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BMC medical genomics

An array CGH based genomic instability index (G2I) is predictive of clinical outcome in breast cancer and reveals a subset of tumors without lymph node involvement but with poor prognosis.


PMID 23186559

Abstract

Despite entering complete remission after primary treatment, a substantial proportion of patients with early stage breast cancer will develop metastases. Prediction of such an outcome remains challenging despite the clinical use of several prognostic parameters. Several reports indicate that genomic instability, as reflected in specific chromosomal aneuploidies and variations in DNA content, influences clinical outcome but no precise definition of this parameter has yet been clearly established. To explore the prognostic value of genomic alterations present in primary tumors, we performed a comparative genomic hybridization study on BAC arrays with a panel of breast carcinomas from 45 patients with metastatic relapse and 95 others, matched for age and axillary node involvement, without any recurrence after at least 11 years of follow-up. Array-CGH data was used to establish a two-parameter index representative of the global level of aneusomy by chromosomal arm, and of the number of breakpoints throughout the genome. Application of appropriate thresholds allowed us to distinguish three classes of tumors highly associated with metastatic relapse. This index used with the same thresholds on a published set of tumors confirms its prognostic significance with a hazard ratio of 3.24 [95CI: 1.76-5.96] p = 6.7 x 10(-5) for the bad prognostic group with respect to the intermediate group. The high prognostic value of this genomic index is related to its ability to individualize a specific group of breast cancers, mainly luminal type and axillary node negative, showing very high genetic instability and poor outcome. Indirect transcriptomic validation was obtained on independent data sets. Accurate evaluation of genetic instability in breast cancers by a genomic instability index (G2I) helps individualizing specific tumors with previously unexpected very poor prognosis.