A nonparametric method of comparing the partial areas under two correlated receiver operating characteristic curves

A non-parametric method was developed and tested to compare the partial areas under two correlated Receiver Operating Characteristic curves. Based on the theory of generalized U-statistics the mathematical formulas have been derived for computing ROC area, and the variance and covariance between the portions of two ROC curves. A practical SAS application also has been developed to facilitate the calculations. The accuracy of the non-parametric method was evaluated by comparing it to other methods. By applying our method to the data from a published ROC analysis of CT image, our results are very close to theirs. A hypothetical example was used to demonstrate the effects of two crossed ROC curves. The two ROC areas are the same. However each portion of the area between two ROC curves were found to be significantly different by the partial ROC curve analysis. For computation of ROC curves with large scales, such as a logistic regression model, we applied our method to the breast cancer study with Medicare claims data. It yielded the same ROC area computation as the SAS Logistic procedure. Our method also provides an alternative to the global summary of ROC area comparison by directly comparing the true-positive rates for two regression models and by determining the range of false-positive values where the models differ. ^

RNA polymerase II large subunit degradation induced by ecteinascidin 743: Molecular characterization and subsequent rational investigation of antitumor mechanisms in cancers with clinical response

Ecteinascidin 743 (Et-743), which is a novel DNA minor groove alkylator with a unique spectrum of antitumor activity, is currently being evaluated in phase II/III clinical trials. Although the precise molecular mechanisms responsible for the observed antitumor activity are poorly understood, recent data suggests that post-translational modifications of RNA polymerase II Large Subunit (RNAPII LS) may play a central role in the cellular response to this promising anticancer agent. The stalling of an actively transcribing RNAPII LS at Et-743-DNA adducts is the initial cellular signal for transcription-coupled nucleotide excision repair (TC-NER). In this manner, Et-743 poisons TC-NER and produces DNA single strand breaks. Et-743 also inhibits the transcription and RNAPII LS-mediated expression of selected genes. Because the poisoning of TC-NER and transcription inhibition are critical components of the molecular response to Et-743 treatment, we have investigated if changes in RNAPII LS contribute to the disruption of these two cellular pathways. In addition, we have studied changes in RNAPII LS in two tumors for which clinical responses were reported in phase I/II clinical trials: renal cell carcinoma and Ewing's sarcoma. Our results demonstrate that Et-743 induces degradation of the RNAPII LS that is dependent on active transcription, a functional 26S proteasome, and requires functional TC-NER, but not global genome repair. Additionally, we have provided the first experimental data indicating that degradation of RNAPII LS might lead to the inhibition of activated gene transcription. A set of studies performed in isogenic renal carcinoma cells deficient in von Hippel-Lindau protein, which is a ubiquitin-E3-ligase for RNAPII LS, confirmed the central role of RNAPII LS degradation in the sensitivity to Et-743. Finally, we have shown that RNAPII LS is also degraded in Ewing's sarcoma tumors following Et-743 treatment and provide data to suggest that this event plays a role in decreased expression of the Ewing's sarcoma oncoprotein, EWS-Fli1. Altogether, these data implicate degradation of RNAPII LS as a critical event following Et-743 exposure and suggest that the clinical activity observed in renal carcinoma and Ewing's sarcoma may be mediated by disruption of molecular pathways requiring a fully functional RNAPII LS. ^

Preclinical modeling of multi-drug cancer therapies

Anticancer drugs typically are administered in the clinic in the form of mixtures, sometimes called combinations. Only in rare cases, however, are mixtures approved as drugs. Rather, research on mixtures tends to occur after single drugs have been approved. The goal of this research project was to develop modeling approaches that would encourage rational preclinical mixture design. To this end, a series of models were developed. First, several QSAR classification models were constructed to predict the cytotoxicity, oral clearance, and acute systemic toxicity of drugs. The QSAR models were applied to a set of over 115,000 natural compounds in order to identify promising ones for testing in mixtures. Second, an improved method was developed to assess synergistic, antagonistic, and additive effects between drugs in a mixture. This method, dubbed the MixLow method, is similar to the Median-Effect method, the de facto standard for assessing drug interactions. The primary difference between the two is that the MixLow method uses a nonlinear mixed-effects model to estimate parameters of concentration-effect curves, rather than an ordinary least squares procedure. Parameter estimators produced by the MixLow method were more precise than those produced by the Median-Effect Method, and coverage of Loewe index confidence intervals was superior. Third, a model was developed to predict drug interactions based on scores obtained from virtual docking experiments. This represents a novel approach for modeling drug mixtures and was more useful for the data modeled here than competing approaches. The model was applied to cytotoxicity data for 45 mixtures, each composed of up to 10 selected drugs. One drug, doxorubicin, was a standard chemotherapy agent and the others were well-known natural compounds including curcumin, EGCG, quercetin, and rhein. Predictions of synergism/antagonism were made for all possible fixed-ratio mixtures, cytotoxicities of the 10 best-scoring mixtures were tested, and drug interactions were assessed. Predicted and observed responses were highly correlated (r2 = 0.83). Results suggested that some mixtures allowed up to an 11-fold reduction of doxorubicin concentrations without sacrificing efficacy. Taken together, the models developed in this project present a general approach to rational design of mixtures during preclinical drug development. ^