Poor Prognosis Signature Research Articles

Gene signatures as predictors of response to neoadjuvant chemotherapy (NCT) with docetaxel, doxorubicin, cyclophosphamide (TAC), or AC and nab-paclitaxel (nab-P) and carboplatin ± trastuzumab in patients (pts) with stage II-III and inflammatory breast cancer (IBC).

540 Background: Pathologic complete response (pCR) and residual cancer burden (RCB) scores of 0 (pCR) and 1 (near CR) after NCT predict for better survival (Symmans et al. J Clin Oncol 25:4414-22, 2007). Improved NCTs and molecular markers predicting for response are needed. Methods: Pts with HER2- BC were randomized to receive 6 cycles of docetaxel 75 mg/m2, doxorubicin 50 mg/m2, cyclophosphamide 500 mg/m2 (TAC, Arm A) versus A 60 mg/m2 and C 600 mg/m2 given every 2 weeks × 4, followed by 3 weekly doses of carboplatin (carbo) at an AUC 2,and nab- P 100 mg/m2, repeated as 28 day cycles × 3 ( Arm B). Pts with HER2+ BC (by immunohistochemistry [IHC] and/or FISH) received NCT as per Arm B, but, with trastuzumab (trast) given weekly with carbo and nab (Arm C). Core biopsies were taken pre-NCT. 70-gene (MammaPrint) profiling for poor vs. good risk features, and 80-gene profiling for basal, HER2+, and luminal subtypes were performed. We assessed the predictive value of gene profiling for response to NCT, as a function of RCB scores of 0-1 vs. > 1. Results: Pre-NCT fresh tissue was procured from 97 pts. Pts were treated for stage II (49%), III (locally advanced [51%] and IBC [16%]) BC; 57% of BCs were ER and/or PR+ and 36% were HER2+ (by IHC/or FISH). RCB scores were available in 90% of pts enrolled (10% is awaiting surgery): scores of 0-1 were observed in 29% pts treated on either Arms A or B, and in 64% of pts treated on Arm C. There was 98% concordance of HER2- BCs by IHC/FISH vs. gene profiling; 35% of HER2+ BCs by IHC/FISH were HER2- by gene profiling. Of the 25 pts with response data on Arm C, 8 of 19 pts who were HER2+ by both IHC/FISH and HER2+ gene profiling, achieved pCR (RCB 0), vs. 2 of 6 pts with HER2+ BC by IHC/FISH, but with HER2- gene profiles (p-value: 0.04). A poor-prognosis signature by the 70-gene assay was observed in 76% of BCs. Conclusions: BC with HER2+ gene profile is the most likely to respond with pCR (RCB 0) to trastuzumab-containing NCT. RCB scores as a function of NCT, molecular subtypes, and poor vs. good risk profile in the entire pt population will be presented. Author Disclosure Employment or Leadership Position Consultant or Advisory Role Stock Ownership Honoraria Research Funding Expert Testimony Other Remuneration Agendia Abraxis BioScience Agendia Abraxis BioScience

The 70-gene prognosis signature predicts early metastasis in breast cancer patients between 55 and 70 years of age

BackgroundThe majority of breast cancer patients are postmenopausal women who are increasingly being offered adjuvant chemotherapy. Since the beneficial effect of chemotherapy in postmenopausal patients predominantly occurs in the first 5 years after diagnosis, a prognostic marker for early events can be of use for adjuvant treatment decision making. The aim of this study was to evaluate the prognostic value of the 70-gene prognosis signature for early events in postmenopausal patients. MethodsFrozen tumor samples from 148 patients aged 55–70 years were selected (T1–2, N0) and classified by the 70-gene prognosis signature (MammaPrint™) into good or poor prognosis. Eighteen percent received hormonal therapy. ResultsBreast cancer-specific survival (BCSS) at 5 years was 99% for the good-prognosis signature versus 80% for the poor-prognosis signature group (P=0.036). The 70-gene prognosis signature was a significant and independent predictor of BCCS during the first 5 years of follow-up with an adjusted hazard ratio of 14.4 (95% confidence interval 1.7–122.2; P=0.01) at 5 years. ConclusionThe 70-gene prognosis signature can accurately select postmenopausal patients at low risk of breast cancer-related death within 5 years of diagnosis and can be of clinical use in selecting postmenopausal women for adjuvant chemotherapy.

Basal-, Luminal-, and HER2- Molecular Subtype, and the MammaPrint 70-Gene Signature as Predictors of Response to Neoadjuvant Chemotherapy (NCT) with Docetaxel, Doxorubicin, Cyclophosphamide (TAC), or AC and Nab-Paclitaxel and Carboplatin +/- Trastuzumab in Patients (Pts) with Stage II-III and Inflammatory Breast Cancer (BC).

Abstract Background: Pathologic complete response (pCR) and minimal residual cancer burden (RCB scores of 0 [pCR]-1[near CR]) after NCT may predict for improved survival (Symmans et al. J Clin Oncol 25:4414-22, 2007). Hence, improved NCT regimens in conjunction with molecular markers that predict for both response and/or resistance are needed. Materials and Methods: 115 pts with stages II-III BC were to be prospectively randomized to receive 6 cycles of docetaxel 75 mg/m2, doxorubicin 50 mg/m2, cyclophosphamide 500 mg/m2 with filgrastim support (TAC, arm A) versus a novel regimen of A 60 mg/m2 and C 600 mg/m2 given every 2 weeks x 4, followed by 3 weekly doses of carboplatin (AUC 2) and nab-paclitaxel 100 mg/m2 repeated as 28 day cycles x 3 (arm B). Pts with HER2 + BC received NCT similar to arm B, but with the addition of 12 weekly doses of trastuzumab given together with carboplatin and nab-paclitaxel (arm C). Core biopsies were performed prior to NCT and were preserved fresh frozen. 70-gene (MammaPrint™) profiling and 80-gene profiling (van de Vijver et al. NEJM 347:1999-2009, 2002) to categorize all tumors for basal-, HER2-, and luminal subtypes were carried out. We set out to assess the predictive value of Mammaprint scores (poor vs. good), as well as basal, vs. luminal, vs. HER2 molecular subtype profiling, for response to treatment on arms A vs. B vs. C. Responses were dichotomized as complete or near complete response (Symmans RCB scores of 0-1) vs. suboptimal response (RCB score > 1). Results: Sufficient amount of BC tissue and good quality RNA for gene array assessment were procured in 64% of the first 90 patients who have undergone pre-treatment core biopsies, and then proceeded to NCT, followed by definitive surgery. Here we report on the first 50 pts with complete set of data analyzed. The median age was 50 years (range:31-69). Pts were treated for stage II (49%) and III locally advanced (41%), and inflammatory BC (10%). By gene profiling, 28% of the tumors were HER2-type (vs. 38% by IHC 3+, or FISH, representing all pts treated on arm C), 26% basal-type, 42% luminal-type, and 4% borderline luminal-type. Poor-prognosis signature by the 70-gene (MammaPrint) assay was observed in 74% of pts: 92% of HER2-type, 100% of basal-type, and 52% of luminal-type tumors were characterized as poor-risk by the 70-gene assay. Following NCT, Symmans RCB scores of 0-1 were observed in 71% of pts with HER2-type, in 38% with basal-type, and 28% of pts with luminal-type molecular subtype characteristics. Conclusion: BC with HER2- and basal-molecular subtypes are more likely to respond to NCT and is frequently associated with poor-risk characteristics as determined by the 70-gene assay. The complete analysis of correlations among response to specific sets of NCT, molecular subtype, and 70-gene assay results in the entire pt population will be presented. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 2026.

Breast tumour stroma is a prognostic indicator and target for therapy

The development of the breast is exquisitely sensitive to interactions between the epithelium and stroma. Experimental evidence indicates that a reduction in signalling between any of the stromal cell types (fibroblasts, macrophages, endothelial cells and adipocytes) results in reduced or absent breast development [1], although all interactions appear to be orchestrated by the epithelial cell oestrogen receptor alpha [2]. The epithelial-stromal interactions that occur in tumours are less well characterised but there is no doubt there is expansion of the stroma as well as of the epithelium during tumour development [3,4]. Recent data indicate that the prognosis after breast cancer diagnosis relates to stromal type, and experimental and clinical studies directed at modifying the stroma (for example, angiogenesis inhibitors) suggest that the stroma is a target for therapy that is worthy of further exploration Studies of separately microdissected breast stroma and epithelium from normal lobules compared with ductal carcinoma in situ (DCIS) and invasive cancer indicate that extensive changes in gene expression in both the epithelial and stromal compartments occur during cancer development. These data strongly support the hypothesis that performing microdissections can be less optimal for gene expression profiling studies or to exclude cancers with a prominent stroma. Some array-based studies have had a requirement of more than 50% of cancer cells in the biopsies taken for array profiling; this may result in exclusion of biologically important cancers. Compared with the intralobular stroma of the normal breast lobule, Ma and colleagues reported that 2,338 genes were upregulated and 1,234 genes were downregulated in the stroma of DCIS [5]. A further 76 genes were upregulated and 229 genes were downregulated in the stroma of invasive tumours, indicating that most of the changes had occurred in DCIS suggesting that paracrine and endocrine influences are driving stroma formation rather than cell interactions, since the basement membrane is largely intact in DCIS. In a similar study examining stroma separated from the epithelium, Casey and colleagues demonstrated that the major changes of gene expression were upregulation of genes for the extracellular matrix and proteases in the stroma and downregulation of cytoskeletal proteins such as keratins, tubulins and adhesion molecules leading to increased cell motility in the tumour epithelium [6]. Invasive tumours have been likened to ‘wounds that do not heal’ [7]. In order to establish whether tumours induced gene expression similar to wounds, Chang and colleagues investigated whether they expressed the genes induced by serum in fibroblasts (the equivalent of wounding) [8,9]. The expression of 422 selected genes changed by serum in tumours was associated with a poor prognosis, whereas tumours with no change tended to have a good prognosis. In this study, although the genes were produced in serum-treated fibroblasts, they could have been expressed in epithelial cells of the tumours studied. In order to assess the prognostic and predictive significance of genes strictly of stromal origin, Finak and colleagues isolated stroma from normal lobules and tumours by laser capture microdissection, and derived a 26-gene expression signature that was a poor prognostic indicator irrespective of breast tumour subtype and standard prognostic indicators and that also indicated resistance to standard treatments [10]. The stromal signature, however, has been described to be associated with a basal type of breast cancer in three independent datasets, including the Canadian study [11]. Other gene signatures derived from the whole tumour and searched for potential stromal genes were also able to detect a poor prognosis signature [12] and to detect a stromal signature that indicated failure to respond to neoadjuvant 5-fluorouracil, epirubicin, and cyclophosphamide chemotherapy [13]. More recently two groups have demonstrated downregulation of a protein (caveolin-1) that acts as a scaffold protein in cell surface pits or caveolae (important for * Correspondence: Anthony.Howell@christie.nhs.uk Breakthrough Breast Cancer Research Unit, Paterson Institute for Cancer Research, University of Manchester, The Christie NHS Foundation Trust, University Hospital of South Manchester, Manchester M20 4BX, UK Howell et al. Breast Cancer Research 2009, 11:S16 http://breast-cancer-research.com/content/11/S3/S16

Use of laser capture microdissection to derive poor prognosis gene expression signatures of epithelial abdominal malignant mesothelioma

e22098 Background: Prior work in abdominal malignant mesothelioma (AMM) has established a poor prognosis profile of biphasic AMM when compared to more favorable prognosis epithelial AMM. It is evident, however, that epithelial AMM shows heterogeneity in biologic behavior and prognosis. Given improvements in therapy and survival in AMM, stratification of patients with epithelial AMM into favorable and poor prognosis subgroups is desirable and gene expression profiles may provide poor prognosis signatures to further guide therapy. Methods: Favorable and poor prognosis subgroups were defined as 1000 days of survival time (based on median survival of 90 patients with AMM at Columbia Presbyterian as of Sept 2007). Fourteen frozen epithelial AMM (7 favorable prognosis, 7 poor prognosis) with high quality RNA were laser capture microdissected using a PALM Zeiss microscope and analyzed using Affymetrix U133 Plus 2 microarrays. Unsupervised hierarchical clustering and supervised subgroup analysis were performed using BRB tools version 3.2. Results: Unsupervised hierarchical clustering revealed 2 main clades that contained 6 of 7 poor prognosis and 6 of 7 favorable prognosis tumors, respectively. Supervised class comparison revealed a 39-gene signature that distinguished the 2 classes (permutation values of p < .001). High expression of Nde1, UHRF1, and EZH2 by qPCR on a set of 32 AMM (using an upper quartile cutoff) was associated with poor survival (Log rank statistic p<.01, p<.003 and p<.03 respectively). Immunohistochemistry performed on tissue microarrays of 122 AMM for p16 loss and increased EZH2, Col3A1 and UHRF1 immunoreactivity was associated with poor survival (Log rank statistic p<.0001, p<.009, p<.0001 and p<.012, respectively). Conclusions: Using a training set of laser captured favorable and poor prognosis epithelial AMM, a 39-gene signature was obtained that is associated with prognosis. Validation of several of the candidate genes by qPCR expression and immunohistochemistry was performed, confirming favorable and poor prognosis strata among the epithelial AMM. Identification of poor prognosis epithelial AMM at the time of initial biopsy/tumor cytoreduction could lead to more aggressive primary therapeutic intervention. No significant financial relationships to disclose.

Meta-Analysis and Gene Set Enrichment Relative to ER Status Reveal Elevated Activity of MYC and E2F in the “Basal” Breast Cancer Subgroup

BackgroundBreast cancers lacking the estrogen receptor (ER) can be distinguished from other breast cancers on the basis of poor prognosis, high grade, distinctive histopathology and unique molecular signatures. These features further distinguish estrogen receptor negative (ER−) tumor subtypes, but targeted therapy is currently limited to tumors over-expressing the ErbB2 receptor.Methodology/Principal FindingsTo uncover the pathways against which future therapies could be developed we undertook a meta-analysis of gene expression from five large microarray datasets relative to ER status. A measure of association with ER status was calculated for every Affymetrix HG-U133A probe set and the pathways that distinguished ER− tumors were defined by testing for enrichment of biologically defined gene sets using Gene Set Enrichment Analysis (GSEA). As expected, the expression of the direct transcriptional targets of the ER was muted in ER− tumors, but the expression of genes indirectly regulated by estrogen was enhanced. We also observed enrichment of independent MYC- and E2F-driven transcriptional programs. We used a cell model of estrogen and MYC action to define the interaction between estrogen and MYC transcriptional activity in breast cancer. We found that the basal subgroup of ER− breast cancer showed a strong MYC transcriptional response that reproduced the indirect estrogen response seen in estrogen receptor positive (ER+) breast cancer cells.Conclusions/SignificanceIncreased transcriptional activity of MYC is a characteristic of basal breast cancers where it mimics a large part of an estrogen response in the absence of the ER, suggesting a mechanism by which these cancers achieve estrogen-independence and providing a potential therapeutic target for this poor prognosis sub group of breast cancer.

The 70-gene signature as a response predictor for neoadjuvant chemotherapy in breast cancer

The 70-gene signature (MammaPrint) is a prognostic tool used to guide adjuvant treatment decisions. The aim of this study was to assess its value to predict chemosensitivity in the neoadjuvant setting. We obtained the 70-gene profile of stage II-III patients prior to neoadjuvant chemotherapy and classified the prognosis-signatures. Pathological complete remission (pCR) was used to measure chemosensitivity. Among 167 patients, 144 (86%) were having a poor and 23 (14%) a good prognosis-signature. None of the good prognosis-signature patients achieved a pCR (0/23), whereas 29/144 patients (20%) in the poor prognosis-signature group did (P = 0.015). All triple-negative tumors (n = 38) had a poor prognosis-signature. Within the non triple-negative subgroup, the response of the primary tumor remained associated with the classification of the prognosis-signature (P = 0.023). A pCR is unlikely to be achieved in tumors that have a good prognosis-signature. Tumors with a poor prognosis-signature are more sensitive to chemotherapy.

Biology of breast cancers that present as interval cancers and at young age should inform how we approach early detection and prevention.

Abstract Abstract #6034 Population screening has increased the absolute number of breast cancer cases. In spite of widespread screening, there are still many patients who present with locally advanced breast cancer, often women who are young or who present with a mass between annual screens (“interval cancers”). Molecular profiling provides the opportunity to determine whether mammographically detected cancers are biologically distinct from locally advanced breast cancers. Methods: Tumor samples from the time of diagnosis (2002-2006), from patients with locally advanced breast cancers enrolled on the multi-site I-SPY TRIAL (CALGB 150007/150012 and ACRIN 6657) were compared to stage 1 & 2 tumor samples collected in 2004 from multiple community Netherlands hospitals (RASTER dataset). The NKI 70 gene set was performed on 100 of 221 patients (80 more samples pending) from I-SPY and 228 of 242 patients from the RASTER dataset. The proportion of good vs. poor prognosis profiles were compared. Additionally, we have collected data on response to chemotherapy from the I-SPY patients. Finally, mammograms from the I-SPY and RASTER datasets are being reviewed to enable classification of tumors by presentation (palpable, screen detected, and interval cancer and negative prior mammogram within 1 year). Results: I-SPY data includes 221 patients with tumors ≥3cm in size; median tumor size is 6cm; 55% were <50 years old; 44% were ER negative. Only 20% had an NKI 70 gene good prognosis at time of diagnosis. The percentage of women under <40, 41-50, and >50, with a good prognosis is 10%, 23%, and 22%, respectively. The RASTER dataset included 427 patients from age 30 to 61. NKI 70 gene profiles are available in 219 patients: 66% were Stage 1; 33% were Stage 2. Overall, 51% had a NKI 70 gene good prognosis profile. For women under the age of 40, 31% had a NKI 70 gene poor prognosis. For women 40-50, and >50, the fraction of NKI 70 gene poor prognosis is 55% and 56%, respectively. Data will be presented based on mode of detection: screen vs. palpation (never screened vs. interval). Conclusion: The key observation is that the fraction of good prognosis tumors substantially increases with age. In patients too young for screening, the fraction of poor prognosis tumors is 69%. Patients who present with locally advanced breast cancers who are too young to be screened almost always have a poor prognosis signature. This may extend to those with interval cancers. This suggests that the tumors identified by screening, although they are early stage cancers, are not the precursors of the poor prognosis tumors in young women and those with locally advanced disease. Therefore, we may not be able to rely on current screening approaches to improve outcomes for young women and those with interval cancers. Further analysis will include reviewing interval cancer cases within the I-SPY dataset and assessing prognostic indicators. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 6034.

Validation of 70-gene prognosis signature in node-negative breast cancer

The 70-gene prognosis signature (van't Veer et al., Nature 415(6871):530-536, 2002) may improve the selection of lymph node-negative breast cancer patients for adjuvant systemic therapy. Optimal validation of prognostic classifiers is of great importance and we therefore wished to evaluate the prognostic value of the 70-gene prognosis signature in a series of relatively recently diagnosed lymph node negative breast cancer patients. We evaluated the 70-gene prognosis signature in an independent representative series of patients with invasive breast cancer (N = 123; <55 years; pT1-2N0; diagnosed between 1996 and 1999; median follow-up 5.8 years) by classifying these patients as having a good or poor prognosis signature. In addition, we updated the follow-up of the node-negative patients of the previously published validation-series (Van de Vijver et al., N Engl J Med 347(25):1999-2009, 2002; N = 151; median follow-up 10.2 years). The prognostic value of the 70-gene prognosis signature was compared with that of four commonly used clinicopathological risk indexes. The endpoints were distant metastasis (as first event) free percentage (DMFP) and overall survival (OS). The 5-year OS was 82 +/- 5% in poor (48%) and 97 +/- 2% in good prognosis signature (52%) patients (HR 3.4; 95% CI 1.2-9.6; P = 0.021). The 5-years DMFP was 78 +/- 6% in poor and 98 +/- 2% in good prognosis signature patients (HR 5.7; 95% CI 1.6-20; P = 0.007). In the updated series (N = 151; 60% poor vs. 40% good), the 10-year OS was 51 +/- 5% and 94 +/- 3% (HR 10.7; 95% CI 3.9-30; P < 0.01), respectively. The DMFP was 50 +/- 6% in poor and 86 +/- 5% in good prognosis signature patients (HR 5.5; 95% CI 2.5-12; P < 0.01). In multivariate analysis, the prognosis signature was a strong independent prognostic factor in both series, outperforming the clinicopathological risk indexes. The 70-gene prognosis signature is also an independent prognostic factor in node-negative breast cancer patients for women diagnosed in recent years.

Prognostic Breast Cancer Signature Identified from 3D Culture Model Accurately Predicts Clinical Outcome across Independent Datasets

BackgroundOne of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasets having 295, 286, and 118 samples, respectively.Methods and FindingsOur results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER− patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome.ConclusionsThe 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds prognostic value for both ER-positive and ER-negative breast cancer. The signature was selected using a novel biological approach and hence holds promise to represent the key biological processes of breast cancer.

Novel Molecular Subtypes of Serous and Endometrioid Ovarian Cancer Linked to Clinical Outcome

The study aim to identify novel molecular subtypes of ovarian cancer by gene expression profiling with linkage to clinical and pathologic features. Microarray gene expression profiling was done on 285 serous and endometrioid tumors of the ovary, peritoneum, and fallopian tube. K-means clustering was applied to identify robust molecular subtypes. Statistical analysis identified differentially expressed genes, pathways, and gene ontologies. Laser capture microdissection, pathology review, and immunohistochemistry validated the array-based findings. Patient survival within k-means groups was evaluated using Cox proportional hazards models. Class prediction validated k-means groups in an independent dataset. A semisupervised survival analysis of the array data was used to compare against unsupervised clustering results. Optimal clustering of array data identified six molecular subtypes. Two subtypes represented predominantly serous low malignant potential and low-grade endometrioid subtypes, respectively. The remaining four subtypes represented higher grade and advanced stage cancers of serous and endometrioid morphology. A novel subtype of high-grade serous cancers reflected a mesenchymal cell type, characterized by overexpression of N-cadherin and P-cadherin and low expression of differentiation markers, including CA125 and MUC1. A poor prognosis subtype was defined by a reactive stroma gene expression signature, correlating with extensive desmoplasia in such samples. A similar poor prognosis signature could be found using a semisupervised analysis. Each subtype displayed distinct levels and patterns of immune cell infiltration. Class prediction identified similar subtypes in an independent ovarian dataset with similar prognostic trends. Gene expression profiling identified molecular subtypes of ovarian cancer of biological and clinical importance.

Gene Expression Profile Analysis of Human Breast Cancer Using cDNA Microarrys

negative tumors and they showed more higher gene expression levels of cell replication and cycle, invasion and metastasis, those considered poor prognosis signature. The other group mostly consists of ER positive tumors. Conclusion: These results support the feasibility and usefulness of this systematic approach to studying variation in gene expression patterns in human cancers as a means to dissect and classify solid tumors. We believe, this gene expression profile will outperform all currently available clinical parameters in predicting disease outcome. (Journal of Korean Breast Cancer Society 2003;6:58-67)

Gene expression profiling predicts clinical outcome of breast cancer.

Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70-80% of patients receiving this treatment would have survived without it. None of the signatures of breast cancer gene expression reported to date allow for patient-tailored therapy strategies. Here we used DNA microarray analysis on primary breast tumours of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases ('poor prognosis' signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph node negative). In addition, we established a signature that identifies tumours of BRCA1 carriers. The poor prognosis signature consists of genes regulating cell cycle, invasion, metastasis and angiogenesis. This gene expression profile will outperform all currently used clinical parameters in predicting disease outcome. Our findings provide a strategy to select patients who would benefit from adjuvant therapy.