Catherine Nguyen

The chemokine TECK is expressed by thymic and intestinal epithelial cells and attracts double- and single-positive thymocytes expressing the TECK receptor CCR9

Chemokines are key regulators of migration in lymphoid tissues. In the thymus, maturing thymocytes move from the outer capsule to the inner medulla and thereby interact with different types of stromal cells that control their maturation and selection. In the process of searching for molecules specifically expressed at different stages of mouse thymic differentiation, we have characterized the cDNA coding for the thymus‐expressed chemokine (TECK) and its receptor CCR9. The TECK receptor gene was isolated and shown to be localized on the mouse chromosome 9F1‐F4. Thymic dendritic cells have been initially thought to be a prevalent source of TECK. In contrast, our results indicate that thymic epithelial cells constitute the predominant source of TECK. Consistent with the latter distribution, the TECK receptor is highly expressed by double‐positive thymocytes, and TECK can chemoattract both double‐positive and single‐positive thymocytes. The TECK transcript is also abundantly expressed in the epithelial cells lining the small intestine. In conclusion, the interplay of TECK and its receptor CCR9 is likely to have a significant role in the recruitment of developing thymocytes to discrete compartments of the thymus.

Gene expression profiling of colon cancer by DNA microarrays and correlation with histoclinical parameters

Different diagnostic and prognostic groups of colorectal carcinoma (CRC) have been defined. However, accurate diagnosis and prediction of survival are sometimes difficult. Gene expression profiling might improve these classifications and bring new insights into underlying molecular mechanisms. We profiled 50 cancerous and noncancerous colon tissues using DNA microarrrays consisting of ∼8000 spotted human cDNA. Global hierarchical clustering was to some extent able to distinguish clinically relevant subgroups, normal versus cancer tissues and metastatic versus nonmetastatic tumours. Supervised analyses improved these segregations by identifying sets of genes that discriminated between normal and tumour tissues, tumours associated or not with lymph node invasion or genetic instability, and tumours from the right or left colon. A similar approach identified a gene set that divided patients with significantly different 5-year survival (100% in one group and 40% in the other group; P=0.005). Discriminator genes were associated with various cellular processes. An immunohistochemical study on 382 tumour and normal samples deposited onto a tissue microarray subsequently validated the upregulation of NM23 in CRC and a downregulation in poor prognosis tumours. These results suggest that microarrays may provide means to improve the classification of CRC, provide new potential targets against carcinogenesis and new diagnostic and/or prognostic markers and therapeutic targets.

Gene expression profiling of primary breast carcinomas using arrays of candidate genes

Breast cancer is characterized by an important histoclinical heterogeneity that currently hampers the selection of the most appropriate treatment for each case. This problem could be solved by the identification of new parameters that better predict the natural history of the disease and its sensitivity to treatment. A large-scale molecular characterization of breast cancer could help in this context. Using cDNA arrays, we studied the quantitative mRNA expression levels of 176 candidate genes in 34 primary breast carcinomas along three directions: comparison of tumor samples, correlations of molecular data with conventional histoclinical prognostic features and gene correlations. The study evidenced extensive heterogeneity of breast tumors at the transcriptional level. A hierarchical clustering algorithm identified two molecularly distinct subgroups of tumors characterized by a different clinical outcome after chemotherapy. This outcome could not have been predicted by the commonly used histoclinical parameters. No correlation was found with the age of patients, tumor size, histological type and grade. However, expression of genes was differential in tumors with lymph node metastasis and according to the estrogen receptor status; ERBB2 expression was strongly correlated with the lymph node status (P < 0.0001) and that of GATA3 with the presence of estrogen receptors (P < 0.001). Thus, our results identified new ways to group tumors according to outcome and new potential targets of carcinogenesis. They show that the systematic use of cDNA array testing holds great promise to improve the classification of breast cancer in terms of prognosis and chemosensitivity and to provide new potential therapeutic targets.

Gene Expression Profiling Identifies Molecular Subtypes of Inflammatory Breast Cancer

Breast cancer is a heterogeneous disease. Comprehensive gene expression profiles obtained using DNA microarrays have revealed previously indistinguishable subtypes of noninflammatory breast cancer (NIBC) related to different features of mammary epithelial biology and significantly associated with survival. Inflammatory breast cancer (IBC) is a rare, particular, and aggressive form of disease. Here we have investigated whether the five molecular subtypes described for NIBC (luminal A and B, basal, ERBB2 overexpressing, and normal breast-like) were also present in IBC. We monitored the RNA expression of approximately 8,000 genes in 83 breast tissue samples including 37 IBC, 44 NIBC, and 2 normal breast samples. Hierarchical clustering identified the five subtypes of breast cancer in both NIBC and IBC samples. These subtypes were highly similar to those defined in previous studies and associated with similar histoclinical features. The robustness of this classification was confirmed by the use of both alternative gene set and analysis method, and the results were corroborated at the protein level. Furthermore, we show that the differences in gene expression between NIBC and IBC and between IBC with and without pathologic complete response that we have recently reported persist in each subtype. Our results show that the expression signatures defining molecular subtypes of NIBC are also present in IBC. Obtained using different patient series and different microarray platforms, they reinforce confidence in the expression-based molecular taxonomy but also give evidence for its universality in breast cancer, independently of a specific clinical form.

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.

Distinct and Complementary Information Provided by Use of Tissue and DNA Microarrays in the Study of Breast Tumor Markers

Emerging high-throughput screening technologies are rapidly providing opportunities to identify new diagnostic and prognostic markers and new therapeutic targets in human cancer. Currently, cDNA arrays allow the quantitative measurement of thousands of mRNA expression levels simultaneously. Validation of this tool in hospital settings can be done on large series of archival paraffin-embedded tumor samples using the new technique of tissue microarray. On a series of 55 clinically and pathologically homogeneous breast tumors, we compared for 15 molecules with a proven or suspected role in breast cancer, the mRNA expression levels measured by cDNA array analysis with protein expression levels obtained using tumor tissue microarrays. The validity of cDNA array and tissue microarray data were first verified by comparison with quantitative reverse transcriptase-polymerase chain reaction measurements and immunohistochemistry on full tissue sections, respectively. We found a good correlation between cDNA and tissue array analyses in one-third of the 15 molecules, and no correlation in the remaining two-thirds. Furthermore, protein but not RNA levels may have prognostic value; this was the case for MUC1 protein, which was studied further using a tissue microarray containing approximately 600 tumor samples. For THBS1 the opposite was observed because only RNA levels had prognostic value. Thus, differences extended to clinical prognostic information obtained by the two methods underlining their complementarity and the need for a global molecular analysis of tumors at both the RNA and protein levels.

Differential expression assay of chromosome arm 8p genes identifies Frizzled-related (FRP1/FRZB) and Fibroblast Growth Factor Receptor 1 (FGFR1) as candidate breast cancer genes.

Deletions and amplifications are frequent alterations of the short arm of chromosome 8 associated with various types of cancers, including breast cancers. This indicates the likely presence of tumor suppressor genes and oncogenes. In the present study, we have used the expressed sequence tag (EST) map of 8p11-21 to assemble a set of available cDNAs representing genes from this region. DNA arrays were prepared for expression analysis and search for genes potentially involved in breast cancer. Underexpresion in tumoral breast cells (versus normal breast) was observed for 15 transcripts. Among these, the Frizzled-related gene FRP1/FRZB, was turned off in 78% of breast carcinomas, suggesting that the lack of its product may be associated with malignant transformation. Overexpression in tumoral breast cells was observed for 13 genes. The FGFR1 gene, that encodes a tyrosine kinase receptor for members of the fibroblast growth factor family, was identified as a good candidate for one amplification unit. Taken together, our results demonstrate that such a strategy can rapidly identify genes with an altered pattern of expression and provide candidate genes for malignancies.

Responder and nonresponder patients exhibit different peripheral transcriptional signatures during major depressive episode.

To date, it remains impossible to guarantee that short-term treatment given to a patient suffering from a major depressive episode (MDE) will improve long-term efficacy. Objective biological measurements and biomarkers that could help in predicting the clinical evolution of MDE are still warranted. To better understand the reason nearly half of MDE patients respond poorly to current antidepressive treatments, we examined the gene expression profile of peripheral blood samples collected from 16 severe MDE patients and 13 matched controls. Using a naturalistic and longitudinal design, we ascertained mRNA and microRNA (miRNA) expression at baseline, 2 and 8 weeks later. On a genome-wide scale, we detected transcripts with roles in various biological processes as significantly dysregulated between MDE patients and controls, notably those involved in nucleotide binding and chromatin assembly. We also established putative interactions between dysregulated mRNAs and miRNAs that may contribute to MDE physiopathology. We selected a set of mRNA candidates for quantitative reverse transcriptase PCR (RT-qPCR) to validate that the transcriptional signatures observed in responders is different from nonresponders. Furthermore, we identified a combination of four mRNAs (PPT1, TNF, IL1B and HIST1H1E) that could be predictive of treatment response. Altogether, these results highlight the importance of studies investigating the tight relationship between peripheral transcriptional changes and the dynamic clinical progression of MDE patients to provide biomarkers of MDE evolution and prognosis.

Modulation of interleukin-7 receptor expression characterizes differentiation of CD8 T cells specific for HIV, EBV and CMV.

Objectives:To further understand differentiation and homeostasis of CD8 T cells specific for HIV, Epstein–Barr Virus (EBV) and cytomegalovirus (CMV) during HIV infection, we investigated interleukin-7 receptor α (IL-7Rα) expression on those virus-specific T cells. Methods:Microarrays and cytometry analyses were performed on peripheral blood mononuclear cells (PBMC), total and tetramer-binding virus-specific CD8 T cells from 66 HIV-infected patients. Results:Microarray analysis revealed reduced levels of IL-7Rα and increased levels of perforin with disease progression in total PBMC. This loss of IL-7Rα expression was observed on CD8 T cells and was inversely related to perforin expression. The relative expression of both molecules defined three new subsets: IL-7RαposPerforinneg; IL-7RαlonegPerforinlo; and IL-7RαlonegPerforinhi corresponding to naive and effector-memory CD8 differentiation, as assessed by CD45RA/CD11a. The IL-7Rα expression decreased along the CD8 differentiation pathway defined by CD27 and CD28. In contrast, IL-7Rα expression was down-modulated on all the CD8 T cells specific for HIV, EBV and CMV that were almost exclusively IL-7Rαlo/negPerforinlo and was parallel with the CD27 expression. In addition, this low IL-7Rα expression on HIV-specific CD8 T cells was independent of virus load and T-cell activation and remained stable during the first 6 months of antiretroviral therapy despite successful control of HIV replication. Conclusion:The relative expression of IL-7Rα, perforin reveals new aspects of virus-specific CD8 T cell differentiation, independently of T-cell activation and virus load. This opens new perspectives for understanding homeostasis of those cells and immune-based therapeutic strategies.

Genomic and Expression Profiling of Chromosome 17 in Breast Cancer Reveals Complex Patterns of Alterations and Novel Candidate Genes

Chromosome 17 is severely rearranged in breast cancer. Whereas the short arm undergoes frequent losses, the long arm harbors complex combinations of gains and losses. In this work we present a comprehensive study of quantitative anomalies at chromosome 17 by genomic array-comparative genomic hybridization and of associated RNA expression changes by cDNA arrays. We built a genomic array covering the entire chromosome at an average density of 1 clone per 0.5 Mb, and patterns of gains and losses were characterized in 30 breast cancer cell lines and 22 primary tumors. Genomic profiles indicated severe rearrangements. Compiling data from all samples, we subdivided chromosome 17 into 13 consensus segments: 4 regions showing mainly losses, 6 regions showing mainly gains, and 3 regions showing either gains or losses. Within these segments, smallest regions of overlap were defined (17 for gains and 16 for losses). Expression profiles were analyzed by means of cDNA arrays comprising 358 known genes at 17q. Comparison of expression changes with quantitative anomalies revealed that about half of the genes were consistently affected by copy number changes. We identified 85 genes overexpressed when gained (39 of which mapped within the smallest regions of overlap), 67 genes underexpressed when lost (32 of which mapped to minimal intervals of losses), and, interestingly, 32 genes showing reduced expression when gained. Candidate genes identified in this study belong to very diverse functional groups, and a number of them are novel candidates.