Functional genomic analysis of drug sensitivity pathways to guide adjuvant strategies in breast cancer , Breast Cancer

The widespread introduction of high throughput RNA interference screening technology has revealed tumour drug sensitivity pathways to common cytotoxics such as paclitaxel, doxorubicin and 5fluorouracil, targeted agents such as trastuzumab and inhibitors of AKT and Poly(ADP-ribose) polymerase (PARP) as well as endocrine therapies such as tamoxifen. Given the limited power of microarray signatures to predict therapeutic response in associative studies of small clinical trial cohorts, the use of functional genomic data combined with expression or sequence analysis of genes and microRNAs implicated in drug response in human tumours may provide a more robust method to guide adjuvant treatment strategies in breast cancer that are transferable across different expression platforms and patient cohorts. Introduction The fact that a large number of patients, who could otherwise be spared, are exposed to the severe side effects of chemotherapy necessitates an improved classification of breast cancer in the adjuvant setting in order to define patient groups who will benefit from the addition of adjuvant chemotherapy (and as a consequence, those patients who will not benefit from treatment). At the recent St Gallen conference it was recognised that: “perhaps the most difficult decision in current adjuvant therapy is selection of patients with highly or incompletely responsive disease for whom additional chemotherapy should be given.” Traditional clinical and pathological factors, based on several decades of clinical experience, guide this decision process. The introduction of genome-scale gene expression profiling has promised an improved understanding of breast cancer and identified distinct gene expression signatures that reflect prognosis in retrospective patient cohorts. However, conceptual and technical difficulties, such as tumour heterogeneity, the use of different array platforms and diverse statistical methods to analyse data, together with small sample sizes for each individual study, have caused serious concern [1]. While there are positive signs, such as that in independent datasets there is concordance amongst different gene expression signatures, it is not clear whether gene expression profiling simply captures already well known biological characteristics derived from careful phenotypic observations [2]. In this review, we present current evidence supporting the introduction of prognostic and chemosensitivity signatures into clinical practice and argue that uncertainties associated with these signatures may be resolved through the functional validation of genes involved in drug sensitivity and response. Prognostic gene expression signatures Considerable effort has been dedicated to establishing new prognostic strategies using genomic expression signatures in breast cancer, with the ultimate goal of optimising the stratification of patients into high and low risk groups to guide adjuvant decision-making [3-7]. The strategy is similar to the current adjuvant prescribing model in breast cancer that is focussed on administering chemotherapy to those most at risk whilst minimising harm by defining good prognostic cohorts who will not benefit from chemotherapy. However, it Review Functional genomic analysis of drug sensitivity pathways to guide adjuvant strategies in breast cancer Charles Swanton1,2, Zoltan Szallasi3,4, James D Brenton5 and Julian Downward6 1Translational Cancer Therapeutics Laboratory, Cancer Research UK London Research Institute, 44 Lincoln’s Inn Fields, London WC2A 3PX, UK 2Royal Marsden Hospital, Breast Unit, Department of Medicine, Downs Road Sutton, Surrey, SM2 5PT, UK 3Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Lyngby, Denmark 4Children’s Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology (CHIP@HST), Harvard Medical School, Boston, MA 02115, USA 5Functional Genomics of Ovarian Cancer Laboratory, Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK 6Signal Transduction Laboratory, Cancer Research UK London Research Institute, 44 Lincoln’s Inn Fields, London WC2A 3PX, UK Corresponding author: Charles Swanton, [email protected] Published: 31 October 2008 Breast Cancer Research 2008, 10:214 (doi:10.1186/bcr2159) This article is online at http://breast-cancer-research.com/content/10/5/214 © 2008 BioMed Central Ltd COXEN = coexpression extrapolation; EORTC = European Organisation for Research and Treatment of Cancer; ER = estrogen receptor; FU = fluorouracil; PARP = Poly(ADP-ribose) polymerase; RNAi = RNA interference; siRNA = small interfering RNA.