Citations

Abstract Glioblastoma (GBM) is the most common and malignant form of primary brain tumor. Average survival rate of GBM is 12-15 months from diagnosis. Due to its highly invasive nature, current treatment options have not been able to improve the survival rate in past 20 years. In order to discover GBM therapeutic targets, omics technologies have been widely used in order to identify potential biomarkers. Identification of cancer biomarkers for diagnosis and prognosis continues to evolve as clinical specimen pipelines and technologies are optimized. This research study focused on investigating proteins, metabolites and lipids biomarkers and patterns of human GBM xenografts employing mass spectrometry. Human tumor cell lines GBM 10 and GBM 43 were injected in the right cerebral hemisphere and flank sites in NOD/SCID mice (n = 10 mice per group). After harvesting brain and flank tumors and control brain from mouse models, protein, metabolites, and lipids of tumor samples were collected through one simple extraction procedure, which may solve sample preparation difficulties in omics studies, developed by Dr. Jiri Adamec. These samples were analyzed by reverse phase high performance liquid chromatography - Fourier transform ion cyclotron resonance mass spectrometry (LC-FTMS). FTMS analyzers have the highest resolving power of all MS instruments, which is ideal for complex mixtures such as GBM tissue. Spectra obtained from the FTMS analysis were analyzed using Student t-tests to detect significant differences in tissue profiles at a level of p = 0.05. Compounds below this threshold were identified through a database using the m/z ratio. Lipidomic analysis indicated the possible up and down-regulation of several lipids classes in GBM tissue connected to metabolic pathways, tumor proliferation and immunodepression, such as glycerophospholipids, both up- and down-regulated (e.g. PC38:4 or PG 35:2, respectively), and down-regulated glycosphingolipids (Galβ1-3GlcNAcβ1-3Galβ1-4Glc-Cer, Galβ1-4GlcNAcβ1-3Galβ1-4Glc-Cer), and triglycerides (TG 55:9, 53:4,52:8). We expect that integrated studies of proteomics, metabolomics, and lipidomics increase the confidence of identification of potential biomarkers. Accompanying these studies is a collaborative effort to improve the effectiveness and efficiency of computational pipelines that are imperative to the analytics, visualization, identification, and interpretation of the omics data. Only by carefully integrating the computational pipelines can we successfully perform the types of integrative studies needed to advance the identification of cancer biomarkers for diagnosis and prognosis, and our integrative studies serve as a case study for our pipeline advancement efforts. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):B37. Citation Format: Soo Jung Ha, Gordon Showalter, Jenna Rickus, Shanbao Cai, Haiyan Wang, Wei Michael Liu, Jann N. Sarkaria, John Springer, Jiri Adamec, Karen E. Pollok, Kari L. Clase. Proteomic and metabolomic analyses of glioblastoma using mass spectrometry. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr B37.

Subjects were exposed to two sets of paragraphs that probed their auditory comprehension abilities under various prestimulation conditions. In Experiment 1, aphasic and control subjects listened to four paragraphs. Each paragraph was preceded by a control condition (no antecedent) or one of the following contextual antecedents: picture, verbal, or picture and verbal combined. Prestimulation with a picture alone was detrimental, but prestimulation with a picture used in combination with a verbal context appeared to be of value. In Experiment 2, subjects heard four paragraphs that were less cohesive than those in Experiment 1. Only verbal and control conditions were contrasted. The provision of prior verbal contexts facilitated the auditory comprehension of the paragraphs.

A novel, hand-held Reference Point Indentation (RPI) instrument, measures how well the bone of living patients and large animals resists indentation. The results presented here are reported in terms of Bone Material Strength, which is a normalized measure of how well the bone resists indentation, and is inversely related to the indentation distance into the bone. We present examples of the instrument's use in: (1) laboratory experiments on bone, including experiments through a layer of soft tissue, (2) three human clinical trials, two ongoing in Barcelona and at the Mayo Clinic, and one completed in Portland, OR, and (3) two ongoing horse clinical trials, one at Purdue University and another at Alamo Pintado Stables in California. The instrument is capable of measuring consistent values when testing through soft tissue such as skin and periosteum, and does so handheld, an improvement over previous Reference Point Indentation instruments. Measurements conducted on horses showed reproducible results when testing the horse through tissue or on bare bone. In the human clinical trials, reasonable and consistent values were obtained, suggesting the Osteoprobe® is capable of measuring Bone Material Strength in vivo, but larger studies are needed to determine the efficacy of the instrument's use in medical diagnosis.

Abstract Background: A statement of measurement uncertainty describes the quality of a clinical assay analysis result, and uncertainty models of clinical assays can be used to evaluate and optimize laboratory protocols designed to minimize the measurement uncertainty associated with an assay. In this study, we propose a methodology to lend systematic structure to the uncertainty modeling process. Methods: Clinical laboratory assays are typically classified based on the chemical reaction involved, and therefore, based on the assay analysis methodology. We use this fact to demonstrate that uncertainty models for assays within the same category are structurally identical in all respects except for the values of certain model parameters. This is accomplished by building uncertainty models for assays belonging to two categories – substrate assays based on optical absorbance analysis of endpoint reactions, and ion selective electrode (ISE) assays based on potentiometric measurements of electromotive force. Results: Uncertainty models for the substrate assays and the ISE assays are built, and for each category, a general mathematical framework for the uncertainty model is developed. The parameters of the general framework that vary from assay to assay for each category are identified and listed. Conclusions: Estimates of measurement uncertainty from the models were compared with estimates of uncertainty from quality control data from the clinical laboratory. We demonstrate that building a general modeling framework for each assay category and plugging in parameter values for each assay is sufficient to generate uncertainty models for an assay within a given category.

Abstract Background: Laboratory protocols used to calibrate commercial clinical chemistry systems affect test result quality. Mathematical models of system uncertainty can be developed using performance parameters provided by the manufacturer for various subsystems. These models can be used to evaluate protocols for specific laboratory operations. Methods: A mathematical model was developed to estimate the uncertainty inherent in the Roche Diagnostics P-Modular system, and included uncertainties associated with the sample and reagent pipettes, spectrometer and the calibration process. The model was then used to evaluate various alternate calibration protocols: calibration based on mean of replicate measurements (n=1–6) and calibration based on conditional acceptance when the following quality control specimen was within one standard deviation of target. The effect of calibrator concentrations on assay measurement uncertainty was also studied, and calibrator concentrations that minimize uncertainty at a specific concentration were identified. Results: The simulation model produced uncertainty estimates of 3.5% for the serum cholesterol assay and identified sample pipette (40%) and spectrometer (21%) as the largest contributors to measurement uncertainty. Each additional replicate calibrator measurements result in diminishing reductions in measurement uncertainty, with maximum reductions (19%) achieved with five replicate measurements. The conditional acceptance of calibration only when the control was within 1s resulted in an 18% reduction. Conclusions: The model can be used to evaluate the utility of laboratory protocols and establish realistic assay performance targets. The model also can help instrument manufacturers and laboratorians identify major contributors to assay measurement uncertainty, which helps improve performance in future assay systems.

The genome sequence of a lycophyte hints at ancient evolutionary transitions.

Abnormal expression of genes for energy regulation in Parkinson’s disease patients identifies a master regulator as a possible therapeutic target for early intervention.