Public Data from our Genomics and Proteomics Studies
1. Data from the following links are used in the Price et al. PNAS paper published in 2007
http://www3.mdanderson.org/~genomics/sarcoma_data_matrix_for_supplemental.zip
http://www3.mdanderson.org/~genomics/SupplementalTable2caption.doc
http://www3.mdanderson.org/~genomics/SupplementalTableSampleClassifications.pdf
Press release on this work.
Simple Two-Gene Test Sorts Out Similar Gastrointestinal Cancers Top Scoring Pair Analysis Applicable to Other Cancers, Personalized Care M. D. Anderson News Release 02/12/07 A powerful two-gene test distinguishes between a pair of nearly identical gastrointestinal cancers that require radically different courses of treatment, researchers report this week in the online Early Edition of the Proceedings of the National Academy of Sciences. "This simple and accurate test has the potential to be relatively quickly implemented in the clinic to benefit patients by guiding appropriate treatment," says senior author Wei Zhang, Ph.D., professor in the Department of Pathology at The University of Texas M. D. Anderson Cancer Center. The analytical technique employed to tell gastrointestinal stromal tumor (GIST) from leiomyosarcoma (LMS) with near perfect accuracy will have wider application in more individualized diagnosis and treatment of other types of cancer, study co-authors from M. D. Anderson and the Institute for Systems Biology in Seattle conclude. GIST was once thought to be a type of leiomyosarcoma because both originate in the smooth muscle cells of the gastrointestinal tract. However, GIST is treatable with the targeted medication known as Gleevec and is relatively unresponsive to chemotherapy. The opposite is true of LMS. An existing test distinguishes among the two cancers with about 87% accuracy, but intensive and time-consuming additional analyses are required for uncertain cases, Zhang says. The researchers used common whole genome microarrays to measure gene expression in 68 GIST or LMS tumors, but then applied an analytical twist. Rather than identifying multiple genes that might distinguish each type of cancer, the researchers instead analyzed every possible pair of genes, says first author Nathan Price, Ph.D., research scientist at the Institute for Systems Biology, a process called Top Scoring Pair analysis. The result was a cancer classifier based on the expression ratio of two genes. If the gene OBSCN expresses more of its RNA than the gene C9orf65, then the tumor is GIST. If C9orf65 is more abundant, it's LMS. The test accurately identified 67 of the 68 microarrayed tumors, with the exception being one tumor with nearly a 50-50 split between the two expressed genes upon which no diagnosis could be made. An additional test using a more accurate measurement procedure on the two genes identified 20 of the original samples (including the sample with near equal gene expression) and 19 independent samples with 100% accuracy, the authors report. Genomic approaches to diagnosing, selecting treatment and determining a cancer patient's prospects of responding to care are beginning to work their way into the clinic, the researchers note. These approaches can rely on dozens of genes as biomarkers. Top scoring pair analysis allows the use of fewer genes to distinguish between similar cancers or between groups of patients who have one type of cancer yet respond differently based on genetic indicators, the authors note. For example, paired gene analysis may be used to determine which patients benefit from different types of chemotherapy and which patients are at risk of relapse. Zhang said the research group is using this analytical strategy to identify gene pairs that can predict which GIST patients respond to Gleevec and how other types of cancer respond to treatment as well. Co-authors with Zhang and Price are: Jonathan Trent, M.D., Ph.D., of M. D. Anderson Department of Sarcoma Medical Oncology; Adel El-Naggar, M.D., Ph.D., David Cogdell, and Ellen Taylor, all of M. D. Anderson's Department of Pathology; Kelly Hunt, M.D., and Raphael E. Pollock, M.D., Ph.D., of M. D. Anderson Department of Surgical Oncology; and Leroy Hood, Ph.D., M.D. and president, and Ilya Shmulevich, Ph.D., of the Institute for Systems Biology. This research was funded by the National Cancer Institute and the National Institute of General Medical Sciences, both of the National Institutes of Health; the Commonwealth Foundation for Cancer Research, the American Cancer Society, the Texas Tobacco Settlement Fund, and by grants from the Michael and Betty Kadoorie Foundation and the Goodwin Fund.
2. Data used in the Natarajan Mendes et al. Journal of Proteome Research paper 2007.
http://www3.mdanderson.org/~genomics/data 90 cell line array 12_06-06.csv
http://www3.mdanderson.org/~genomics/lysate.zip
Press coverage about this paper
Breaking News on Biopharmaceutical Science and Business Identifying the connections between cancer pathways By Dr Matt Wilkinson
13/06/2007- Researchers have used a protein lysate array to profile and classify multiple components of aberrant cell signalling pathways in 90 cancer cell lines. The new research, published in an early view article in the Journal of Proteome Research not only identified potential biomarkers but also common pathways and crosstalking between those pathways. This information could help develop treatment strategies that simultaneously inhibit multiple key kinase signalling pathways that optimise the overall therapeutic benefit of anticancer agents. The researchers, led by Dr Wei Zhang of the University of Texas M.D. Anderson Cancer Center, US, used custom designed protein microarrays to study the relationships between different signalling pathways in different types of cancer cell. These cancer cell lines comprised 12 different types of cancer cell, including breast, colon, glioma (a cancer of the central nervous system), kidney, leukaemia, lung, lymphoma, melanoma, ovarian, pancreatic, prostate and sarcoma (a soft tissue cancer). By profiling multiple components of aberrant signalling pathways at the same time using the array the researchers could determine potential relationships between the signalling pathways of different types of cancer. The cell lines were first lysed and diluted before being printed on PVDF-coated slides using a G3 robotic array spotter from Genomic Solutions. Each slide was then incubated with 53 antibodies for specific signalling pathways including: PI3-K, growth factor / integrin signalling, apoptosis, cell cycle and others. Protein expression levels and their phosphorylation status were then observed using a DakoCytomation catalysed signal amplification kit from Dako, US. The results showed that the PI3-K pathway was up-regulated in several different tumour types while the VEGF angiogenesis (vascular endothelial growth factor) pathway was down-regulated in haematopoetic tumours. Analysis of individual cancer types showed the up-regulation of the PI3-K (phosphoinositide 3-kinase) signalling pathway in glioma, with the cyclin-dependant kinase (CDK), a protein that couples mitogenic and antimitogenic extracellular signals with the cell cycle, being most significantly overexpressed. Colon cancers have high levels of retinoblastoma (Rb) and the proto-oncogene c-myc proteins compared to the other types of cancer, whereas sarcoma have significantly lower levels of Rb and RSK (Ribosomal s6 kinase) along with a high expression level of CDK4 inhibitor p16ink. Lung cancer cell lines were shown to exhibit lower expression of MAPK (mitogen-activated protein kinase) and cytochrome IV-2 than other cancer cell lines while expressing higher levels of total and phosphorylated pyruvate dehydrogenase kinase (PDK). Both pancreatic and prostate cancers showed increased levels of IGFBP2 (insulin-like growth factor binding protein 2) expression although pancreatic cancer was the only type of cancer where EGFR was significantly differently expressed and clustered with another protein - phosphorylated c-Src (a proto-oncogenic tyrosine kinase).