Validation of Claims Algorithms for Progression to Metastatic Cancer in Patients with Breast, Non-small Cell Lung, and Colorectal Cancer.

BACKGROUND: Validated algorithms for identifying progression to metastatic cancer could permit the use of administrative claims databases for research in this area. OBJECTIVE: To identify simple algorithms that could accurately detect cancer progression to metastatic breast, non-small cell lung, and colorectal cancer (CRC) using medical and pharmacy claims data. METHODS: Adults with stage I-III breast, non-small cell lung cancer (NSCLC), or CRC in the Geisinger Health System from 2004 to 2011 were selected. Evidence of progression was extracted via manual chart review as the reference standard. In addition to secondary malignancy diagnosis (ICD-9 code for metastases), diagnoses, procedures, and treatments were selected with clinician input as indicators of cancer progression. Random forests models provided variable importance scores. In addition to codes for secondary malignancy, several more complex algorithms were constructed and performance measures calculated. RESULTS: Among those with breast cancer [17/502 (3.4%) progressed], the performance of a secondary malignancy code was suboptimal [sensitivity: 64.7%; specificity: 86.0%; positive predictive value (PPV): 13.9; negative predictive value (NPV): 98.6%]; requiring malignancy at another site or initiation of immunotherapy increased PPV and specificity but decreased sensitivity. For NSCLC [61/236 (25.8%) progressed], codes for secondary malignancy alone (PPV: 47.4%; NPV: 84.8%; sensitivity: 60.7%; specificity: 76.6%) performed similarly or better than more complex algorithms. For CRC [33/276 (12.0%) progressed], secondary malignancy codes had good specificity (92.7%) and NPV (92.3%) but low sensitivity (42.4%) and PPV (43.8%); an algorithm with change in chemotherapy increased sensitivity but decreased other metrics. CONCLUSION: Selected algorithms performed similarly to the presence of a secondary tumor diagnosis code, with low sensitivity/PPV and higher specificity/NPV. Accurate identification of cancer progression likely requires verification through chart review.

Monotonicity of interleukin-1 receptor-ligand binding with respect to antagonist in the presence of decoy receptor.

We consider the interaction between interleukin-1 IL-1, its receptor IL-1RI, the receptor antagonist IL-1Ra and a decoy receptor (or trap) that binds both with the ligand and the antagonist. We study how the interaction between IL-1Ra and the decoy receptor influences the effect of either reagent on reducing the equilibrium concentration of the receptor-ligand complex. We obtain that, given a certain relationship among the equilibrium constants and the total concentrations of solutes, IL-1Ra can reverse the effect of the decoy receptor of decreasing the equilibrium concentration of the receptor-ligand complex. This finding derives from a mathematical result applicable to any reversible chemical reaction system comprising four species arranged in a square such that each species binds its two immediate neighbors. The result gives the monotonicity of the equilibrium concentrations of the complex species as functions of the total concentrations of the simple species.

Robust regression for high throughput drug screening.

Effective analysis of high throughput screening (HTS) data requires automation of dose-response curve fitting for large numbers of datasets. Datasets with outliers are not handled well by standard non-linear least squares methods, and manual outlier removal after visual inspection is tedious and potentially biased. We propose robust non-linear regression via M-estimation as a statistical technique for automated implementation. The approach of finding M-estimates by Iteratively Reweighted Least Squares (IRLS) and the resulting optimization problem are described. Initial parameter estimates for iterative methods are important, so self-starting methods for our model are presented. We outline the software implementation, done in Matlab and deployed as an Excel application via the Matlab Excel Builder Toolkit. Results of M-estimation are compared with least squares estimates before and after manual editing.

Scan-rate-dependent melting transitions of interleukin-1 receptor (type II): elucidation of meaningful thermodynamic and kinetic parameters of aggregation acquired from DSC simulations.

The role of thermal unfolding as it pertains to thermodynamic properties of proteins and their stability has been the subject of study for more than 50 years. Moreover, exactly how the unfolding properties of a given protein system may influence the kinetics of aggregation has not been fully characterized. In the study of recombinant human Interleukin-1 receptor type II (rhuIL-1R(II)) aggregation, data obtained from size exclusion chromatography and differential scanning calorimetry (DSC) were used to model the thermodynamic and kinetic properties of irreversible denaturation. A break from linearity in the initial aggregation rates as a function of 1/T was observed in the vicinity of the melting transition temperature (T(m) approximately 53.5 degrees C), suggesting significant involvement of protein unfolding in the reaction pathway. A scan-rate dependence in the DSC experiment testifies to the nonequilibrium influences of the aggregation process. A mechanistic model was developed to extract meaningful thermodynamic and kinetic parameters from an irreversibly denatured process. The model was used to simulate how unfolding properties could be used to predict aggregation rates at different temperatures above and below the T(m) and to account for concentration dependence of reaction rates. The model was shown to uniquely identify the thermodynamic parameters DeltaC(P) (1.3 +/- 0.7 kcal/mol-K), DeltaH(m) (74.3 +/- 6.8 kcal/mol), and T(m) with reasonable variances.