Serum alpha 2-HS glycoprotein predicts survival in patients with glioblastoma.

BACKGROUND: Glioblastoma, the most common primary brain tumor, has variable prognosis. We aimed to identify serum biomarkers that predict survival of patients with glioblastoma. METHODS: In phase 1 (biomarker discovery), SELDI-TOF mass spectra were studied in 200 serum samples from 58 control subjects and 36 patients with grade II astrocytoma, 15 with anaplastic astrocytoma, and 91 with glioblastoma. To identify potential biomarkers, we searched for peptide peaks that changed progressively in size with increasing malignancy. One peak, identified as the B-chain of alpha 2-Heremans-Schmid glycoprotein (AHSG), was less prominent with increasing tumor grade. We therefore investigated AHSG as a survival predictor in glioblastoma. We measured serum AHSG by turbidimetry and determined indices of malignancy, including tumor proliferation (Ki67 immunolabel) and necrosis (tumor lipids on magnetic resonance spectroscopy). In phase 2 (biomarker validation), the prognostic power of AHSG was validated in an independent group of 72 glioblastoma patients. RESULTS: Median survival was longer (51 vs 29 weeks) in glioblastoma patients with normal vs low serum AHSG concentrations (hazard ratio 2.7, 95% CI 1.5-5.0, P <0.001), independent of age and Karnofsky score. Serum AHSG inversely correlated with Ki-67 immunolabeling and tumor lipids. A prognostic index combining serum AHSG with patient age and Karnofsky score separated glioblastoma patients with short (<3 months) and long (>2 years) median survival. The prognostic value of serum AHSG was validated in a different cohort of glioblastoma patients. CONCLUSIONS: We conclude that serum AHSG concentration, measured before starting treatment, predicts survival in patients with glioblastoma.

OMICS and brain tumour biomarkers.

Currently, brain tumours are diagnosed by surgical biopsy and light microscopic examination of tissue, with immunohistochemistry in difficult cases. We review research in the field of brain tumour diagnosis and discuss several new approaches. In future, tumour type, optimal treatment, and prognosis could be obtained by studying the gene (genomics), protein (proteomics) or metabolite (metabolomics) content of tumour cells. These techniques generate complex data, analysed using techniques such as pattern recognition software to identify biomarker signatures of different tumours. Compared with individual biomarkers, biomarker signatures appear to increase diagnostic accuracy and may produce an improved brain tumour classification system.

Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum.

BACKGROUND: We investigated the potential of proteomic fingerprinting with mass spectrometric serum profiling, coupled with pattern recognition methods, to identify biomarkers that could improve diagnosis of tuberculosis. METHODS: We obtained serum proteomic profiles from patients with active tuberculosis and controls by surface-enhanced laser desorption ionisation time of flight mass spectrometry. A supervised machine-learning approach based on the support vector machine (SVM) was used to obtain a classifier that distinguished between the groups in two independent test sets. We used k-fold cross validation and random sampling of the SVM classifier to assess the classifier further. Relevant mass peaks were selected by correlational analysis and assessed with SVM. We tested the diagnostic potential of candidate biomarkers, identified by peptide mass fingerprinting, by conventional immunoassays and SVM classifiers trained on these data. FINDINGS: Our SVM classifier discriminated the proteomic profile of patients with active tuberculosis from that of controls with overlapping clinical features. Diagnostic accuracy was 94% (sensitivity 93.5%, specificity 94.9%) for patients with tuberculosis and was unaffected by HIV status. A classifier trained on the 20 most informative peaks achieved diagnostic accuracy of 90%. From these peaks, two peptides (serum amyloid A protein and transthyretin) were identified and quantitated by immunoassay. Because these peptides reflect inflammatory states, we also quantitated neopterin and C reactive protein. Application of an SVM classifier using combinations of these values gave diagnostic accuracies of up to 84% for tuberculosis. Validation on a second, prospectively collected testing set gave similar accuracies using the whole proteomic signature and the 20 selected peaks. Using combinations of the four biomarkers, we achieved diagnostic accuracies of up to 78%. INTERPRETATION: The potential biomarkers for tuberculosis that we identified through proteomic fingerprinting and pattern recognition have a plausible biological connection with the disease and could be used to develop new diagnostic tests.

Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra.

A computer-based decision support system to assist radiologists in diagnosing and grading brain tumours has been developed by the multi-centre INTERPRET project. Spectra from a database of 1H single-voxel spectra of different types of brain tumours, acquired in vivo from 334 patients at four different centres, are clustered according to their pathology, using automated pattern recognition techniques and the results are presented as a two-dimensional scatterplot using an intuitive graphical user interface (GUI). Formal quality control procedures were performed to standardize the performance of the instruments and check each spectrum, and teams of expert neuroradiologists, neurosurgeons, neurologists and neuropathologists clinically validated each case. The prototype decision support system (DSS) successfully classified 89% of the cases in an independent test set of 91 cases of the most frequent tumour types (meningiomas, low-grade gliomas and high-grade malignant tumours--glioblastomas and metastases). It also helps to resolve diagnostic difficulty in borderline cases. When the prototype was tested by radiologists and other clinicians it was favourably received. Results of the preliminary clinical analysis of the added value of using the DSS for brain tumour diagnosis with MRS showed a small but significant improvement over MRI used alone. In the comparison of individual pathologies, PNETs were significantly better diagnosed with the DSS than with MRI alone.

A novel and accurate diagnostic test for human African trypanosomiasis.

INTRODUCTION: Human African trypanosomiasis (sleeping sickness) affects up to half a million people every year in sub-Saharan Africa. Because current diagnostic tests for the disease have low accuracy, we sought to develop a novel test that can diagnose human African trypanosomiasis with high sensitivity and specificity. METHODS: We applied serum samples from 85 patients with African trypanosomiasis and 146 control patients who had other parasitic and non-parasitic infections to a weak cation exchange chip, and analysed with surface-enhanced laser desorption-ionisation time-of-flight mass spectrometry. Mass spectra were then assessed with three powerful data-mining tools: a tree classifier, a neural network, and a genetic algorithm. FINDINGS: Spectra (2-100 kDa) were grouped into training (n=122) and testing (n=109) sets. The training set enabled data-mining software to identify distinct serum proteomic signatures characteristic of human African trypanosomiasis among 206 protein clusters. Sensitivity and specificity, determined with the testing set, were 100% and 98.6%, respectively, when the majority opinion of the three algorithms was considered. This novel approach is much more accurate than any other diagnostic test. INTERPRETATION: Our report of the accurate diagnosis of an infection by use of proteomic signature analysis could form the basis for diagnostic tests for the disease, monitoring of response to treatment, and for improving the accuracy of patient recruitment in large-scale epidemiological studies.