A whack at using SELDI for H&N cancer doesn't do so well:
>>Clinical Cancer Research Vol. 10, 4806-4812, July 15, 2004
Molecular Oncology, Markers, Clinical Correlates
The Use of Plasma Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry Proteomic Patterns for Detection of Head and Neck Squamous Cell Cancers
Scott G. Soltys1, Quynh-Thu Le1, Gongyi Shi1, Robert Tibshirani2, A. J. Giaccia1 and Albert C. Koong1
1 Department of Radiation Oncology, and 2 Health Policy and Research, Stanford University, Stanford, California
Purpose: Our study was undertaken to determine the utility of plasma proteomic profiling using surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry for the detection of head and neck squamous cell carcinomas (HNSCCs).
Experimental Design: Pretreatment plasma samples from HNSCC patients or controls without known neoplastic disease were analyzed on the Protein Biology System IIc SELDI-TOF mass spectrometer (Ciphergen Biosystems, Fremont, CA). Proteomic spectra of mass:charge ratio (m/z) were generated by the application of plasma to immobilized metal-affinity-capture (IMAC) ProteinChip arrays activated with copper. A total of 37,356 data points were generated for each sample. A training set of spectra from 56 cancer patients and 52 controls were applied to the "Lasso" technique to identify protein profiles that can distinguish cancer from noncancer, and cross-validation was used to determine test errors in this training set. The discovery pattern was then used to classify a separate masked test set of 57 cancer and 52 controls. In total, we analyzed the proteomic spectra of 113 cancer patients and 104 controls.
Results: The Lasso approach identified 65 significant data points for the discrimination of normal from cancer profiles. The discriminatory pattern correctly identified 39 of 57 HNSCC patients and 40 of 52 noncancer controls in the masked test set. These results yielded a sensitivity of 68% and specificity of 73%. Subgroup analyses in the test set of four different demographic factors (age, gender, and cigarette and alcohol use) that can potentially confound the interpretation of the results suggest that this model tended to overpredict cancer in control smokers.
Conclusions: Plasma proteomic profiling with SELDI-TOF mass spectrometry provides moderate sensitivity and specificity in discriminating HNSCC. Further improvement and validation of this approach is needed to determine its usefulness in screening for this disease.<<
The problem with this "approach" may be the the Lasso technique, as opposed to SELDI itself. Much better results have been obtained via decision tree algorithms (and perhaps different chips?):
>>Arch Otolaryngol Head Neck Surg. 2004 Jan;130(1):98-104.
Identification of patients with head and neck cancer using serum protein profiles.
Wadsworth JT, Somers KD, Stack BC Jr, Cazares L, Malik G, Adam BL, Wright GL Jr, Semmes OJ.
Department of Otolaryngology-Head & Neck Surgery, Eastern Virginia Medical School, Norfolk 23507, USA. wadswojt@evms.edu
BACKGROUND: New and more consistent biomarkers of head and neck squamous cell carcinoma (HNSCC) are needed to improve early detection of disease and to monitor successful patient management. OBJECTIVE: To determine if a new proteomic technology can correctly identify protein expression profiles for cancer in patient serum samples as well as detect the presence of a known tumor marker. DESIGN: Direct proteomic analysis and comparison. METHODS: The surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF) ProteinChip system was used to screen for differentially expressed proteins in serum samples from 99 patients with HNSCC, 25 "healthy" smokers, and 102 healthy (normal) controls. Protein peak clustering and classification analyses of the SELDI spectral data were performed. RESULTS: Several proteins, with masses ranging from 2778 to 20,800 Da, were differentially expressed between patients with HNSCC and the normal controls. The serum protein expression profiles were used to develop a classification tree algorithm, which achieved a sensitivity of 83.3% and a specificity of 90% in discriminating HNSCC from normal and healthy smoker controls. The positive and negative predictive values were 80% and 92%, respectively. A peak with an average mass of 10,068 Da was detected in sera from HNSCC patients and identified as the known biomarker metallopanstimulin-1 (MPS-1), based on mass. Peak relative intensity of the 10,068-Da protein correlated consistently with MPS-1 levels detected by radioimmunoassay in serum samples of HNSCC patients and controls. The 10,068-Da peak was provisionally identified as MPS-1 by SELDI immunoassay. CONCLUSION: We propose that this technique may allow for the development of a reliable screening test for the early detection and diagnosis of HNSCC, as well as the potential identification of tumor biomarkers.<<
Cheers, Tuck |
