Rebecca Tagett

Gene Expression Profiling for Molecular Characterization of Inflammatory Breast Cancer and Prediction of Response to Chemotherapy

Inflammatory breast cancer (IBC) is a rare but aggressive form of breast cancer with a 5-year survival limited to ∼40%. Diagnosis, based on clinical and/or pathological criteria, may be difficult. Optimal systemic neoadjuvant therapy and accurate predictors of pathological response have yet to be defined for increasing response rate and survival. Using DNA microarrrays containing ∼8,000 genes, we profiled breast cancer samples from 81 patients, including 37 with IBC and 44 with noninflammatory breast cancer (NIBC). Global unsupervised hierarchical clustering was able to some extent to distinguish IBC and NIBC cases and revealed subclasses of IBC. Supervised analysis identified a 109-gene set the expression of which discriminated IBC from NIBC samples. This molecular signature was validated in an independent series of 26 samples, with an overall performance accuracy of 85%. Discriminator genes were associated with various cellular processes possibly related to the aggressiveness of IBC, including signal transduction, cell motility, adhesion, and angiogenesis. A similar approach, with leave-one-out cross-validation, identified an 85-gene set that divided IBC patients with significantly different pathological complete response rate (70% in one group and 0% in the other group). These results show the potential of gene expression profiling to contribute to a better understanding of IBC, and to provide new diagnostic and predictive factors for IBC, as well as for potential therapeutic targets.

Expression scanning of an array of growth control genes in human tumor cell lines

Analysis of gene expression on a medium- or large-scale is an increasingly recognized method for functional and clinical investigations based on the now extensive catalog of known or partially sequenced genes. The accessibility of this approach can be enhanced by using readily available technology (macroarrays on Nylon, radioactive detection) and the IMAGE resource to assemble sets of targets. We have set up such a medium-scale, flexible system and validated it by the study of quantitative expression levels for 120 genes in six cell lines, including three mammary carcinoma cell lines. A number of important parameters are identified as necessary for the assembly of a valid set and the obtention of good-quality quantitative data. The extensive data assembled in this survey identified potential targets of carcinogenesis, for example the CRABP2 and GATA3 transcription factor genes. We also demonstrate the feasibility of this procedure for relatively small tumor samples, without recourse to probe amplification methods.

DNA arrays in clinical oncology: promises and challenges.

Cancer is a complex genetic disease characterized by the accumulation of multiple molecular alterations. Current diagnostic and prognostic classifications, based on clinical and pathologic factors, are insufficient to reflect the whole clinical heterogeneity of tumors. Most current anticancer agents do not differentiate between cancerous and normal cells, leading sometimes to disastrous adverse effects. Recent advances in human genome research and high-throughput molecular technologies make it possible finally to tackle the molecular complexity of malignant tumors. With DNA array technology, mRNA expression levels of thousands of genes can be measured simultaneously in a single assay. Oncology is benefiting on multiple fronts. Gene expression profiles are revealing new biologically and clinically relevant tumor subclasses previously indistinguishable and are identifying new diagnostic and prognostic biomarkers as well as new potential therapeutic targets. Here, we review the technology and present clinical applications for which promising results have been obtained. Finally, we discuss issues that must be resolved in the near future to allow DNA arrays to translate into benefits for cancer patients.

MicroArray Facility: a laboratory information management system with extended support for Nylon based technologies

BackgroundHigh throughput gene expression profiling (GEP) is becoming a routine technique in life science laboratories. With experimental designs that repeatedly span thousands of genes and hundreds of samples, relying on a dedicated database infrastructure is no longer an option.GEP technology is a fast moving target, with new approaches constantly broadening the field diversity. This technology heterogeneity, compounded by the informatics complexity of GEP databases, means that software developments have so far focused on mainstream techniques, leaving less typical yet established techniques such as Nylon microarrays at best partially supported.ResultsMAF (MicroArray Facility) is the laboratory database system we have developed for managing the design, production and hybridization of spotted microarrays. Although it can support the widely used glass microarrays and oligo-chips, MAF was designed with the specific idiosyncrasies of Nylon based microarrays in mind. Notably single channel radioactive probes, microarray stripping and reuse, vector control hybridizations and spike-in controls are all natively supported by the software suite. MicroArray Facility is MIAME supportive and dynamically provides feedback on missing annotations to help users estimate effective MIAME compliance. Genomic data such as clone identifiers and gene symbols are also directly annotated by MAF software using standard public resources. The MAGE-ML data format is implemented for full data export. Journalized database operations (audit tracking), data anonymization, material traceability and user/project level confidentiality policies are also managed by MAF.ConclusionMicroArray Facility is a complete data management system for microarray producers and end-users. Particular care has been devoted to adequately model Nylon based microarrays. The MAF system, developed and implemented in both private and academic environments, has proved a robust solution for shared facilities and industry service providers alike.

Expression profiling in mouse fetal thymus reveals clusters of coordinately expressed genes that mark individual stages of T-cell ontogeny

Abstract. To search for genes that participate in regulatory networks sustaining mouse embryonic T-cell development, we have performed expression profiling using nylon macroarrays. Labeled samples representative of individual developmental stages were utilized, taking advantage of cell homogeneity during early thymus ontogeny. cDNAs revealing differential expression were further selected using labeled samples derived from lymphoid versus non-lymphoid tissues, and from mutant thymi exhibiting T-cell developmental defects. We thus identified clusters of coexpressed genes during T-cell embryogenesis and characterized their sequences through bioinformatics. We compare our results with those from other profiling analyses in the immune system, and discuss their implications for the definition of genes whose products are involved in T-cell development.