Protein microarray: sensitive and effective immunodetection for drug residues

<p>Abstract</p> <p>Background</p> <p>Veterinary drugs such as clenbuterol (CL) and sulfamethazine (SM₂) are low molecular weight (<1000 Da) compounds, or haptens, that are difficult to develop immunoassays due to their low immunogenicity. In this study, we conjugated the drugs to ovalbumin to increase their immunogenicity for antiserum production in rabbits and developed a protein microarray immunoassay for detection of clenbuterol and sulfamethazine. The sensitivity of this approach was then compared to traditional ELISA technique.</p> <p>Results</p> <p>The artificial antigens were spotted on microarray slides. Standard concentrations of the compounds were added to compete with the spotted antigens for binding to the antisera to determine the IC₅₀. Our microarray assay showed the IC₅₀were 39.6 ng/ml for CL and 48.8 ng/ml for SM₂, while the traditional competitive indirect-ELISA (ci-ELISA) showed the IC₅₀were 190.7 ng/ml for CL and 156.7 ng/ml for SM₂. We further validated the two methods with CL fortified chicken muscle tissues, and the protein microarray assay showed 90% recovery while the ci-ELISA had 76% recovery rate. When tested with CL-fed chicken muscle tissues, the protein microarray assay had higher sensitivity (0.9 ng/g) than the ci-ELISA (0.1 ng/g) for detection of CL residues.</p> <p>Conclusions</p> <p>The protein microarrays showed 4.5 and 3.5 times lower IC₅₀than the ci-ELISA detection for CL and SM₂, respectively, suggesting that immunodetection of small molecules with protein microarray is a better approach than the traditional ELISA technique.</p

Analysis of Cancer Mutation Signatures in Blood by a Novel Ultra-Sensitive Assay: Monitoring of Therapy or Recurrence in Non-Metastatic Breast Cancer

BACKGROUND: Tumor DNA has been shown to be present both in circulating tumor cells in blood and as fragments in the plasma of metastatic cancer patients. The identification of ultra-rare tumor-specific mutations in blood would be the ultimate marker to measure efficacy of cancer therapy and/or early recurrence. Herein we present a method for detecting microinsertions/deletions/indels (MIDIs) at ultra-high analytical selectivity. MIDIs comprise about 15% of mutations. METHODS AND FINDINGS: We describe MIDI-Activated Pyrophosphorolysis (MAP), a method of ultra-high analytical selectivity for detecting MIDIs. The high analytical selectivity of MAP is putatively due to serial coupling of two rare events: heteroduplex slippage and mis-pyrophosphorolysis. MAP generally has an analytical selectivity of one mutant molecule per >1 billion wild type molecules and an analytical sensitivity of one mutant molecule per reaction. The analytical selectivity of MAP is about 100,000-fold better than that of our previously described method of Pyrophosphorolysis Activated Polymerization-Allele specific amplification (PAP-A) for detecting MIDIs. The utility of this method is illustrated in two ways. 1) We demonstrate that two EGFR deletions commonly found in lung cancers are not present in tissue from four normal human lungs (10(7) copies of gDNA each) or in blood samples from 10 healthy individuals (10(7) copies of gDNA each). This is inconsistent, at least at an analytical sensitivity of 10(-7), with the hypotheses of (a) hypermutation or (b) strong selection of these growth factor-mutated cells during normal lung development leads to accumulation of pre-neoplastic cells with these EGFR mutations, which sometimes can lead to lung cancer in late adulthood. Moreover, MAP was used for large scale, high throughput "gene pool" analysis. No germline or early embryonic somatic mosaic mutation was detected (at a frequency of >0.3%) for the 15/18 bp EGFR deletion mutations in 6,400 individuals, suggesting that early embryonic EGFR somatic mutation is very rare, inconsistent with hypermutation or strong selection of these deletions in the embryo. 2) The second illustration of MAP utility is in personalized monitoring of therapy and early recurrence in cancer. Tumor-specific p53 mutations identified at diagnosis in the plasma of six patients with stage II and III breast cancer were undetectable after therapy in four women, consistent with clinical remission, and continued to be detected after treatment in two others, reflecting tumor progression. CONCLUSIONS: MAP has an analytical selectivity of one part per billion for detection of MIDIs and an analytical sensitivity of one molecule. MAP provides a general tool for monitoring ultra-rare mutations in tissues and blood. As an example, we show that the personalized cancer signature in six out of six patients with non-metastatic breast cancer can be detected and that levels over time are correlated with the clinical course of disease

Tumours of the thymus: a cohort study of prognostic factors from the European Society of Thoracic Surgeons database

OBJECTIVES A retrospective database was developed by the European Society of Thoracic Surgeons, collecting patients submitted to surgery for thymic tumours to analyse clinico-pathological prognostic predictors. METHODS A total of 2151 incident cases from 35 institutions were collected from 1990 to 2010. Clinical-pathological characteristics were analysed, including age, gender, associated myasthenia gravis stage (Masaoka), World Health Organization histology, type of thymic tumour [thymoma, thymic carcinoma (TC), neuroendocrine thymic tumour (NETT)], type of resection (complete/incomplete), tumour size, adjuvant therapy and recurrence. Primary outcome was overall survival (OS); secondary outcomes were the proportion of incomplete resections, disease-free survival and the cumulative incidence of recurrence (CIR). RESULTS A total of 2030 patients were analysed for OS (1798 thymomas, 191 TCs and 41 NETTs). Ten-year OS was 0.73 (95% confidence interval 0.69-0.75). Complete resection (R0) was achieved in 88% of the patients. Ten-year CIR was 0.12 (0.10-0.15). Predictors of shorter OS were increased age (P < 0-001), stage [III vs I HR 2.66, 1.80-3.92; IV vs I hazard ratio (HR) 4.41, 2.67-7.26], TC (HR 2.39, 1.68-3.40) and NETT (HR 2.59, 1.35-4.99) vs thymomas and incomplete resection (HR 1.74, 1.18-2.57). Risk of recurrence increased with tumour size (P = 0.003), stage (III vs I HR 5.67, 2.80-11.45; IV vs I HR 13.08, 5.70-30.03) and NETT (HR 7.18, 3.48-14.82). Analysis using a propensity score indicates that the administration of adjuvant therapy was beneficial in increasing OS (HR 0.69, 0.49-0.97) in R0 resections. CONCLUSIONS Masaoka stages III-IV, incomplete resection and non-thymoma histology showed a significant impact in increasing recurrence and in worsening survival. The administration of adjuvant therapy after complete resection is associated with improved surviva

Design space exploration of stochastic system-of-systems simulations using adaptive sequential experiments

The complexities of our surrounding environments are becoming increasingly diverse, more integrated, and continuously more difficult to predict and characterize. These modeling complexities are ever more prevalent in System-of-Systems (SoS) simulations where computational times can surpass real-time and are often dictated by stochastic processes and non-continuous emergent behaviors. As the number of connections continue to increase in modeling environments and the number of external noise variables continue to multiply, these SoS simulations can no longer be explored with traditional means without significantly wasting computational resources. This research develops and tests an adaptive sequential design of experiments to reduce the computational expense of exploring these complex design spaces. Prior to developing the algorithm, the defining statistical attributes of these spaces are researched and identified. Following this identification, various techniques capable of capturing these features are compared and an algorithm is synthesized. The final algorithm will be shown to improve the exploration of stochastic simulations over existing methods by increasing the global accuracy and computational speed, while reducing the number of simulations required to learn these spaces.PhDCommittee Chair: Mavris, Dimitri; Committee Member: Dongwook Lim; Committee Member: German, Brian; Committee Member: Goldsman, David; Committee Member: Holmberg, Gunnar; Committee Member: Ravachol, Miche