The successful treatment of cancer is dependent upon an accurate diagnosis of the tumor. It has become clear that while many tumors appear indistinguishable at the morphological level, they are in fact molecularly distinct, and such molecular distinctions can be predictive of clinical outcome. The present research proposal lays out a strategy for developing a molecular classification system for two of the most common human tumors: adenocarcinoma of the lung and prostate. The classification system will be based upon gene expression profiles obtained using DNA microarray technologies. There are three phases to the proposed project: 1) gene expression data collection for 42,000 genes and ESTs using oligonucleotide arrays for a series of lung and prostate adenocarcinoma patients with known clinical outcome, 2) classification model building using both supervised and unsupervised learning techniques, and 3) testing of the validity of these models on an independent set of lung and prostate adenocarcinoma samples. It is hoped that the development of a molecular classification system for these common tumors will help to optimize the use of existing anti-cancer therapies, and may also lay the groundwork for the development of new therapeutic strategies targeted to patients with particular subsets of these diseases.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
1U01CA084995-01
Application #
6074263
Study Section
Special Emphasis Panel (ZCA1-SRRB-7 (O2))
Program Officer
Jacobson, James W
Project Start
1999-09-30
Project End
2004-03-31
Budget Start
1999-09-30
Budget End
2000-03-31
Support Year
1
Fiscal Year
1999
Total Cost
Indirect Cost
Name
Dana-Farber Cancer Institute
Department
Type
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02215
Director's Challenge Consortium for the Molecular Classification of Lung Adenocarcinoma; Shedden, Kerby; Taylor, Jeremy M G et al. (2008) Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat Med 14:822-7
Dobbin, Kevin K; Beer, David G; Meyerson, Matthew et al. (2005) Interlaboratory comparability study of cancer gene expression analysis using oligonucleotide microarrays. Clin Cancer Res 11:565-72
Febbo, Phillip G; Lowenberg, Mark; Thorner, Aaron R et al. (2005) Androgen mediated regulation and functional implications of fkbp51 expression in prostate cancer. J Urol 173:1772-7
Sansal, Isabelle; Sellers, William R (2004) The biology and clinical relevance of the PTEN tumor suppressor pathway. J Clin Oncol 22:2954-63
Lieberfarb, Marshall E; Lin, Ming; Lechpammer, Mirna et al. (2003) Genome-wide loss of heterozygosity analysis from laser capture microdissected prostate cancer using single nucleotide polymorphic allele (SNP) arrays and a novel bioinformatics platform dChipSNP. Cancer Res 63:4781-5
Rossi, Sabrina; Graner, Edgard; Febbo, Phillip et al. (2003) Fatty acid synthase expression defines distinct molecular signatures in prostate cancer. Mol Cancer Res 1:707-15
Singh, Dinesh; Febbo, Phillip G; Ross, Kenneth et al. (2002) Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1:203-9
Signoretti, Sabina; Di Marcotullio, Lucia; Richardson, Andrea et al. (2002) Oncogenic role of the ubiquitin ligase subunit Skp2 in human breast cancer. J Clin Invest 110:633-41
Bhattacharjee, A; Richards, W G; Staunton, J et al. (2001) Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci U S A 98:13790-5