PHENOTYPIC ASSOCIATION OF GENE AMPLIFICATION WITH MULTIPLE DRUG RESISTANCE IN VINCRISTINE RESISTANT CHINESE HAMSTER OVARY CELLS

Resistance of tumors to pharmacologic agents poses a significant problem in the treatment of human malignancies. This study overviews the scope of clinical resistance and focuses upon current research attempts toward investigation of the phenomenon of multidrug resistance (MDR).^ The objective of this investigation was to determine whether gene amplification had a role in the development of the MDR phenotype in Chinese hamster ovary cells (CHO) primarily selected for resistance to vincristine (VCR). A DNA fragment, previously shown to be amplified in two independently derived Chinese hamster cell lines exhibiting the MDR phenotype, was also amplified in VCR hamster lines. Sequences flanking this fragment were shown to contain coding information for a 4.3 kb transcript overproduced in VCR cells. These sequences were not enriched in double minute DNA preparations isolated from VCR cells. There was an approximately forty-fold increase in both the level of gene amplification and transcript overproduction in the VCR cell lines, independent of the level of primary resistance. This DNA amplification and overproduction of the 4.3 kb transcript was also demonstrated in CHO cells independently selected for resistance to Adriamycin and vinblastine.^ All the DNA sequences of two hamster cDNA clones containing 785 and 932 base pair inserts showed direct homology to the published mouse mdr sequences (about 90%). This sequence conservation held for only portions of the gene when the human mdr1 sequences were compared with those from either the mouse or hamster.^ Somatic cell hybrids, constructed between VCR CHO cells and sensitive murine cells, were used to determine whether there was a functional relationship between the chromosome bearing the amplified sequences and the MDR phenotype. Concordant segregation between vincristine resistance, the MDR phenotype, the presence of MDR-associated amplified sequences, overexpression of the mRNA encoded by these sequences, overexpression of the mRNA encoded by these sequences, and CHO chromosome Z1 was consistent with the hypothesis that there is an amplified gene on chromosome Z1 of the VCR CHO cells which is responsible for MDR in these cells. ^

Slope estimation in a parametric measurement error model: A simulation study

In regression analysis, covariate measurement error occurs in many applications. The error-prone covariates are often referred to as latent variables. In this proposed study, we extended the study of Chan et al. (2008) on recovering latent slope in a simple regression model to that in a multiple regression model. We presented an approach that applied the Monte Carlo method in the Bayesian framework to the parametric regression model with the measurement error in an explanatory variable. The proposed estimator applied the conditional expectation of latent slope given the observed outcome and surrogate variables in the multiple regression models. A simulation study was presented showing that the method produces estimator that is efficient in the multiple regression model, especially when the measurement error variance of surrogate variable is large.^

Analyzing left-truncated right-censored data with uncertain onset time with parametric models

Prevalent sampling is an efficient and focused approach to the study of the natural history of disease. Right-censored time-to-event data observed from prospective prevalent cohort studies are often subject to left-truncated sampling. Left-truncated samples are not randomly selected from the population of interest and have a selection bias. Extensive studies have focused on estimating the unbiased distribution given left-truncated samples. However, in many applications, the exact date of disease onset was not observed. For example, in an HIV infection study, the exact HIV infection time is not observable. However, it is known that the HIV infection date occurred between two observable dates. Meeting these challenges motivated our study. We propose parametric models to estimate the unbiased distribution of left-truncated, right-censored time-to-event data with uncertain onset times. We first consider data from a length-biased sampling, a specific case in left-truncated samplings. Then we extend the proposed method to general left-truncated sampling. With a parametric model, we construct the full likelihood, given a biased sample with unobservable onset of disease. The parameters are estimated through the maximization of the constructed likelihood by adjusting the selection bias and unobservable exact onset. Simulations are conducted to evaluate the finite sample performance of the proposed methods. We apply the proposed method to an HIV infection study, estimating the unbiased survival function and covariance coefficients. ^