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Lecture 8. Functional Genomics: Gene Expression Profiling using DNA microarrays. Part II

Lecture 8. Functional Genomics: Gene Expression Profiling using DNA microarrays. Part II. Clark EA, Golub TR, Lander ES, Hynes RO.(2000) Genomic analysis of metastasis reveals an essential role for RhoC. Nature . 406:532-535.

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Lecture 8. Functional Genomics: Gene Expression Profiling using DNA microarrays. Part II

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  1. Lecture 8. Functional Genomics: Gene Expression Profiling using DNA microarrays. Part II Clark EA, Golub TR, Lander ES, Hynes RO.(2000) Genomic analysis of metastasis reveals an essential role for RhoC. Nature. 406:532-535. **Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS, Thorsen T, Quist H, Matese JC, Brown, PO, Botstein D, Eystein Lonning P, Borresen-Dale AL. 2001. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. PNAS 98:10869-10874.

  2. Two ways to Use Gene Expression Profiling Golub et al: Discovery Hypothesis Experimentation

  3. Clark EA, Golub TR, Lander ES, Hynes RO.(2000) Genomic analysis of metastasis reveals an essential role for RhoC. Nature. 406:532-535

  4. Gene Profiling with 7000 gene human chip or 6000 gene mouse chip Discovery: RhoC is upregulated in metastatic tumors RNAse Protection assay confirms the results

  5. Hypothesis: Overexpression of RhoC will increase tumor metastasis Test: Use a retrovirus vector to introduce RhoC into low metastatic cell line or RhoC dominant negative form into highly metastatic cell line Parents: low high +RhoC-DN +RhoC

  6. In vitro sssays confirm the role of RhoC in cell migration and invasiveness

  7. RhoC alters cell morphology Parents: low high +RhoC-DN +RhoC

  8. Two ways to Use Gene Expression Profiling Golub et al: Discovery Hypothesis Experimentation Perou et al: Discovery: Can Gene expression profile be a diagnostic/ prognostic tool for human cancer?

  9. Tumor Clusters Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, Fluge O, Pergamenschikov A, Williams C, Zhu SX, Lonning PE, Borresen-Dale AL, Brown PO, Botstein D. 2000. Molecular portraits of human breast tumours. Nature. 406:747-52. Gene Clusters RNA isolated from 40 breast tumors and four normal breast samples; compared to RNA pooled from 11 different human tumor cell lines; cDNA microarray containing ~8000 gene used. Analysis: Hierarcial clustering Result: Tumors (top) are heterogeneous and many clusters are found; functional gene clusters among all tumors can be identified

  10. Tumor Clusters Select genes whose expression differs the MOST BETWEEN tumor samples: 456 set of “Intrinsic Genes” Repeat Cluster analysis With these Genes: Result: The 40 Tumors are organized into 4 clusters Gene Clusters

  11. Question: Do Gene Profiles Have Clinical Significance? Sorlie T, et al. 2001. PNAS 98:10869-10874. RNA from 78 breast tumors, 3 benign breast lesions, 4 normal breast samples tested with the 456 gene set identified in the previous study. Control was the same as last time {RNA from 11 different human tumor cell lines). Result: Tumors can be organized into 5 (or 6) clusters.

  12. Clinical Outcome Can be Correlated to Gene Expression Clusters 49 tumor samples (non-metastatic) correlated to patient survival Relapse Free Survival Overall Survival

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