The Current State of Proteomics in GI Oncology

Proteomics refers to the study of the entire set of proteins in a given cell or tissue. With the extensive development of protein separation, mass spectrometry, and bioinformatics technologies, clinical proteomics has shown its potential as a powerful approach for biomarker discovery, particularly in the area of oncology. More than 130 exploratory studies have defined candidate markers in serum, gastrointestinal (GI) fluids, or cancer tissue. In this article, we introduce the commonly adopted proteomic technologies and describe results of a comprehensive review of studies that have applied these technologies to GI oncology, with a particular emphasis on developments in the last 3 years. We discuss reasons why the more than 130 studies to date have had little discernible clinical impact, and we outline steps that may allow proteomics to realize its promise for early detection of disease, monitoring of disease recurrence, and identification of targets for individualized therapy

Reciprocal and Nonreciprocal Recombination at the Glucocerebrosidase Gene Region: Implications for Complexity in Gaucher Disease

Gaucher disease results from an autosomal recessive deficiency of the lysosomal enzyme glucocerebrosidase. The glucocerebrosidase gene is located in a gene-rich region of 1q21 that contains six genes and two pseudogenes within 75 kb. The presence of contiguous, highly homologous pseudogenes for both glucocerebrosidase and metaxin at the locus increases the likelihood of DNA rearrangements in this region. These recombinations can complicate genotyping in patients with Gaucher disease and contribute to the difficulty in interpreting genotype-phenotype correlations in this disorder. In the present study, DNA samples from 240 patients with Gaucher disease were examined using several complementary approaches to identify and characterize recombinant alleles, including direct sequencing, long-template polymerase chain reaction, polymorphic microsatellite repeats, and Southern blots. Among the 480 alleles studied, 59 recombinant alleles were identified, including 34 gene conversions, 18 fusions, and 7 downstream duplications. Twenty-two percent of the patients evaluated had at least one recombinant allele. Twenty-six recombinant alleles were found among 310 alleles from patients with type 1 disease, 18 among 74 alleles from patients with type 2 disease, and 15 among 96 alleles from patients with type 3 disease. Several patients carried two recombinations or mutations on the same allele. Generally, alleles resulting from nonreciprocal recombination (gene conversion) could be distinguished from those arising by reciprocal recombination (crossover and exchange), and the length of the converted sequence was determined. Homozygosity for a recombinant allele was associated with early lethality. Ten different sites of crossover and a shared pentamer motif sequence (CACCA) that could be a hotspot for recombination were identified. These findings contribute to a better understanding of genotype-phenotype relationships in Gaucher disease and may provide insights into the mechanisms of DNA rearrangement in other disorders

Glucosylsphingosine accumulation in tissues from patients with Gaucher disease: correlation with phenotype and genotype

Gaucher disease, the inherited deficiency of lysosomal glucocerebrosidase, presents with a wide spectrum of clinical manifestations including neuronopathic and non-neuronopathic forms. While the lipid glucosylceramide is stored in both patients with Gaucher disease and in a null allele mouse model of Gaucher disease, elevated levels of a second potentially toxic substrate, glucosylsphingosine, are also found. Using high performance liquid chromatography, glucosylsphingosine levels were measured in tissues from patients with type 1, 2, and 3 Gaucher disease. Glucosylsphingosine was measured in 16 spleen samples (8 type 1; 4 type 2; and 4, type 3) and levels ranged from 54 to 728 ng/mg protein in the patients with type 1 disease, 133 to 1200 ng/mg protein in the patients with type 2, and 109 to 1298 ng/mg protein in the type 3 samples. The levels of splenic glucosylsphingosine bore no relation to the type of Gaucher disease, the age of the patient, the genotype, nor the clinical course. In the same patients, hepatic glucosylsphingosine levels were lower than in spleen. Glucosylsphingosine was also measured in brains from 13 patients (1 type 1; 8 type 2; and 4 type 3). While the glucosylsphingosine level in the brain from the type 1 patient, 1.0 ng/mg protein, was in the normal range, the levels in the type 3 samples ranged from 14 to 32 ng/mg protein, and in the type 2 samples from 24 to 437 ng/mg protein, with the highest values detected in two fetuses with hydrops fetalis. The elevated levels found in brains from patients with neuronopathic Gaucher disease support the hypothesis that glucosylsphingosine may contribute to the nervous system involvement in these patients

Analysis of MALDI-TOF serum profiles for biomarker selection and sample classification

Abstract- Mass spectrometric profiles of peptides and proteins obtained by current technologies are characterized by complex spectra, high dimensionality, and substantial noise. These characteristics generate challenges in discovery of proteins and protein-profiles that distinguish disease states, e.g. cancer patients from healthy individuals. A challenging aspect of biomarker discovery in serum is the interference of abundant proteins with identification of disease-related proteins and peptides. We present data processing methods and computational intelligence that combines support vector machines (SVM) with particle swarm optimization (PSO) for biomarker selection from MALDI-TOF spectra of enriched serum. SVM classifiers were built for various combinations of m/z windows guided by the PSO algorithm. The method identified mass points that achieved high classification accuracy in distinguishing cancer patients from non-cancer controls. Based on their frequency of occurrence in multiple runs, six m/z windows were selected as candidate biomarkers. These biomarkers yielded 100% sensitivity and 91 % specificity in distinguishing liver cancer patients from healthy individuals in an independent dataset. I