Author
Lawrence D. True
Other affiliations: United States Department of Veterans Affairs, Emory University, University of California, Los Angeles ...read more
Bio: Lawrence D. True is an academic researcher from University of Washington. The author has contributed to research in topics: Prostate cancer & Prostate. The author has an hindex of 72, co-authored 289 publications receiving 29134 citations. Previous affiliations of Lawrence D. True include United States Department of Veterans Affairs & Emory University.
Topics: Prostate cancer, Prostate, Cancer, Stromal cell, Metastasis
Papers published on a yearly basis
Papers
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Broad Institute1, Harvard University2, Novartis3, Brigham and Women's Hospital4, Jikei University School of Medicine5, Boston Children's Hospital6, University of North Carolina at Chapel Hill7, University of Texas Southwestern Medical Center8, Nagoya City University9, Autonomous University of Barcelona10, University Health Network11, Cornell University12, Beth Israel Deaconess Medical Center13, Memorial Sloan Kettering Cancer Center14, University of Pennsylvania15, University of Michigan16, University of Washington Medical Center17, Howard Hughes Medical Institute18, Massachusetts Institute of Technology19
TL;DR: It is demonstrated that cancer cells containing amplifications surrounding the MCL1 and BCL2L1 anti-apoptotic genes depend on the expression of these genes for survival, and a large majority of SCNAs identified in individual cancer types are present in several cancer types.
Abstract: A powerful way to discover key genes with causal roles in oncogenesis is to identify genomic regions that undergo frequent alteration in human cancers. Here we present high-resolution analyses of somatic copy-number alterations (SCNAs) from 3,131 cancer specimens, belonging largely to 26 histological types. We identify 158 regions of focal SCNA that are altered at significant frequency across several cancer types, of which 122 cannot be explained by the presence of a known cancer target gene located within these regions. Several gene families are enriched among these regions of focal SCNA, including the BCL2 family of apoptosis regulators and the NF-kappaBeta pathway. We show that cancer cells containing amplifications surrounding the MCL1 and BCL2L1 anti-apoptotic genes depend on the expression of these genes for survival. Finally, we demonstrate that a large majority of SCNAs identified in individual cancer types are present in several cancer types.
3,375 citations
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University of Michigan1, Harvard University2, Massachusetts Institute of Technology3, Memorial Sloan Kettering Cancer Center4, University of Washington5, The Royal Marsden NHS Foundation Trust6, Institute of Cancer Research7, Cornell University8, Brigham and Women's Hospital9, Beth Israel Deaconess Medical Center10, University of Washington Medical Center11, University of Trento12, Wayne State University13, Johns Hopkins University14
TL;DR: This cohort study provides clinically actionable information that could impact treatment decisions for affected individuals and identified new genomic alterations in PIK3CA/B, R-spondin, BRAF/RAF1, APC, β-catenin, and ZBTB16/PLZF.
2,713 citations
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TL;DR: The Cancer Genome Atlas (TCGA) has been used for a comprehensive molecular analysis of primary prostate carcinomas as discussed by the authors, revealing substantial heterogeneity among primary prostate cancers, evident in the spectrum of molecular abnormalities and its variable clinical course.
2,109 citations
01 Nov 2015
TL;DR: A comprehensive molecular analysis of 333 primary prostate carcinomas revealed a molecular taxonomy in which 74% of these tumors fell into one of seven subtypes defined by specific gene fusions (ERG, ETV1/4, and FLI1) or mutations (SPOP, FOXA1, and IDH1).
Abstract: There is substantial heterogeneity among primary prostate cancers, evident in the spectrum of molecular abnormalities and its variable clinical course. As part of The Cancer Genome Atlas (TCGA), we present a comprehensive molecular analysis of 333 primary prostate carcinomas. Our results revealed a molecular taxonomy in which 74% of these tumors fell into one of seven subtypes defined by specific gene fusions (ERG, ETV1/4, and FLI1) or mutations (SPOP, FOXA1, and IDH1). Epigenetic profiles showed substantial heterogeneity, including an IDH1 mutant subset with a methylator phenotype. Androgen receptor (AR) activity varied widely and in a subtype-specific manner, with SPOP and FOXA1 mutant tumors having the highest levels of AR-induced transcripts. 25% of the prostate cancers had a presumed actionable lesion in the PI3K or MAPK signaling pathways, and DNA repair genes were inactivated in 19%. Our analysis reveals molecular heterogeneity among primary prostate cancers, as well as potentially actionable molecular defects.
1,794 citations
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TL;DR: The aim was to develop a universally acceptable classification system for bladder neoplasia that could be used effectively by pathologists, urologists, and oncologists.
Abstract: In October 1997, Dr. F.K. Mostofi assembled a group of individuals interested in bladder neoplasia at a meeting in Washington DC. The participants included urologic pathologists, urologists, urologic oncologists, and basic scientists with an interest in bladder neoplasia. The purpose of this meeting was to discuss bladder terminology and make recommendations to the World Health Organization (WHO) Committee on urothelial tumors. Following this meeting, a group of the urologic pathologists who attended the Washington meeting decided to broaden the representation of the group and arranged a meeting primarily of the members of the International Society of Urologic Pathologists (ISUP) at the 1998 United States and Canadian Academy of Pathology Meeting held in Boston. Massachusetts. At this meeting. issues regarding terminology of bladder lesions, primarily neoplastic and putative preneoplastic lesions, were discussed, resulting in a consensus statement. The WHO/ ISUP consensus classification arises from this consensus conference committee's recommendations to the WHO planning committee and their agreement with virtually all of the proposals presented herein. 29 The effort involved in reaching such a consensus was often considerable. Many of those involved in this process have compromised to arrive at a consensus. The aim was to develop a universally acceptable classification system for bladder neoplasia that could be used effectively by pathologists, urologists, and oncologists.
1,484 citations
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TL;DR: The ability to prospectively identify tumorigenic cancer cells will facilitate the elucidation of pathways that regulate their growth and survival and strategies designed to target this population may lead to more effective therapies.
Abstract: Breast cancer is the most common malignancy in United States women, accounting for >40,000 deaths each year. These breast tumors are comprised of phenotypically diverse populations of breast cancer cells. Using a model in which human breast cancer cells were grown in immunocompromised mice, we found that only a minority of breast cancer cells had the ability to form new tumors. We were able to distinguish the tumorigenic (tumor initiating) from the nontumorigenic cancer cells based on cell surface marker expression. We prospectively identified and isolated the tumorigenic cells as CD44+CD24−/lowLineage− in eight of nine patients. As few as 100 cells with this phenotype were able to form tumors in mice, whereas tens of thousands of cells with alternate phenotypes failed to form tumors. The tumorigenic subpopulation could be serially passaged: each time cells within this population generated new tumors containing additional CD44+CD24−/lowLineage− tumorigenic cells as well as the phenotypically diverse mixed populations of nontumorigenic cells present in the initial tumor. The ability to prospectively identify tumorigenic cancer cells will facilitate the elucidation of pathways that regulate their growth and survival. Furthermore, because these cells drive tumor development, strategies designed to target this population may lead to more effective therapies.
10,058 citations
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TL;DR: A unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs is presented.
Abstract: Recent advances in sequencing technology make it possible to comprehensively catalogue genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (1) initial read mapping; (2) local realignment around indels; (3) base quality score recalibration; (4) SNP discovery and genotyping to find all potential variants; and (5) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We discuss the application of these tools, instantiated in the Genome Analysis Toolkit (GATK), to deep whole-genome, whole-exome capture, and multi-sample low-pass (~4×) 1000 Genomes Project datasets.
10,056 citations
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TL;DR: The molecular pathways of this cancer-related inflammation are now being unravelled, resulting in the identification of new target molecules that could lead to improved diagnosis and treatment.
Abstract: The mediators and cellular effectors of inflammation are important constituents of the local environment of tumours. In some types of cancer, inflammatory conditions are present before a malignant change occurs. Conversely, in other types of cancer, an oncogenic change induces an inflammatory microenvironment that promotes the development of tumours. Regardless of its origin, 'smouldering' inflammation in the tumour microenvironment has many tumour-promoting effects. It aids in the proliferation and survival of malignant cells, promotes angiogenesis and metastasis, subverts adaptive immune responses, and alters responses to hormones and chemotherapeutic agents. The molecular pathways of this cancer-related inflammation are now being unravelled, resulting in the identification of new target molecules that could lead to improved diagnosis and treatment.
9,282 citations
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TL;DR: The Integrative Genomics Viewer (IGV) is a high-performance viewer that efficiently handles large heterogeneous data sets, while providing a smooth and intuitive user experience at all levels of genome resolution.
Abstract: Data visualization is an essential component of genomic data analysis. However, the size and diversity of the data sets produced by today’s sequencing and array-based profiling methods present major challenges to visualization tools. The Integrative Genomics Viewer (IGV) is a high-performance viewer that efficiently handles large heterogeneous data sets, while providing a smooth and intuitive user experience at all levels of genome resolution. A key characteristic of IGV is its focus on the integrative nature of genomic studies, with support for both array-based and next-generation sequencing data, and the integration of clinical and phenotypic data. Although IGV is often used to view genomic data from public sources, its primary emphasis is to support researchers who wish to visualize and explore their own data sets or those from colleagues. To that end, IGV supports flexible loading of local and remote data sets, and is optimized to provide high-performance data visualization and exploration on standard desktop systems. IGV is freely available for download from http://www.broadinstitute.org/igv, under a GNU LGPL open-source license.
6,930 citations
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TL;DR: The results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents and the generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of ‘personalized’ therapeutic regimens.
Abstract: The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of 'personalized' therapeutic regimens.
6,417 citations