Comparative study of classification algorithms for immunosignaturing data

BACKGROUND

High-throughput technologies such as DNA, RNA, protein, antibody and peptide microarrays are often used to examine differences across drug treatments, diseases, transgenic animals, and others. Typically one trains a classification system by gathering large amounts of probe-level data, selecting informative features, and classifies test samples using a small number of features. As new microarrays are invented, classification systems that worked well for other array types may not be ideal. Expression microarrays, arguably one of the most prevalent array types, have been used for years to help develop classification algorithms. Many biological assumptions are built into classifiers that were designed for these types of data. One of the more problematic is the assumption of independence, both at the probe level and again at the biological level. Probes for RNA transcripts are designed to bind single transcripts. At the biological level, many genes have dependencies across transcriptional pathways where co-regulation of transcriptional units may make many genes appear as being completely dependent. Thus, algorithms that perform well for gene expression data may not be suitable when other technologies with different binding characteristics exist. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides. It relies on many-to-many binding of antibodies to the random sequence peptides. Each peptide can bind multiple antibodies and each antibody can bind multiple peptides. This technology has been shown to be highly reproducible and appears promising for diagnosing a variety of disease states. However, it is not clear what is the optimal classification algorithm for analyzing this new type of data.

RESULTS

We characterized several classification algorithms to analyze immunosignaturing data. We selected several datasets that range from easy to difficult to classify, from simple monoclonal binding to complex binding patterns in asthma patients. We then classified the biological samples using 17 different classification algorithms. Using a wide variety of assessment criteria, we found 'Naïve Bayes' far more useful than other widely used methods due to its simplicity, robustness, speed and accuracy.

CONCLUSIONS

'Naïve Bayes' algorithm appears to accommodate the complex patterns hidden within multilayered immunosignaturing microarray data due to its fundamental mathematical properties.

Thyroid hormone-dependent development in Xenopus laevis: a sensitive screen of thyroid hormone signaling disruption by municipal wastewater treatment plant effluent

Because thyroid hormones (THs) are conserved modulators of development and physiology, identification of compounds adversely affecting TH signaling is critical to human and wildlife health. Anurans are an established model for studying disruption of TH signaling because metamorphosis is dependent upon the thyroid system. In order to strengthen this model and identify new gene transcript biomarkers for TH disruption, we performed DNA microarray analysis of Xenopus laevis tadpole tail transcriptomes following treatment with triiodothyronine (T(3)). Comparison of these results with previous studies in frogs and mammals identified 36 gene transcripts that were TH-sensitive across clades. We then tested molecular biomarkers for sensitivity to disruption by exposure to wastewater effluent (WWE). X. laevis tadpoles, exposed to WWE from embryo through metamorphosis, exhibited an increased developmental rate compared to controls. Cultured tadpole tails showed dramatic increases in levels of four TH-sensitive gene transcripts (thyroid hormone receptor β (TRβ), deiodinase type II (DIO2), and corticotropin releasing hormone binding protein (CRHBP), fibroblast activation protein α (FAPα)) when exposed to T(3) and WWE extracts. TRβ, DIO2, and CRHBP were identified as TH sensitive in other studies, while FAPα mRNA transcripts were highly TH sensitive in our array. The results validate the array and demonstrate TH-disrupting activity by WWE. Our findings demonstrate the usefulness of cross-clade analysis for identification of gene transcripts that provide sensitivity to endocrine disruption. Further, the results suggest that development is disrupted by exposure to complex mixes of compounds found in WWE possibly through interference with TH signaling.

The ketogenic diet is an effective adjuvant to radiation therapy for the treatment of malignant glioma

Introduction

The ketogenic diet (KD) is a high-fat, low-carbohydrate diet that alters metabolism by increasing the level of ketone bodies in the blood. KetoCal® (KC) is a nutritionally complete, commercially available 4∶1 (fat∶ carbohydrate+protein) ketogenic formula that is an effective non-pharmacologic treatment for the management of refractory pediatric epilepsy. Diet-induced ketosis causes changes to brain homeostasis that have potential for the treatment of other neurological diseases such as malignant gliomas.

Methods

We used an intracranial bioluminescent mouse model of malignant glioma. Following implantation animals were maintained on standard diet (SD) or KC. The mice received 2×4 Gy of whole brain radiation and tumor growth was followed by in vivo imaging.

Results

Animals fed KC had elevated levels of β-hydroxybutyrate (p = 0.0173) and an increased median survival of approximately 5 days relative to animals maintained on SD. KC plus radiation treatment were more than additive, and in 9 of 11 irradiated animals maintained on KC the bioluminescent signal from the tumor cells diminished below the level of detection (p<0.0001). Animals were switched to SD 101 days after implantation and no signs of tumor recurrence were seen for over 200 days.

Conclusions

KC significantly enhances the anti-tumor effect of radiation. This suggests that cellular metabolic alterations induced through KC may be useful as an adjuvant to the current standard of care for the treatment of human malignant gliomas.

Abstract 3217: Mechanistic analysis of the ketogenic diet versus KetoCal® as adjuvant treatments for malignant glioma

Patients with malignant brain tumors have a median survival of approximately one year following diagnosis, regardless of currently available treatments which include surgery followed by radiation and chemotherapy. Improvement in the survival of brain cancer patients requires the design of new therapeutic modalities that take advantage of common phenotypes. One such phenotype is the metabolic dysregulation that is a hallmark of cancer cells. It has therefore been postulated that one approach to treating brain tumors may be by metabolic alteration such as that which occurs through the use of the ketogenic diet (KD). The KD is high-fat, low-carbohydrate diet that has been utilized for the non-pharmacologic treatment of refractory epilepsy. We and others have shown that this diet enhances survival in mouse models of malignant gliomas. There are varying formulations of the diet that alter the ratio of fats to carbohydrates & protein, and it is not clear whether any one is more effective than another. Bio-Serv F3666 (Frenchtown, NJ) is a rodent KD with a 6:1 ratio of fat:carbohydrate & protein. We have previously shown that ad libitum feeding of this diet significantly increased survival in albino C57BL/6 mice (NCI, Frederick, MD) stereotactically implanted with GL261-luc2 cells, a syngeneic bioluminescent mouse model of malignant glioma. Radiation in combination with KD was synergistic, and survival was significantly increased over either treatment alone. Ad libitum feeding of KetoCal® (KC; Nutricia North America, Gaithersburg, MD); a nutritionally complete, commercially available 4:1 (fat: carbohydrate & protein) ketogenic formula used for the treatment of pediatric epilepsy, also resulted in a significant increase in survival. However, the combination of radiation with KetoCal® caused the tumors to completely regress in 9 of 11 mice, and the tumors did not recur when these animals were switched back to standard rodent chow. The mechanism(s) by which the KD and KC exert their anti-tumor effects are not completely understood. We have begun to compare these 2 formulations to identify their anti-tumor effects. Animals on KC showed a more pronounced drop in blood glucose than those maintained on KD. Blood levels of α-hydroxybutyrate were significantly higher in animals fed KC than those in animals fed KD. Total AKT was reduced in tumor tissue from animals fed KC, but not in animals fed KD; however, phospho-AKT(Thr308) was reduced in tumor and non-tumor tissue from animals maintained on KD, but not those maintained on KC. Furthermore, tumor tissue from animals fed KD had a more pronounced decrease in insulin growth factor-1 than did tumor tissue from animals fed KC. These data suggest that the mechanisms leading to increased survival may be different in animals fed KC versus those fed KD. A greater understanding of the effects of different ketogenic formulations will allow for a more rational approach to its clinical use.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3217. doi:1538-7445.AM2012-3217

Physical characterization of the "immunosignaturing effect"

Identifying new, effective biomarkers for diseases is proving to be a challenging problem. We have proposed that antibodies may offer a solution to this problem. The physical features and abundance of antibodies make them ideal biomarkers. Additionally, antibodies are often elicited early in the ontogeny of different chronic and infectious diseases. We previously reported that antibodies from patients with infectious disease and separately those with Alzheimer's disease display a characteristic and reproducible "immunosignature" on a microarray of 10,000 random sequence peptides. Here we investigate the physical and chemical parameters underlying how immunosignaturing works. We first show that a variety of monoclonal and polyclonal antibodies raised against different classes of antigens produce distinct profiles on this microarray and the relative affinities are determined. A proposal for how antibodies bind the random sequences is tested. Sera from vaccinated mice and people suffering from a fugal infection are individually assayed to determine the complexity of signals that can be distinguished. Based on these results, we propose that this simple, general and inexpensive system could be optimized to generate a new class of antibody biomarkers for a wide variety of diseases.

GuiTope: an application for mapping random-sequence peptides to protein sequences

BACKGROUND

Random-sequence peptide libraries are a commonly used tool to identify novel ligands for binding antibodies, other proteins, and small molecules. It is often of interest to compare the selected peptide sequences to the natural protein binding partners to infer the exact binding site or the importance of particular residues. The ability to search a set of sequences for similarity to a set of peptides may sometimes enable the prediction of an antibody epitope or a novel binding partner. We have developed a software application designed specifically for this task.

RESULTS

GuiTope provides a graphical user interface for aligning peptide sequences to protein sequences. All alignment parameters are accessible to the user including the ability to specify the amino acid frequency in the peptide library; these frequencies often differ significantly from those assumed by popular alignment programs. It also includes a novel feature to align di-peptide inversions, which we have found improves the accuracy of antibody epitope prediction from peptide microarray data and shows utility in analyzing phage display datasets. Finally, GuiTope can randomly select peptides from a given library to estimate a null distribution of scores and calculate statistical significance.

CONCLUSIONS

GuiTope provides a convenient method for comparing selected peptide sequences to protein sequences, including flexible alignment parameters, novel alignment features, ability to search a database, and statistical significance of results. The software is available as an executable (for PC) at http://www.immunosignature.com/software and ongoing updates and source code will be available at sourceforge.net.

Segmentation and intensity estimation for microarray images with saturated pixels

Background

Microarray image analysis processes scanned digital images of hybridized arrays to produce the input spot-level data for downstream analysis, so it can have a potentially large impact on those and subsequent analysis. Signal saturation is an optical effect that occurs when some pixel values for highly expressed genes or peptides exceed the upper detection threshold of the scanner software (2¹⁶ - 1 = 65, 535 for 16-bit images). In practice, spots with a sizable number of saturated pixels are often flagged and discarded. Alternatively, the saturated values are used without adjustments for estimating spot intensities. The resulting expression data tend to be biased downwards and can distort high-level analysis that relies on these data. Hence, it is crucial to effectively correct for signal saturation.

Results

We developed a flexible mixture model-based segmentation and spot intensity estimation procedure that accounts for saturated pixels by incorporating a censored component in the mixture model. As demonstrated with biological data and simulation, our method extends the dynamic range of expression data beyond the saturation threshold and is effective in correcting saturation-induced bias when the lost information is not tremendous. We further illustrate the impact of image processing on downstream classification, showing that the proposed method can increase diagnostic accuracy using data from a lymphoma cancer diagnosis study.

Conclusions

The presented method adjusts for signal saturation at the segmentation stage that identifies a pixel as part of the foreground, background or other. The cluster membership of a pixel can be altered versus treating saturated values as truly observed. Thus, the resulting spot intensity estimates may be more accurate than those obtained from existing methods that correct for saturation based on already segmented data. As a model-based segmentation method, our procedure is able to identify inner holes, fuzzy edges and blank spots that are common in microarray images. The approach is independent of microarray platform and applicable to both single- and dual-channel microarrays.

The ketogenic diet for the treatment of glioma: insights from genetic profiling

Seizures, particularly first onset seizures in adults, are a diagnostic hallmark of brain tumors (Giglio and Villano, 2010). Unfortunately, malignant brain tumors are almost uniformly fatal due, in part, to the limitations of available therapies. Improvement in the survival of brain cancer patients requires the design of new therapeutic modalities including those that enhance currently available therapies. One potential strategy is to exploit differences in metabolic regulation between normal cells and tumor cells through dietary approaches. Previous studies have shown that a high-fat, low-carbohydrate ketogenic diet (KD) extends survival in animal models of glioma; however, the mechanism for this effect is not entirely known. We examined the effects of an experimental KD on a mouse model of glioma, and compared patterns of gene expression in tumors versus contralateral non-tumor containing brain from animals fed either a KD or a standard diet. We found that the KD reduced reactive oxygen species (ROS) production in tumor cells. Gene expression profiling demonstrated that the KD induces an overall reversion to expression patterns seen in non-tumor specimens, and a number of genes involved in modulating ROS levels and oxidative stress were altered in tumor cells. In addition, there was reduced expression of genes involved in signal transduction from growth factors known to be involved in glioma growth. These results suggest that the anti-tumor effect of the KD is multifactorial, and elucidation of genes whose expression is altered will help identify mechanisms through which ketones inhibit tumor growth, reduce seizure activity and provide neuroprotection.

Application of immunosignatures to the assessment of Alzheimer's disease

OBJECTIVE

Accurate assessment of Alzheimer's disease (AD), both presymptomatically and at different disease stages, will become increasingly important with the expanding elderly population. There are a number of indications that the immune system is engaged in AD. Here we explore the ability of an antibody-profiling technology to characterize AD and screen for peptides that may be used for a simple diagnostic test.

METHODS

We developed an array-based system to profile the antibody repertoire of transgenic mice with cerebral amyloidosis (TG) and elderly individuals with or without AD. The array consists of 10,000 random sequence peptides (20-mers) capable of detecting antibody binding patterns, allowing the identification of peptides that mimic epitopes targeted by a donor's serum.

RESULTS

TG mice exhibited a distinct immunoprofile compared to nontransgenic littermates. Further, we show that dementia patients, including autopsy-confirmed AD subjects, have distinguishable profiles compared to age-matched nondemented people. Using antibodies to different forms of Aβ peptide and blocking protocols, we demonstrate that most of this signature is not due to the subject's antibodies raised against Aβ.

INTERPRETATION

We propose that "immunosignaturing" technology may have potential as a diagnostic tool in AD.

Statistical methods for analyzing immunosignatures

BACKGROUND

Immunosignaturing is a new peptide microarray based technology for profiling of humoral immune responses. Despite new challenges, immunosignaturing gives us the opportunity to explore new and fundamentally different research questions. In addition to classifying samples based on disease status, the complex patterns and latent factors underlying immunosignatures, which we attempt to model, may have a diverse range of applications.

METHODS

We investigate the utility of a number of statistical methods to determine model performance and address challenges inherent in analyzing immunosignatures. Some of these methods include exploratory and confirmatory factor analyses, classical significance testing, structural equation and mixture modeling.

RESULTS

We demonstrate an ability to classify samples based on disease status and show that immunosignaturing is a very promising technology for screening and presymptomatic screening of disease. In addition, we are able to model complex patterns and latent factors underlying immunosignatures. These latent factors may serve as biomarkers for disease and may play a key role in a bioinformatic method for antibody discovery.

CONCLUSION

Based on this research, we lay out an analytic framework illustrating how immunosignatures may be useful as a general method for screening and presymptomatic screening of disease as well as antibody discovery.

Abstract 624: Efficacy of KetoCal as an adjuvant therapy for malignant gliomas

The ketogenic diet (KD) is a high-fat, low-carbohydrate diet that alters the body's metabolism to burn fats in preference to carbohydrates. The use of fats as a primary energy source leads to a state of ketosis and the accumulation of ketone bodies in the blood. KetoCal (Nutricia North America, Gaithersburg, MD) is a nutritionally complete, commercially available 4:1 (fat: carbohydrate + protein) ketogenic formula that is an effective non-pharmacologic treatment for the management of pediatric epilepsy. Diet-induced changes to brain homeostasis as a result of ketosis have shown potential as an alternative treatment for other neurological diseases including malignant gliomas. We previously showed that Bio-Serv F3666 (Frenchtown, NJ), a rodent KD with a fat to carbohydrate + protein ratio of 6:1, potentiates the activity of both radiation and temozolomide (TMZ) therapy. We studied the efficacy of KetoCal as an adjuvant treatment for malignant glioma in combination with radiation or TMZ.

GL261-luc cells expressing luciferase were stereotactically implanted into the right frontal lobe of albino C57BL/6 mice (NCI, Frederick, MD). Following intracranial implantation, animals were fed standard rodent chow (SD) and were randomized to treatment groups on day 3 (SD vs KetoCal with and without radiation or TMZ) based on bioluminescent signal (IVIS® Spectrum, Caliper Life Sciences, Hopkinton, MA). Blood levels of β-hydroxybutyrate (βHB) and glucose were measured using a Keto-Site reflectance meter (GDS Technologies, Elkhart, IN) and HemoCue Glucose System (HemoCue AB, Ängelholm, Sweden). βHB and glucose levels were checked one and two weeks following the initiation of diet treatment. Mice were maintained on a 12 hour light/dark cycle and KetoCal or SD was available ad libitum.

Animals fed KetoCal had elevated levels of βHB (p=0.0173), decreased levels of glucose (p=0.0224) and increased median survival of approximately 6 days relative to animals on SD. To determine if KetoCal enhanced the effect of radiation; mice received 2 × 4 Gy whole brain radiation in combination with SD or KetoCal. Irradiated animals fed SD had a median survival of 44.5 days, while irradiated animals maintained on KetoCal demonstrated a complete loss of bioluminescence, suggesting the absence of viable tumor cells. These animals have been followed for &gt;170 days with no visible signs of recurrent tumor (p=0.0061). Animals fed KetoCal in combination with TMZ (5×50mg/kg) had a median survival of 37 days compared to 28 days for animals treated with TMZ and maintained on SD (p=0.0043).

In summary, the 4:1 fat: carbohydrate + protein ratio in KetoCal appears to provide a higher anti-tumor effect than a 6:1 formulation when used in addition to radiation or TMZ. The effect is particularly pronounced when used in combination with radiation. This suggests that cellular metabolic alterations brought about through changes in the diet may be useful for the treatment of human malignant gliomas.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 624. doi:10.1158/1538-7445.AM2011-624

Exploring antibody recognition of sequence space through random-sequence peptide microarrays

A universal platform for efficiently mapping antibody epitopes would be of great use for many applications, ranging from antibody therapeutic development to vaccine design. Here we tested the feasibility of using a random peptide microarray to map antibody epitopes. Although peptide microarrays are physically constrained to ∼10(4) peptides per array, compared with 10(8) permitted in library panning approaches such as phage display, they enable a much more high though put and direct measure of binding. Long (20 mer) random sequence peptides were chosen for this study to look at an unbiased sampling of sequence space. This sampling of sequence space is sparse, as an exact epitope sequence is unlikely to appear. Commercial monoclonal antibodies with known linear epitopes or polyclonal antibodies raised against engineered 20-mer peptides were used to evaluate this array as an epitope mapping platform. Remarkably, peptides with the most sequence similarity to known epitopes were only slightly more likely to be recognized by the antibody than other random peptides. We explored the ability of two methods singly and in combination to predict the actual epitope from the random sequence peptides bound. Though the epitopes were not directly evident, subtle motifs were found among the top binding peptides for each antibody. These motifs did have some predictive ability in searching for the known epitopes among a set of decoy sequences. The second approach using a windowing alignment strategy, was able to score known epitopes of monoclonal antibodies well within the test dataset, but did not perform as well on polyclonals. Random peptide microarrays of even limited diversity may serve as a useful tool to prioritize candidates for epitope mapping or antigen identification.

The ketogenic diet reverses gene expression patterns and reduces reactive oxygen species levels when used as an adjuvant therapy for glioma

BACKGROUND

Malignant brain tumors affect people of all ages and are the second leading cause of cancer deaths in children. While current treatments are effective and improve survival, there remains a substantial need for more efficacious therapeutic modalities. The ketogenic diet (KD) - a high-fat, low-carbohydrate treatment for medically refractory epilepsy - has been suggested as an alternative strategy to inhibit tumor growth by altering intrinsic metabolism, especially by inducing glycopenia.

METHODS

Here, we examined the effects of an experimental KD on a mouse model of glioma, and compared patterns of gene expression in tumors vs. normal brain from animals fed either a KD or a standard diet.

RESULTS

Animals received intracranial injections of bioluminescent GL261-luc cells and tumor growth was followed in vivo. KD treatment significantly reduced the rate of tumor growth and prolonged survival. Further, the KD reduced reactive oxygen species (ROS) production in tumor cells. Gene expression profiling demonstrated that the KD induces an overall reversion to expression patterns seen in non-tumor specimens. Notably, genes involved in modulating ROS levels and oxidative stress were altered, including those encoding cyclooxygenase 2, glutathione peroxidases 3 and 7, and periredoxin 4.

CONCLUSIONS

Our data demonstrate that the KD improves survivability in our mouse model of glioma, and suggests that the mechanisms accounting for this protective effect likely involve complex alterations in cellular metabolism beyond simply a reduction in glucose.

CT colonography computer-aided polyp detection: Effect on radiologist observers of polyp identification by CAD on both the supine and prone scans

RATIONALE AND OBJECTIVES

To determine whether the display of computer-aided detection (CAD) marks on individual polyps on both the supine and prone scans leads to improved polyp detection by radiologists compared to the display of CAD marks on individual polyps on either the supine or the prone scan, but not both.

MATERIALS AND METHODS

The acquisition of patient data for this study was approved by the Institutional Review Board and was Health Insurance Portability and Accountability Act-compliant. Subsequently, the use of the data was declared exempt from further institutional review board review. Four radiologists interpreted 33 computed tomography colonography cases, 21 of which had one adenoma 6-9 mm in size, with the assistance of a CAD system in the first reader mode (ie, the radiologists reviewed only the CAD marks). The radiologists were shown each case twice, with different sets of CAD marks for each of the two readings. In one reading, a true-positive CAD mark for the same polyp was displayed on both the supine and prone scans (a double-mark reading). In the other reading, a true-positive CAD mark was displayed either on the supine or prone scan, but not both (a single-mark reading). True-positive marks were randomized between readings and there was at least a 1-month delay between readings to minimize recall bias. Sensitivity and specificity were determined and receiver operating characteristic (ROC) and multiple-reader multiple-case analyses were performed.

RESULTS

The average per polyp sensitivities were 60% (38%-81%) versus 71% (52%-91%) (P = .03) for single-mark and double-mark readings, respectively. The areas (95% confidence intervals) under the ROC curves were 0.76 (0.62-0.88) and 0.79 (0.58-0.96), respectively (P = NS). Specificities were similar for the single-mark compared with the double-mark readings.

CONCLUSION

The display of CAD marks on a polyp on both the supine and prone scans led to more frequent detection of polyps by radiologists without adversely affecting specificity for detecting 6-9 mm adenomas.

Discovery of high-affinity protein binding ligands--backwards

Background

There is a pressing need for high-affinity protein binding ligands for all proteins in the human and other proteomes. Numerous groups are working to develop protein binding ligands but most approaches develop ligands using the same strategy in which a large library of structured ligands is screened against a protein target to identify a high-affinity ligand for the target. While this methodology generates high-affinity ligands for the target, it is generally an iterative process that can be difficult to adapt for the generation of ligands for large numbers of proteins.

Methodology/Principal Findings

We have developed a class of peptide-based protein ligands, called synbodies, which allow this process to be run backwards – i.e. make a synbody and then screen it against a library of proteins to discover the target. By screening a synbody against an array of 8,000 human proteins, we can identify which protein in the library binds the synbody with high affinity. We used this method to develop a high-affinity synbody that specifically binds AKT1 with a K_d<5 nM. It was found that the peptides that compose the synbody bind AKT1 with low micromolar affinity, implying that the affinity and specificity is a product of the bivalent interaction of the synbody with AKT1. We developed a synbody for another protein, ABL1 using the same method.

Conclusions/Significance

This method delivered a high-affinity ligand for a target protein in a single discovery step. This is in contrast to other techniques that require subsequent rounds of mutational improvement to yield nanomolar ligands. As this technique is easily scalable, we believe that it could be possible to develop ligands to all the proteins in any proteome using this approach.

Abstract 4767: Neo-peptides from gene fusions as cancer vaccine antigens

Background: Of all possible sources of antigens for cancer vaccines, frameshift (FS) peptides are attractive in that they may represent neo-antigens. FS peptides may be induced by insertions/deletions or by chimeric transcripts that result from mis-splicing, chromosomal translocations or trans-splicing of mRNA. Aim: In order to discover FS neo-antigens, we have taken a bioinformatical approach at identifying tumor specific, neo-peptides that are generated when chimeric transcripts are translated. Specifically we have focused our attention on identifying chimeric transcripts that create a frameshift when the exons of two different genes are joined. As a result of this gene fusion, approximately 50% of the time a frameshift occurs thus creating a neo-peptide. Method: In order to quickly identify these types of transcripts that are specific to cancer and not present in normal tissue, we have developed an algorithm that can be used to screen publically available databases and high-throughput sequencing data. As proof of principle we have applied this algorithm to the NCBI Expressed Sequence Tag database that contains cDNA sequencing information for normal and cancer tissues. Results: Based on this approach, we have identified and validated 35 chimeric transcripts that are present in breast cancer cell lines and in primary breast tumors. In addition, 9 chimeric transcripts are also present in seven different pancreatic cancer cell lines. Of the 35 chimeric transcripts, 11 chimeric transcripts produce frameshift peptides that are at least nine amino acids or longer and are predicted by SYFPEITHI and Rankpep to be immunogenic and capable of being presented in the most frequent human class I MHC alleles. Moreover, our analyses also identified chimeric transcripts that have already been confirmed by 454 sequencing for breast and prostate cancer. Discussion: Defining the chimeric transcriptome in both healthy and cancer patients could be critical for the development of new therapeutic agents. Of more interest to us, is their potential contribution to a prophylactic cancer vaccine. (This work is supported by a DoD Innovator Award and a Keck Foundation grant to SAJ)

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4767.

Abstract 638: Mechanistic studies of the ketogenic diet as an adjuvant therapy for malignant gliomas

The ketogenic diet (KD) is a high-fat, low carbohydrate diet which leads to high rates of fatty acid oxidation and an increase in the production of acetyl-CoA. When the level of acetyl-CoA exceeds the capacity of the tricarboxylic acid cycle there is an increase in the production of the ketone bodies β-hydroxybutyrate (BHB) and acetoacetate (ACA) which are then used as an energy source in the brain. The utility of the KD for the treatment of epilepsy has been known for many years; however, its potential for use in other disease states is just beginning to be recognized. We and others have demonstrated that when used alone, the KD can prolong survival in intracranial mouse models of malignant glioma. We have also shown that there is a statistically significant increase in the survival of animals fed KD and treated with radiation or temozolomide versus those receiving the same treatment but maintained on a standard diet (SD). The addition of the gene encoding luciferase to our mouse glioma cells allowed us to follow tumor growth using non-invasive in vivo imaging. We showed that tumors grow slower in mice fed KD versus SD. Additional in vivo analyses of tumors and surrounding brain using dihydroethidium demonstrated the presence of increased reactive oxygen species (ROS) in mice maintained on SD, and an overall reduction in ROS in the tumors from animals fed KD. Despite this, the KD potentiates the anti-tumor activity of radiation. Whether the KD actually blocks the increase in ROS that occurs immediately following radiation treatment is under investigation. To further identify mechanisms that contribute to the enhanced survival seen in tumor bearing animals fed KD, total RNA was isolated from tumors and contralateral normal brain of animals fed either ketogenic or standard diets and gene expression profiling analysis using the Affymetrix GeneChip® Mouse Genome 430 2.0 array was done. Despite reports suggesting that tumor inhibition was due to nutrient deprivation in the tumor, genes involved in autophagy were not uniformly up regulated in the tumor versus the normal brain of animals fed a KD. A 2-way ANOVA for interaction showed that the KD drives overall gene expression back toward non-tumor SD conditions, and further analyses suggest this may be due, in part, to genes involved in oncogenic signaling pathways. An analysis of tumor tissue from animals fed a KD versus those fed SD showed the down regulation of genes involved in the insulin-like growth factor 1 (IGF-1), platelet-derived growth factor (PDGF) and epidermal growth factor receptor (EGFR) signaling pathways. Furthermore, many of the genes involved in glycolysis are up regulated in tumors from animals fed KD versus those fed SD. These data suggest that when used alone, the KD may inhibit tumor growth by down-regulating growth factor pathways. Gene expression profiling analyses of tissue from animals treated with the KD in addition to radiation and temozolomide are underway.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 638.

ICRPfinder: a fast pattern design algorithm for coding sequences and its application in finding potential restriction enzyme recognition sites

Background

Restriction enzymes can produce easily definable segments from DNA sequences by using a variety of cut patterns. There are, however, no software tools that can aid in gene building -- that is, modifying wild-type DNA sequences to express the same wild-type amino acid sequences but with enhanced codons, specific cut sites, unique post-translational modifications, and other engineered-in components for recombinant applications. A fast DNA pattern design algorithm, ICRPfinder, is provided in this paper and applied to find or create potential recognition sites in target coding sequences.

Results

ICRPfinder is applied to find or create restriction enzyme recognition sites by introducing silent mutations. The algorithm is shown capable of mapping existing cut-sites but importantly it also can generate specified new unique cut-sites within a specified region that are guaranteed not to be present elsewhere in the DNA sequence.

Conclusion

ICRPfinder is a powerful tool for finding or creating specific DNA patterns in a given target coding sequence. ICRPfinder finds or creates patterns, which can include restriction enzyme recognition sites, without changing the translated protein sequence. ICRPfinder is a browser-based JavaScript application and it can run on any platform, in on-line or off-line mode.

A man-made ATP-binding protein evolved independent of nature causes abnormal growth in bacterial cells

Recent advances in de novo protein evolution have made it possible to create synthetic proteins from unbiased libraries that fold into stable tertiary structures with predefined functions. However, it is not known whether such proteins will be functional when expressed inside living cells or how a host organism would respond to an encounter with a non-biological protein. Here, we examine the physiology and morphology of Escherichia coli cells engineered to express a synthetic ATP-binding protein evolved entirely from non-biological origins. We show that this man-made protein disrupts the normal energetic balance of the cell by altering the levels of intracellular ATP. This disruption cascades into a series of events that ultimately limit reproductive competency by inhibiting cell division. We now describe a detailed investigation into the synthetic biology of this man-made protein in a living bacterial organism, and the effect that this protein has on normal cell physiology.