Tamoxifen significantly improves outcome for estrogen receptor-positive (ER+) breast cancer, but the 15-year recurrence rate remains 30%. The aim of this study was to identify gene profiles that accurately predicted the outcome of ER+ breast cancer patients who received adjuvant Tamoxifen mono-therapy.
Post-menopausal breast cancer patients diagnosed no later than 2002, being ER+ as defined by >1% IHC staining and having a frozen tumor sample with >50% tumor content were included. Tumor samples from 108 patients treated with adjuvant Tamoxifen were analyzed for the expression of 59 genes using quantitative-PCR. End-point was clinically verified recurrence to distant organs or ipsilateral breast. Gene profiles were identified using a model building procedure based on conditional logistic regression and leave-one-out cross-validation, followed by a non-parametric bootstrap (1000x re-sampling). The optimal profiles were further examined in 5 previously-reported datasets containing similar patient populations that were either treated with Tamoxifen or left untreated (n = 623). Three gene signatures were identified, the strongest being a 2-gene combination of BCL2-CDKN1A, exhibiting an accuracy of 75% for prediction of outcome. Independent examination using 4 previously-reported microarray datasets of Tamoxifen-treated patient samples (n = 503) confirmed the potential of BCL2-CDKN1A. The predictive value was further determined by comparing the ability of the genes to predict recurrence in an additional, previously-published, cohort consisting of Tamoxifen-treated (n = 58, p = 0.015) and untreated patients (n = 62, p = 0.25).
A novel gene expression signature predictive of outcome of Tamoxifen-treated patients was identified. The validation suggests that BCL2-CDKN1A exhibit promising predictive potential.
During the last years, several groups have identified prognostic gene expression signatures with apparently similar performances. However, signatures were never compared on an independent population of untreated breast cancer patients, where risk assessment was computed using the original algorithms and microarray platforms.
We compared three gene expression signatures, the 70-gene, the 76-gene and the Gene expression Grade Index (GGI) signatures, in terms of predicting distant metastasis free survival (DMFS) for the individual patient. To this end, we used the previously published TRANSBIG independent validation series of node-negative untreated primary breast cancer patients. We observed agreement in prediction for 135 of 198 patients (68%) when considering the three signatures. When comparing the signatures two by two, the agreement in prediction was 71% for the 70- and 76-gene signatures, 76% for the 76-gene signature and the GGI, and 88% for the 70-gene signature and the GGI. The three signatures had similar capabilities of predicting DMFS and added significant prognostic information to that provided by the classical parameters.
Despite the difference in development of these signatures and the limited overlap in gene identity, they showed similar prognostic performance, adding to the growing evidence that these prognostic signatures are of clinical relevance.
We hypothesize that measurement of gene expression related to estrogen receptor α (ER; gene name ESR1) within a breast cancer sample represents intrinsic tumoral sensitivity to adjuvant endocrine therapy.
A genomic index for sensitivity to endocrine therapy (SET) index was defined from genes coexpressed with ESR1 in 437 microarray profiles from newly diagnosed breast cancer, unrelated to treatment or outcome. The association of SET index and ESR1 levels with distant relapse risk was evaluated from microarrays of ER-positive breast cancer in two cohorts who received 5 years of tamoxifen alone as adjuvant endocrine therapy (n = 225 and 298, respectively), a cohort who received neoadjuvant chemotherapy followed by tamoxifen and/or aromatase inhibition (n = 122), and two cohorts who received no adjuvant systemic therapy (n = 208 and 133, respectively).
The SET index (165 genes) was significantly associated with distant relapse or death risk in both tamoxifen-treated cohorts (hazard ratio [HR] = 0.70, 95% CI, 0.56 to 0.88, P = .002; and HR = 0.76, 95% CI, 0.63 to 0.93, P = .007) and in the chemo-endocrine–treated cohort (HR = 0.19; 95% CI, 0.05 to 0.69, P = .011) independently from pathologic response to chemotherapy, but was not prognostic in two untreated cohorts. No distant relapse or death was observed after tamoxifen alone if node-negative and high SET or after chemo-endocrine therapy if intermediate or high SET.
The SET index of ER-related transcription predicted survival benefit from adjuvant endocrine therapy, not inherent prognosis. Prior chemotherapy seemed to enhance the efficacy of adjuvant endocrine therapy related to SET index.
The 70-gene signature (MammaPrint™) has been developed on retrospective series of breast cancer patients to predict the risk of breast cancer distant metastases. The microarRAy-prognoSTics-in-breast-cancER (RASTER) study was the first study designed to prospectively evaluate the performance of the 70-gene signature, which result was available for 427 patients (cT1–3N0M0). Adjuvant systemic treatment decisions were based on the Dutch CBO 2004 guidelines, the 70-gene signature and doctors' and patients' preferences. Five-year distant-recurrence-free-interval (DRFI) probabilities were compared between subgroups based on the 70-gene signature and Adjuvant! Online (AOL) (10-year survival probability <90% was defined as high-risk). Median follow-up was 61.6 months. Fifteen percent (33/219) of the 70-gene signature low-risk patients received adjuvant chemotherapy (ACT) versus 81% (169/208) of the 70-gene signature high-risk patients. The 5-year DRFI probabilities for 70-gene signature low-risk (n = 219) and high-risk (n = 208) patients were 97.0% and 91.7%. The 5-year DRFI probabilities for AOL low-risk (n = 132) and high-risk (n = 295) patients were 96.7% and 93.4%. For 70-gene signature low-risk–AOL high-risk patients (n = 124), of whom 76% (n = 94) had not received ACT, 5-year DRFI was 98.4%. In the AOL high-risk group, 32% (94/295) less patients would be eligible to receive ACT if the 70-gene signature was used. In this prospective community-based observational study, the 5-year DRFI probabilities confirmed the additional prognostic value of the 70-gene signature to clinicopathological risk estimations such as AOL. Omission of adjuvant chemotherapy as judged appropriate by doctors and patients and instigated by a low-risk 70-gene signature result, appeared not to compromise outcome.
breast cancer; gene expression profiling; prognosis prediction; adjuvant systemic treatment
Basal breast cancers (BCs) represent ~15% of BCs. Although overall poor, prognosis is heterogeneous. Identification of good- versus poor-prognosis patients is difficult or impossible using the standard histoclinical features and the recently defined prognostic gene expression signatures (GES). Kinases are often activated or overexpressed in cancers, and constitute targets for successful therapies. We sought to define a prognostic model of basal BCs based on kinome expression profiling.
DNA microarray-based gene expression and histoclinical data of 2515 early BCs from thirteen datasets were collected. We searched for a kinome-based GES associated with disease-free survival (DFS) in basal BCs of the learning set using a metagene-based approach. The signature was then tested in basal tumors of the independent validation set.
A total of 591 samples were basal. We identified a 28-kinase metagene associated with DFS in the learning set (N = 73). This metagene was associated with immune response and particularly cytotoxic T-cell response. On multivariate analysis, a metagene-based predictor outperformed the classical prognostic factors, both in the learning and the validation (N = 518) sets, independently of the lymphocyte infiltrate. In the validation set, patients whose tumors overexpressed the metagene had a 78% 5-year DFS versus 54% for other patients (p = 1.62E-4, log-rank test).
Based on kinome expression, we identified a predictor that separated basal BCs into two subgroups of different prognosis. Tumors associated with higher activation of cytotoxic tumor-infiltrative lymphocytes harbored a better prognosis. Such classification should help tailor the treatment and develop new therapies based on immune response manipulation.
breast cancer; basal-like; gene expression profiling; prognosis; immune response
Gene expression profiling (GEP) is increasingly used in the rapidly evolving field of personalized medicine. We sought to evaluate the association between GEP-assessed of breast cancer recurrence risk and patients’ well-being.
Participants were Dutch women from 10 hospitals being treated for early stage breast cancer who were enrolled in the MINDACT trial (Microarray In Node-negative and 1 to 3 positive lymph node Disease may Avoid ChemoTherapy). As part of the trial, they received a disease recurrence risk estimate based on a 70-gene signature and on standard clinical criteria as scored via a modified version of Adjuvant! Online. \Women completed a questionnaire 6–8 weeks after surgery and after their decision regarding adjuvant chemotherapy. The questionnaire assessed perceived understanding, knowledge, risk perception, satisfaction, distress, cancer worry and health-related quality of life (HRQoL), 6–8 weeks after surgery and decision regarding adjuvant chemotherapy.
Women (n = 347, response rate 62%) reported high satisfaction with and a good understanding of the GEP information they received. Women with low risk estimates from both the standard and genomic tests reported the lowest distress levels. Distress was higher predominately among patients who had received high genomic risk estimates, who did not receive genomic risk estimates, or who received conflicting estimates based on genomic and clinical criteria. Cancer worry was highest for patients with higher risk perceptions and lower satisfaction. Patients with concordant high-risk profiles and those for whom such profiles were not available reported lower quality of life.
Patients were generally satisfied with the information they received about recurrence risk based on genomic testing. Some types of genomic test results were associated with greater distress levels, but not with cancer worry or HRQoL.
Personalized medicine; Genomic profile; 70-gene signature; Patient-centered care; Breast cancer; Chemotherapy
In premenopausal women, endocrine adjuvant therapy for breast cancer primarily consists of tamoxifen alone or with ovarian suppressive strategies. Toremifene is a chlorinated derivative of tamoxifen, but with a superior risk-benefit profile. In this retrospective study, we sought to establish the role of toremifene as an endocrine therapy for premenopausal patients with estrogen and/or progesterone receptor positive breast cancer besides tamoxifen.
Patients with early invasive breast cancer were selected from the breast tumor registries at the Sun Yat-Sen Memorial Hospital (China). Premenopausal patients with endocrine responsive breast cancer who underwent standard therapy and adjuvant therapy with toremifene or tamoxifen were considered eligible. Patients with breast sarcoma, carcinosarcoma, concurrent contralateral primary breast cancer, or with distant metastases at diagnosis, or those who had not undergone surgery and endocrine therapy were ineligible. Overall survival and recurrence-free survival were the primary outcomes measured. Toxicity data was also collected and compared between the two groups.
Of the 810 patients reviewed, 452 patients were analyzed in the study: 240 received tamoxifen and 212 received toremifene. The median and mean follow up times were 50.8 and 57.3 months, respectively. Toremifene and tamoxifen yielded similar overall survival values, with 5-year overall survival rates of 100% and 98.4%, respectively (p = 0.087). However, recurrence-free survival was significantly better in the toremifene group than in the tamoxifen group (p = 0.022). Multivariate analysis showed that recurrence-free survival improved independently with toremifene (HR = 0.385, 95% CI = 0.154-0.961; p = 0.041). Toxicity was similar in the two treatment groups with no women experiencing severe complications, other than hot flashes, which was more frequent in the toremifene patients (p = 0.049). No patients developed endometrial cancer.
Toremifene may be a valid and safe alternative to tamoxifen in premenopausal women with endocrine-responsive breast cancer.
Tamoxifen; Toremifene; Breast cancer; Adjuvant endocrine therapy; Premenopausal
Given the large number of genes purported to be prognostic for breast cancer, it would be optimal if the genes identified are not confounded by the continuously changing systemic therapies. The aim of this study was to discover and validate a breast cancer prognostic expression signature for distant metastasis in untreated, early stage, lymph node-negative (N-) estrogen receptor-positive (ER+) patients with extensive follow-up times.
197 genes previously associated with metastasis and ER status were profiled from 142 untreated breast cancer subjects. A "metastasis score" (MS) representing fourteen differentially expressed genes was developed and evaluated for its association with distant-metastasis-free survival (DMFS). Categorical risk classification was established from the continuous MS and further evaluated on an independent set of 279 untreated subjects. A third set of 45 subjects was tested to determine the prognostic performance of the MS in tamoxifen-treated women.
A 14-gene signature was found to be significantly associated (p < 0.05) with distant metastasis in a training set and subsequently in an independent validation set. In the validation set, the hazard ratios (HR) of the high risk compared to low risk groups were 4.02 (95% CI 1.91–8.44) for the endpoint of DMFS and 1.97 (95% CI 1.28 to 3.04) for overall survival after adjustment for age, tumor size and grade. The low and high MS risk groups had 10-year estimates (95% CI) of 96% (90–99%) and 72% (64–78%) respectively, for DMFS and 91% (84–95%) and 68% (61–75%), respectively for overall survival. Performance characteristics of the signature in the two sets were similar. Ki-67 labeling index (LI) was predictive for recurrent disease in the training set, but lost significance after adjustment for the expression signature. In a study of tamoxifen-treated patients, the HR for DMFS in high compared to low risk groups was 3.61 (95% CI 0.86–15.14).
The 14-gene signature is significantly associated with risk of distant metastasis. The signature has a predominance of proliferation genes which have prognostic significance above that of Ki-67 LI and may aid in prioritizing future mechanistic studies and therapeutic interventions.
Breast cancer clinically represents a heterogeneous disease. Over the last decades, the integration of prognostic and predictive markers in treatment decisions has led to a more individualized and optimized therapy. While prognosis describes the risk of disease recurrence and disease-related death after diagnosis without the influence of therapy, prediction illustrates the probability of efficacy or response of a specific therapeutic measure. The substantial decline in breast cancer mortality seen over the last 20 years is primarily due to the delivery of adjuvant systemic therapy. It is important that clinical decisions are made to minimize overtreatment, under-treatment, and incorrect treatment. Improved understanding of breast cancer biology together with the utilization of classical biomarkers and the identification of new markers or profiles is increasingly defining who should receive cancer therapy and what therapy offers the best efficacy. The molecular targets as the prerequisite for successful concepts of specific therapies like anti-estrogens, antibodies, or small molecules, have therefore high clinical value in regards to prognosis as well as prediction.
Prognosis; Prediction; Early breast cancer; Biomarkers
Discrepancies in the prognosis of triple negative breast cancer exist between Caucasian and Asian populations. Yet, the gene signature of triple negative breast cancer specifically for Asians has not become available. Therefore, the purpose of this study is to construct a prediction model for recurrence of triple negative breast cancer in Taiwanese patients. Whole genome expression profiling of breast cancers from 185 patients in Taiwan from 1995 to 2008 was performed, and the results were compared to the previously published literature to detect differences between Asian and Western patients. Pathway analysis and Cox proportional hazard models were applied to construct a prediction model for the recurrence of triple negative breast cancer. Hierarchical cluster analysis showed that triple negative breast cancers from different races were in separate sub-clusters but grouped in a bigger cluster. Two pathways, cAMP-mediated signaling and ephrin receptor signaling, were significantly associated with the recurrence of triple negative breast cancer. After using stepwise model selection from the combination of the initial filtered genes, we developed a prediction model based on the genes SLC22A23, PRKAG3, DPEP3, MORC2, GRB7, and FAM43A. The model had 91.7% accuracy, 81.8% sensitivity, and 94.6% specificity under leave-one-out support vector regression. In this study, we identified pathways related to triple negative breast cancer and developed a model to predict its recurrence. These results could be used for assisting with clinical prognosis and warrant further investigation into the possibility of targeted therapy of triple negative breast cancer in Taiwanese patients.
The key to optimising our approach in early breast cancer is to individualise care. Each patient has a tumour with innate features that dictate their chance of relapse and their responsiveness to treatment. Often patients with similar clinical and pathological tumours will have markedly different outcomes and responses to adjuvant intervention. These differences are encoded in the tumour genetic profile. Effective biomarkers may replace or complement traditional clinical and histopathological markers in assessing tumour behaviour and risk. Development of high-throughput genomic technologies is enabling the study of gene expression profiles of tumours. Genomic fingerprints may refine prediction of the course of disease and response to adjuvant interventions. This review will focus on the role of multiparameter gene expression analyses in early breast cancer, with regards to prognosis and prediction. The prognostic role of genomic signatures, particularly the Mammaprint and Rotterdam signatures, is evolving. With regard to prediction of outcome, the Oncotype Dx multigene assay is in clinical use in tamoxifen treated patients. Extensive research continues on predictive gene identification for specific chemotherapeutic agents, particularly the anthracyclines, taxanes and alkylating agents.
We used bioluminescence imaging to reveal patterns of metastasis formation by human breast cancer cells in immunodeficient mice. Individual cells from a population established in culture from the pleural effusion of a breast cancer patient showed distinct patterns of organ-specific metastasis. Single-cell progenies derived from this population exhibited markedly different abilities to metastasize to the bone, lung, or adrenal medulla, which suggests that metastases to different organs have different requirements. Transcriptomic profiling revealed that these different single-cell progenies similarly express a previously described “poor-prognosis” gene expression signature. Unsupervised classification using the transcriptomic data set supported the hypothesis that organ-specific metastasis by breast cancer cells is controlled by metastasis-specific genes that are separate from a general poor-prognosis gene expression signature. Furthermore, by using a gene expression signature associated with the ability of these cells to metastasize to bone, we were able to distinguish primary breast carcinomas that preferentially metastasized to bone from those that preferentially metastasized elsewhere. These results suggest that the bone-specific metastatic phenotypes and gene expression signature identified in a mouse model may be clinically relevant.
To tailor local treatment in breast cancer patients there is a need for predicting ipsilateral recurrences after breast-conserving therapy. After adequate treatment (excision with free margins and radiotherapy), young age and incompletely excised extensive intraductal component are predictors for local recurrence, but many local recurrences can still not be predicted. Here we have used gene expression profiling by microarray analysis to identify gene expression profiles that can help to predict local recurrence in individual patients.
By using previously established gene expression profiles with proven value in predicting metastasis-free and overall survival (wound-response signature, 70-gene prognosis profile and hypoxia-induced profile) and training towards an optimal prediction of local recurrences in a training series, we establish a classifier for local recurrence after breast-conserving therapy.
Validation of the different gene lists shows that the wound-response signature is able to separate patients with a high (29%) or low (5%) risk of a local recurrence at 10 years (sensitivity 87.5%, specificity 75%). In multivariable analysis the classifier is an independent predictor for local recurrence.
Our findings indicate that gene expression profiling can identify subgroups of patients at increased risk of developing a local recurrence after breast-conserving therapy.
Our purpose was to collect preliminary data on newly diagnosed breast cancer patient knowledge of prognosis before and after oncology visits. Many oncologists use a validated prognostic software model, Adjuvant!, to estimate 10-year recurrence and mortality outcomes for breast cancer local and adjuvant therapy. Some oncologists are printing Adjuvant! screens to use as visual aids during consultations. No study has reported how such use of Adjuvant! printouts affects patient knowledge of prognosis. We hypothesized that Adjuvant! printouts would be associated with significant changes in the proportion of patients with accurate understanding of local therapy prognosis.
We recruited a convenience sample of 20 patients seen by 2 senior oncologists using Adjuvant! printouts of recurrence and mortality screens in our academic medical center. We asked patients for their estimates of local therapy recurrence and mortality risks and counted the number of patients whose estimates were within ± 5% of Adjuvant! before and after the oncology visit, testing whether pre/post changes were significant using McNemar's two-sided test at a significance level of 5%.
Two patients (10%) accurately estimated local therapy recurrence and mortality risks before the oncology visit, while seven out of twenty (35%) were accurate afterwards (p = 0.125).
A majority of patients in our sample were inaccurate in estimating their local therapy recurrence and mortality risks, even after being shown printouts summarizing these risks during their oncology visits. Larger studies are needed to replicate or repudiate these preliminary findings, and test alternative methods of presenting risk estimates. Meanwhile, oncologists should be wary of relying exclusively on Adjuvant! printouts to communicate local therapy recurrence and mortality estimates to patients, as they may leave a majority of patients misinformed.
Although human epidermal growth factor receptor 2 (HER2) positive or estrogen receptor (ER) positive breast cancers are treated with clinically validated anti-HER2 or anti-estrogen therapies, intrinsic and acquired resistance to these therapies appears in a substantial proportion of breast cancer patients and new therapies are needed. Identification of additional molecular factors, especially those characterized by aggressive behavior and poor prognosis, could prioritize interventional opportunities to improve the diagnosis and treatment of breast cancer.
We compiled a collection of 4,010 breast tumor gene expression data derived from 23 datasets that have been posted on the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. We performed a genome-scale survival analysis using Cox-regression survival analyses, and validated using Kaplan-Meier Estimates survival and Cox Proportional-Hazards Regression survival analyses. We conducted a genome-scale analysis of chromosome alteration using 481 breast cancer samples obtained from The Cancer Genome Atlas (TCGA), from which combined expression and copy number data were available. We assessed the correlation between somatic copy number alterations and gene expression using analysis of variance (ANOVA).
Increased expression of each of the heat shock protein (HSP) 90 isoforms, as well as HSP transcriptional factor 1 (HSF1), was correlated with poor prognosis in different subtypes of breast cancer. High-level expression of HSP90AA1 and HSP90AB1, two cytoplasmic HSP90 isoforms, was driven by chromosome coding region amplifications and were independent factors that led to death from breast cancer among patients with triple-negative (TNBC) and HER2-/ER+ subtypes, respectively. Furthermore, amplification of HSF1 was correlated with higher HSP90AA1 and HSP90AB1 mRNA expression among the breast cancer cells without amplifications of these two genes. A collection of HSP90AA1, HSP90AB1 and HSF1 amplifications defined a subpopulation of breast cancer with up-regulated HSP90 gene expression, and up-regulated HSP90 expression independently elevated the risk of recurrence of TNBC and poor prognosis of HER2-/ER+ breast cancer.
Up-regulated HSP90 mRNA expression represents a confluence of genomic vulnerability that renders HER2 negative breast cancers more aggressive, resulting in poor prognosis. Targeting breast cancer with up-regulated HSP90 may potentially improve the effectiveness of clinical intervention in this disease.
Adjuvant! Online is a web-based application designed to provide 10 years survival probability of patients with breast cancer. Several predictors have not been assessed in the original Adjuvant! Online study. We provide the validation of Adjuvant! Online algorithm on two breast cancer datasets, and we determined whether the accuracy of Adjuvant! Online is improved with other well-known prognostic factors.
Patients and Methods
The French data set is composed of 456 women with early breast cancer. The Dutch data set is composed of 295 women less than 52 years of age. Agreement between observation and Adjuvant! Online prediction was checked, and logistic models were performed to estimate the prognostic information added by risk factors to Adjuvant! Online prediction.
Adjuvant! Online prediction was overall well-calibrated in the French data set but failed in some subgroups of such high grade and HER2 positive patients. HER2 status, Mitotic Index and Ki67 added significant information to Adjuvant! Online prediction. In the Dutch data set, the overall 10-year survival was overestimated by Adjuvant! Online, particularly in patients less than 40 years old.
Adjuvant! Online needs to be updated to adjust overoptimistic results in young and high grade patients, and should consider new predictors such as Ki67, HER2 and Mitotic Index.
Identification of a molecular signature predicting the relapse of tamoxifen-treated primary breast cancers should help the therapeutical management of ER-positive cancers.
A series of 132 primary tumors from patients who received adjuvant tamoxifen were analysed for expression profiles at the whole genome level by 70-mer oligonucleotide microarrays. A supervised analysis was performed to identify an expression signature.
We defined a 36-gene signature that classified correctly 78% of patients with relapse and 80% of relapse-free patients (79% accuracy). Using 23 independent tumors, we confirmed the accuracy of the signature (78%), whose relevance was further demonstrated by using published microarray data from 60 tamoxifen-treated patients (63% accuracy).
Univariate analysis using the validation set of 83 tumors demonstrated that the 36-gene classifier was more efficient to predict disease-free survival than the traditional histo-pathological prognostic factors and as effective as the Nottingham Prognostic Index or the “Adjuvant!“ software. Multivariate analysis demonstrated that the molecular signature was the only independent prognostic factor. Comparison with several already published signatures demonstated that the 36-gene signature was among the best to classify tumors from both training and validation sets. Kaplan-Meier analyses emphasized its prognostic power both on the whole cohort of patients and on a subgroup with an intermediate risk of recurrence as defined by the St Gallen criteria.
This study identifies a molecular signature specifying a subgroup of patients who do not gain benefits from tamoxifen treatment. These patients may therefore be eligible for alternative endocrine therapies and/or chemotherapy.
Adult; Aged; Aged, 80 and over; Antineoplastic Agents, Hormonal; therapeutic use; Breast Neoplasms; diagnosis; drug therapy; genetics; Carcinoma; diagnosis; drug therapy; genetics; Chemotherapy, Adjuvant; Cluster Analysis; Disease-Free Survival; Drug Resistance, Neoplasm; genetics; Female; Follow-Up Studies; Gene Expression Profiling; Humans; Middle Aged; Neoplasm Recurrence, Local; diagnosis; genetics; Oligonucleotide Array Sequence Analysis; Prognosis; Receptors, Estrogen; genetics; Receptors, Progesterone; genetics; Sensitivity and Specificity; Tamoxifen; therapeutic use; Treatment Outcome; gene expression profiling; classifier; tamoxifen; breast cancer
Accurate prognosis of breast cancer can spare a significant number of breast cancer patients from receiving unnecessary adjuvant systemic treatment and its related expensive medical costs. Recent studies have demonstrated the potential value of gene expression signatures in assessing the risk of post-surgical disease recurrence. However, these studies all attempt to develop genetic marker-based prognostic systems to replace the existing clinical criteria, while ignoring the rich information contained in established clinical markers. Given the complexity of breast cancer prognosis, a more practical strategy would be to utilize both clinical and genetic marker information that may be complementary.
A computational study is performed on publicly available microarray data, which has spawned a 70-gene prognostic signature. The recently proposed I-RELIEF algorithm is used to identify a hybrid signature through the combination of both genetic and clinical markers. A rigorous experimental protocol is used to estimate the prognostic performance of the hybrid signature and other prognostic approaches. Survival data analyses is performed to compare different prognostic approaches.
The hybrid signature performs significantly better than other methods, including the 70-gene signature, clinical makers alone and the St. Gallen consensus criterion. At the 90% sensitivity level, the hybrid signature achieves 67% specificity, as compared to 47% for the 70-gene signature and 48% for the clinical makers. The odds ratio of the hybrid signature for developing distant meta-stases within five years between the patients with a good prognosis signature and the patients with a bad prognosis is 21.0 (95% CI: 6.5–68.3), far higher than either genetic or clinical markers alone.
It remains a critical issue to improve the survival rate in patients with recurrent or metastatic breast cancer. This study sought to develop a prognostic scheme based on a 28-gene signature in a broad patient population, including those with advanced disease. Clinically annotated transcriptional profiles of 1,734 breast cancer patients were obtained to validate the 28-gene signature in prognostic categorization. The 28-gene signature generated significant patient stratification with regard to breast cancer disease-free survival (log-rank P < 0.0001; n = 1,337) and overall survival (log-rank P < 0.0001; n = 806) in Kaplan-Meier analyses. The gene expression signature provides refined prognosis of disease-free survival (log-rank P < 0.006; Kaplan-Meier analysis) within each classic clinicopathologic factor-defined subgroup, including LN-, LN+, ER-, ER+, and tumor Grade II. Furthermore, it was investigated whether this gene signature predicts chemoresponse to drugs commonly used to treat breast cancer. The mRNA expression levels of this gene signature in NCI-60 cell lines were used to predict chemoresponse to CMF, Tamoxifen, Paclitaxel, Docetaxel, and Doxorubicin (Adriamycin). The 28-gene prognostic signature accurately (P < 0.02) predicted chemotherapeutic response to the studied drugs. This study confirmed the prognostic applicability of the breast cancer gene signature in a broad clinical setting. This prognostic signature is also predictive of drug response in cancer cell lines.
gene signature; breast cancer prognosis; chemosensitivity prediction; NCI-60 cell lines
Breast cancer is a prevalent disease and a major cause of morbidity and cancer-related deaths among women worldwide. A significant number of patients at the time of primary diagnosis present metastatic disease, at least to locoregional lymph nodes, which results in somewhat unpredictable prognosis that often prompts adjuvant systemic therapies of various kinds. The time course of distant recurrence is also unpredictable with some patients sustaining a recurrence within months after diagnosis, even during adjuvant treatments, while others can experience recurrence years or decades after initial diagnosis. To date, clinically approved therapeutics yielded marginal benefits for patients with systemic metastatic breast disease, since despite high clinical responses to various therapies, the patients virtually always become resistant and tumor relapses. Molecular profiling studies established that breast cancer is highly heterogeneous and encompasses diverse histological and molecular subtypes with distinct biological and clinical implications in particular in relation to the incidence of progression to metastasis. The latter has been recognized to result from late genetic events during the multistep progression proposed by the dominant theory of carcinogenesis. However, there is evidence that the dissemination of primary cancer can also be initiated at a very early stage of cancer development, originating from rare cell variants, possibly cancer stem-like cells (CSC), with invasive potential. These precursor metastatic cancer cells with stem-like properties are defined by their ability to self-renew and to regenerate cell variants, which have high plasticity and intrinsic invasive properties required for dissemination and tropism toward specific organs. Equally relevant to the CSC hypothesis for metastasis formation is the epithelial-mesenchymal transition (EMT) process, which is critical for the acquisition of cancer cell invasive behavior and for selection/gain of CSC properties. These exciting concepts have led to the formulation of various approaches for targeting precursor metastatic cells, and these have taken on greater priority in therapeutic drug discovery research by both academia and pharmaceuticals. In this review, we focus on current efforts in medicinal chemistry to develop small molecules able to target precursor metastatic cells via interference with the CSC/EMT differentiation program, self-renewal, and survival. It is not meant to be comprehensive and the reader is referred to selected reviews that provide coverage of related basic aspects. Rather, emphasis is given to promising molecules with CSC/EMT signaling at the preclinical stage and in clinical trials that are paving the way to new generations of anti-metastasis drugs.
Breast cancer; metastasis; cancer stem cells; EMT; experimental therapy
Although most patients with estrogen receptor α (ER)-positive breast cancer initially respond to endocrine therapy, many ultimately develop resistance to antiestrogens. However, mechanisms of antiestrogen resistance and biomarkers predictive of such resistance are underdeveloped.
We adapted four ER+ human breast cancer cell lines to grow in an estrogen-depleted medium. A gene signature of estrogen independence was developed by comparing expression profiles of long-term estrogen-deprived (LTED) cells to their parental counterparts. We evaluated the ability of the LTED signature to predict tumor response to neoadjuvant therapy with an aromatase inhibitor, and disease outcome following adjuvant tamoxifen. We utilized Gene Set Analysis (GSA) of LTED cell gene expression profiles and a loss-of-function approach to identify pathways causally associated with resistance to endocrine therapy.
The LTED gene expression signature was predictive of high tumor cell proliferation following neoadjuvant therapy with anastrozole and letrozole, each in different patient cohorts. This signature was also predictive of poor recurrence-free survival in two studies of patients treated with adjuvant tamoxifen. Bioinformatic interrogation of expression profiles in LTED cells revealed a signature of MYC activation. The MYC activation signature and high MYC protein levels were both predictive of poor outcome following tamoxifen therapy. Finally, knockdown of MYC inhibited LTED cell growth.
A gene expression signature derived from ER+ breast cancer cells with acquired hormone independence predicted tumor response to aromatase inhibitors and associated with clinical markers of resistance to tamoxifen. In some cases, activation of the MYC pathway was associated with this resistance.
aromatase; tamoxifen; myc; LTED; breast
Estrogen signaling plays an essential role in breast cancer progression, and estrogen receptor (ER) status has long been a marker of hormone responsiveness. However, ER status alone has been an incomplete predictor of endocrine therapy, as some ER+ tumors, nevertheless, have poor prognosis. Here we sought to use expression profiling of ER+ breast cancer cells to screen for a robust estrogen-regulated gene signature that may serve as a better indicator of cancer outcome. We identified 532 estrogen-induced genes and further developed a 73-gene signature that best separated a training set of 286 primary breast carcinomas into prognostic subtypes by stepwise cross-validation. Notably, this signature predicts clinical outcome in over 10 patient cohorts as well as their respective ER+ subcohorts. Further, this signature separates patients who have received endocrine therapy into two prognostic subgroups, suggesting its specificity as a measure of estrogen signaling, and thus hormone sensitivity. The 73-gene signature also provides additional predictive value for patient survival, independent of other clinical parameters, and outperforms other previously reported molecular outcome signatures. Taken together, these data demonstrate the power of using cell culture systems to screen for robust gene signatures of clinical relevance.
Adjuvant systemic therapy has led to markedly improved outcome in early-stage breast cancer. However, the absolute gains from chemotherapy might be modest in node-negative patients. Adjuvant chemotherapy is the only option for triple-negative breast cancer patients and should be used with trastuzumab in HER2-positive patients. Considering the large group of patients with some degree of endocrine responsiveness, adding chemotherapy according to risk is an option. At present, we guide our therapeutic decisions using clinicopathologic risk classifications like the St. Gallen risk category or Adjuvant! online. A downside of these risk estimations is a low specificity and consequently the risk for overtreatment of a considerable number of patients. To spare patients unnecessary toxicities we need more reliable prognostic factors or tumor markers. From the plethora of tumor markers, only urokinase-type plasminogen activator (uPA)/plasminogen activator inhibitor 1 (PAI-1) and certain multiparameter gene expression assays are recommended by the American Society of Clinical Oncology. These tumor markers are presently investigated in clinical trials in node-negative breast cancer (NNBC-3, MINDACT, TAILORx). These studies will hopefully allow us to quantify the risk of progression in the individual patient and to tailor treatment accordingly. This should lead to a more personalized treatment recommendation.
Breast cancer; Node-negative; Risk category; Adjuvant!; uPA/PAI-1; Gene expression profiling
Multiple breast cancer gene expression profiles have been developed that appear to provide similar abilities to predict outcome and may outperform clinical-pathologic criteria; however, the extent to which seemingly disparate profiles provide additive prognostic information is not known, nor do we know whether prognostic profiles perform equally across clinically defined breast cancer subtypes. We evaluated whether combining the prognostic powers of standard breast cancer clinical variables with a large set of gene expression signatures could improve on our ability to predict patient outcomes.
Using clinical-pathological variables and a collection of 323 gene expression "modules", including 115 previously published signatures, we build multivariate Cox proportional hazards models using a dataset of 550 node-negative systemically untreated breast cancer patients. Models predictive of pathological complete response (pCR) to neoadjuvant chemotherapy were also built using this approach.
We identified statistically significant prognostic models for relapse-free survival (RFS) at 7 years for the entire population, and for the subgroups of patients with ER-positive, or Luminal tumors. Furthermore, we found that combined models that included both clinical and genomic parameters improved prognostication compared with models with either clinical or genomic variables alone. Finally, we were able to build statistically significant combined models for pathological complete response (pCR) predictions for the entire population.
Integration of gene expression signatures and clinical-pathological factors is an improved method over either variable type alone. Highly prognostic models could be created when using all patients, and for the subset of patients with lymph node-negative and ER-positive breast cancers. Other variables beyond gene expression and clinical-pathological variables, like gene mutation status or DNA copy number changes, will be needed to build robust prognostic models for ER-negative breast cancer patients. This combined clinical and genomics model approach can also be used to build predictors of therapy responsiveness, and could ultimately be applied to other tumor types.
Recent high profile clinical trials demonstrate that microarray-based gene expression profiling has the potential to become an important tool for predicting prognosis in breast cancer. Earlier work in our laboratory using mouse models and human breast cancer populations has enabled us to demonstrate that metastasis susceptibility is an inherited trait. This same combined approach facilitated the identification of a number of candidate genes that when dysregulated, have the potential to induce prognostic gene expression profiles in human datasets. To investigate if these gene expression signatures were of somatic or germline origin, and to assess the contribution of different cell types to the induction of these signatures, we have performed a series of expression profiling experiments in a mouse model of metastatic breast cancer. These results demonstrate that both the tumor epithelium and invading stromal tissues contribute to the development of prognostic gene signatures. Furthermore, analysis of normal tissues and tumor transplants suggests that prognostic signatures result from both somatic and inherited components, with the inherited components being more consistently predictive.