Proteomic prediction of disease outcome in cancer : clinical framework and current status

Quantitative Pathology in Tissue MR Spectroscopy Based Human Prostate Metabolomics

At present, the clinical utility of metabolomic profiles of human prostate tissue relies on the establishment of correlations between metabolite data and clinical measurements, particularly pathological findings. Because metabolomics is a quantitative study, its clinical value can be rigorously investigated by determining its association with other quantitative measures. The human visual assessment of prostate tissue, however, introduces both inter- and intra-observer biases that may limit the reliability of its quantitations, and therefore, the strength of its correlations with metabolomic profiles. The aim of this study was to develop a simple, feasible protocol for the computer-aided image analysis (CAIA) of prostate pathology slides in order to achieve quantitative pathology from tissue samples, following metabolomic measurement with high-resolution magic angle spinning (HRMAS) magnetic resonance spectroscopy (MRS). Thirty-eight samples from 29 prostatectomy cases were studied with HRMAS MRS. After spectroscopy analysis, samples were serial-sectioned, stained and visually assessed by pathologists. Cross-sections from these samples were then measured with the CAIA protocol. Results showed a two-fold difference between human visual assessments of the area percentages of tissue pathologies and CAIA area percentages obtained for the same features. Linear correlations were found between both metabolites indicative of normal epithelium and those indicative of prostate cancer, and the CAIA quantitative results. CAIA based quantitative pathology is more reliable than human visual assessment in establishing correlations useful for disease diagnosis between prostate pathology and metabolite concentrations.

Comparative proteomics analysis of human pituitary adenomas: current status and future perspectives

This article will review the published research on the elucidation of the mechanisms of pituitary adenoma formation. Mass spectrometry (MS) plays a key role in those studies. Comparative proteomics has been used with the long-term goal to locate, detect, and characterize the differentially expressed proteins (DEPs) in human pituitary adenomas; to identify tumor-related and -specific biomarkers; and to clarify the basic molecular mechanisms of pituitary adenoma formation. The methodology used for comparative proteomics, the current status of human pituitary proteomics studies, and future perspectives are reviewed. The methodologies that are used in comparative proteomics studies of human pituitary adenomas are readily exportable to other different areas of cancer research.

Genome-Scale Analysis of Lung Cancer Progression

Among cancers, lung cancer is the single biggest killer in the US. It is estimated that lung cancer was responsible for 171900 newly diagnosed cases of cancer in the US in 2003, and for 157200 deaths. Over many years, however, there has been little improvement in the clinical outcome of lung cancer, and any improvement in the incidence or mortality from lung cancer can largely be attributed to smoking cessation and not to the success of therapy. The histopathology of lung cancer reveals that it is a disease with many faces. Lung cancer is often nonresponsive to traditional therapy, leaving few, if any, alternatives in the management of the advanced stages of the disease. The molecular pathogenesis of lung cancer, only recently illuminated, involves numerous molecular and cell biological changes revealing a very complex disease progression. Large-scale mRNA expression analysis has been recently used to classify lung cancers molecularly. These techniques have been used successfully to differentiate lung cancer histotypes based on patterns of genes expressed. The use of protein analysis to this end has also been attempted, with limited correlation with RNA experiments. This likely reflects the limited sensitivity of the technologies and complex, poorly understood post-synthesis protein modifications. In any event, there have been great strides made in understanding the nature of lung cancer from a molecular perspective; these effects represent a great advancement in the diagnosis and prognosis of lung cancer. Moreover, these advances may lead to the improvement of patient survival by guiding the choice of more efficacious therapy.