methylation analysis RESULTS—AML genomes have fewer mutations than most other adult cancers, with an average of only 13 mutations found in genes Of these, an average of 5 are in genes that are recurrently mutated in AML A total of 23 genes were significantly mutated, and another 237 were mutated in two or more samples Nearly all samples had at least 1 nonsynonymous mutation in one of nine categories of genes that are almost certainly relevant for pathogenesis, including transcriptionfactor fusions (18% of cases), the gene encoding nucleophosmin (NPM1) (27%), tumorsuppressor genes (16%), DNA-methylation–related genes (44%), signaling genes (59%), chromatin-modifying genes (30%), myeloid transcription-factor genes (22%), cohesin-complex genes (13%), and spliceosome-complex genes (14%) Patterns of cooperation and mutual exclusivity suggested strong biologic relationships among several of the genes and categories CONCLUSIONS—We identified at least one potential driver mutation in nearly all AML samples and found that a complex interplay of genetic events contributes to AML pathogenesis in individual patients The databases from this study are widely available to serve as a foundation for further investigations of AML pathogenesis, classification, and risk stratification (Funded by the National Institutes of Health) The molecular pathogenesis of acute myeloid leukemia (AML) has been studied with the use of cytogenetic analysis for more than three decades Recurrent chromosomal structural variations are well established as diagnostic and prognostic markers, suggesting that acquired genetic abnormalities (ie, somatic mutations) have an essential role in pathogenesis 1,2 However, nearly 50% of AML samples have a normal karyotype, and many of these genomes lack structural abnormalities, even when assessed with high-density comparative genomic hybridization or single-nucleotide polymorphism (SNP) arrays 3-5 (see Glossary) Targeted sequencing has identified recurrent mutations in FLT3, NPM1, KIT, CEBPA, and TET2 6-8 Massively parallel sequencing enabled the discovery of recurrent mutations in DNMT3A 9,10 and IDH1 11 Recent studies have shown that many patients with

3,980 citations

Journal ArticleDOI
24 Jun 2021-Cell
TL;DR: Weighted-nearest neighbor analysis as mentioned in this paper is an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities.

3,369 citations

Journal ArticleDOI
TL;DR: Age-related clonal hematopoiesis is a common condition that is associated with increases in the risk of hematologic cancer and in all-cause mortality, with the latter possibly due to an increased risk of cardiovascular disease.
Abstract: Background The incidence of hematologic cancers increases with age. These cancers are associated with recurrent somatic mutations in specific genes. We hypothesized that such mutations would be detectable in the blood of some persons who are not known to have hematologic disorders. Methods We analyzed whole-exome sequencing data from DNA in the peripheral-blood cells of 17,182 persons who were unselected for hematologic phenotypes. We looked for somatic mutations by identifying previously characterized single-nucleotide variants and small insertions or deletions in 160 genes that are recurrently mutated in hematologic cancers. The presence of mutations was analyzed for an association with hematologic phenotypes, survival, and cardiovascular events. Results Detectable somatic mutations were rare in persons younger than 40 years of age but rose appreciably in frequency with age. Among persons 70 to 79 years of age, 80 to 89 years of age, and 90 to 108 years of age, these clonal mutations were observed in 9.5% (219 of 2300 persons), 11.7% (37 of 317), and 18.4% (19 of 103), respectively. The majority of the variants occurred in three genes: DNMT3A, TET2, and ASXL1. The presence of a somatic mutation was associated with an increase in the risk of hematologic cancer (hazard ratio, 11.1; 95% confidence interval [CI], 3.9 to 32.6), an increase in all-cause mortality (hazard ratio, 1.4; 95% CI, 1.1 to 1.8), and increases in the risks of incident coronary heart disease (hazard ratio, 2.0; 95% CI, 1.2 to 3.4) and ischemic stroke (hazard ratio, 2.6; 95% CI, 1.4 to 4.8). Conclusions Age-related clonal hematopoiesis is a common condition that is associated with increases in the risk of hematologic cancer and in all-cause mortality, with the latter possibly due to an increased risk of cardiovascular disease. (Funded by the National Institutes of Health and others.)

3,183 citations