Silicon Investor (SI) -- The First Internet Community

STOCKTALK

We've detected that you're using an ad content blocking browser plug-in or feature. Ads provide a critical source of revenue to the continued operation of Silicon Investor. We ask that you disable ad blocking while on Silicon Investor in the best interests of our community. If you are not using an ad blocker but are still receiving this message, make sure your browser's tracking protection is set to the 'standard' level.

Biotech / Medical : Ciphergen Biosystems(CIPH): -- Ignore unavailable to you. Want to Upgrade?

To: tuck who wrote (123)	5/27/2003 12:08:22 PM
From: tuck	Read Replies (1) \| Respond to of 510

The Hopkins team mentioned in the previous post receives funding from CIPH in return for licensing rights to the biomarkers. So is this team, which previously dedicated its efforts to prostate cancer biomarkers. This is the first study I've seen for head and neck cancer: >>Serum Protein Expression Profiles Identify Head and Neck Cancer Lisa Cazares, J.T. Wadsworth, K.D. Somers, Bao-Ling Adam, B.C. Stack Jr., O.J. Semmes, G.L. Wright Jr., Eastern Virginia Medical School, Norfolk, VA; Penn State College of Medicine, Hershey, PA. Head and neck squamous cell cancer (HNSCC) comprises over 5% of all cancers in the United States and an even larger proportion of cancers worldwide. Aside from a complete head and neck physical examination and imaging studies in those patients with suspicious findings, there are no accepted methods to screen for these cancers. New screening tools are needed to improve early detection of this disease and to monitor successful patient management. We performed a study using surface-enhanced laser desorption/ionization (seldi) mass spectrometry to screen for differentially expressed proteins in serum from 100 patients with HNSCC and 100 normal controls. Protein peak clustering and classification analyses were performed using the Biomarker Wizard and Biomarker Patterns software (version 3.0), respectively (Ciphergen Biosystems, Inc.). Using the IMAC3 ProteinChip® surface, an average of 80 protein peaks, with masses ranging from 2,778 to 20,800 Da, were resolved. Thirty-two of these were found to be differentially expressed in HNSCC and the healthy control group. The serum protein expression profiles were used to train and develop decision tree classification algorithms which achieved a sensitivity of 90% and a specificity of 92.5% in discriminating HNSCC from normal controls. Thirteen protein peaks were utilized for the generation of the best classification tree, which predicted HNSCC with a sensitivity of 83.3% and specificity of 100%, from a test set of 51 serum samples not used to train the algorithm. Based on these results, we propose that seldi protein profiling followed by classification tree analysis has tremendous potential for the development of a screening test for the early detection/diagnosis of HNSCC. << Cheers, Tuck