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Medullary breast cancer (MBC) is a rare but enigmatic pathologic type of breast cancer. Despite features of aggressiveness, MBC is associated with a favorable prognosis. Morphologic diagnosis remains difficult in many cases. Very little is known about the molecular alterations involved in MBC. Notably, it is not clear whether MBC and ductal breast cancer (DBC) represent molecularly distinct entities and what genes/proteins might account for their differences. Using whole-genome oligonucleotide microarrays, we compared gene expression profiles of 22 MBCs and 44 grade III DBCs. We show that MBCs are less heterogeneous than DBCs. Whereas different molecular subtypes (luminal A, luminal B, basal, ERBB2-overexpressing, and normal-like) exist in DBCs, 95% MBCs display a basal profile, similar to that of basal DBCs. Supervised analysis identified gene expression signatures that discriminated MBCs from DBCs. Discriminator genes are associated with various cellular processes related to MBC features, in particular immune reaction and apoptosis. As compared with MBCs, basal DBCs overexpress genes involved in smooth muscle cell differentiation, suggesting that MBCs are a distinct subgroup of basal breast cancer with limited myoepithelial differentiation. Finally, MBCs overexpress a series of genes located on the 12p13 and 6p21 chromosomal regions known to contain pluripotency genes. Our results contribute to a better understanding of MBC and of mammary oncogenesis in general. (Cancer Res 2006; (66)9: 4634-44)

Supplementary Figure 1 from Gene Expression Profiling Shows Medullary Breast Cancer Is a Subgroup of Basal Breast Cancers

Supplementary Figure 2 from Gene Expression Profiling Shows Medullary Breast Cancer Is a Subgroup of Basal Breast Cancers

Supplementary Tables 1-4 from Gene Expression Profiling Shows Medullary Breast Cancer Is a Subgroup of Basal Breast Cancers

Supplementary Section and Figure Legends from Gene Expression Profiling Shows Medullary Breast Cancer Is a Subgroup of Basal Breast Cancers

The ability of flow cytometry to resolve multiple parameters was used in a microsphere-based flow cytometric assay for the simultaneous determination of several cytokines in a sample. The flow cytometer microsphere-based assay (FMBA) for cytokines consists of reagents and dedicated software, specifically designed for the quantitative determination of cytokines. We have made several improvements in the multiplex assay: (i) dedicated software specific for the quantitative multiplex assay that processes data automatically, (ii) a stored master calibration curve with a two-point recalibration to adjust the stored curve periodically, and (iii) an internal standard to normalize the detection step in each sample. Overall analytical performance, including sensitivity, reproducibility, and dynamic range, was investigated for interleukin-4 (IL-4), IL-6, IL-10, IL-12, gamma interferon (IFN-γ), and tumor necrosis factor alpha. These assays were found to be reproducible and accurate, with a sensitivity in the picograms-per-milliliter range. Results obtained with FMBA correlate well with commercial enzyme-linked immunosorbent assay data ( r > 0.98) for all cytokines assayed. This multiplex assay was applied to the determination of cytokine profiles in whole blood from atopic and nonatopic patients. Our results show that atopic subjects' blood produces more IL-4 ( P = 0.003) and less IFN-γ ( P = 0.04) than the blood of nonatopic subjects. However, atopic asthmatic subjects' blood produces significantly more IFN-γ than that of atopic nonasthmatic subjects ( P = 0.03). The results obtained indicate that the FMBA technology constitutes a powerful system for the quantitative, simultaneous determination of secreted cytokines in immune diseases.

Medullary breast cancer (MBC) is a rare but enigmatic pathologic type of breast cancer. Despite features of aggressiveness, MBC is associated with a favorable prognosis. Morphologic diagnosis remains difficult in many cases. Very little is known about the molecular alterations involved in MBC. Notably, it is not clear whether MBC and ductal breast cancer (DBC) represent molecularly distinct entities and what genes/proteins might account for their differences. Using whole-genome oligonucleotide microarrays, we compared gene expression profiles of 22 MBCs and 44 grade III DBCs. We show that MBCs are less heterogeneous than DBCs. Whereas different molecular subtypes (luminal A, luminal B, basal, ERBB2-overexpressing, and normal-like) exist in DBCs, 95% MBCs display a basal profile, similar to that of basal DBCs. Supervised analysis identified gene expression signatures that discriminated MBCs from DBCs. Discriminator genes are associated with various cellular processes related to MBC features, in particular immune reaction and apoptosis. As compared with MBCs, basal DBCs overexpress genes involved in smooth muscle cell differentiation, suggesting that MBCs are a distinct subgroup of basal breast cancer with limited myoepithelial differentiation. Finally, MBCs overexpress a series of genes located on the 12p13 and 6p21 chromosomal regions known to contain pluripotency genes. Our results contribute to a better understanding of MBC and of mammary oncogenesis in general. (Cancer Res 2006; (66)9: 4634-44)

Background High 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. Results MAF (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. Conclusion MicroArray 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.

Conventional cytogenetic analysis currently stratifies acute myelogenous leukaemia (AML) into prognostically relevant groups. However, approximately 50% of adult AMLs have normal cytogenetics (NC-AMLs), and represent a heterogeneous and poorly understood group. We analysed gene expression in 55 AML samples including 53 cases from adult patients with NC-AML (n = 36), trisomy 8, t(15;17), t(8;21), t(11;19), 7q deletion, and two cell lines using 9000-gene DNA microarrays. Global hierarchical clustering showed that NC-AMLs are a heterogeneous group. Supervised analysis distinguished two subgroups of NC-AML: one subgroup constituted a homogeneous NC cluster ('pure NC-AML'), and the other NC-AMLs were close to the AML cases with translocations ('translocation like'). Gene expression signatures were also derived for patients with trisomy 8, as well as FLT3 and MLL gene duplications. Importantly, samples from 24 NC-AML patients who could be evaluated for clinical outcome were analysed. In all, 43 genes that discriminated two classes of patients with significantly different prognosis were identified. The poor prognosis class contained a majority of 'pure NC-AMLs', whereas the 'translocation-like' AMLs were in the good prognosis class. Discriminator genes included genes involved in drug resistance (TOP2B), protein transport (MTX2, SLC35A2), and cell signalling (MAPK1, PRKAB2). Our results demonstrate the transcriptional heterogeneity of NC-AMLs, and suggest the existence of 'translocation-like' NC-AMLs and of a gene expression signature that may predict response to chemotherapy.

International audience; ERBB2 is a transmembrane tyrosine kinase receptor encoded by a gene located in chromosome region 17q12. Overexpression of ERBB2, generally by way of gene amplification, plays a role in mammary oncogenesis. This alteration can be overcome by use of the humanized monoclonal antibody trastuzumab (Herceptin). Accurate determination of ERBB2 status is required for appropriate use of this targeted therapy and is currently analysed by immunohistochemistry (IHC) on tissue sections and/or fluorescence in situ hybridisation (FISH) on interphase chromosomes. We have studied the gene expression profiles of a series of 213 breast tumours and 16 breast cancer cell lines with known ERBB2 status, using Ipsogen's DiscoveryChip microarrays with approximately 9000 cDNAs. We have identified 36 genes and expressed sequence tags that were differentially expressed in tumours and in cell lines with and without ERBB2 protein overexpression. This ERBB2-specific gene expression signature (GES) contained 29 overexpressed genes including the ERBB2 gene itself, five genes located in its immediate vicinity on 17q12, non-17q genes such as GATA4 and eight downregulated genes including oestrogen receptor alpha (ER). Some correlations were validated at the protein level using IHC on tissue microarrays. The GES was able to distinguish ERBB2-negative and -positive cancer samples, as well as FISH-negative and FISH-positive ERBB2 2+ IHC samples.