J. Fernebro, P. Francis, P. Edén, Å. Borg, I. Panagopoulos, F. Mertens, J. Vallon-Christersson, M. Åkerman, N. Mandahl, M. Nilbert Departments of Oncology,

Slides:

Advertisements

Similar presentations

Analysis of Microarray Genomic Data of Breast Cancer Patients Hui Liu, MS candidate Department of statistics Prof. Eric Suess, faculty mentor Department.

Advertisements

Basic Gene Expression Data Analysis--Clustering

 2013 Genentech USA, Inc. All rights reserved. Disclosure/Disclaimer The Molecular Biomarkers in Cancer (MBiC) slide presentation is not an independent.

Rarer Sarcoma Subtypes Andrew J. Wagner, MD, PhD Center for Sarcoma and Bone Oncology Dana-Farber Cancer Institute Boston, MA.

Gene regulation in cancer 11/14/07. Overview The hallmark of cancer is uncontrolled cell proliferation. Oncogenes code for proteins that help to regulate.

The origin of metastatic disease: clues from genomics 7/13/2011.

Expression profiles for prognosis and prediction Laura J. Van ‘t Veer The Netherlands Cancer Institute, Amsterdam.

1 MicroArray -- Data Analysis Cecilia Hansen & Dirk Repsilber Bioinformatics - 10p, October 2001.

Microarray technology and analysis of gene expression data Hillevi Lindroos.

MOLECULAR GENETICS OF B CELL LYMPHOMAS: AN UPDATE Michel Trudel, MD, FRCPC Shaikh Khalifa Medical Center.

Microarrays Dr Peter Smooker,

Parallel human genome analysis: Microarray-based expression monitoring of 1000 genes Mark Schena, Dari Shalon, Renu Heller, Andrew Chai, Patrick O. Brown,

Figure 1: (A) A microarray may contain thousands of ‘spots’. Each spot contains many copies of the same DNA sequence that uniquely represents a gene from.

Microarrays and Gene Expression Analysis. 2 Gene Expression Data Microarray experiments Applications Data analysis Gene Expression Databases.

1 April, 2005 Chapter C4.1 and C5.1 DNA Microarrays and Cancer.

 MicroRNAs (miRNAs) are a class of small RNA molecules, about ~21 nucleotide (nt) long.  MicroRNA are small non coding RNAs (ncRNAs) that regulate.

DNA Microarrays Examining Gene Expression. Prof. GrossBiology 4 DNA MicroArrays DNA MicroArrays use hybridization technology to examine gene expression.

Genomics I: The Transcriptome RNA Expression Analysis Determining genomewide RNA expression levels.

Analysis of microarray data

Comprehensive Gene Expression Analysis of Prostate Cancer Reveals Distinct Transcriptional Programs Associated With Metastatic Disease Kevin Paiz-Ramirez.

Malignant Melanoma and CDKN2A

Amplification of COPS3 in High-grade Osteosarcoma: Relationship to TP53 Mutation and Patient Outcome Samuel Lunenfeld Research Institute Mount Sinai Hospital,

E2F3 in Soft Tissue Sarcoma Development M. Scurr; A Feber; J Shipley; A Fletcher; N Dennis; I Judson; C Cooper.

Kerrington Smith, M.D. CTOS Nov 14, 2008

From motif search to gene expression analysis

Clustering of DNA Microarray Data Michael Slifker CIS 526.

Table S1. Characteristics of breast tumor and normal breast tissue samples. Relevant characteristics of breast tumor and normal breast tissue samples analyzed.

Molecular classification of renal cell carcinoma subtypes using microRNA signatures Zaravinos A 1, Lambrou GI 2, Mourmouras N 3, Delakas D 3, Deltas C.

Kyoto University, Japan

Finish up array applications Move on to proteomics Protein microarrays.

Scenario 6 Distinguishing different types of leukemia to target treatment.

Marco Magistri , Journal Club. A non-coding RNA (ncRNA) is any RNA molecule that is not translated into a protein “Structural genes encode proteins.

Apostolos Zaravinos and Constantinos C Deltas Molecular Medicine Research Center and Laboratory of Molecular and Medical Genetics, Department of Biological.

Microarrays and Gene Expression Analysis. 2 Gene Expression Data Microarray experiments Applications Data analysis Gene Expression Databases.

1 FINAL PROJECT- Key dates –last day to decided on a project * 11-10/1- Presenting a proposed project in small groups A very short presentation (Max.

Ranjit Ganta, Raj Acharya, Shruthi Prabhakara Department of Computer Science and Engineering, Penn State University DATA WAREHOUSE FOR BIO-GEO HEALTH CARE.

Lecture 8. Functional Genomics: Gene Expression Profiling using DNA microarrays. Part II Clark EA, Golub TR, Lander ES, Hynes RO.(2000) Genomic analysis.

Gene Expression Analysis. 2 DNA Microarray First introduced in 1987 A microarray is a tool for analyzing gene expression in genomic scale. The microarray.

Whole Genome Approaches to Cancer 1. What other tumor is a given rare tumor most like? 2. Is tumor X likely to respond to drug Y?

DIFFERENTIALLY EXPRESSED GENES AND THEIR ASSOCIATED NETWORKS IN CLEAR-CELL RENAL CELL CARCINOMA (RCC) Apostolos Zaravinos and Constantinos C. Deltas Molecular.

Idea: measure the amount of mRNA to see which genes are being expressed in (used by) the cell. Measuring protein might be more direct, but is currently.

Prof. Yechiam Yemini (YY) Computer Science Department Columbia University (c)Copyrights; Yechiam Yemini; Lecture 2: Introduction to Paradigms 2.3.

The type of target influence the type of drug Main drugs’ categories –Small molecules –Biologicals (e.g. antibodies, hormones, etc) The drug discovery.

BIOLOGICAL MARKERS OF PROGNOSIS IN EWING'S SARCOMA: THE ROLE OF GAL3BP CTOS meeting, Seattle 2007 Katia Scotlandi, Diana Zambelli, Monia Zuntini, and Piero.

Use of gene expression to identify heterogeneity of metastatic behavior among high-grade pleomorphic soft tissue sarcomas Keith Skubitz 1, Princy Francis.

Annals of Oncology 23: 298–304, 2012 종양혈액내과 R4 김태영 / prof. 김시영.

R2 김재민 / Prof. 정재헌 Journal conference 1.

Fig. 6. Hierarchical clustering analysis of gene expression in LSMC and MSMC pretreated with TGF-βRII antisense for 24 h, followed by TGF-β treatment for.

FINAL PROJECT- Key dates

Gene Expression Analysis

Loyola Marymount University

Genetics and pathology of pancreatic cancer

Van’t Veer et al, Nature 415: (2002)

Nat. Rev. Clin. Oncol. doi: /nrclinonc

Loyola Marymount University

Opening a Chromatin Gate to Metastasis

Class Prediction Based on Gene Expression Data Issues in the Design and Analysis of Microarray Experiments Michael D. Radmacher, Ph.D. Biometric Research.

Loyola Marymount University

Tracing the Innate Genetic Evolution and Spatial Heterogeneity in Treatment Naïve Lung Cancer Lesions Jihye Kim Translational Bioinformatics and Cancer.

Loyola Marymount University

Loyola Marymount University

Loyola Marymount University

A, unsupervised hierarchical clustering of the expression of probe sets differentially expressed in the oral mucosa of smokers versus never smokers. A,

CXCL14-positive CAFs induce overexpression of LINC00092 in ovarian cancer. CXCL14-positive CAFs induce overexpression of LINC00092 in ovarian cancer. A,

EN1 expression in breast cancer and clinical outcome.

Extraction diagram of candidate genes identified by global expression analysis. Extraction diagram of candidate genes identified by global expression analysis.

Identification of putative TET1 targets in TNBC

Global analysis of the chemical–genetic interaction map.

Highly metastatic PDAC cells have a unique gene signature, which is not preserved in metastases but predicts poor patient outcome. Highly metastatic PDAC.

Kaplan-Meier (K-M) curves of progression-free survival (PFS) in 54 patients with metastatic gastric cancer treated with RAD001. Kaplan-Meier (K-M) curves.

Presentation transcript:

J. Fernebro, P. Francis, P. Edén, Å. Borg, I. Panagopoulos, F. Mertens, J. Vallon-Christersson, M. Åkerman, N. Mandahl, M. Nilbert Departments of Oncology, Theoretical Physics, Clinical Genetics, and Pathology Lund University, Sweden Gene expression profiles relate to gene fusion type and metastasis in synovial sarcoma

Aims Groups of genes discriminating the monophasic from biphasic have been previously identified (Allander et al., 2002; Nagayama et al., 2002) Do gene expression profiles in synovial sarcoma correlate with:  Histopathology  Cytogenetics  Gene fusion type  Metastatic potential

23 synovial sarcoma specimens from 21 patients mean age 46, 12 males, 16 primary tumors, 3 local recurrences, 4 metastases  Histopathology monophasic (17) vs biphasic (4)  Cytogenetics t(X;18) (4) vs complex karyotype (12)  Gene fusion type SS18/SSX1 (12) vs SS18/SSX2 (7)  Metastatic potential metastasis (13) vs metastsis-free (5) SS18 SSX Material

Scan Process Image Labelled cDNA hrs incubation at 42 o C Hybridize to microarray slide Reference RNA Tumour RNA Image and Data Analysis cDNA microarray, IMAGE clones distinct reporters unique UniGene clusters Hierarchical clustering based on ~5800 genes - Unsupervised - Supervised Golub scores, random permutation tests Methods

 Expression profiles did not relate to histopathology or karyotype  Significant discriminators for gene fusion type and metastatic potential Metastasis  No  Yes Gene fusion type  Type 1  Type 2 Supervised clustering

Up SS18/SSX1 - TCF7- LEF1 - RTP801- CKB - NOTCH2- Metallothioneins Up SS18/SSX2 - BENE- MN1 - NEDD4- FRAG1 - MSX2- TIEG Gene fusion type  Transcription factors  Signalling molecules  Multiple metallothioneins Discriminating genes Up metastasis - TYMS- TOP2A - PTTG1- IGFBP2 - BUB1- COL18A1 Up without metastasis - ProSAPiP1- TNNT1 - ICAM1- DDX21 - FRAG1- TCF8 Metastatic potential  Cell proliferation  Cell cycle progression  Transcriptional regulation

 45 from top 200 common to both lists  Up in metastasis also up in SS18/SSX1 - BSG- FRAG1 - NOTCH2- RGS2 - TCF7- TCF8 Common discriminators Up SS18/SSX1 - TCF7- LEF1 - RTP801- CKB - NOTCH2- Metallothioneins Up SS18/SSX2 - BENE- MN1 - NEDD4- FRAG1 - MSX2- TIEG Gene fusion type Up metastasis - TYMS- TOP2A - PTTG1- IGFBP2 - BUB1- COL18A1 Up without metastasis - ProSAPiP1- TNNT1 - ICAM1- DDX21 - FRAG1- TCF8 Metastatic potential Discriminating genes

Targets for chemotherapeutic drugs Up SS18/SSX1 - TCF7- LEF1 - RTP801- CKB - NOTCH2- Metallothioneins Up SS18/SSX2 - BENE- MN1 - NEDD4- FRAG1 - MSX2- TIEG Gene fusion type Up metastasis - TYMS- TOP2A - PTTG1- IGFBP2 - BUB1- COL18A1 Up without metastasis - ProSAPiP1- TNNT1 - ICAM1- DDX21 - FRAG1- TCF8 Metastatic potential Discriminating genes

Previously reported in synovial sarcoma Up SS18/SSX1 - TCF7- LEF1 - RTP801- CKB - NOTCH2- Metallothioneins Up SS18/SSX2 - BENE- MN1 - NEDD4- FRAG1 - MSX2- TIEG Gene fusion type Up metastasis - TYMS- TOP2A - PTTG1- IGFBP2 - BUB1- COL18A1 Up without metastasis - ProSAPiP1- TNNT1 - ICAM1- DDX21 - FRAG1- TCF8 Metastatic potential Discriminating genes

Previously reported in metastasizing tumors Up SS18/SSX1 - TCF7- LEF1 - RTP801- CKB - NOTCH2- Metallothioneins Up SS18/SSX2 - BENE- MN1 - NEDD4- FRAG1 - MSX2- TIEG Gene fusion type Up metastasis - TYMS- TOP2A - PTTG1- IGFBP2 - BUB1- COL18A1 Up without metastasis - ProSAPiP1- TNNT1 - ICAM1- DDX21 - FRAG1- TCF8 Metastatic potential Discriminating genes

 Differentially expressed genes relative to gene fusion type and metastatic potential in synovial sarcoma  Signaling molecules, transcription factors and metallothioneins among the fusion type discriminating genes  Known targets for chemotherapy (TYMS and TOP2A) upregulated in metastasizing tumors Conclusions