Suppression of the Malignant Phenotype of Bladder Cancer Cells Investigations of the Proteome in Relation to Phenotype and Gene Expression

Transcription

1 Suppression of the Malignant Phenotype of Bladder Cancer Cells Investigations of the Proteome in Relation to Phenotype and Gene Expression

2 Topics Modulation of Phenotype by Extracellular Matrix. Bioinformatics techniques for identifying significant genes and proteins. Integration of Transcriptomic and Proteomic levels of information concerning phenotype.

3 Cancer Is A Tissue Level Disease 6 1 Genetically Initiated Cell 5 Phenotypically Altered Field Cells Stromal Cell 1. Paracrine interactions from initiated cell. 2. Paracrine interactions through stromal cells. 3. Juxtacrine interactions with neighboring cells. 4. Matrix-mediated interactions (normal or altered). 5. Epigenetic promotion of initiated cell. 6. Epigenetic suppression of malignant phenotype. 2 4

4 Cancer Cells and Extracellular Matrix (ECM) An established tumor modulates the ECM. Evolving tumors and metastases are exposed to NORMAL matrix. Normal matrix may be suppressive. Progression requires escape from tissue level controls modulated by matrix. Most cancer research is done with cells grown on plastic. Tissue level controls are not active.

5 Model System for Effect of ECM Matrigel: EHS tumor ECM. Representative of matrix remodeled by cancer. SISgel: Gel made by pepsin treatment of Small Intestinal Submucosa. Representative of normal matrix. Plastic: Eliminates most matrix-dependent features of growth and differentiation.

6 Bladder Cancer 56,000 new cases, 12,500 deaths in US. 2 pathways Papillary (85%) limited invasive or metastatic potential. 15% progress. Flat (15%) develop from CIS. Highly invasive with poor prognosis. What are the functional genomics and proteomics of these phenotypes and can markers or targets be identified?

7 TCCSUP High Grade Human Invasive J82 High Grade Human Invasive JB-V- Metastatic/Invasive variant of JP253. JP253 Human Low Grade, slight invasion RT4-Human Papilloma HUC- Immortalized normal Human Urothelial Cells

8 Does this Represent Real Biology?

9 Growth of J82 bladder cancer cell xenografts. Tumor Growth at Indicated Time Substrate Coinjection 7 Days 21 Days Plastic Matrigel + ++ Plastic SISgel 0 ++ SISgel Matrigel 0 + SISgel SISgel 0 0 Matrigel Matrigel + ++ Matrigel SISgel 0 0

10 The ECM can suppress the malignant phenotype What we see in culture represents real biological differences in the phenotype. The malignant phenotype can be suppressed by normal matrix. Understanding the mechanism might identify targets for therapy or biomarkers with which to stratify disease or risk.

11 The Genomic/Proteomic Approach Can we identify mechanisms that produce the malignant phenotype? Can we identify genes or proteins of mechanistic interest with a low false positive chance? Do we have bioinformatic techniques that allow us to assemble a system-level description of the mechanism that integrates genomics and proteomics?

12 Transcriptome Analysis 1186 cancer-related related genes on nylon. Filter out genes not expressed 3 sd above background. Leaves 311 expressed genes, all well annotated. More recently, 22,465 genes in genome-wide scan.

13 Expression Ratio of 311 Expressed Genes by Cell Type, Matrigel vs. SISgel M/S Expression Ratio Gene Number HUC RT4 JP253 JBV J82 TCCSUP

14 Expression Ratio of Genes in J82 Cells M/S Gene Number

15 Complexity and Simplicity Pairwise comparisons often add complexity to an already complex situation. Not really the answer to the questions we asked. The simplicity this side of complexity is worth nothing. The simplicity on the other side of complexity is worth everything. Oliver Wendell Holmes.

16 Supervised vs Unsupervised Analysis Unsupervised: Let the data talk to me. Finding relationships among data points. Biology is not inherent in such analysis. Requires replicates for statistical power. Supervised: : Data is referred to external identifiers that encapsulate biological relationships among experiments. Linkages increases power by raising a pattern above the noise. noise.

17 IFITM1 (Found by Statistical Analysis) HUC RT4 JP JB J82 SUP 3-D Column 1 3-D Plastic SIS Column 2 3-D Column Matrigel 3 SIS Plastic Matrigel Interferon inducible protein 9-27 (IFITM1) is a signaling component up-regulated in 3-D growth. Proposed protein-level functionality of IFITM1 deduced from literature and expression patterns.

18 Expr. Of Malignancy Initial "Template Model" for Systems Study of Transcriptome and Proteome Expressed Malignancy Matrigel (+) SISgel plastic HUC RT4 JP253 JB-V J82 TCCSUP Inherent Malignancy

19 Final Fit of "Template Model" for Systems Study of Transcriptome and Proteome Expressed Malignancy Expr. Of Malignancy 0 Matrigel (+) SISgel Plastic HUC RT4 JP253 JB-V J82 TCCSUP Inherent Malignancy

20 Genes Matching Template Number of genes with correlations exceeding the value in the first column as a function of model. Correlation coefficient # Genes Matching Model Complete Data Set # Genes Matching Model TCCSUP Excluded # Genes Matching Model Randomized Data > > > > >

21 Discriminant Function Analysis 15 genes distinguish the matrix on which the cells are growing. 14 genes identify the inherent malignancy of bladder cancer cells grown in 3-dimensional culture.

22 DFA with 8 genes distinguishes the growth substrate Root Root 1 Plastic SISgel Matrigel

23 Pathway Construction C10orf7 + S-Phase XRCC1 - PARP2 EGF PARP1 + EGR1 + TP53 + CCNB1 + Apoptosis

24 The Proteome Is not a direct connection to the transcriptome (i.e. mrna and protein are not equivalent.) Like the transcriptome, is dynamic and ever changing. It is not an entity in the sense the genome is. Post-translational translational modification Degradation Protein-protein interaction Splice variants RNA-level control of gene expression and translation

25 2-D D Gels of TCCSUP on Gels SISGel Matrigel

26 Personal Philosophy on New Methods A method grows beyond instrument development when real biology can be done fairly simply. That time is now for proteomics. It is important to avoid false positives.

27 2-D D Chromatography Automated system with 1 injection. First dimension is pi a a fundamental physical property that can be independently measured. Second dimension is hydrophobicity a fundamental physical property that can be standardized. Fractions are collected free of gel for MS identification.

28 1 st Dimension: J82 on Plastic (Full) UV-1 JA82_Matrigel_ ph Monitor JA82_Matrigel_ AU ph Minutes

29 1 st Dimension: J82 cells Plastic vs SISgel AU Plastic SISgel ph Minutes

30 1 st Dimension SISgel vs. Matrigel vs. Plastic Plastic Matrigel SISgel AU ph Minutes

31 1st Dimension Summary Major differences among first dimension separations. More protein eluted during ph gradient from cells grown on gels than on plastic. (confirms transcriptome findings) Proteomes of cells grown on SISgel and Matrigel show major qualitative and quantitative differences.

32 Components Identified in 2 nd Dimension Separations of 1.5 mg protein J82 cells grown on SISgel: 1984 J82 cells grown on Matrigel: 1582 J82 cells grown on Plastic: 1345

33 DeltaVue plastic vs Matrigel Overlay Plastic Difference Matrigel Matrigel

34 DeltaVue plastic vs Matrigel Overlay lane 15, Zoom to min all lanes Overlay Difference Matrigel

35 DeltaVue plastic vs Matrigel Overlay lane 14, Zoom to min all lanes Overlay Difference Matrigel

36 DeltaVue SISgel vs Matrigel Overlay lane 14, Zoom to min all lanes Overlay Difference Matrigel

37 DeltaVue plastic vs Matrigel Overlay lane 9, Zoom to min lanes Overlay Difference Matrigel

38 DeltaVue plastic vs Matrigel Overlay lane 7, Zoom to min lanes Overlay Difference Matrigel

39 Proteomes of J82 Cells on Matrigel and SISgel Matrigel SISgel

40 MS Identification of Proteins 28 components identified with major differences in expression. Lyophilization and trypsin digestion at ~30:1 molar ratio (estimated from UV absorbance). Zip-tip clean-up. MALDI-TOF and MS-MS for identification. Indications some fractions contain >1 proteins.

41 MS Analysis of Fractions ms2(2047) cid 230 MF33 F6+F70002, ms2(1919.1) cid 230 MF33 F6+F70002 Kratos PCAxima QIT V2.3.4 %Int. 4.7 mv 8.0 mv ER60 (protein disulfide isomerase) HSP [c].E Mass/Charge 3[c].E

42 Proteome vs Transcriptome Chaperones and protein-processing processing proteins have been identified tentatively as overrepresented in 13 tentative identifications. So far, based on 13 tentative protein identifications, the proteins identified as being differentially expressed show only modest correlation with the RNAs shown to be differentially expressed.

43 The Next Steps Build a database of components by pi-hydrophobocity and identify interesting components by techniques used in microarray studies as biology is varied systematically. Characterize the two gels. Attempt to identify pathways rather than individual proteins. Major emphasis to abundant proteins. Glycosylation a a major question. Integrate with transcriptome data to build dynamic picture of regulation of phenotype.

44 Integration of Transcriptome and Proteome The answer lies not in statistics but in cell biology. Discovery or hypothesis-generating phase. Hypothesis-testing testing phase.

45 Summary There are major differences among the same J82 cells grown on Matrigel, SISgel and plastic. Proteome is not simply a reflection of transcriptome It is not important to identify all proteins, but rather to identify with confidence differences related to mechanism. The Beckman 2-D 2 D chromatographic system for proteomics is reproducible and yields a picture of the proteome that captures its complexity.

46 Thanks to: My lab group: Kim Kyker Jean Coffman Norma McElwee Bioinformatics group (OMRF) Mike Centola Igor Dozmorov Nick Knowlton Beckman Coulter Mike Simonian, Edna Betgovarez MS Group Hiro Matsumoto Nobu Takemori