Relevance of multiple biological parameters in breast cancer prognosis

Abstract

A multitude of clinical, pathological and biological parameters have been reliably associated with prognosis in breast cancer patients. Recently much interest has been engendered by multifactorial computing methods attempting to produce higher prognostic accuracy as well as being of clinical utility for treatment selection. Clinical, pathological and a number of more recent biological parameters were evaluated retrospectively in a population of 196 breast cancer patients who had been treated with first line surgery and followed for a median of 7.3 years. Clinical tumour size, menopausal status, type of treatment as well as tumour grade, pathological tumour size, node invasion, the presence of vascular emboli and steroid hormone receptor status were evaluated together with gene amplification (by Southern blot) of c-erbB2/neu, c-myc and int-2/FGF3 as well as overexpression (by immunohistochemistry, IHC) of c-erbB2/neu, EGF receptor, CSF-1 and CSF-1 receptor. The presence and abundance of inflammatory cell infiltrates (T, B cells and monocytes) were also evaluated by IHC. The risk of cancer related death as tested in univariate and multivariate analyses was consistently higher in patients with positive axillary nodes and in those whose tumours showed evidence of int-2/FGF3 gene amplification, of overexpression of c-erbB2/neu at the cellular membrane, of vascular invasion by tumour cells and of abundant CD45RO+ T cells infiltrates. A prognostic score was calculated for each patient by computing the prognostic index associated with each of these five parameters and risk profiles were established by order of increasing risk. Survival curves drawn for three groups of high, intermediate or lowest risk showed a highly significant poorer survival for the highest risk group. The model we present, although far from the complexity of a 'neural network' analysis, does discriminate effectively between low, moderate and high risk groups. Future prospective studies should test these independent prognostic markers together with more recently established markers in an attempt to determine not only the most predictive but also the most cost-effective 'prognostikit' for breast cancer patients. Such a molecular prognostic index will allow the evaluation of current therapies in the light of specific molecular alterations and may aid the design of new therapeutic approaches geared to the genetic profile of a given tumour.