Gene expression profile predicts outcome after anthracycline-based adjuvant chemotherapy in early breast cancer.

Prognosis of early beast cancer is heterogeneous. Today, no histoclinical or biological factor predictive for clinical outcome after adjuvant anthracycline-based chemotherapy (CT) has been validated and introduced in routine use. Using DNA microarrays, we searched for a gene expression signature associated with metastatic relapse after adjuvant anthracycline-based CT without taxane. We profiled a multicentric series of 595 breast cancers including 498 treated with such adjuvant CT. The identification of the prognostic signature was done using a metagene-based supervised approach in a learning set of 323 patients. The signature was then tested on an independent validation set comprising 175 similarly treated patients, 128 of them from the PACS01 prospective clinical trial. We identified a 3-metagene predictor of metastatic relapse in the learning set, and confirmed its independent prognostic impact in the validation set. In multivariate analysis, the predictor outperformed the individual current prognostic factors, as well as the Nottingham Prognostic Index-based classifier, both in the learning and the validation sets, and added independent prognostic information. Among the patients treated with adjuvant anthracycline-based CT, with a median follow-up of 68 months, the 5-year metastasis-free survival was 82% in the "good-prognosis" group and 56% in the "poor-prognosis" group. Our predictor refines the prediction of metastasis-free survival after adjuvant anthracycline-based CT and might help tailoring adjuvant CT regimens.

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

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.

Gene expression profiling shows medullary breast cancer is a subgroup of basal breast cancers.

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.

Identification of new classes among acute myelogenous leukaemias with normal karyotype using gene expression profiling.

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.

Identification and validation of an ERBB2 gene expression signature in breast cancers.

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.