Showing papers by "Alexander Krasnitz published in 2018"
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
Henry Ford Health System1, Harvard University2, Stanford University3, University of Hasselt4, University of Texas MD Anderson Cancer Center5, Nencki Institute of Experimental Biology6, École Polytechnique Fédérale de Lausanne7, Sage Bionetworks8, Université libre de Bruxelles9, Poznan University of Medical Sciences10, George Washington University11, Cold Spring Harbor Laboratory12, University of Kansas13, University of California, Santa Cruz14, University of North Carolina at Chapel Hill15, Van Andel Institute16
TL;DR: Novel stemness indices for assessing the degree of oncogenic dedifferentiation are provided and it is found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors.
1,099 citations
Cold Spring Harbor Laboratory1, Johns Hopkins University2, Ontario Institute for Cancer Research3, École Polytechnique Fédérale de Lausanne4, Stony Brook University5, Memorial Sloan Kettering Cancer Center6, University of California, Davis7, Thomas Jefferson University8, SUNY Downstate Medical Center9, Utrecht University10, Broad Institute11, Hofstra University12, University of Pennsylvania13, University of Nebraska Medical Center14, Eppley Institute for Research in Cancer and Allied Diseases15, Princess Margaret Cancer Centre16, Cornell University17, University Health Network18, University of Toronto19
TL;DR: A pancreatic cancer patient-derived organoid (PDO) library is generated that recapitulates the mutational spectrum and transcriptional subtypes of primary Pancreatic cancer and proposes that combined molecular and therapeutic profiling of PDOs may predict clinical response and enable prospective therapeutic selection.
Abstract: Pancreatic cancer is the most lethal common solid malignancy. Systemic therapies are often ineffective and predictive biomarkers to guide treatment are urgently needed. We generated a pancreatic cancer patient-derived organoid (PDO) library that recapitulates the mutational spectrum and transcriptional subtypes of primary pancreatic cancer. New driver oncogenes were nominated and transcriptomic analyses revealed unique clusters. PDOs exhibited heterogeneous responses to standard-of-care chemotherapeutics and investigational agents. In a case study manner, we find that PDO therapeutic profiles paralleled patient outcomes and that PDOs enable longitudinal assessment of chemo-sensitivity and evaluation of synchronous metastases. We derived organoid-based gene expression signatures of chemo-sensitivity that predicted improved responses for many patients to chemotherapy in both the adjuvant and advanced disease settings. Finally, we nominated alternative treatment strategies for chemo-refractory PDOs using targeted agent therapeutic profiling. We propose that combined molecular and therapeutic profiling of PDOs may predict clinical response and enable prospective therapeutic selection.
608 citations
TL;DR: It is demonstrated that sparse DNA sequencing of single-cell nuclei from prostate core biopsies is a rich source of quantitative parameters for evaluating neoplastic growth and aggressiveness, yielding novel genomic measures with the potential to improve the accuracy of diagnosis and prognosis in prostate cancer.
Abstract: A distinction between indolent and aggressive disease is a major challenge in diagnostics of prostate cancer. As genetic heterogeneity and complexity may influence clinical outcome, we have initiated studies on single tumor cell genomics. In this study, we demonstrate that sparse DNA sequencing of single-cell nuclei from prostate core biopsies is a rich source of quantitative parameters for evaluating neoplastic growth and aggressiveness. These include the presence of clonal populations, the phylogenetic structure of those populations, the degree of the complexity of copy-number changes in those populations, and measures of the proportion of cells with clonal copy-number signatures. The parameters all showed good correlation to the measure of prostatic malignancy, the Gleason score, derived from individual prostate biopsy tissue cores. Remarkably, a more accurate histopathologic measure of malignancy, the surgical Gleason score, agrees better with these genomic parameters of diagnostic biopsy than it does with the diagnostic Gleason score and related measures of diagnostic histopathology. This is highly relevant because primary treatment decisions are dependent upon the biopsy and not the surgical specimen. Thus, single-cell analysis has the potential to augment traditional core histopathology, improving both the objectivity and accuracy of risk assessment and inform treatment decisions.Significance: Genomic analysis of multiple individual cells harvested from prostate biopsies provides an indepth view of cell populations comprising a prostate neoplasm, yielding novel genomic measures with the potential to improve the accuracy of diagnosis and prognosis in prostate cancer. Cancer Res; 78(2); 348-58. ©2017 AACR.
24 citations
01 Jul 2018
TL;DR: This book presents a meta-modelling framework called “Smart grids” that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of designing and implementing smart grids.
Abstract: [Malta, Tathiane M.; Poisson, Laila; Noushmehr, Houtan] Henry Ford Hlth Syst, Detroit, MI USA. [Sokolov, Artem] Harvard Med Sch, Boston, MA 02115 USA. [Gentles, Andrew J.; Gevaert, Olivier] Stanford Univ, Palo Alto, CA 94304 USA. [Burzykowski, Tomasz] Hasselt Univ, Diepenbeek, Belgium. [Weinstein, John; Lazar, Alexander J.] Univ Texas MD Anderson Canc Ctr, Houston, TX 77030 USA. [Kaminska, Bozena] PAS, Nencki Inst Expt Biol, Warsaw, Poland. [Huelsken, Joerg] Ecole Polytech Fed Lausanne, Swiss Inst Technol, Lausanne, Switzerland. [Omberg, Larsson] Sage Bionetworks, Seattle, WA USA. [Colaprico, Antonio] Univ Libre Bruxelles, Brussels, Belgium. [Czerwinska, Patrycja; Mazurek, Sylwia] Poznan Univ Med Sci, Poznan, Poland. [Mishra, Lopa] George Washington Univ, Washington, DC USA. [Heyn, Holger] Ctr Genom Regulat CNAG CRG, Barcelona, Spain. [Krasnitz, Alex] Cold Spring Harbor Lab, POB 100, Cold Spring Harbor, NY 11724 USA. [Godwin, Andrew K.] Univ Kansas, Med Ctr, Kansas City, KS 66103 USA. [Stuart, Joshua M.] Univ Calif Santa Cruz, Santa Cruz, CA 95064 USA. [Hoadley, Katherine] Univ N Carolina, Chapel Hill, NC 27515 USA. [Laird, Peter W.] Van Andel Res Inst, Grand Rapids, MI USA. [Wiznerowicz, Maciej] Int Inst Mol Oncol, Poznan, Poland.
1 citations