Fractal methods to analyze ion channel kinetics.

We describe the traditional nonfractal and the new fractal methods used to analyze the currents through ion channels in the cell membrane. We discuss the hidden assumptions used in these methods and how those assumptions lead to different interpretations of the same experimental data. The nonfractal methods assumed that channel proteins have a small number of discrete states separated by fixed energy barriers. The goal was to determine the parameters of the kinetic diagram, which are the number of states, the pathways between them, and the kinetic rate constants of those pathways. The discovery that these data have fractal characteristics suggested that fractal approaches might provide more appropriate tools to analyze and interpret these data. The fractal methods determine the characteristics of the data over a broad range of time scales and how those characteristics depend on the time scale at which they are measured. This is done by using a multiscale method to accurately determine the probability density function over many time scales and by determining how the effective kinetic rate constant, the probability of switching states, depends on the effective time scale at which it is measured. These fractal methods have led to new information about the physical properties of channel proteins in terms of the number of conformational substates, the distribution of energy barriers between those states, and how those energy barriers change with time. The new methods developed from the fractal paradigm shifted the analysis of channel data from determining the parameters of a kinetic diagram to determining the physical properties of channel proteins in terms of the distribution of energy barriers and/or their time dependence.

HER-2/neu expression in archival non-small cell lung carcinomas using FDA-approved Hercep test.

HER-2/neu is a 185 kDa glycoprotein related to the epidermal growth factor receptor. Overexpressed in 25-30% of primary breast carcinomas, HER-2/neu is associated with a poor clinical outcome. Recently the FDA approved an antibody to HER-2/neu, trastuzumab (Herceptin), for the treatment of HER-2/neu overexpressing metastatic breast cancers. Relatively little is known about HER-2/neu status and lung cancers. We reasoned that if HER-2/neu status could be ascertained in non-small cell lung carcinomas (NSCLCs), and a clinical correlation can be established, a rationale for the use of Herceptin in this tumor type could be established. Using a FDA-approved standardized diagnostic kit, HercepTest, for detection of HER-2/neu in clinical specimens, we examined the expression of HER-2/neu in NSCLCs in archival paraffin-embedded specimens (N = 81). In normal epithelium, HER-2/neu expression was not detected in a majority of samples (74/81). HER-2/neu overexpression was detected in 27% of the tumors of different histological types including adenocarcinomas, large cell carcinomas, and squamous cell carcinomas. Poor to moderately differentiated, but not well differentiated tumors showed overexpression of HER-2/neu. The specificity of HercepTest was further increased (from 27% to 21%) when the expression in the few normal tissues was subtracted from the tumor score. HER-2/neu may offer an attractive predictive and prognostic factor for NSCLC.

Cancer gene discovery using digital differential display.

The Cancer Gene Anatomy Project database of the National Cancer Institute has thousands of expressed sequences, both known and novel, in the form of expressed sequence tags (ESTs). These ESTs, derived from diverse normal and tumor cDNA libraries, offer an attractive starting point for cancer gene discovery. Using a data-mining tool called Digital Differential Display (DDD) from the Cancer Gene Anatomy Project database, ESTs from six different solid tumor types (breast, colon, lung, ovary, pancreas, and prostate) were analyzed for differential expression. An electronic expression profile and chromosomal map position of these hits were generated from the Unigene database. The hits were categorized into major classes of genes including ribosomal proteins, enzymes, cell surface molecules, secretory proteins, adhesion molecules, and immunoglobulins and were found to be differentially expressed in these tumorderived libraries. Genes known to be up-regulated in prostate, breast, and pancreatic carcinomas were discovered by DDD, demonstrating the utility of this technique. Two hundred known genes and 500 novel sequences were discovered to be differentially expressed in these select tumor-derived libraries. Test genes were validated for expression specificity by reverse transcription-PCR, providing a proof of concept for gene discovery by DDD. A comprehensive database of hits can be accessed at http:// www.fau.edu/cmbb/publications/cancergenes. htm. This solid tumor DDD database should facilitate target identification for cancer diagnostics and therapeutics.