Selnur Erdal

Pharmacobiologically Based Scheduling of Capecitabine and Docetaxel Results in Antitumor Activity in Resistant Human Malignancies

PURPOSE Capecitabine and docetaxel have demonstrated preclinical antitumor synergy. This synergy is thought to occur from docetaxel-mediated upregulation of thymidine phosphorylase (dThdPase), an enzyme responsible for the relative tumor selectivity of capecitabine. On the basis of the time-dependency and transiency for this upregulation, we performed a phase I study of capecitabine in combination with weekly docetaxel. We hypothesized that weekly docetaxel would result in sustained dThdPase expression and that capecitabine administration at times of maximum dThdPase upregulation would increase the therapeutic index for this combination. PATIENTS AND METHODS Patients with advanced solid malignancies received docetaxel on days 1, 8, and 15, and capecitabine bid on days 5 to 18, every 4 weeks. Docetaxel was fixed at 36 mg/m(2)/wk, whereas capecitabine was escalated in successive patients cohorts. RESULTS Sixteen patients received 77 courses at capecitabine doses from 950 to 1,500 mg/m(2)/d. The most common toxicities were hand-foot syndrome, diarrhea, nausea/vomiting, and asthenia. Grades 3 to 4 hematologic toxicities were infrequent and no treatment-related hospitalizations occurred. Three of three patients treated at 1,500/36 mg/m(2) capecitabine/docetaxel developed grade 3 hand-foot syndrome or diarrhea during either their first or second course, whereas only two of 13 patients at 1,250/36 mg/m(2) doses developed significant toxicity. Antitumor responses (n = 7) occurred in patients with hepatocellular, non-small-cell lung, and chemotherapy-refractory breast, bladder, and colorectal carcinomas. Prolonged stabilizations occurred in patients with metastatic mesothelioma (n = 2), chemorefractory non-small-cell lung carcinoma, and bronchioloalveolar carcinoma. CONCLUSION Capecitabine in combination with weekly docetaxel is well tolerated. Recommended doses are capecitabine 1,250 mg/m(2)/d (625 mg/m(2) bid) with docetaxel 36 mg/m(2)/wk. The acceptable toxicity profile in this dose schedule, and the antitumor activity observed, warrant further evaluation of this regimen.

A time series analysis of microarray data

As the capture and analysis of single-time-point microarray expression data becomes routine, investigators are turning to time-series expression data to investigate complex gene regulation schemes and metabolic pathways. These investigations are facilitated by algorithms that can extract and cluster related behaviors from the full population of time-series behaviors observed. Although traditional clustering techniques have shown to be effective for certain types of expression analysis, they do not take the biological nature of the process into account, and therefore are clearly not optimized for this purpose. Moreover, the current approaches provide internal comparisons for the experiments utilized for clustering, but cross-comparisons between clustered results are qualitative and subjective. We present a combination of current and novel methods for the analysis of time series gene expression data. We focus on an actual study we have performed for Haemophilus influenzae which is a major cause of otitis media in children. We first perform a discretization of the gene expression data that takes both positive and negative correlations into consideration and then develop a clustering algorithm optimized for such data that allows elucidation and searching of time-series patterns. The resulting approach allows time-series data to be usefully compared across multiple experiments. We demonstrate the success of our algorithm by showing some of the genes that it finds to be co-regulated are not detected by current methods. As a result we are able to identify several signal pathways that initiate competence development, and to characterize the transcriptomes of wild-type and an adenylate cyclase mutant (cya) strains under both nutrient-limiting and nutrient-complete growth conditions.

A Knowledge-Anchored Integrative Image Search and Retrieval System

Clinical data that may be used in a secondary capacity to support research activities are regularly stored in three significantly different formats: (1) structured, codified data elements; (2) semi-structured or unstructured narrative text; and (3) multi-modal images. In this manuscript, we will describe the design of a computational system that is intended to support the ontology-anchored query and integration of such data types from multiple source systems. Additional features of the described system include (1) the use of Grid services-based electronic data interchange models to enable the use of our system in multi-site settings and (2) the use of a software framework intended to address both potential security and patient confidentiality concerns that arise when transmitting or otherwise manipulating potentially privileged personal health information. We will frame our discussion within the specific experimental context of the concept-oriented query and integration of correlated structured data, narrative text, and images for cancer research.

A Multi-metric Similarity Based Analysis of Microarray Data

Clustering has been shown to be effective in analyzing functional relationships of genes. However, no single clustering method with single distance metric is capable of capturing all types of relationships that a gene may have with other genes. In this paper we introduce a framework which groups genes around a query gene, and ranks them in order corresponding to different levels of similarity utilizing multiple metrics. The focus of our efforts is to create gene centric clusters. The notion of Strong Group (SG) is presented as a cluster definition where no two genes are distant from each other, greater than a threshold value. The genes are then ranked on their frequency of co-occurrence. The grouping and rankings are drawn by applying set operations over results of multiple distance metrics, each capturing particular similarities such as shifted relationships, negative correlations and strong positive relationships. The effectiveness of the algorithm is demonstrated on two case studies. In the first one, a single yeast cell cycle dataset is used. It is shown that different combination of set operations reveals different kinds of interactions between genes. Finally, to provide further analysis on our techniques, we have tested them on multiple microarray datasets obtained from Stanford Microarray Database.