Gene expression profiling of pediatric acute myelogenous leukemia

Slides:



Advertisements
Similar presentations
Original Figures for "Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring"
Advertisements

Dimension reduction : PCA and Clustering Slides by Agnieszka Juncker and Chris Workman.
Scenario 6 Distinguishing different types of leukemia to target treatment.
Ishida et al. Supplementary Figures 1-3 Page 1 Supplementary Fig. 1. Stepwise determination of genomic aberrations on chr-13 in medulloblastomas from Ptch1.
ALK-positive plasmablastic B-cell lymphoma with expression of the NPM-ALK fusion transcript: report of 2 cases by Mihaela Onciu, Frederick G. Behm, James.
EQTLs.
CellExpress Tutorial A Comprehensive Microarray-Based Cancer Cell Line and Clinical Sample Gene Expression Analysis Online System :8080 NTU.
Comparison of the Predictive Accuracy of DNA Array-Based Multigene Classifiers across cDNA Arrays and Affymetrix GeneChips  James Stec, Jing Wang, Kevin.
M. Fu, G. Huang, Z. Zhang, J. Liu, Z. Zhang, Z. Huang, B. Yu, F. Meng 
Generation of induced pluripotent stem cells from primary chronic myelogenous leukemia patient samples by Keiki Kumano, Shunya Arai, Masataka Hosoi, Kazuki.
Volume 44, Issue 1, Pages (January 2016)
Hyperdiploid Acute Lymphoblastic Leukemia With 51 to 65 Chromosomes: A Distinct Biological Entity With a Marked Propensity to Undergo Apoptosis by Chikako.
Genes with Bimodal Expression Are Robust Diagnostic Targets that Define Distinct Subtypes of Epithelial Ovarian Cancer with Different Overall Survival 
Volume 21, Issue 11, Pages (December 2017)
Gene expression profiling of multiple myeloma reveals molecular portraits in relation to the pathogenesis of the disease by Florence Magrangeas, Valéry.
Bead Array–Based microRNA Expression Profiling of Peripheral Blood and the Impact of Different RNA Isolation Approaches  Andrea Gaarz, Svenja Debey-Pascher,
Leukemia in twins: lessons in natural history
Tracking CD40 signaling during germinal center development
IMMUNOPHENOTYPING LEUKEMIAS AND LYMPHOMAS
Michael D. Onken, Lori A. Worley, Rosa M. Dávila, Devron H. Char, J
Two splice-factor mutant leukemia subgroups uncovered at the boundaries of MDS and AML using combined gene expression and DNA-methylation profiling by.
Christos Sotiriou, Chand Khanna, Amir A
Volume 131, Issue 3, Pages (September 2006)
Volume 1, Issue 2, Pages (March 2002)
Gene expression profiling of human peri-implantation endometria between natural and stimulated cycles  Yunao Liu, M.Sc., Kai-Fai Lee, Ph.D., Ernest H.Y.
Volume 1, Issue 1, Pages (February 2002)
Genome-wide epigenetic analysis delineates a biologically distinct immature acute leukemia with myeloid/T-lymphoid features by Maria E. Figueroa, Bas J.
Expression profiling of snoRNAs in normal hematopoiesis and AML
Volume 10, Issue 6, Pages (December 2006)
Global approach to the diagnosis of leukemia using gene expression profiling by Torsten Haferlach, Alexander Kohlmann, Susanne Schnittger, Martin Dugas,
Accurate Molecular Characterization of Formalin-Fixed, Paraffin-Embedded Tissues by microRNA Expression Profiling  Anna E. Szafranska, Timothy S. Davison,
Arjun Pennathur, MD, Liqiang Xi, MD, Virginia R. Litle, MD, William E
by Vladia Monsurrò, Ena Wang, Yoshisha Yamano, Stephen A
Volume 7, Issue 4, Pages (April 2005)
Volume 129, Issue 3, Pages (September 2005)
Qing Cheng, Cheng Cheng, Kristine R. Crews, Raul C
by Gregory H. Underhill, David George, Eric G. Bremer, and Geoffrey S
Acute mixed lineage leukemia in children: the experience of St Jude Children's Research Hospital by Jeffrey E. Rubnitz, Mihaela Onciu, Stanley Pounds,
Molecular Profiling to Diagnose a Case of Atypical Dermatomyositis
Loss of imprinting at the 14q32 domain is associated with microRNA overexpression in acute promyelocytic leukemia by Floriana Manodoro, Jacek Marzec, Tracy.
Dimension reduction : PCA and Clustering
QuantiGene Plex Represents a Promising Diagnostic Tool for Cell-of-Origin Subtyping of Diffuse Large B-Cell Lymphoma  John S. Hall, Suzanne Usher, Richard.
Molecular Classification of Renal Tumors by Gene Expression Profiling
Comparison of the Predictive Accuracy of DNA Array-Based Multigene Classifiers across cDNA Arrays and Affymetrix GeneChips  James Stec, Jing Wang, Kevin.
Volume 3, Issue 1, Pages (July 2016)
RNA-Stabilized Whole Blood Samples but Not Peripheral Blood Mononuclear Cells Can Be Stored for Prolonged Time Periods Prior to Transcriptome Analysis 
Jianing Yu, David Ferster  Neuron 
Optimal gene expression analysis by microarrays
Volume 21, Issue 11, Pages (December 2017)
Gene expression correlates of clinical prostate cancer behavior
Microarray Gene Expression Analysis of Fixed Archival Tissue Permits Molecular Classification and Identification of Potential Therapeutic Targets in Diffuse.
Medial Axis Shape Coding in Macaque Inferotemporal Cortex
Volume 16, Issue 6, Pages (December 2004)
Genome-wide promoter methylation of hairy cell leukemia
Volume 10, Issue 6, Pages (December 2006)
Unraveling the Mysteries of IGF-1 Signaling in Melanoma
Volume 122, Issue 6, Pages (September 2005)
Volume 12, Issue 9, Pages (April 2002)
Volume 118, Issue 2, Pages (July 2004)
Cecal metabolome during C. difficile colonization and infection.
Volume 7, Issue 2, Pages (August 2010)
Volume 1, Issue 1, Pages (July 2015)
Brandon Ho, Anastasia Baryshnikova, Grant W. Brown  Cell Systems 
Qing-Rong Chen, Gordon Vansant, Kahuku Oades, Maria Pickering, Jun S
A, unsupervised hierarchical clustering of the expression of probe sets differentially expressed in the oral mucosa of smokers versus never smokers. A,
PD-L1 expression correlates with T-cell markers and an IFN response signature in human melanomas. PD-L1 expression correlates with T-cell markers and an.
Pancreatic adenocarcinoma, chronic pancreatitis, and normal pancreas samples can be distinguished on the basis of gene expression profiling. Pancreatic.
Distinct subtypes of CAFs are detected in human PDAC
Gene expression heatmap of non–T-cell-inflamed, intermediate, and T-cell–inflamed testicular germ cell tumors from TCGA. Genes are on the row, and samples.
Qing Cheng, Cheng Cheng, Kristine R. Crews, Raul C
Presentation transcript:

Gene expression profiling of pediatric acute myelogenous leukemia by Mary E. Ross, Rami Mahfouz, Mihaela Onciu, Hsi-Che Liu, Xiaodong Zhou, Guangchun Song, Sheila A. Shurtleff, Stanley Pounds, Cheng Cheng, Jing Ma, Raul C. Ribeiro, Jeffrey E. Rubnitz, Kevin Girtman, W. Kent Williams, Susana C. Raimondi, Der-Cherng Liang, Lee-Yung Shih, Ching-Hon Pui, and James R. Downing Blood Volume 104(12):3679-3687 December 1, 2004 ©2004 by American Society of Hematology

Unsupervised cluster analysis of pediatric AMLs Unsupervised cluster analysis of pediatric AMLs. Expression profiles of the diagnostic leukemic blasts from 130 cases of pediatric AML were obtained using the U133A Affymetrix microarray. Unsupervised cluster analysis of pediatric AMLs. Expression profiles of the diagnostic leukemic blasts from 130 cases of pediatric AML were obtained using the U133A Affymetrix microarray. The expression data were then filtered to remove any probe sets that failed to show significant variation in expression across the data set. The remaining 17 051 probe sets were then used in an unsupervised 2-dimensional hierarchical clustering algorithm, and the resultant dendrogram is shown. Indicated below the dendrogram are the genetic subtype and FAB morphology for each case according to the indicated color codes. Mary E. Ross et al. Blood 2004;104:3679-3687 ©2004 by American Society of Hematology

Expression profiles of pediatric AMLs Expression profiles of pediatric AMLs. (A) Hierarchical clustering of 130 diagnostic pediatric AML samples (columns) versus 250 class discriminating genes (rows). Expression profiles of pediatric AMLs. (A) Hierarchical clustering of 130 diagnostic pediatric AML samples (columns) versus 250 class discriminating genes (rows). The genes used in this analysis are the top 50–ranked genes per group as selected by SAM. For genes that had more than one probe set selected as a class discriminator, the highest-ranked probe set was used for this figure. Probe set signal values were normalized to the mean across the entire data set and the relative value for each case is represented by a color, with red representing high expression and green representing low expression (scale shown in the lower right). The genetic subtype of each case is indicated by colored bars across the top and bottom of the panel. (B) Similarity plot of 130 pediatric AML diagnostic samples using the top 50–ranked genes (1 probe set per gene) for each subgroup as selected by SAM. Similarities are plotted using a scale that is based on Pearson correlation coefficients calculated for pairwise comparisons using the expression data. The degree of similarity between cases is displayed using the blue color scale at the bottom of the figure. Genetic groups are indicated by the color bars along the top and side of the similarity plot and are arranged identically as in panel A. Mary E. Ross et al. Blood 2004;104:3679-3687 ©2004 by American Society of Hematology

AML subtype-specific class discriminating genes. AML subtype-specific class discriminating genes. Shown are representative genes that are highly correlated with the individual genetic subtypes of AML. Probe set signal values are normalized to the mean for the data set and the expression for each case is then represented by color, with red representing deviation above the mean and green representing deviation below the mean. The leukemia subtype is indicated at the top of the figure, and the Affymetrix probe set number and gene symbol are listed on the right side of the figure. Mary E. Ross et al. Blood 2004;104:3679-3687 ©2004 by American Society of Hematology

Expression signature of core-binding factor AMLs Expression signature of core-binding factor AMLs. Two-dimensional hierarchical clustering of the 130 AML cases using the top 50–ranked discriminating probe sets for the core-binding factor (CBF) leukemias (AML1-ETO and CBFβ-MYH11 cases). Expression signature of core-binding factor AMLs. Two-dimensional hierarchical clustering of the 130 AML cases using the top 50–ranked discriminating probe sets for the core-binding factor (CBF) leukemias (AML1-ETO and CBFβ-MYH11 cases). The genetic subtype of each case is presented by a color-coded bar at the bottom of the figure, using the same color scheme used in Figures 1, 2, 3. The probe set number and gene symbol for the discriminating genes are listed on the right. The normalized expression level for each gene is represented by a color using the scale shown in the lower left corner. Cases were clustered using a cosine function. Mary E. Ross et al. Blood 2004;104:3679-3687 ©2004 by American Society of Hematology

Gene expression profiles of pediatric acute leukemia with MLL chimeric fusion genes. Gene expression profiles of pediatric acute leukemia with MLL chimeric fusion genes. (A) Multidimensional scaling plot generated using unsupervised principle components analysis with a combined data set containing 130 AML cases, 132 ALL cases,41 and 5 additional T-lineage ALL (T-ALL) cases that contain MLL chimeric fusion genes. A variation filter was applied to remove any probe sets that showed minimal variation in expression across this data set, and the analysis was performed with the remaining 17 944 probe sets. Each case is represented by a colored sphere, with AML cases indicated by blue, B-progenitor lineage ALL (B-ALL) by yellow, and T-ALL by green. Acute leukemia cases cluster based on lineage. (B) The same PCA analysis as shown in panel A, except that cases that contain an MLL chimeric fusion gene are indicated in red. The cases containing the MLL chimeric fusion gene continue to cluster according to lineage. (C) Multidimensional scaling plot generated using the supervised learning algorithm, discriminants analysis with variance (DAV) with the expression data from the 267 acute leukemia samples generated using the 17 944 probe sets that passed the variation filter. Cases are color coded as described for panel B. Cases with an MLL chimeric fusion gene (in red) can be separated in gene space from the leukemias that lack this genetic lesion. (D) Expression profile of the top 50–ranked MLL discriminating genes. The probe set number and gene symbol for the discriminating genes are listed on the right. The normalized expression level for each gene is represented by color using the scale shown. Mary E. Ross et al. Blood 2004;104:3679-3687 ©2004 by American Society of Hematology