Gene Fusion in Cancer

Our overall hypothesis posits that global gene expression analysis will help us elucidate a select set of genes that dictate whether a clinically localized prostate cancer exhibits a progressive or non-progressive phenotype. Complementary global protein studies (proteomics) will help identify tumor and circulating proteins as biomarker candidates. We recognize that the underlying predispositions from genetic and environmental/ nutritional/hormonal mechanisms will be heterogeneous, so that multiple patterns of carcinogenesis and of response to treatments must be expected and must be differentiated. Some of these molecular patterns will be specific to prostate cancer, while others will be shared by subsets of cancers arising in other organs.

Overall Goals

1. To create an integrated molecular model to explore at a systems level the roles of androgen receptor (AR) signaling and regulation.
2. To understand the roles of transcription factor families in progression, invasion, and metastasis of prostate cancers.

Themes and Specific Aims

1. Transcription Factor Network Controlled by the Androgen Receptor in Prostate Cancer Cells:
   To create an integrated model for prostate cancer progression using microarray gene expression, MPSS transcripts, proteomics, and protein-protein interaction data and text.
2. Successful Genomic-Scale Screening for Gene Fusions in Human Solid Tumors by Integrative Biomedical Informatics:
   To generate an integrative translational bioinformatics model for the prediction of novel gene fusions in human solid tumors, with experimental validation using fluorescent in situ hybridization (FISH) with split probes located at the side of each gene, by RT-PCR or 5'-RACE.
3. Discovery and design of small molecules for treatment of cancers, using a systems biology approach:
   To mine data from MiMI and Oncomine for targets, and combine those findings with structure and ligand information in PDB and other databases to develop system-level approaches to identify novel small molecules for NCIBI prostate cancer therapies.
4. Development of the Metabolome Ontology:
   To integrate metabolomic data-sets and use metabolite patterns as biomarkers for disease state, progression and treatment outcome.

Related Publications

Laxman B, Morris DS, Yu J, Siddiqui J, Cao J, Mehra R, Lonigro RJ, Tsodikov A, Wei JT, Tomlins SA, Chinnaiyan AM. A first-generation multiplex biomarker analysis of urine for the early detection of prostate cancer. Cancer Res 2008; 68(3): 645-9. PMID: 18245462.

Taylor BS, Pal M, Yu J, Laxman B, Kalyana-Sundaram S, Zhao R, Menon A, Wei JT, Nesvizhskii AI, Ghosh D, Omenn GS, Lubman DM, Chinnaiyan AM, Sreekumar A. Humoral response profiling reveals pathways to prostate cancer progression. Mol Cell Proteomics 2008; 7(3): 600-11. PMID: 18077443.

Tomlins SA, Rhodes DR, Yu J, Varambally S, Mehra R, Perner S, Demichelis F, Helgeson BE, Laxman B, Morris DS, Cao Q, Cao X, Andrén O, Fall K, Johnson L, Wei JT, Shah RB, Al-Ahmadie H, Eastham JA, Eggener SE, Fine SW, Hotakainen K, Stenman UH, Tsodikov A, Gerald WL, Lilja H, Reuter VE, Kantoff PW, Scardino PT, Rubin MA, Bjartell AS, Chinnaiyan AM. The role of SPINK1 in ETS rearrangement-negative prostate cancers. Cancer Cell 2008; 13(6): 519-28. PMID: 18538735.

Morris DS, Tomlins SA, Rhodes DR, Mehra R, Shah RB, Chinnaiyan AM. Integrating biomedical knowledge to model pathways of prostate cancer progression. Cell Cycle 2007; 6(10): 1177-87. PMID: 17495538.

Rhodes DR, Kalyana-Sundaram S, Mahavisno V, Varambally R, Yu J, Briggs BB, Barrette TR, Anstet MJ, Kincead-Beal C, Kulkarni P, Varambally S, Ghosh D, Chinnaiyan AM. Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. Neoplasia 2007; 9(2): 166-80. PMID: 17356713.

Tomlins SA, Mehra R, Rhodes DR, Cao X, Wang L, Dhanasekaran SM, Kalyana-Sundaram S, Wei JT, Rubin MA, Pienta KJ, Shah RB, Chinnaiyan AM. Integrative molecular concept modeling of prostate cancer progression. Nat Genet 2007; 39(1): 41-51. PMID: 17173048.

Mo F, Hong X, Gao F, Du L, Wang J, Omenn GS, Lin B. A compatible exon-exon junction database for the identification of exon skipping events using tandem mass spectrum data. BMC Bioinformatics (in press).

Tu LC, Yan X, Hood L, Lin B. Proteomics analysis of the interactome of N-myc downstream regulated gene 1 and its interactions with the androgen response program in prostate cancer cells. Mol Cell Proteomics 2007; 6(4): 575-88. PMID: 17220478.

Lin B, White JT, Lu W, Xie T, Utleg AG, Yan X, Yi EC, Shannon P, Khrebtukova I, Lange PH, Goodlett DR, Zhou D, Vasicek TJ, Hood L. Evidence for the presence of disease-perturbed networks in prostate cancer cells by genomic and proteomic analyses: a systems approach to disease. Cancer Res 2005; 65(8): 3081-91. PMID: 15833837.

Yu J, Yu J, Almal AA, Dhanasekaran SM, Ghosh D, Worzel WP, Chinnaiyan AM. Feature selection and molecular classification of cancer using genetic programming. Neoplasia 2007; 9(4): 292-303. PMID: 17460773.

Tomlins SA, Chinnaiyan AM. Of mice and men: cancer gene discovery using comparative oncogenomics. Cancer Cell 2006; 10(1): 2-4. PMID: 16843259.

Tomlins SA, Mehra R, Rhodes DR, Smith LR, Roulston D, Helgeson BE, Cao X, Wei JT, Rubin MA, Shah RB, Chinnaiyan AM. TMPRSS2:ETV4 gene fusions define a third molecular subtype of prostate cancer. Cancer Res 2006; 66(7): 3396-400. PMID: 16585160.

Tomlins SA, Rhodes DR, Perner S, Dhanasekaran SM, Mehra R, Sun XW, Varambally S, Cao X, Tchinda J, Kuefer R, Lee C, Montie JE, Shah RB, Pienta KJ, Rubin MA, Chinnaiyan AM. Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science 2005; 310(5748): 644-8. PMID: 16254181.

Wang X, Yu J, Sreekumar A, Varambally S, Shen R, Giacherio D, Mehra R, Montie JE, Pienta KJ, Sanda MG, Kantoff PW, Rubin MA, Wei JT, Ghosh D, Chinnaiyan AM. Autoantibody signatures in prostate cancer. N Engl J Med 2005; 353(12): 1224-35. PMID: 16177248.