Sherene Loi a,b,, Martine Piccart a, Christos Sotiriou a. Contents

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1 Critical Reviews in Oncology/Hematology 61 (2007) The use of gene-expression profiling to better understand the clinical heterogeneity of estrogen receptor positive breast cancers and tamoxifen response Sherene Loi a,b,, Martine Piccart a, Christos Sotiriou a a Translational Unit, Microarray Laboratories and Department of Medical Oncology, Jules Bordet Institute, Brussels, Belgium b Department of Medical Oncology and Haematology, Peter MacCallum Cancer Centre, St. Andrew s Place, East Melbourne, Victoria 3002, Australia Accepted 21 September 2006 Contents 1. Introduction Briefly on microarray technique Novel molecular classification of breast cancer by microarray profiling Estrogen receptor positive molecular subtypes can be distinguished through use of genomic grade and estrogen-regulated genes Gene classifiers predicting tamoxifen resistance Thoughts on integrating current knowledge and future directions Reviewers References Biographies Abstract In a short period of time DNA microarray technology has revolutionized our understanding of human cancer biology. This has been particularly impressive in the field of breast cancer research, where the clinical heterogeneity long observed by physicians seems to be mirrored by different molecular phenotypes exposed by microarray analysis. Gene-expression signatures have been developed to predict prognosis and treatment response and pending adequate validation, are on the verge of entry into the clinical setting. In this review article we explore how gene-expression profiling has influenced our understanding of the ER-positive breast cancers: that proliferation and cell-cycle genes seem to be the strongest predictor for metastasis and relapse in this group, and discuss the various gene predictors and molecular subtype classifications that exist that may help us individualize therapy for these women Elsevier Ireland Ltd. All rights reserved. Keywords: Breast cancer; Gene-expression profiling; Prognosis 1. Introduction Microarray technology is changing our understanding of cancer biology at the molecular level, as it allows us to inter- Corresponding author at: Department of Medical Oncology and Haematology, Peter MacCallum Cancer Centre, St. Andrew s Place, East Melbourne, Victoria 3002, Australia. Tel.: ; fax: address: Sherene.loi@petermac.org (S. Loi). rogate tens of thousands of expressed genes simultaneously. In a short period of time, molecular subtypes of cancer have been proposed as well as expression signatures that claim to predict prognosis and treatment response better than currently used methods, promising to facilitate the era of individualized medicine. In particular, the molecular heterogeneity of breast cancer has been investigated leading physicians to place more emphasis on biology in the clinical setting. Factors such as hormone receptor and HER2/neu status are now playing an important part in treatment decision-making in /$ see front matter 2006 Elsevier Ireland Ltd. All rights reserved. doi: /j.critrevonc

2 188 S. Loi et al. / Critical Reviews in Oncology/Hematology 61 (2007) the early stage breast cancer, which has previously focused almost exclusively on anatomical stage. This heterogeneity is particularly applicable to the ER-positive breast cancers. In this review article we discuss how findings derived from microarray studies may help us gain new biological insight into this group of breast cancer and how the molecular heterogeneity of ER-positive breast cancers may define a subset of women who may not be the best candidates for adjuvant tamoxifen therapy. 2. Briefly on microarray technique The concept behind DNA chip or microarray technology is not a new one: it relies on the accurate binding, or hybridization, of strands of DNA with their precise complementary copies in experimental conditions where one sequence is bound onto a solid-state substrate. Currently there are several DNA microarray platforms in use for genome-wide gene-expression studies. The cdna microarray is a common array type in which double stranded PCR products amplified from expressed sequence tag (EST) clones, are spotted onto glass slides. The main advantage of this approach is that gene-expression profiles can be determined relatively cheaply compared with commercial platforms. However, the use of cdna can be challenging due to frequent cross-hybridization amongst homologous genes, alternative splice variants and antisense RNA. The advent of oligonucleotide-based microarrays has addressed many of these technological limitations. Oligonucleotide arrays differ as the probes they employ are shorter and have uniform length. There are two major approaches to constructing oligonucleotide arrays: (a) microarrays composed of short oligonucleotides (25 bases) may be synthesized directly onto a solid matrix using photolithographic technology (Affymetrix) or (b) microarrays composed of long oligonucleotides (55 70 bases) may either be deposited by an ink-jet printing process or spotted by a robotic printing process onto glass slides (i.e. Agilent, CodeLink). The advantage of the commercially available oligonucleotide platforms is that as the arrays are standardized, the raw data produced on the same platform can be exchanged and results compared between local or international institutions with relative ease. To ensure the reliability of results, proper validation is a crucial part of genomic studies and the widespread use of commercial platforms may facilitate this. 3. Novel molecular classification of breast cancer by microarray profiling Unsupervised hierarchical clustering analysis has identified multiple tumour classes within breast cancer, the principal two classes named luminal and basal corresponding to differences in expression of the estrogen receptor (ER-positive and negative, respectively). The most commonly cited microarray classification divides these tumours into smaller subgroups: luminal A, luminal B, basal-like, normal-like and ERBB2 [1], though these subdivisions were reproduced in another study using different samples and microarray platform [2]. These molecular phenotypes were associated with distinct clinical outcomes: when originally described, the luminal A subgroup had the highest expression of ER and ER-regulated genes and a better clinical outcome compared with the luminal B group, suggesting that the molecular portrait of a breast tumour at an early stage may dictate prognosis. These results were extremely provoking as they suggested a group of ER-positive breast cancers that have an unfavourable outcome even when treated with adjuvant tamoxifen. Overall, these observations suggested that molecular profiling could significantly advance our understanding of breast cancer biology and improve our management of breast cancer patients. However despite their early promise, classifications generated by hierarchical cluster analysis are extremely unstable: there is currently no operational definition of what constitutes each subtype, i.e. no diagnostic test or consistent gene list and no pre-specified rules or cut-offs when applying the intrinsic gene set as originally described by Sorlie et al. [1]. Indeed, as a demonstration of their instability, when more samples are added, the dendogram changes substantially, as demonstrated by the disappearance of the luminal C subtype and the luminal B class clustering with the ER-negative samples the basal and ERBB2 subgroups [3]. Hierarchical clustering is therefore not a suitable method for assigning a newly diagnosed tumour to a molecular class with any certainty across different laboratories. Furthermore, it can be argued that these analyses do not give more information than what can be at this time provided by ER-positive or negative receptor status and histological grade. Particularly for the group of patients with ER- and ERBB2-positive tumours, it is unclear where they fall molecularly (i.e. the luminal or ERBB2 subtype). Hence, whilst of immense potential implications, clinicians are currently unable to use these subtypes in clinical practise for treatment decision-making through lack of definition, adequate validation and easy implementation. Indeed, further investigation into the underlying biology is also limited. 4. Estrogen receptor positive molecular subtypes can be distinguished through use of genomic grade and estrogen-regulated genes When the molecular subtype classification was originally reported, various gene-expression clusters were described which had systematic variation between the subgroups of tumours described above [4]. One of these clusters was described as a proliferation cluster, whose expression of genes correlated with cellular proliferation rates, including genes such as KI-67 and PCNA (proliferating cell nuclear antigen) which link well in the clinical setting with high grade, highly proliferative tumours. The expression of this

3 S. Loi et al. / Critical Reviews in Oncology/Hematology 61 (2007) cluster in relation to the various subtypes has been explored: the luminal B and the basal-like subtypes had high expression of this cluster, with the other subtypes having mostly negative expression [2]. Over-expression of proliferation-related and cell-cycle genes in breast cancer has been associated with a poor outcome suggesting that these genes may contribute strongly to the luminal B subgroup s poor prognosis. Supporting this concept, Dai and colleagues have reported a cell proliferation signature as the key marker of poor outcome in a population of young women (<55 years) whose breast cancers had high expression of ER for their age [5]. Of note, the majority of these women had not received systemic adjuvant treatment making conclusions about the signature and response to tamoxifen impossible. In order to further investigate the relationship between cell-cycle genes and prognosis, we have defined a grade geneexpression index (GGI) [6]. The index is a linear combination of the expression of 128 probe sets (97 genes) that were found to be differentially expressed between histological grades 1 and 3 (as defined by the Elston Ellis grading system [7]), which effectively is a quantification of the degree of similarity between the tumour expression profile and tumour grade: a high gene-expression grade index corresponds to a high genomic grade and vice versa. The development of such an index allows us to investigate the expression of the genes in the GGI in relation to the subtypes and prognosis in a supervised and reproducible manner. It also facilitates looking at many breast cancer samples across the various microarray platforms. This ability addresses some of the concerns raised by many critics about results from the various microarray studies. Applying the GGI genes to the previously defined subtypes [1] demonstrates that whilst the ER-negative tumours are predominately high genomic grade, the luminal tumours have a diverse range of GGI levels with the luminal B/high GGI group having a worst outcome compared with the luminal A/low GGI group [8]. Using a dataset of 417 ER-positive breast cancer samples which had received no systemic adjuvant treatment derived from two publicly available datasets [9,10] and our own currently unpublished dataset (manuscript submitted for publication), we determined that the prognostic value (as defined by time to first distant metastasis) of the ER-positive subgroups as divided by the GGI (luminal A/low GGI versus luminal B/high GGI). These two groups had statistically significant different outcomes as estimated by Kaplan Meier analysis (HR 2.6; 95% CI: ; p < 0.001) [8]. In order to investigate the relationship with adjuvant tamoxifen treatment, we looked at the GGI s prognostic ability in our currently unpublished dataset involving 249 early breast cancer samples treated with adjuvant tamoxifen only (manuscript submitted for publication). The median follow-up of this dataset was in excess of 8 years and 53% of this population were node positive. Similar to the untreated samples, the ER-positive tumours could be divided into two groups with statistically different outcome despite treatment with adjuvant tamoxifen (HR 3.1; 95% CI: ; p < 0.001) [8,11]. The low genomic grade ER-positive subgroup had a 10-year time to distant metastases of 83% (95% CI: 71 95%). Of note, these prognostic classes as defined by genomic grade were an improvement over standard stratification by quantitative ER expression levels, which correlate well to immunohistochemical protein values [10]. Thus it seems that the luminal B/high genomic grade subgroup s poor natural disease history does not seem to be substantially altered by adjuvant tamoxifen (note that none of these women had received adjuvant chemotherapy). Of note, less than 10% of this population had over-expression of ERBB2 by immunohistochemistry and nearly all of these samples were included in the high genomic grade subgroup. Together, these data could highlight a group of ER-positive breast cancers requiring further investigation from both a biologic and therapeutic perspective as it seems that the most important biological mechanism for metastasis and outcome is related to cell-cycle genes [6]. Furthermore, definition of these prognostic subgroups is simple and reproducible using the GGI. The GGI is easy to use, does not require all 97 genes to be mapped and is highly reproducible amongst various microarray-produced data [6]. Using a analogous approach where genes were selected based on correlation with a biological variable rather than clinical outcome, Oh and colleagues have used estrogeninduced genes identified from MCF-7 cells to define two prognostic groups of patients within 400 ER-positive samples, some of whom had been treated with adjuvant tamoxifen [12]. Of note, a significant association existed between these prognostic groups and histologic grade, which is probably not surprising given that many of the genes induced by estradiol were cell-cycle related. Symmans and colleagues have proposed a similar concept, using the mrna levels of an Affymetrix estrogen receptor probe set to define a ER-reporter index and to predict distant relapse free survival in ER-positive breast cancer samples treated with tamoxifen [13]. 5. Gene classifiers predicting tamoxifen resistance Recently, gene signatures derived from microarray studies have been reported to be able to predict outcome in women with ER-positive breast cancer treated with tamoxifen better than currently used clinico-pathological factors. The clinical need is obvious: whilst ER is used to predict response to tamoxifen, it is not a perfect biomarker as up to 40% of patients will fail on tamoxifen treatment leading to incurable metastatic disease [14]. Data from recent large prospective randomized controlled trials involving aromatase inhibitors are now emerging and herald new standards in adjuvant endocrine treatment [15 20]. At present it is unclear the optimal sequence and duration of AI and tamoxifen therapy, and which women we should offer these strategies to. A robust gene predictor that can identify a subset of women who do poorly on tamoxifen makes them ideal candidates to study the benefits of alternative endocrine strategies, particularly upfront or early sequential aromatase inhibition and/or

4 190 S. Loi et al. / Critical Reviews in Oncology/Hematology 61 (2007) chemotherapy. Furthermore, tamoxifen is still a clearly beneficial and cost effective treatment for certain women. Two studies using microarray technology investigating tamoxifen resistance have been reported. Using a training set of 46 tumours from ER-positive women with advanced disease, Jansen and colleagues identified 81 genes using a cdna microarray platform that predicted for response to tamoxifen (as measured by progressive or responsive disease by RECIST criteria). This set was reduced to 44 genes and applied to a small independent validation set of 66 tumours. It correctly predicted the outcome of response to tamoxifen treatment in 77% of progressive disease cases and 48% of responders [21]. Functional annotation of the 81 genes revealed involvement in estrogen action (26%), apoptosis (14%), extracellular matrix formation (9%), and immune response (6%). In the other study, an expression signature predictive of disease free survival was developed from 60 patients treated with adjuvant tamoxifen. The signature was reduced to a two-gene-expression ratio, HOXB13 versus IL17BR [22]. The expression ratio was subsequently validated on an independent set of 20 patients using PCR-based analysis on standard formalin-fixed paraffin embedded tissue. The overall accuracy was 80% (95% CI: 56 94%). In addition, the authors demonstrated in HOXB13 infected MCF-10A cells that HOXB13 seems to have a role stimulating cell invasion. The function of HOXB13 in regulating tamoxifen response was previously uncharacterised. Little is also known about the relevance of the other gene in tamoxifen response: IL17BR is one of the cognate receptors for interleukin 17, a proinflammatory cytokine generally secreted by activated T cells. Of note, immune function-related gene clusters have been reported to be prominent in defining tumour subtypes [4], though the immunological aspects of breast cancer prognosis have yet to be characterised. External and preferably independent validation of gene classifiers is now essential due to the immense susceptibility for error in microarray studies, especially given the large variable (often over 30,000 genes) to sample ratio both of these studies deriving their classifiers from less than 60 patients and the lack of a predefined hypothesis. It is also hard to believe that the heterogeneity of breast cancer can be accounted for in such small training and validation sets. The two gene signature was recently shown not to have any prognostic value in an independent validation on a cohort of 58 ER-positive samples also treated with adjuvant tamoxifen monotherapy [23]. However, compared with the original dataset, this group had more lymph node positive disease, larger tumours with more samples that over-expressed HER2/neu. These factors, the relatively small cohort size and the different primers that were used compared with the original study may partly explain the negative result. The authors have recently claimed that the two-gene ratio has better prognostication in lymph-node negative rather than positive disease [24 26], though this seems difficult to understand biologically given that the molecular portrait of a breast cancer seems to be remarkably stable from primary to metastatic disease progression [27] and that their initial discovery set was balanced for nodal disease [22]. Keeping these concerns in mind, we have recently developed a gene classifier for adjuvant tamoxifen resistance from a training set of 99 patients and validated the classifier on 156 independent samples [28]. The gene signature was developed to help the clinician identify which patients would have a low risk of relapse on adjuvant tamoxifen in the first 3 years, i.e. a good prognostic group. The signature was the strongest covariate in univariate and multivariate Cox analysis, subsuming commonly used clinical factors such tumour grade, size and ER levels. However, in order to try and account for the vast heterogeneity in breast cancer and complex issues emerging with design and validation of gene predictors [29,30], we have redeveloped our signature using all 255 patients from three different institutions as the training set (the largest training set to address this clinical problem) and have sought a collaboration with an external group to perform an independent validation using a large cohort. This will ensure the predictor is robust, applicable to a diverse population and on another microarray platform which is yet to be achieved by the two studies described above. Genomic Health, using quantitative RT-PCR, has developed an algorithm based on the expression of 16-genes that can predict the risk of recurrence in women receiving adjuvant tamoxifen [31]. This assay was derived from analysing the results from three preliminary studies involving 447 samples from a heterogeneously treated population (chemotherapy and tamoxifen) and 250 candidate genes selected after a literature search of the most important microarray experiments relating to breast cancer prognosis. Of these, 21 genes were chosen, 5 of which were control genes. The multigene predictor assigns a Recurrence Score to predict low, intermediate and high risk of distant recurrence for women receiving 5 years of adjuvant tamoxifen (±chemotherapy). The predictor was subsequently validated prospectively on 675 archival patients who had received tamoxifen only in the NSABP B-14 trial. The Recurrence Score accurately predicted patients at high versus low risk of recurrence on tamoxifen, hence identifying those who do poorly with tamoxifen treatment (p < ). The advantage of this approach is that the test can be easily performed on paraffin-embedded tissue with a high degree of accuracy using a small amount of tissue [32]. Disadvantages include the preselection of genes and a final algorithm which some may argue does not encompass more than quantitative estrogen, progesterone receptor status, proliferation and HER2/neu measurements, all currently widely available and hence provides no new biological insights into tamoxifen response. Indeed, an algorithm containing just these four genes has been developed but not validated [33]. Recently the Recurrence Score [31] has been reported to be both prognostic and predictive of response to tamoxifen [34]. The assay also claims to be able to predict the magnitude of response to CMF and anthracycline/taxane chemother-

5 S. Loi et al. / Critical Reviews in Oncology/Hematology 61 (2007) Table 1 Comparison of published ER-positive breast cancer multi-gene classifiers Assay name Method of discovery RNA source Level of evidence Number of categories Recurrence Score [31] Candidate gene approach FFPET 3 3 Intrinsic gene set [1] Whole genome approach, hierarchical clustering Frozen tissue Two-gene ratio [22] Whole genome approach, outcome-based FFPET 4 2 Genomic grade [6] Whole genome approach, biology-based Frozen tissue 4 2 Estrogen-regulated genes [12] Whole genome approach, biology-based Frozen tissue 4 2 FFPET, formalin-fixed paraffin embedded tissue. Levels of evidence [37]: (1) prospective, high powered, controlled, and ideally randomized trial, with the marker and clinical outcome data as the primary objective. The trial should use a validated and reproducible assay as a means to guide diagnostic/therapeutic decisions which are based on assay results in one arm and using a standard clinical assay on the other arm; (2) prospective cohort study using a validated and reproducible assay as a means to identify patient with certain outcomes more effectively than that of a standard clinical algorithm, with the assay study not necessarily being the primary objective of the study, but specimen collection and statistical analysis defined in the study protocol; (3) evidence from large retrospective studies (which may or may not have had prospective follow-up) from which a variable number of samples have been selected and which statistical analysis was not dictated at the time of the trial; (4) evidence from small retrospective studies that do not have prospectively defined clinical follow-up data and analysis; (5) evidence from small pilot studies not designed to test marker clinical utility. apies [35,36]. Patients with a low Recurrence Score were found not to benefit from chemotherapy, whereas those with a high Recurrence Score had a larger chemotherapy benefit and a higher rate of complete pathological response. These studies suggest that the relationship between the Recurrence Score and chemotherapy benefit is not specific a certain regimen and supports anecdotal observations that not all women benefit equally from chemotherapy. Thus, multiple ways of classifying ER-positive tumours have been reported using a number of discovery strategies and each with varying levels of independent validation [37] on different archival populations (Table 1). In the near future, it will be important to further compare the different categorizations in order to understand their relationships and to develop an optimal and widely accepted system. The identification of a common biological theme will be preferable for clinicians rather than a complicated diagnostic algorithm. The definitive test for all of these classifiers remains prospective validation to determine their utility in the clinical setting and how they can guide treatment decision-making compared with current standard clinical algorithms. For example, the current St. Gallen consensus guideline [38] allocates treatment based on endocrine-responsiveness based on estrogen receptor status by immunohistochemistry. Should these multi-gene predictors prove to be better than the estrogen receptor at predicting endocrine responsiveness, then one can envisage a further stratification in the group labelled as endocrine responsive regarding magnitude of chemotherapy benefit. Whilst at present the obvious advantage of the Recurrence Score is that it can be performed on paraffin-embedded tissue, in the future however, one can imagine that a single small frozen tumour sample (3 6 mm) could undergo a multitude of tests using microarray technology to assess (for example) prognosis, ER functional status, genomic grade and response to individual chemo- and biological therapies. 6. Thoughts on integrating current knowledge and future directions Gene-expression profiling has already given us new insights into the biology of ER-positive breast cancers. It seems that the clinical heterogeneity that physicians have observed can be related to molecular phenotypes: ER-positive breast cancers can be divided into at least two prognostically different subtypes that can be identified in early stage breast cancer. Biologically, it is clear that cell-cycle and proliferation related genes play a strong part in driving metastasis and relapse in ER-positive tumours and adjuvant tamoxifen monotherapy does not seem to significantly affect this mechanism. The effect of other endocrine agents and chemotherapy is unknown. In the Recurrence Score algorithm the proliferation set of genes have the largest weighting in the score, highlighting their importance in outcome prediction. However, determining what is driving the cell-cycle machinery in the luminal B/high genomic grade group is the next challenge. Further biological understanding from microarray experiments is daunting even for the most knowledgeable molecular biologist due to the huge volume of expression data produced. New integrative computational approaches such as functional enrichment analysis [39] may be able to extract deeper biological insight and its power has already been demonstrated in a study which found significance for a functionally related gene set and not for the individual genes [40,41]. This will ultimately aid in the identification of other potential pharmacological targets and the development of new and more effective therapies. Despite the above approaches successfully predicting outcome, the overlap between gene classifiers is minimal and similarities are restricted to cell cycle/proliferation related genes. This has evoked wide interest and also some cynicism at what could account for this disparity and whether it can be accounted for by trivial reasons such as different platforms, different training populations and different types of analyses. This question was addressed in a study that reanalysed a breast cancer dataset [42] and found that many equally predictive gene lists could be produced by the same analysis depending on the composition of the training set, as many genes were correlated with survival. Therefore, a gene signature classifier that is good at prediction may not necessarily contain the genes that are the crucial directors of breast cancer outcome. This does not imply that a gene set can not provide useful prediction if it has undergone ade-

6 192 S. Loi et al. / Critical Reviews in Oncology/Hematology 61 (2007) quate outcome validation [43,44] as useful biomarkers serve to predict (not explain) and in the short term are probably more pragmatic than attempting a better understanding of breast cancer biology (which may not necessarily lead to better biomarkers or prediction). As a result, at present it is difficult to understand completely how the subgrouping achieved by unsupervised hierarchical clustering, genomic grade and the above described gene classifiers relate to each other. However, despite the different discovery strategies it is likely, given the dominance of cell-cycle genes in these predictors, that they classify patients similarly, though this has yet to be demonstrated. Another issue yet to be adequately elucidated is the whether these gene classifiers truly predict response to tamoxifen or solely prognosis, i.e. the intrinsic aggressiveness of the tumour. The only way this question will be answered is by applying these predictors to datasets that have been treated by the alternative therapy, i.e. an aromatase inhibitor or chemotherapy or non treatment, ideally in the context of a randomized clinical trial. Response to therapy can only defined in the metastatic setting for patients with measurable disease and it is possible that this may be the only way to develop a true gene predictor for treatment response. Finally, the incorporation and acceptance of genomic information and classifiers into clinical practise has evoked much debate. What extent of validation is required before physicians believe in the reliability of the results? Is external and independent validation in a large cohort by investigators not involved in the discovery phase enough? Is prospective analysis in a randomised trial always necessary to correct biases inherent from use of retrospective archival tissue? What does it truly mean to have a poor prognosis and can any of our currently available treatments alter its natural disease history, given the immense implications for women given such a label? The authors believe at least one prospective trial should be done to address these issues. Can microarray analyses be routinely incorporated given their cost and technical issues? How much better are gene predictors than currently widely available clinico-pathological factors, though it is noted that old routine markers such as ER status and histological grade still suffer inconsistencies from laboratory to laboratory. What is the best statistical method to assess this? In our opinion, given the constancy of their performance over old biomarkers, gene predictors seem to incorporate more than just ER status and grade to provide a far more sensitive and objective quantitative signal of prognosis than what can be currently achieved. Will there be just one ultimate predictor for tamoxifen resistance? Perhaps we should begin stratifying ER-positive tumours by high and low genomic grade in clinical trials given their diverse prognosis? Will the cost of these tests offset the potential saving in life lost, cost of excessive treatment especially given the increasing price of health care today and prescription of more effective therapies? Some studies claim that this will be so [45]. Ifso, how can this technology become widely available to the rest of the world, particularly in developing and poor countries where the cost savings are most crucial and the infrastructure for such tests does not exist? These issues are complex and will not be resolved easily. However, the progress already achieved in the study of cancer and the considerable evidence that such research may help us in the task of personalised medicine is compelling. 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J Natl Cancer Inst 2005;97(12): [45] Hornberger J, Cosler LE, Lyman GH. Economic analysis of targeting chemotherapy using a 21-gene RT-PCR assay in lymph-node-negative, estrogen-receptor-positive, early-stage breast cancer. Am J Manag Care 2005;11(5): Biographies Dr. Sherene Loi is a medical oncologist specialising in breast cancer. She completed her medical oncology training at the Peter MacCallum Cancer Centre in Melbourne, Australia, before undertaking a fellowship at the Institut Jules Bordet in Brussels, Belgium in 2004 under the supervision of Dr. Martine Piccart. In Brussels, her focus has been on translational research, in particular the incorporation of microarray technology into breast cancer clinical trials, through the newly formed research consortium TRANSBIG. In 2005 Dr. Loi received an ASCO merit award and Young Investigator Award for her work.

8 194 S. Loi et al. / Critical Reviews in Oncology/Hematology 61 (2007) Martine J. Piccart-Gebhart, MD, PhD, is Professor of Oncology at the Université Libre de Bruxelles (ULB) and Director of the Medicine Department at the Institut Jules Bordet, Belgium. She received her medical degree (1978, summa cum laude, Prix Fleury Mercier) and her internal medicine certification (1983) at the Université Libre de Bruxelles, Belgium. Dr. Piccart-Gebhart earned her oncology qualification ( ) from Division of Oncology at the New York University (NYU) Medical Center. She became certified in European Medical Oncology in London in 1989, and earned her PhD from the ULB in Dr. Piccart-Gebhart is a member of numerous professional organizations, including ASCO, the American Association for Cancer Research (AACR), the European Society for Medical Oncology (ESMO), and the European Organisation for Research and Treatment of Cancer (EORTC). She has served ASCO in various capacities, such as a member of the Nominating Committee ( ), of the International Affairs Committee and the Cancer Education Committee. She has recently been elected member of the ASCO board. Dr. Piccart-Gebhart has participated in several ASCO Annual Meetings, serving as faculty, discussant, and Session Chair. She is also the current President of EORTC. She plays an active role in new drug development and has been particularly involved in the development of paclitaxel, docetaxel, capecitabine, trastuzumab, letrozole, exemestane, and pegfilgrastim. In 1996, Dr. Piccart-Gebhart founded the Breast International Group (BIG) and currently serves as chair. Created to facilitate breast cancer clinical trials and to reduce the unnecessary duplication of efforts, BIG coordinates 35 clinical research groups based primarily in Europe, South America, and Australasia. A translational research consortium created to complement BIG s clinical research network, TransBIG, was founded by Dr. Piccart-Gebhart in Dr. Piccart-Gebhart has served as author or co-author of more than 200 scientific publications in peer-reviewed journals. She has received numerous awards in recognition of her achievements in the clinical research field including the ESMO Award for Exceptional Contribution to the Advancement of Medical Oncology in Europe (1997). In 2004, she received an award from the Breast Cancer Research Foundation and the Prix Mois du Cancer du Sein. In 2005, Dr. Piccart-Gebhart and her research team were awarded a Freedom to Discover grant from Bristol-Myers Squibb. She also received a Jacqueline Seroussi Memorial Foundation for Cancer Research Award, for International leadership in translational and clinical research that has improved treatment outcomes for women with early stage and advanced breast cancer (2005, Tel Aviv, Israel). She was awarded the 14th Claude Jacquillat Award for achievements in clinical oncology during the 17th ICACT meeting in Paris, in February 2006, and, together with Dr. Aron Goldhirsch, the ESMO-GSK Lifetime Achievement Award on behalf of BIG in September Christos Sotiriou, MD, PhD, Jules Bordet Institute/Head of Functional Genomics & Translational Research Unit (Université Libre de Bruxelles, Brussels, Belgium). Dr. Christos Sotiriou earned a medical degree from the Université Libre de Bruxelles, Belgium in He did his internal medicine/oncology residency at the Jules Bordet Institute (Profs. J. Klastersky, M. Piccart), and he earned his specialty in internal medicine and medical oncology in July 1999 at the Université Libre de Bruxelles. During his last year of residency, he worked as research fellow at the Laboratory of Endocrinology, Bone Metabolism and Breast Oncology (Dr. J. Body), at the Jules Bordet Institute, supported by an educational grant from the Belgian National Foundation of Scientific Research. From October 1999 till September 2001, he worked as basic research fellow, at the Division of Clinical Sciences, Microarray Facility, National Cancer Institute (Pr Edison Liu), National Institutes of Health, Bethesda, MD, USA, supported by an educational grant from the Association Yvonne et Thomas Rucquois Les Amis de l Institut Jules Bordet Belgium, and the National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. During his fellowship he received a Scholar in Training Award for the study of the expression profiling analysis of von Hippel-Lindau tumor suppressor gene from the American Association for Cancer Research, March 24 28, 2001, New Orleans, USA. Dr. Sotiriou earned his doctor of philosophy degree (PhD) from the Université Libre de Bruxelles, Belgium in September He was promoted Head of the Functional Genomics and Translational Research Unit of the Jules Bordet Institute in October 2004 and, in October 2005, he become a research associate professor at the Belgian National Foundation of Scientific Research (FNRS). Dr. Sotiriou s research is focusing on genomics and molecular biology in breast cancer. Dr. Sotiriou is a full member of ASCO, AACR, and ESMO and also member of the Steering and Executive committee of TRANSBIG, a sister network of the Breast International Group (BIG aisbl), dedicated multinational translational research linked to prospective clinical trials, since He received several educational and research grants from the Swiss Foundation MEDIC, the National Foundation of Cancer Research, the Université Libre de Bruxelles, the Fonds Jean-Claude Heuson, the Fondation Lambeau-Marteau, the Belgian Federation against Cancer, and the European Union Framework VI Programme.