Mathematical Prognostic Biomarker Models for Treatment Response and Survival in Epithelial Ovarian Cancer

Following initial standard chemotherapy (platinum/taxol), more than 75% of those patients with advanced stage epithelial ovarian cancer (EOC) experience a recurrence. There are currently no accurate prognostic tests that, at the time of the diagnosis/surgery, can identify those patients with advanced stage EOC who will respond to chemotherapy. Using a novel mathematical theory, we have developed three prognostic biomarker models (complex mathematical functions) that—based on a global gene expression analysis of tumor tissue collected during surgery and prior to the commencement of chemotherapy—can identify with a high accuracy those patients with advanced stage EOC who will respond to the standard chemotherapy [long-term survivors (>7 yrs)] and those who will not do so [short-term survivors (<3 yrs)]. Our three prognostic biomarker models were developed with 34 subjects and validated with 20 unknown (new and different) subjects. Both the overall biomarker model sensitivity and specificity ranged from 95.83% to 100.00%. The 12 most significant genes identified, which are also the input variables to the three mathematical functions, constitute three distinct gene networks with the following functions: 1) production of cytoskeletal components, 2) cell proliferation, and 3) cell energy production. The first gene network is directly associated with the mechanism of action of anti-tubulin chemotherapeutic agents, such as taxanes and epothilones. This could have a significant impact in the discovery of new, more effective pharmacological treatments that may significantly extend the survival of patients with advanced stage EOC

An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk

It remains elusive whether some of the associations identified in genome-wide association studies of prostate cancer (PrCa) may be due to regulatory effects of genetic variants on CpG sites, which may further influence expression of PrCa target genes. To search for Cp

Structural features of ribonucleotide reductase

Herpes simplex virus type 1 (HSV-1) encodes a ribonucleotide reductase which comprises two polypeptides with sizes of 136,000 (RR1) and 38,000 mol. wt. (RR2). We have determined the entire DNA sequence specifying HSV-1 RR1 and have identified two adjacent open reading frames in varicella-zoster virus (VZV) which have homology to HSV RR1 and RR2; the predicted sizes for the VZV RR1 and RR2 polypeptides are 87,000 and 35,000 mol. wt. respectively. Amino acid comparisons with RR1 and RR2 polypeptides from other organisms indicate that HSV-1 RR1 contains a unique N-terminal domain which is absent from other RR1 polypeptides apart from HSV-2 RR1. These N-terminal amino acid sequences are poorly conserved between HSV-1 and HSV-2 in contrast to the remainder of the protein which shows greater than 90% homology. Polypeptide structural predictions suggest that the HSV-1 N-terminal domain may be separated into two regions, namely, a beta-sheet structure followed by a nonstructured area. Across the remainder of RR1 and RR2, comparisons also reveal blocks of amino acids conserved between the different ribonucleotide reductases, and these may be important for enzyme activity. From predictions on the structure of these conserved blocks, we have proposed that the location of a substrate binding site within RR1 is centered on three conserved glycine residues in a region which is predicted to adopt a beta-sheet/turn/alpha-helical structure; this approximates to the structure for ADP nucleotide binding folds. Finally, we propose that the promoters for the HSV and Epstein-Barr virus (EBV) RR2 transcripts have evolved by separate evolutionary routes