While this provides further compelling evidence that tumour biology is a key variable required for decision making in personalised BC management, the heterogeneity within these groups and its incorporation with the currently validated variables and prognostic indices add complexity. Further support for this hypothesis is provided by gene expression profiling (GEP) that have identified distinct molecular tumour groups with direct clinical relevance ( Perou et al, 1999 Sorlie et al, 2001 van de Vijver et al, 2002 van't Veer et al, 2003 Darb-Esfahani et al, 2009 Parker et al, 2009 Nielsen et al, 2010). It is now recognised that the biological characteristics of BC are important for clinical management and incorporation into the NPI could significantly improve the delivery of personalised medicine in BC patients.Ĭurrent data imply that BC is a heterogeneous group of diseases with complex and distinctive underlying molecular pathogenesis ( Beckmann et al, 1997 Ellis et al, 1999 Lishman and Lakhani, 1999). However, the NPI cannot reveal the full clinical/survival outcome heterogeneity currently observed in BC and would benefit from greater sophistication to support more accurate personalised management of BC patients. The NPI has been confirmed after long-term follow-up ( Galea et al, 1992), validated independently in large multicentre studies ( Brown et al, 1993 Balslev et al, 1994) and revised to stratify patients into five prognostic groups ( Blamey et al, 2007). Prognosis worsens as the NPI numerical value increases and by using cutoff points patients may be stratified into good, moderate and poor prognostic groups ( Ellis et al, 1987 Blamey et al, 2007). The current NPI is based on a combination of histopathological examination of tumour size, lymph-node (LN) stage and tumour grading assembled in a prognostic index formula ( Haybittle et al, 1982) and can be used as a risk stratifier in unselected cohorts of operable early-stage primary BC patients. Examples of such methods include the Nottingham Prognostic Index (NPI) ( Galea et al, 1992 Balslev et al, 1994 D'Eredita et al, 2001), St Gallen consensus criteria ( Goldhirsch et al, 2009), the National Comprehensive Cancer Network (NCCN) guidelines ( Carlson et al, 2006) and Adjuvant! Online ( Ravdin et al, 2001). Methods have been developed to assist in predicting patient outcome and to support clinical decision making in BC management. Clinical decision making in personalised BC management requires robust and accurate risk stratification based not only on outcome prediction but also on a biological basis ( Clark, 1994). This, in addition to numbers and complexity of available treatment options, has resulted in decision making difficulties regarding the most appropriate treatment choice. Breast cancer (BC), the most common cancer and the second leading cause of cancer death in women, represents a heterogeneous group of tumours with varied genotypic and phenotypic features, behaviour and response to therapy.
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