Mining Databases to Find the Genes that Determine the Intractability of Human Tumors
Add to your Conference/Group
Add your comments:
Insert YouTube Videos inside your Slideworld presentation Copy and paste the video URL from YouTube, choose where to insert the video, and press “Submit”. The video will play in your slideshow after sometime.
Enter YouTube video URL
Enter Slide No where you want to insert youtube videos
Post a comment
Post Comment on Twitter
Post Comment on SlideWorld
Subscribe to follow-up comments
SlideWorld will not store your password. SlideWorld will maintain your privacy.
Subscribe to follow-up comments
Slide 1 :
Mining Databases to Find the Genes that Determine the Intractability of Human Tumors Stein WD1, Fojo T2, Steadman, K 2, Litman T3, and Bates SE21Hebrew University, Jerusalem, Israel; 2National Cancer Institute, Bethesda MD, USA; 3University of Copenhagen, Denmark Why are intractable cancers so intractable? The Axis of Intractability SAGE and microarray analyses of gene expression levels Bulk tumors vs. cell lines Intractable vs. tractable tumors
Slide 2 :
We can use the SEER (Surveillance, Epidemiology, and End-Results) database to get a surrogate measure of the intractability of the different tumor histologies
Slide 3 :
Slide 4 :
The triangles are tissues that highly express the MDR1 protein : p-glycoprotein (ABCB1)
Slide 5 :
Direct scans of patient treated with a blocker of P-glycoprotein, XR9576 (tariquidar)(Agrawal et al Clin Cancer Res 2003) Probe is sestamibi, labeled with technetium Testing the effectiveness of the MDR1 blocker tariquidar, in vivo minutes
Slide 6 :
Increased visibility of a tumor in a patient (arrow) after treatment with tariquidar
Slide 7 :
So why is chemotherapy so unsuccessful in the intractable tissue tumors? The drugs that have been developed against cancer have mostly been tested on cells from cancers, but grown as cell lines in culture. How good a model are they for the solid tumors?
Slide 8 :
SAGESerial Analysis of Gene Expression Two basic principles: Short sequence tags (14 bp) can identifya transcript Concatenation of tags allows efficient sequencing Gene-mining part I :
Slide 9 :
Seven tissues in the SAGE database with matching pairsof tumor and cell line Tissue Bulk CL Breast 2 2 Colon 2 4 Glioma 2 2 Medullablastoma 4 2 Ovary 2 2 Pancreas 2 2 Prostate 1 1
Slide 10 :
Slide 11 :
Slide 12 :
Correlating gene expression with intractability
Slide 13 :
Conclusion Bulk tumors over-express ECM-related genes and under-express protein synthesis genes. Thus, they are tethered and inert, as compared with cell lines. Perhaps this explains why tumors are less sensitive to chemotherapeutic reagents.
Slide 14 :
Molecular Classification of Human Carcinomas by Use of Gene Expression Signatures1 Andrew I. Su, John B. Welsh, Lisa M. Sapinoso, Suzanne G. Kern, Petre Dimitrov, Hilmar Lapp, Peter G. Schultz, Steven M. Powell, Christopher A. Moskaluk, Henry F. Frierson, Jr. and Garret M. Hampton2 Department of Chemistry, The Scripps Research Institute, La Jolla, California 92037 [A. I. S., P. G. S.]; Genomics Institute of the Novartis Research Foundation, San Diego, California 92121 [J. B. W., L. M. S., S. G. K., P. D., H. L., P. G. S., G. M. H.]; and Departments of Medicine [S. M. P.] and Pathology [C. A. M., H. F. F.], University of Virginia Health System, Charlottesville, Virginia 22908 We have explored published data bases in an attempt to correlate gene expression with intractability For instance :
Slide 15 :
GENE-MINING PART II: We used the Su et al data to identify possible genes that are concerned in determining the position of a tumor along the Axis of Intractability. To do this we simply calculated, for the 13,562 genes, the CORRELATIONS between the SEER measure of tractability (in % survivors), tissue-by-tissue, against the relative expression level for that gene. We found quite a number of genes that correlate closely with sensitivity or with resistance to chemotherapy. These are clearly targets for pharmaceutical investigations.
Slide 16 :
The False Discovery Rate tests the significance of the fingered genes
Slide 17 :
What does this gene mining tell us? It gives a clue as to what organelles might be important in intractability: For hemidesmosomes we have the integrins a6ß4, laminin-5, KRT8, CDC42EP1 For focal adhesion complexes, ezrin, vinexin, VASP, SYN4 No desmosomal genes, so it looks like adhesion to the basal matrix, rather than cell-to-cell adhesion that is crucial Apart from these, galectin-3, syndecan-1,CD44, ICAM2, ECM1, KRT19 and PLS2 are often found, and all have ECM and adhesion connotations.
Slide 18 :
Slide 19 :
Where are we going? Developing an assay system for searching for new drugs: Cells growing in spheroids – can manipulate them with Mabs, antisense, disintegrins, and then look for synergy with cytotoxins
Slide 20 :
Slide 21 :
An excel spreadsheet of one set of the spheroid data
Slide 22 :
OVERLAP HIGH IN DAY 6 MONOLAYER HIGH IN DAY 3 SPHEROIDS (both compared with day 3 monolayers)
Slide 23 :
Conclusions Cells grown as spheroids for 3 days on polyhema (rather than uncoated plates) yield very many of the same genes overexpressed as growing them 6 days, rather than 3, as monolayers. By 6 days, such monolayers are confluent. This suggests that the drug resistance one gets by growing cells as spheroids is simply due to confluence. The changes when one grows the cells on polyhema (or grows them to confluence) result in over-expression of genes concerned with cell growth and maintenance.
Slide 24 :
“INTRACTABILITY” SIGNATURES TURNING OUR OLD TRACT/INTRACT PLOTS INTO THE FORM OF THE NCI 60 CELL LINES/DRUG SCREEN PICTURES
Slide 25 :
First we will look at averages on a tissue-by-tissue basis across all the biopsies of that tumor histology Then we will look at the 144 individual biopsies In each case, these are log plots of the relative gene expression, with the over-expressed genes plotted on the right, under-expressed on the left I have looked only at the 18 cell adhesion genes and the 9 oxido-reductase genes, these being the ontology categories that were most highly fingered in the analysis
Slide 26 :
Gene signatures tissue-by-tissue
Slide 27 :
Slide 28 :
NOW WE WILL LOOK AT THE SPHEROID VERSUS ATTACHED CELLS GENE PROFILE. THE SAME GENES AS BEFORE, 18 AND 9
Slide 29 :
Adhesion and oxido-reductase genes Looking at the genes over-expressed in spheroids compared with monolayers
Slide 30 :
Slide 31 :
LOCAL CONCLUSION: The spheroid model is interesting in itself……. but doesn’t appear to reflect those aspects of cell-cell interactions that determine resistance to chemotherapy. We need a better model for the high-throughput drug searching…..
Genital Human Papill...
Flexible Careers for...
Ratecoding of spinal...
Plateletrich plasma ...
MRIBased Human Ventr...
Free Powerpoint Templates
5 Years ago.
1132 Views, 0 favourite
PowerPoint Presentation on Mining Databases to Find the Genes that Determine the Intractability of
PowerPoint Presentation on Mining Databases to Find the Genes that Determine the Intractability of Human Tumors or PowerPoint Presentation on a data base search for genes that are up- or down- regulated in intractable cancers
More By User
Flag as inappropriate
Select your reason for flagging this presentation as inappropriate. If needed, use the
form to let us know more details.
Other Terms Of Service Violation