Author
Andrew J. Gentles
Other affiliations: University of California, San Diego, Genetic Information Research Institute
Bio: Andrew J. Gentles is an academic researcher from Stanford University. The author has contributed to research in topics: Diffuse large B-cell lymphoma & Cancer. The author has an hindex of 43, co-authored 119 publications receiving 15352 citations. Previous affiliations of Andrew J. Gentles include University of California, San Diego & Genetic Information Research Institute.
Papers published on a yearly basis
Papers
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TL;DR: CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types when applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors.
Abstract: We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersortstanfordedu/)
6,967 citations
Institute for Systems Biology1, BC Cancer Agency2, University of California, San Francisco3, University of North Carolina at Chapel Hill4, Columbia University5, Discovery Institute6, Massachusetts Institute of Technology7, Arizona State University8, Sage Bionetworks9, Harvard University10, Johns Hopkins University11, Stanford University12, University of Calgary13, Université libre de Bruxelles14, University of Texas MD Anderson Cancer Center15, Medical College of Wisconsin16, Qatar Airways17, Cold Spring Harbor Laboratory18, University of São Paulo19, Henry Ford Hospital20, University of Alabama at Birmingham21, Van Andel Institute22, Stony Brook University23
TL;DR: An extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA identifies six immune subtypes that encompass multiple cancer types and are hypothesized to define immune response patterns impacting prognosis.
3,246 citations
TL;DR: A pan-cancer resource and meta-analysis of expression signatures from ∼18,000 human tumors with overall survival outcomes across 39 malignancies is presented and it is found that expression of favorably prognostic genes, including KLRB1 (encoding CD161), largely reflect tumor-associated leukocytes.
Abstract: Molecular profiles of tumors and tumor-associated cells hold great promise as biomarkers of clinical outcomes. However, existing data sets are fragmented and difficult to analyze systematically. Here we present a pan-cancer resource and meta-analysis of expression signatures from ∼18,000 human tumors with overall survival outcomes across 39 malignancies. By using this resource, we identified a forkhead box MI (FOXM1) regulatory network as a major predictor of adverse outcomes, and we found that expression of favorably prognostic genes, including KLRB1 (encoding CD161), largely reflect tumor-associated leukocytes. By applying CIBERSORT, a computational approach for inferring leukocyte representation in bulk tumor transcriptomes, we identified complex associations between 22 distinct leukocyte subsets and cancer survival. For example, tumor-associated neutrophil and plasma cell signatures emerged as significant but opposite predictors of survival for diverse solid tumors, including breast and lung adenocarcinomas. This resource and associated analytical tools (http://precog.stanford.edu) may help delineate prognostic genes and leukocyte subsets within and across cancers, shed light on the impact of tumor heterogeneity on cancer outcomes, and facilitate the discovery of biomarkers and therapeutic targets.
2,153 citations
TL;DR: The utility of CIBERSORTx is evaluated in multiple tumor types, including melanoma, where single-cell reference profiles were used to dissect bulk clinical specimens, revealing cell-type-specific phenotypic states linked to distinct driver mutations and response to immune checkpoint blockade.
Abstract: Single-cell RNA-sequencing has emerged as a powerful technique for characterizing cellular heterogeneity, but it is currently impractical on large sample cohorts and cannot be applied to fixed specimens collected as part of routine clinical care. We previously developed an approach for digital cytometry, called CIBERSORT, that enables estimation of cell type abundances from bulk tissue transcriptomes. We now introduce CIBERSORTx, a machine learning method that extends this framework to infer cell-type-specific gene expression profiles without physical cell isolation. By minimizing platform-specific variation, CIBERSORTx also allows the use of single-cell RNA-sequencing data for large-scale tissue dissection. We evaluated the utility of CIBERSORTx in multiple tumor types, including melanoma, where single-cell reference profiles were used to dissect bulk clinical specimens, revealing cell-type-specific phenotypic states linked to distinct driver mutations and response to immune checkpoint blockade. We anticipate that digital cytometry will augment single-cell profiling efforts, enabling cost-effective, high-throughput tissue characterization without the need for antibodies, disaggregation or viable cells. CIBERSORTx, a suite of computational tools, enables inference of cell type abundance and cell-type-specific gene expression profiles from bulk RNA profiles.
1,812 citations
TL;DR: All human solid tumor cells require CD47 expression to suppress phagocytic innate immune surveillance and elimination, showing that CD47 is a commonly expressed molecule on all cancers, its function to blockphagocytosis is known, and blockade of its function leads to tumor cell phagcytosis and elimination.
Abstract: CD47, a "don't eat me" signal for phagocytic cells, is expressed on the surface of all human solid tumor cells Analysis of patient tumor and matched adjacent normal (nontumor) tissue revealed that CD47 is overexpressed on cancer cells CD47 mRNA expression levels correlated with a decreased probability of survival for multiple types of cancer CD47 is a ligand for SIRPα, a protein expressed on macrophages and dendritic cells In vitro, blockade of CD47 signaling using targeted monoclonal antibodies enabled macrophage phagocytosis of tumor cells that were otherwise protected Administration of anti-CD47 antibodies inhibited tumor growth in orthotopic immunodeficient mouse xenotransplantation models established with patient tumor cells and increased the survival of the mice over time Anti-CD47 antibody therapy initiated on larger tumors inhibited tumor growth and prevented or treated metastasis, but initiation of the therapy on smaller tumors was potentially curative The safety and efficacy of targeting CD47 was further tested and validated in immune competent hosts using an orthotopic mouse breast cancer model These results suggest all human solid tumor cells require CD47 expression to suppress phagocytic innate immune surveillance and elimination These data, taken together with similar findings with other human neoplasms, show that CD47 is a commonly expressed molecule on all cancers, its function to block phagocytosis is known, and blockade of its function leads to tumor cell phagocytosis and elimination CD47 is therefore a validated target for cancer therapies
1,206 citations
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Journal Article•
28,685 citations
Book•
29 Sep 2017
TL;DR: Thank you very much for reading who classification of tumours of haematopoietic and lymphoid tissues, and maybe you have knowledge that, people have look hundreds of times for their chosen readings like this, but end up in malicious downloads.
Abstract: WHO CLASSIFICATION OF TUMOURS OF HAEMATOPOIETIC AND LYMPHOID TISSUES , WHO CLASSIFICATION OF TUMOURS OF HAEMATOPOIETIC AND LYMPHOID TISSUES , کتابخانه مرکزی دانشگاه علوم پزشکی تهران
13,835 citations
TL;DR: CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types when applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors.
Abstract: We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersortstanfordedu/)
6,967 citations
TL;DR: The revision clarifies the diagnosis and management of lesions at the very early stages of lymphomagenesis, refines the diagnostic criteria for some entities, details the expanding genetic/molecular landscape of numerous lymphoid neoplasms and their clinical correlates, and refers to investigations leading to more targeted therapeutic strategies.
5,321 citations
01 Aug 2000
TL;DR: Assessment of medical technology in the context of commercialization with Bioentrepreneur course, which addresses many issues unique to biomedical products.
Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.
4,833 citations