10:25 Chairperson’s Remarks

10:30 Microarrays: Bench-to-Bedside
Towia Libermann , Ph.D., Associate Professor of Medicine, Beth Israel Deaconess Medical Center
Functional genomics and proteomics approaches have rapidly evolved over the last years and have provided the basis for groundbreaking discoveries in basic and clinical research. As the technologies become more mature and validated, bench-to-bedside clinical applications rapidly emerge. Our focus has been to identify gene signatures in patients with various types of cancer for early detection, cancer progression, metastasis, survival as well as resistance or sensitivity to therapy. We have developed novel bioinformatics approaches for biomarker discovery and individualized gene expression analysis that are enhancing the identification of aberrant signaling pathways in individual patients and clinical application of microarray data for patient stratification and management. Examples of microarray and bioinformatics approaches and their potential clinical applications in renal, prostate and breast cancer will be presented together with the implementation of the new fully automatized Affymetrix platform for high throughput 96 well microarray analysis.

11:00 Molecular Classification of Gliomas: Let the Data Inform Treatment
Jean Claude Zenklusen, Ph.D., Staff Scientist, Neuro-Oncology Branch, National Institutes of Health, National Cancer Institute
Primary brain tumors are the fourth leading cause of cancer mortality in adults under the age of 54 and the leading cause of cancer mortality in children in the United States due to suboptimal therapeutic approaches, hindered by lack of understanding of the biology of these tumors. In the framework of the Glioma Molecular Diagnostic Initiative at the NCI, we have collected and molecularly characterized (at both RNA and DNA) over 500 gliomas with corollary clinical data. Here we present a novel, comprehensive, classification of Gliomas (both high and low grade) based on their mRNA expression profiles, which were analyzed using a variety of class discovery algorithms (HC, K-mean, NMF) without using any pre-conceived notion of their biological or clinical/histopathological grouping. The results were validated in a separate datasets (either produced by our own group or from previously published reports), and classify these tumors into six distinct subclasses having a nested hierarchy correlating with the tumor’s characteristics from both biological and clinical standpoints. This novel classification along with the wealth of genomic data produced by the GMDI may allow for the development of a more rational, objective, diagnostic of gliomas and serve as an important starting point in the search for new molecular therapeutic targets.

11:30 Microarray Data Analysis
Yudong He, Ph.D., Scientist, Rosetta Merck

12:00 pm Lunch and Learn Workshops
(Sponsorship Available) or Lunch on Your Own

1:25 pm Chairperson’s Remarks

1:30 Response Markers to Imatinib Mesylate in the Treatment of Gastrointestinal Stromal Tumor
Andrew Godwin, M.D., Member, Director, Clinical Molecular Genetics Laboratory; Director, Biosample Repository, Medical Oncology, Fox Chase Cancer Center
Most Gastrointestinal Stromal Tumors (GISTs) contain gain-of-function, i.e., oncogenic mutations in c-KIT or in Platelet Derived Growth Factor Receptor alpha (PDGFRa) and GIST patients are treated with imatinib mesylate, originally referred to as STI571 or GleevecTM, an oral 2-phenylaminopyrimidine derivative that acts as a selective inhibitor against oncogenic forms of KIT, PDGFa, and BCR-ABL. What is becoming apparent is that clinical management of GIST may benefit from molecular characterization, i.e., pre-selecting patients for treatment with imatinib or additional first and second line therapies based on c-KIT and PDGFRa mutational status or predictive gene signatures. As part of a recent Phase II Trial of neoadjuvant/adjuvant imatinib for advanced primary and recurrent operable GISTs (RTOG-0132), we used gene profiling of pre- and post-imatinib treated biopsies to better define prognostic markers. The analysis identified 38 genes as differentially expressed in pretreatment samples between responders and nonresponders, with all genes present at higher levels in nonresponders. Interestingly, seven of these genes mapped to a single locus on chromosome 19p and a subset of these faithfully predicted likely response to imatinib-based therapy in naive panel of GISTs.

2:00 COXEN: Genomic Prediction of Patients’ Responses to Combination Chemotherapeutics
Jae Lee, M.D., Associate Professor, Public Health Sciences, University of Virginia School of Medicine
The U.S. National Cancer Institute has used a panel of 60 diverse human cancer cell lines (the NCI-60) to screen >100,000 chemical compounds for anticancer activity. We asked whether it would be possible to identify common chemosensitivity biomarkers from that rich database to predict drug activity in cell types not included in the NCI-60 panel or, even further, clinical responses in patients with tumors. We address that challenge by developing a novel pharmacogenomic prediction approach "Co-eXpression ExtrapolatioN" (COXEN), which can effectively identify concordant genomic chemosensitivity biomarkers between two independent expression profiling data sets, here extrapolating the genomic expression patterns of NCI-60 biomarkers with those of clinical tumors. Applying our COXEN approach in a prospective fashion, we predicted anticancer drug activities on human patients in breast, ovarian, bladder cancer, and other types of cancer, who were treated with commonly used combination chemotherapeutics. We also used COXEN for in silico screening of 45,545 compounds and identify a novel agent with superior growth inhibition activity against human bladder cancer.

2:30 Use of Genomics as a Tool to Identify Stage-Appropriate Therapeutic Intervention Strategies
Marti Jett, M.D., Chief, Department of Molecular Pathology, Walter Reed Army Institute Research
Genomic patterns have the potential to reveal the progression of host responses during illness and help to pinpoint early indicators of tissue involvement or organ failure. In combination with limited proteomics, these findings can be used to identify markers that will permit therapeutic efforts to be focused to divert impending serious outcomes. Our studies using a systems approach to integrate clinical, physiological, -omics and mathematical modeling have revealed stages of illness progression during which time certain standard therapies may no longer be effective, yet they also reveal other therapeutic strategies. The aim is to identify sets of biomarkers that could be rapidly determined for near-real-time assessment of patient status.

3:00 Networking Refreshment Break, Poster and Exhibit Viewing