Towards the identification of a tumor suppressor gene on chromosome 3p12: Physical mapping and candidate gene screening

Renal cell carcinoma (RCC) is the most common malignant tumor of the kidney. Characterization of RCC tumors indicates that the most frequent genetic event associated with the initiation of tumor formation involves a loss of heterozygosity or cytogenetic aberration on the short arm of human chromosome 3. A tumor suppressor locus Nonpapillary Renal Carcinoma-1 (NRC-1, OMIM ID 604442) has been previously mapped to a 5–7 cM region on chromosome 3p12 and shown to induce rapid tumor cell death in vivo, as demonstrated by functional complementation experiments. ^ To identify the gene that accounts for the tumor suppressor activities of NRC-1, fine-scale physical mapping was conducted with a novel real-time quantitative PCR based method developed in this study. As a result, NRC-1 was mapped within a 4.6-Mb region defined by two unique sequences within UniGene clusters Hs.41407 and Hs.371835 (78,545Kb–83,172Kb in the NCBI build 31 physical map). The involvement of a putative tumor suppressor gene Robo1/Dutt1 was excluded as a candidate for NRC-1. Furthermore, a transcript map containing eleven candidate genes was established for the 4.6-Mb region. Analyses of gene expression patterns with real-time quantitative RT-PCR assays showed that one of the eleven candidate genes in the interval (TSGc28) is down-regulated in 15 out of 20 tumor samples compared with matched normal samples. Three exons of this gene have been identified by RACE experiments, although additional exon(s) seem to exist. Further gene characterization and functional studies are required to confirm the gene as a true tumor suppressor gene. ^ To study the cellular functions of NRC-1, gene expression profiles of three tumor suppressive microcell hybrids, each containing a functional copy of NRC-1, were compared with those of the corresponding parental tumor cell lines using 16K oligonucleotide microarrays. Differentially expressed genes were identified. Analyses based on the Gene Ontology showed that introduction of NRC-1 into tumor cell lines activates genes in multiple cellular pathways, including cell cycle, signal transduction, cytokines and stress response. NRC-1 is likely to induce cell growth arrest indirectly through WEE1. ^

The differential staurosporine mediated G1 arrest in normal versus tumor cells is dependent on the retinoblastoma and Chk1 proteins

The ability to regulate cell cycle progression is one of the differences that separates normal from tumor cells. A protein, which is frequently mutated or deleted in a majority of tumor cells, is the retinoblastoma protein (pRb). Previously, we reported that normal cells, which have a wild-type Rb pathway, can be reversibly arrested in the G1 phase of the cell cycle by staurosporine (ST), while tumor cells were unaffected by this treatment. As a result, ST may be used to protect normal cells against the toxic affects of chemotherapy. Here we set out to determine the mechanism(s) by which ST can mediate a reversible G1 arrest in pRb positive cells. To this end, we used an isogenic cell model system of normal human mammary epithelial cells (HMEC) with either intact pRb+ (p53-) or p53+ (pRb-) treated with ST. Our results show that pRb+ cells treated with low concentrations of ST, arrested in the G1 phase of the cell cycle; however, in pRb - cells there was no response. This was verified as a true G 1 arrest in pRb+ cells by two different methods for monitoring cell cycle kinetics and in two additional model systems for Rb (i.e. pRb -/- mouse embryo fibroblasts, and downregulation of RB with siRNA). Our results indicated that ST-mediated G1 arrest required pRb, which in turn initiated a cascade of events leading to inhibition of CDK4 and CDK2 activities and up-regulation of p21 protein. Further assessment of this pathway revealed the novel finding that Chk1 expression and activity were required for the Rb-dependent, ST-mediated G1 arrest. In fact, overexpression of Chk1 facilitated recovery from ST-mediated G1 arrest, an effect only observed in RB+ cells. Collectively, our data suggest pRb is able to cooperate with Chk1 to mediate a G1 arrest in pRb+ cells, but not in pRb- cells. The elucidation of this pathway can help identify novel agents that can be used to protect cancer patients against the debilitating affects of chemotherapy, by targeting only the normal proliferating cells in the body that are otherwise destroyed. ^

STUDIES ON CHOLINERGIC FUNCTION AND MEMORY IN MICE (CHOLINOTOXINS, WORKING, REFERENCE MEMORY, RADIAL ARM MAZE)

Considerable evidence suggests that central cholinergic neurons participate in either acquisition, storage or retrieval of information. Experiments were designed to evaluate information processing in mice following either reversible or irreversible impairment in central cholinergic activity. The cholinergic receptor antagonists, atropine and methylatropine were used to reversibly inhibit cholinergic transmission. Irreversible impairment in central cholinergic function was achieved by central administration of the cholinergic-specific neurotoxins, N-ethyl-choline aziridinium (ECA) and N-ethyl-acetylcholine aziridinium (EACA).^ ECA and EACA appear to act by irreversible inhibition of high affinity choline uptake (proposed rate-limiting step in acetylcholine synthesis). Intraventricular administration of ECA or EACA produced persistent reduction in hippocampal choline acetyltransferase activity. Other neuronal systems and brain regions showed no evidence of toxicity.^ Mice treated with either ECA or EACA showed behavioral deficits associated with cholinergic dysfunction. Passive avoidance behavior was significantly impaired by cholinotoxin treatment. Radial arm maze performance was also significantly impaired in cholinotoxin-treated animals. Deficits in radial arm maze performance were transient, however, such that rapid and apparent complete behavioral recovery was seen during retention testing. The centrally active cholinergic receptor antagonist atropine also caused significant impairment in radial arm maze behavior, while equivalent doses of methylatropine were without effect.^ The relative effects of cholinotoxin and receptor antagonist treatment on short-term (working) memory and long-term (reference) memory in radial arm maze behavior were examined. Maze rotation studies indicated that there were at least two different response strategies which could result in accurate maze performance. One strategy involved the use of response algorithms and was considered to be a function of reference memory. Another strategy appeared to be primarily dependent on spatial working memory. However, all behavioral paradigms with multiple trails have reference memory requirements (i.e. information useful over all trials). Performance was similarly affected following either cholinotoxin or anticholinergic treatment, regardless of the response strategy utilized. In addition, rates of behavioral recovery following cholinotoxin treatment were similar between response groups. It was concluded that both cholinotoxin and anticholinergic treatment primarily resulted in impaired reference memory processes. ^

THE USE OF ARM CIRCUMFERENCE FOR SIMPLIFIED SCREENING FOR MALNUTRITION BY MINIMALLY TRAINED HEALTH WORKERS

Because of its simplicity and low cost, arm circumference (AC) is being used increasingly in screening for protein energy malnutrition among pre-school children in many parts of the developing world, especially where minimally trained health workers are employed. The objectives of this study were as follows: (1) To determine the relationship of the AC measure with weight for age and weight for height in the detection of malnutrition among pre-school children in a Guatemalan Indian village. (2) To determine the performance of minimally trained promoters under field conditions in measuring AC, weight and height. (3) To describe the practical aspects of taking AC measures versus weight, age and height.^ The study was conducted in San Pablo La Laguna, one of four villages situated on the shores of Lake Atitlan, Guatemala, in which a program of simplified medical care was implemented by the Institute for Nutrition for Central America and Panama (INCAP). Weight, height, AC and age data were collected for 144 chronically malnourished children. The measurements obtained by the trained investigator under the controlled conditions of the health post were correlated against one another and AC was found to have a correlation with weight for age of 0.7127 and with weight for height of 0.7911, both well within the 0.65 to 0.80 range reported in the literature. False positive and false negative analysis showed that AC was more sensitive when compared with weight for height than with weight for age. This was fortunate since, especially in areas with widespread chronic malnutrition, weight for height detects those acute cases in immediate danger of complicating illness or death. Moreover, most of the cases identified as malnourished by AC, but not by weight for height (false positives), were either young or very stunted which made their selection by AC better than weight for height. The large number of cases detected by weight for age, but not by AC (false negative rate--40%) were, however, mostly beyond the critical age period and had normal weight for heights.^ The performance of AC, weight for height and weight for age under field conditions in the hands of minimally trained health workers was also analyzed by correlating these measurements against the same criterion measurements taken under ideally controlled conditions of the health post. AC had the highest correlation with itself indicating that it deteriorated the least in the move to the field. Moreover, there was a high correlation between AC in the field and criterion weight for height (0.7509); this correlation was almost as high as that for field weight for height versus the same measure in the health post (0.7588). The implication is that field errors are so great for the compounded weight for height variable that, in the field, AC is about as good a predictor of the ideal weight for height measure.^ Minimally trained health workers made more errors than the investigator as exemplified by their lower intra-observer correlation coefficients. They consistently measured larger than the investigator for all measures. Also there was a great deal of variability between these minimally trained workers indicating that careful training and followup is necessary for the success of the AC measure.^ AC has many practical advantages compared to the other anthropometric tools. It does not require age data, which are often unreliable in these settings, and does not require sophisticated subtraction and two dimensional table-handling skills that weight for age and weight for height require. The measure is also more easily applied with less disturbance to the child and the community. The AC tape is cheap and not easily damaged or jarred out of calibration while being transported in rugged settings, as is often the case with weight scales. Moreover, it can be kept in a health worker's pocket at all times for continual use in a widespread range of settings. ^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.