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.

[Genomics and progress in oncology]. / Place des analyses génomiques centralisées dans la découverte et la validation d'innovations diagnostiques en oncologie.

Genomics and genetics are becoming pillars of clinical research, allowing certain diseases to be understood at the molecular level and leading to improved treatments. Successful genetic analyses in the clinical oncological setting depend on the quality of the samples collected. At clinical sites, attention must be paid to the steps taken prior to analysis, and to the fragility of the molecules to be tested. The development of a standard procedure specifically adapted to the identification and validation of biomarkers through genomics requires an understanding of the different technologies used at each time point, from the analysis to the data processing, and the implication of personnel at all levels: the health care centers, the biotech companies, and/or the pharmaceutical industries.

Identification of TCL1A as an immunohistochemical marker of adverse outcome in diffuse large B-cell lymphomas.

We used a combination of DNA-microarray and tissue-microarray (TMA) analyses to identify markers that could be routinely used to predict the outcome of diffuse large-B-cell lymphoma (DLCL) patients. Gene expression profiling was performed using DNA-microarrays on 52 tumour biopsy samples [31 DLCL and 21 follicular lymphomas (FL)] from 48 patients (28 DLCL and 20 FL). T-cell leukemia/lymphoma-1A (TCL1A) mRNA overexpression was correlated with relapse in DLCL patients. TMA analysis was applied on a distinct series of 36 formalin-fixed, paraffin-embedded DLCL samples and showed that TCL1A immunoexpression was correlated with either higher relapse (p=0.02) or lower 5-year overall survival (p=0.009) rates. Moreover, the prognostic value of TCL1A was independent from IPI in our series. Our data suggest that TCL1A immunodetection is an independent marker of adverse outcome that could be used in routine settings for the management of DLCL patients.

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.