Comparison of gene expression profiles predicting progression in breast cancer patients treated with tamoxifen

The Rochester Modified Magee Algorithm (RoMMa): An Outcomes Based Strategy for Clinical Risk-Assessment and Risk-Stratification in ER Positive, HER2 Negative Breast Cancer Patients Being Considered for Oncotype DX® Testing

INTRODUCTION

Multigene genomic profiling has become the standard of care in the clinical risk-assessment and risk-stratification of ER⁺, HER2^- breast cancer (BC) patients, with Oncotype DX^® (ODX) emerging as the genomic profile test with the most support from the international community. The current state of the health care economy demands that cost-efficiency and access to testing must be considered when evaluating the clinical utility of multigene profile tests such as ODX. Several studies have suggested that certain lower risk patients can be identified more cost-efficiently than simply reflexing all ER⁺, HER2^- BC patients to ODX testing. The Magee equations^TM use standard histopathologic data in a set of multivariable models to estimate the ODX recurrence score. Our group published the first outcome data in 2019 on the Magee equations^TM, using a modification of the Magee equations^TM combined with an algorithmic approach-the Rochester Modified Magee algorithm (RoMMa). There has since been limited published outcome data on the Magee equations^TM. We present additional outcome data, with considerations of the TAILORx risk-stratification recommendations.

METHODS

355 patients with an ODX recurrence score, and at least five years of follow-up or a BC recurrence were included in the study. All patients received either Tamoxifen or an aromatase inhibitor. None of the patients received adjuvant systemic chemotherapy.

RESULTS

There was no significant difference in the risk of recurrence in similar risk categories (very low risk, low risk, and high risk) between the average Modified Magee score and ODX recurrence score with the chi-square test of independence (p > 0.05) or log-rank test (p > 0.05). Using the RoMMa, we estimate that at least 17% of individuals can safely avoid ODX testing.

CONCLUSION

Our study further reinforces that BC patients can be confidently stratified into lower and higher-risk recurrence groups using the Magee equations^TM. The RoMMa can be helpful in the initial clinical risk-assessment and risk-stratification of BC patients, providing increased opportunities for cost savings in the health care system, and for clinical risk-assessment and risk-stratification in less-developed geographies where multigene testing might not be available.

Endonuclease FEN1 Coregulates ERα Activity and Provides a Novel Drug Interface in Tamoxifen-Resistant Breast Cancer

Estrogen receptor α (ERα) is a key transcriptional regulator in the majority of breast cancers. ERα-positive patients are frequently treated with tamoxifen, but resistance is common. In this study, we refined a previously identified 111-gene outcome prediction-classifier, revealing FEN1 as the strongest determining factor in ERα-positive patient prognostication. FEN1 levels were predictive of outcome in tamoxifen-treated patients, and FEN1 played a causal role in ERα-driven cell growth. FEN1 impacted the transcriptional activity of ERα by facilitating coactivator recruitment to the ERα transcriptional complex. FEN1 blockade induced proteasome-mediated degradation of activated ERα, resulting in loss of ERα-driven gene expression and eradicated tumor cell proliferation. Finally, a high-throughput 465,195 compound screen identified a novel FEN1 inhibitor, which effectively blocked ERα function and inhibited proliferation of tamoxifen-resistant cell lines as well as ex vivo-cultured ERα-positive breast tumors. Collectively, these results provide therapeutic proof of principle for FEN1 blockade in tamoxifen-resistant breast cancer. SIGNIFICANCE: These findings show that pharmacologic inhibition of FEN1, which is predictive of outcome in tamoxifen-treated patients, effectively blocks ERα function and inhibits proliferation of tamoxifen-resistant tumor cells.

MammaPrint and BluePrint Molecular Diagnostics Using Targeted RNA Next-Generation Sequencing Technology

Next-generation DNA sequencing is rapidly becoming an indispensable tool for genome-directed cancer diagnostics, but next-generation RNA sequencing (RNA-seq) is currently not standardly used in clinical diagnostics for expression assessment. However, multigene RNA diagnostic assays are used increasingly in the routine diagnosis of early-stage breast cancer. Two of the most widely used tests are currently available only as a central laboratory service, which limits their clinical use. We evaluated the use of RNA-seq as a decentralized method to perform such tests. The MammaPrint and BluePrint RNA-seq tests were found to be equivalent to the clinically validated microarray tests. The RNA-seq tests were highly reproducible when performed in different locations and were stable over time. The MammaPrint RNA-seq test was clinically validated. Our data demonstrate that RNA-seq can be used as a decentralized platform, yielding results substantially equivalent to results derived from the predicate diagnostic device.

IGF1R signaling drives antiestrogen resistance through PAK2/PIX activation in luminal breast cancer

Antiestrogen resistance in estrogen receptor positive (ER⁺) breast cancer is associated with increased expression and activity of insulin-like growth factor 1 receptor (IGF1R). Here, a kinome siRNA screen has identified 10 regulators of IGF1R-mediated antiestrogen with clinical significance. These include the tamoxifen resistance suppressors BMPR1B, CDK10, CDK5, EIF2AK1, and MAP2K5, and the tamoxifen resistance inducers CHEK1, PAK2, RPS6KC1, TTK, and TXK. The p21-activated kinase 2, PAK2, is the strongest resistance inducer. Silencing of the tamoxifen resistance inducing genes, particularly PAK2, attenuates IGF1R-mediated resistance to tamoxifen and fulvestrant. High expression of PAK2 in ER⁺ metastatic breast cancer patients is correlated with unfavorable outcome after first-line tamoxifen monotherapy. Phospho-proteomics has defined PAK2 and the PAK-interacting exchange factors PIXα/β as downstream targets of IGF1R signaling, which are independent from PI3K/ATK and MAPK/ERK pathways. PAK2 and PIXα/β modulate IGF1R signaling-driven cell scattering. Targeting PIXα/β entirely mimics the effect of PAK2 silencing on antiestrogen re-sensitization. These data indicate PAK2/PIX as an effector pathway in IGF1R-mediated antiestrogen resistance.

Systematic review of the clinical and economic value of gene expression profiles for invasive early breast cancer available in Europe

Gene expression profiles with prognostic capacities have shown good performance in multiple clinical trials. However, with multiple assays available and numerous types of validation studies performed, the added value for daily clinical practice is still unclear. In Europe, the MammaPrint, OncotypeDX, PAM50/Prosigna and Endopredict assays are commercially available. In this systematic review, we aim to assess these assays on four important criteria: Assay development and methodology, clinical validation, clinical utility and economic value. We performed a literature search covering PubMed, Embase, Web of Science and Cochrane, for studies related to one or more of the four selected assays. We identified 147 papers for inclusion in this review. MammaPrint and OncotypeDX both have evidence available, including level IA clinical trial results for both assays. Both assays provide prognostic information. Predictive value has only been shown for OncotypeDX. In the clinical utility studies, a higher reduction in chemotherapy was achieved by OncotypeDX, although the number of available studies differ considerably between tests. On average, economic evaluations estimate that genomic testing results in a moderate increase in total costs, but that these costs are acceptable in relation to the expected improved patient outcome. PAM50/prosigna and EndoPredict showed comparable prognostic capacities, but with less economical and clinical utility studies. Furthermore, for these assays no level IA trial data are available yet. In summary, all assays have shown excellent prognostic capacities. The differences in the quantity and quality of evidence are discussed. Future studies shall focus on the selection of appropriate subgroups for testing and long-term outcome of validation trials, in order to determine the place of these assays in daily clinical practice.

Towards decision-making using individualized risk estimates for personalized medicine: A systematic review of genomic classifiers of solid tumors

Recent advances in the understanding of the genetic underpinnings of cancer offer the promise to customize cancer treatments to the individual through the use of genomic classifiers (GCs). At present, routine clinical utilization of GCs is uncommon and their current scope and status, in a broad sense, are unknown. As part of a registered review (PROSPERO 2014:CRD42014013371), we systematically reviewed the literature evaluating the utility of commercially available GCs by searching Ovid Medline (PubMed), EMBASE, the Cochrane Database of Systematic Reviews, and CINAHL on September 2, 2014. We excluded articles involving pediatric malignancies, non-solid or non-invasive cancers, hereditary risk of cancer, non-validated GCs, and GCs involving fewer than 3 biomarkers. A total of 3,625 studies were screened, but only 37 met the pre-specified inclusion criteria. Of these, 15 studies evaluated outcomes and clinical utility of GCs through clinical trials, and the remainder through the use of mathematical models. Most studies (29 of 37) were specific to hormone-receptor positive breast cancer, whereas only 4 studies evaluated GCs in non-breast cancer (prostate, colon, and lung cancers). GCs have spurred excitement across disciplines in recent decades. While there are several GCs that have been validated, the general quality of the data are weak. Further research, including prospective validation is needed, particularly in the non-breast cancer GCs.

Pak1, adjuvant tamoxifen therapy, and breast cancer recurrence risk in a Danish population-based study

Background Adjuvant tamoxifen therapy approximately halves the risk of estrogen receptor-positive (ER+) breast cancer recurrence, but many women do not respond to therapy. Observational studies nested in clinical trial populations suggest that overexpression or nuclear localization of p21-activated kinase 1 (Pak1) in primary tumors predicts tamoxifen failure. Material and methods We measured the association between Pak1 expression and breast cancer recurrence in a Danish population-based case-control study. Pak1 cytoplasmic expression level and nuclear positivity were determined by immunohistochemical staining of primary breast tumors from recurrence cases and matched controls from two breast cancer populations; women diagnosed with ER-positive tumors who received at least one year of tamoxifen therapy (ER+/TAM+), and women diagnosed with ER-negative tumors who survived for at least one year (ER-/TAM-). Pak1 staining was assessed by a single, blinded pathologist, and associations were estimated with conditional logistic regression models. Results We included 541 recurrence cases and 1:1 matched controls from the ER+/TAM + group and 300 recurrence cases and 1:1 matched controls from the ER-/TAM - group. Pak1 cytoplasmic intensity was not associated with breast cancer recurrence in either group (ER+/TAM + ORadj for strong vs. no cytoplasmic staining = 0.91, 95% CI 0.57, 1.5; ER-/TAM - ORadj for strong vs. no cytoplasmic staining = 0.74, 95% CI 0.39, 1.4). Associations between Pak1 nuclear positivity and breast cancer recurrence were similarly near null in both groups. Conclusion Pak1 positivity in primary breast tumors was neither predictive nor prognostic in this prospective, population-based study.

Endocrine therapy resistance in estrogen receptor (ER)-positive breast cancer

Estrogen receptor (ER)-positive breast cancer represents the majority (∼70%) of all breast malignancies. In this subgroup of breast cancers, endocrine therapies are effective both in the adjuvant and recurrent settings, although resistance remains a major issue. Several high-throughput approaches have been used to elucidate mechanisms of resistance and to derive potential predictive markers or alternative therapies. In this review, we cover the state-of-the-art of endocrine-resistance biomarker discovery with regard to the latest technological developments, and discuss current opportunities and restrictions for their implementation into a clinical setting.

A novel gammaretroviral shuttle vector insertional mutagenesis screen identifies SHARPIN as a breast cancer metastasis gene and prognostic biomarker

Breast cancer (BC) is the second leading cause of malignancy among U.S. women. Metastasis results in a poor prognosis and increased mortality, but the molecular mechanisms by which metastatic tumors occur are not well understood. Identifying the genes that drive the metastatic process could provide targets for improved therapy and biomarkers to improve BC patient outcomes. Using a forward mutagenesis screen, BC cells mutagenized with a replication-incompetent gammaretroviral vector (γRV) were xenotransplanted into the mammary fat pad of immunodeficient mice. In this approach the vector provirus dysregulates nearby genes, providing a selective advantage to transduced cells to form metastases. Metastatic tumors were analyzed for proviral integration sites to identify nearby candidate metastasis genes. The γRV has a transgene cassette that allows for rescue in bacteria and rapid identification of vector integration sites. Using this approach, we identified the previously described metastasis gene WWTR1 (TAZ), and three other novel candidate metastasis genes including SHARPIN. SHARPIN was independently validated in vivo as a BC metastasis gene. Analysis of patient data showed that SHARPIN expression predicts metastasis-free survival after adjuvant therapy. Our approach has broad potential to identify genes involved in oncogenic processes for BC and other cancers. We show here it can identify both known (WWTR1) and novel (SHARPIN) BC metastasis genes.

Decreased expression of ABAT and STC2 hallmarks ER-positive inflammatory breast cancer and endocrine therapy resistance in advanced disease

BACKGROUND

Patients with Estrogen Receptor α-positive (ER+) Inflammatory Breast Cancer (IBC) are less responsive to endocrine therapy compared with ER+ non-IBC (nIBC) patients. The study of ER+ IBC samples might reveal biomarkers for endocrine resistant breast cancer.

MATERIALS & METHODS

Gene expression profiles of ER+ samples from 201 patients were explored for genes that discriminated between IBC and nIBC. Classifier genes were applied onto clinically annotated expression data from 947 patients with ER+ breast cancer and validated with RT-qPCR for 231 patients treated with first-line tamoxifen. Relationships with metastasis-free survival (MFS) and progression-free survival (PFS) following adjuvant and first-line endocrine treatment, respectively, were investigated using Cox regression analysis.

RESULTS

A metagene of six genes including the genes encoding for 4-aminobutyrate aminotransferase (ABAT) and Stanniocalcin-2 (STC2) were identified to distinguish 22 ER+ IBC from 43 ER+ nIBC patients and remained discriminatory in an independent series of 136 patients. The metagene and two genes were not prognostic in 517 (neo)adjuvant untreated lymph node-negative ER+ nIBC breast cancer patients. Only ABAT was related to outcome in 250 patients treated with adjuvant tamoxifen. Three independent series of in total 411 patients with advanced disease showed increased metagene scores and decreased expression of ABAT and STC2 to be correlated with poor first-line endocrine therapy outcome. The biomarkers remained predictive for first-line tamoxifen treatment outcome in multivariate analysis including traditional factors or published signatures. In an exploratory analysis, ABAT and STC2 protein expression levels had no relation with PFS after first-line tamoxifen.

CONCLUSIONS

This study utilized ER+ IBC to identify a metagene including ABAT and STC2 as predictive biomarkers for endocrine therapy resistance.

The value of multigene predictors of clinical outcome in breast cancer: an analysis of the evidence

OBJECTIVE

Multigene predictors are being used increasingly in early-stage breast cancer patients for prediction and prognosis. However, one consequence of the increased use of multigene predictors, and the heightened efforts toward their incorporation into routine clinical practice, is the potential for future malpractice litigation. It is, therefore, important to ascertain the strength of the evidence for using the different commercially available multigene predictor assays clinically. We evaluated the literature for evidence of clinical validity of four currently available gene signatures and to assess the influence of the 21-gene-expression assay on changes in treatment recommendations.

METHODS

A systematic search of the peer-reviewed literature from January 2002 to March 2014 for multigene predictor assays was carried out, and a meta-analysis was conducted.

RESULTS

The adjusted Cox hazard ratio average for studies that met the eligibility criteria was 3.538 (95% CI: 1.513-8.469). The 21-gene signature showed the highest stability in the estimation of likelihood of distant risk of recurrence. Using the recurrence scores resulted in changes in treatment recommendations in 31.8% of all patients in the studies.

CONCLUSION

This study may provide insight about the use of multigene predictors in clinical practice for prediction and prognosis of breast cancer.

BreastMark: an integrated approach to mining publicly available transcriptomic datasets relating to breast cancer outcome

Introduction

Breast cancer is a complex heterogeneous disease for which a substantial resource of transcriptomic data is available. Gene expression data have facilitated the division of breast cancer into, at least, five molecular subtypes, namely luminal A, luminal B, HER2, normal-like and basal. Once identified, breast cancer subtypes can inform clinical decisions surrounding patient treatment and prognosis. Indeed, it is important to identify patients at risk of developing aggressive disease so as to tailor the level of clinical intervention.

Methods

We have developed a user-friendly, web-based system to allow the evaluation of genes/microRNAs (miRNAs) that are significantly associated with survival in breast cancer and its molecular subtypes. The algorithm combines gene expression data from multiple microarray experiments which frequently also contain miRNA expression information, and detailed clinical data to correlate outcome with gene/miRNA expression levels. This algorithm integrates gene expression and survival data from 26 datasets on 12 different microarray platforms corresponding to approximately 17,000 genes in up to 4,738 samples. In addition, the prognostic potential of 341 miRNAs can be analysed.

Results

We demonstrated the robustness of our approach in comparison to two commercially available prognostic tests, oncotype DX and MammaPrint. Our algorithm complements these prognostic tests and is consistent with their findings. In addition, BreastMark can act as a powerful reductionist approach to these more complex gene signatures, eliminating superfluous genes, potentially reducing the cost and complexity of these multi-index assays. Known miRNA prognostic markers, mir-205 and mir-93, were used to confirm the prognostic value of this tool in a miRNA setting. We also applied the algorithm to examine expression of 58 receptor tyrosine kinases in the basal-like subtype, identifying six receptor tyrosine kinases associated with poor disease-free survival and/or overall survival (EPHA5, FGFR1, FGFR3, VEGFR1, PDGFRβ, and TIE1). A web application for using this algorithm is currently available.

Conclusions

BreastMark is a powerful tool for examining putative gene/miRNA prognostic markers in breast cancer. The value of this tool will be in the preliminary assessment of putative biomarkers in breast cancer. It will be of particular use to research groups with limited bioinformatics facilities.

Clinical utility of gene-expression profiling in women with early breast cancer: an overview of systematic reviews

PURPOSE

This overview systematically evaluates the clinical utility of using Oncotype DX and MammaPrint gene-expression profiling tests to direct treatment decisions in women with breast cancer. The findings are intended to inform an updated recommendation from the Evaluation of Genomic Applications in Practice and Prevention Working Group.

METHODS

Evidence reported in systematic reviews evaluating the clinical utility of Oncotype DX and MammaPrint, as well as the ability to predict treatment outcomes, change in treatment decisions, and cost-effectiveness, was qualitatively synthesized.

RESULTS

Five systematic reviews found no direct evidence of clinical utility for either test. Indirect evidence showed Oncotype DX was able to predict treatment effects of adjuvant chemotherapy, whereas no evidence of predictive value was found for MammaPrint. Both tests influenced a change in treatment recommendations in 21 to 74% of participants. The cost-effectiveness of Oncotype DX varied with the alternative compared. For MammaPrint, lack of evidence of the predictive value led to uncertainty in the cost-effectiveness.

CONCLUSION

No studies were identified that provided direct evidence that using gene-expression profiling tests to direct treatment decisions improved outcomes in women with breast cancer. Three ongoing studies may provide direct evidence for determining the clinical utility of gene-expression profiling testing.

Luminal B breast cancer: molecular characterization, clinical management, and future perspectives

Gene expression profiling has reshaped our understanding of breast cancer by defining and characterizing four main intrinsic molecular subtypes: human epidermal growth factor receptor 2-enriched, basal-like, luminal A, and luminal B subtypes. Luminal B breast cancer has been reported to have lower expression of hormone receptors, higher expression of proliferation markers, and higher histologic grade than luminal A. It also exhibits worse prognosis and has a distinct profile of response to hormone therapy and chemotherapy. Although luminal cancers share similarities, the studies conducted in recent years using next-generation sequencing technology show that luminal A and B breast cancers should be perceived as distinct entities, with specific oncogenic drivers, rather than more proliferative varieties of luminal tumors. This review discusses the definition and molecular characterization of luminal B breast cancer and presents the available clinical evidence for chemotherapy and endocrine therapy patterns of response. It also provides an overview of ongoing research on molecularly targeted agents for this disease.

The methyltransferase EZH2 is not required for mammary cancer development, although high EZH2 and low H3K27me3 correlate with poor prognosis of ER-positive breast cancers

Enhancer of zeste homolog 2 (EZH2) catalyzes trimethylation of histone H3 lysine 27 (H3K27me3) and its demethylation is catalyzed by UTX. EZH2 levels are frequently elevated in breast cancer and have been proposed to control gene expression through regulating repressive H3K27me3 marks. However, it is not fully established whether breast cancers with different levels of H3K27me3, EZH2 and UTX exhibit different biological behaviors. Levels of H3K27me3, EZH2 and UTX and their prognostic significance were evaluated in 146 cases of breast cancer. H3K27me3 levels were higher in HER2-negative samples. EZH2 expression was higher in cancers that were LN+, size > 20mm, and with higher tumor grade and stage. Using a Cox regression model, H3K27me3 levels and EZH2 expression were identified as independent prognostic factors for overall survival for all the breast cancers studied as well as the ER-positive subgroup. The combination of low H3K27me3 and high EZH2 expression levels were significantly associated with shorter survival. UTX expression was not significantly associated with prognosis and there were no correlations between H3K27me3 levels and EZH2/UTX expression. To determine if EZH2 is required to establish H3K27me3 marks in mammary cancer, Brca1 and Ezh2 were deleted in mammary stem cells in mice. Brca1-deficient mammary cancers with unaltered H3K27me3 levels developed in the absence of EZH2, demonstrating that EZH2 is not a mandatory H3K27 methyltransferase in mammary neoplasia and providing genetic evidence for biological independence between H3K27me3 and EZH2 in this tissue.

PIK3CA mutations, phosphatase and tensin homolog, human epidermal growth factor receptor 2, and insulin-like growth factor 1 receptor and adjuvant tamoxifen resistance in postmenopausal breast cancer patients

Introduction

Inhibitors of the phosphatidylinositol-3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/AKT/mTOR) pathway can overcome endocrine resistance in estrogen receptor (ER) α-positive breast cancer, but companion diagnostics indicating PI3K/AKT/mTOR activation and consequently endocrine resistance are lacking. PIK3CA mutations frequently occur in ERα-positive breast cancer and result in PI3K/AKT/mTOR activation in vitro. Nevertheless, the prognostic and treatment-predictive value of these mutations in ERα-positive breast cancer is contradictive. We tested the clinical validity of PIK3CA mutations and other canonic pathway drivers to predict intrinsic resistance to adjuvant tamoxifen. In addition, we tested the association between these drivers and downstream activated proteins.

Methods

Primary tumors from 563 ERα-positive postmenopausal patients, randomized between adjuvant tamoxifen (1 to 3 years) versus observation were recollected. PIK3CA hotspot mutations in exon 9 and exon 20 were assessed with Sequenom Mass Spectometry. Immunohistochemistry was performed for human epidermal growth factor receptor 2 (HER2), phosphatase and tensin homolog (PTEN), and insulin-like growth factor 1 receptor (IGF-1R). We tested the association between these molecular alterations and downstream activated proteins (like phospho-protein kinase B (p-AKT), phospho-mammalian target of rapamycin (p-mTOR), p-ERK1/2, and p-p70S6K). Recurrence-free interval improvement with tamoxifen versus control was assessed according to the presence or absence of canonic pathway drivers, by using Cox proportional hazard models, including a test for interaction.

Results

PIK3CA mutations (both exon 9 and exon 20) were associated with low tumor grade. An enrichment of PIK3CA exon 20 mutations was observed in progesterone receptor- positive tumors. PIK3CA exon 20 mutations were not associated with downstream-activated proteins. No significant interaction between PIK3CA mutations or any of the other canonic pathway drivers and tamoxifen-treatment benefit was found.

Conclusion

PIK3CA mutations do not have clinical validity to predict intrinsic resistance to adjuvant tamoxifen and may therefore be unsuitable as companion diagnostic for PI3K/AKT/mTOR inhibitors in ERα- positive, postmenopausal, early breast cancer patients.

Hallmarks of aromatase inhibitor drug resistance revealed by epigenetic profiling in breast cancer

Aromatase inhibitors are the major first-line treatment of estrogen receptor-positive breast cancer, but resistance to treatment is common. To date, no biomarkers have been validated clinically to guide subsequent therapy in these patients. In this study, we mapped the genome-wide chromatin-binding profiles of estrogen receptor α (ERα), along with the epigenetic modifications H3K4me3 and H3K27me3, that are responsible for determining gene transcription (n = 12). Differential binding patterns of ERα, H3K4me3, and H3K27me3 were enriched between patients with good or poor outcomes after aromatase inhibition. ERα and H3K27me3 patterns were validated in an additional independent set of breast cancer cases (n = 10). We coupled these patterns to array-based proximal gene expression and progression-free survival data derived from a further independent cohort of 72 aromatase inhibitor-treated patients. Through this approach, we determined that the ERα and H3K27me3 profiles predicted the treatment outcomes for first-line aromatase inhibitors. In contrast, the H3K4me3 pattern identified was not similarly informative. The classification potential of these genes was only partially preserved in a cohort of 101 patients who received first-line tamoxifen treatment, suggesting some treatment selectivity in patient classification.

Boosting the concordance index for survival data--a unified framework to derive and evaluate biomarker combinations

The development of molecular signatures for the prediction of time-to-event outcomes is a methodologically challenging task in bioinformatics and biostatistics. Although there are numerous approaches for the derivation of marker combinations and their evaluation, the underlying methodology often suffers from the problem that different optimization criteria are mixed during the feature selection, estimation and evaluation steps. This might result in marker combinations that are suboptimal regarding the evaluation criterion of interest. To address this issue, we propose a unified framework to derive and evaluate biomarker combinations. Our approach is based on the concordance index for time-to-event data, which is a non-parametric measure to quantify the discriminatory power of a prediction rule. Specifically, we propose a gradient boosting algorithm that results in linear biomarker combinations that are optimal with respect to a smoothed version of the concordance index. We investigate the performance of our algorithm in a large-scale simulation study and in two molecular data sets for the prediction of survival in breast cancer patients. Our numerical results show that the new approach is not only methodologically sound but can also lead to a higher discriminatory power than traditional approaches for the derivation of gene signatures.

Contribution of ER and NF-κB to endocrine resistance in inflammatory breast cancer

SUMMARY Inflammatory breast cancer (IBC) is a very aggressive form of breast cancer with a high mortality rate. Most patients have lymph node metastasis at the time of diagnosis and 30% of patients already have metastases in distant organs. IBC is normally treated with multimodality therapy. Endocrine therapy is administered in cases of ER-positive tumors. Nevertheless, IBC has a high HOXB13:Il17RB ratio, predicting a poor response to tamoxifen treatment. These data suggest a possible role for IBC as a model for endocrine resistance. Previous studies have shown that NF-κB, a transcription factor regulating different cellular processes, is more highly activated in IBC than in non-IBC, while ER is often downregulated in this tumor type. This article summarizes the activity of ER and NF-κB in IBC and their possible contribution to endocrine resistance in this breast cancer subtype.

Two-gene expression ratio as predictor for breast cancer treated with tamoxifen: evidence from meta-analysis

A HOXB13-to-IL17BR expression ratio was previously identified to predict a clinical outcome of breast cancer patients treated with adjuvant tamoxifen. A large number of studies were addressed to confirm its function as a predictor of breast cancer outcome treated with tamoxifen. However, conflicting results were got. In this study, a systematic search of databases was carried out, and other relevant papers were also identified. Then, the analyses were conducted according to the PRISMA and MOOSE guidelines. After full review, 11 studies with a total of 2,958 participants were deemed eligible and were included in the study. Pooled results revealed that women with higher HOXB13-to-IL17BR expression ratio had significantly worse outcomes in breast patients treated with tamoxifen, especially for those who are negative of node.

The impact of the Oncotype Dx breast cancer assay in clinical practice: a systematic review and meta-analysis

The impact of the Oncotype Dx (ODX) breast cancer assay on adjuvant chemotherapy (ACT) treatment decisions has been evaluated in many previous studies. However, it can be difficult to interpret the collective findings, which were conducted in diverse settings with limited sample sizes. We conducted a systematic review and meta-analysis to synthesize the results and provide insights about ODX utility. Studies, identified from PubMed, Embase, ASCO, and SABCS, were included if patients had ER+, node -, early-stage breast cancer, reported use of ODX to inform actual ACT decisions. Information was summarized and pooled according to: (1) distribution of ODX recurrence scores (RS), (2) impact of ODX on ACT recommendations, (3) impact of ODX on ACT use, and (4) proportion of patients following the treatment suggested by the ODX RS. A total of 23 studies met inclusion criteria. The distribution of RS categories was 48.8 % low, 39.0 % intermediate, and 12.2 % high (21 studies, 4,156 patients). ODX changed the clinical-pathological ACT recommendation in 33.4 % of patients (8 studies, 1,437 patients). In patients receiving ODX, receipt of ACT were: 28.2 % overall, 5.8 % low, 37.4 % intermediate, and 83.4 % high. Low RS patients were significantly more likely to follow the treatment suggested by ODX versus high RS patients RR: 1.07 (1.01–1.14) [corrected].The pooled results are consistent with most individual studies to date. The increased proportion of intermediate scores relative to original estimates may have implications for the clinical utility and cost impacts of testing. In addition, low versus high RS patients were significantly more likely to follow the ODX results, suggesting a tendency toward less aggressive treatment, despite a high ODX RS. Finally, there was a lack of studies on the impact of ODX on ACT use versus standard approaches, suggesting that additional studies are warranted.

A gene signature for late distant metastasis in breast cancer identifies a potential mechanism of late recurrences

INTRODUCTION

Breast cancer risk of recurrence is known to span 20 years, yet existing prognostic signatures are best at predicting early recurrences (≤ 5 years). There is a critical need to identify those patients at risk of late-relapse (>5 years), in order to select potential candidates for further treatment and to identify molecular targets for such treatment.

METHODS

A total of 252 breast primary tumors were selected at the Netherlands Cancer Institute from a retrospective series of ER+, HER2- breast cancer patients with a follow-up of at least 10 years. Gene expression analysis was performed using Agilent 4x44K microarrays. Patients were classified in 3 groups: no relapse (M0); relapse before 5 years (M0-5) or after 5 years (M5-15). We assessed the correlation of clinico-pathological variables with late Distant Metastases (DM). We divided the patient series into a training set of untreated patients (n = 140) and a test set of treated patients (n = 112), to investigate whether a gene-signature or single genes could be identified for predicting late DM. Pathway level late DM correlates were identified using PARADIGM and DAVID.

RESULTS

Of the clinico-pathologic variables tested, only lymph node status associated with late DM. A 241-gene signature developed on the NKI training set was able to classify M5-15 patients in the test set with a sensitivity of 77% and a specificity of 33% (AUC 0.654). This signature showed enrichment in genes involved in immune response and extracellular matrix. An alternative analysis of individual genes identified CH25H as an independent predictor of distant metastasis in our patient series.

CONCLUSIONS

We identified a gene signature for late metastasis in breast cancer. Our data are consistent with a model in which suppressed anti-tumoral immunity enables dormant tumor cells to re-enter the cell cycle to form metastases in response to extrinsic events in the microenvironment.

Systematic antibody generation and validation via tissue microarray technology leading to identification of a novel protein prognostic panel in breast cancer

BACKGROUND

Although omic-based discovery approaches can provide powerful tools for biomarker identification, several reservations have been raised regarding the clinical applicability of gene expression studies, such as their prohibitive cost. However, the limited availability of antibodies is a key barrier to the development of a lower cost alternative, namely a discrete collection of immunohistochemistry (IHC)-based biomarkers. The aim of this study was to use a systematic approach to generate and screen affinity-purified, mono-specific antibodies targeting progression-related biomarkers, with a view towards developing a clinically applicable IHC-based prognostic biomarker panel for breast cancer.

METHODS

We examined both in-house and publicly available breast cancer DNA microarray datasets relating to invasion and metastasis, thus identifying a cohort of candidate progression-associated biomarkers. Of these, 18 antibodies were released for extended analysis. Validated antibodies were screened against a tissue microarray (TMA) constructed from a cohort of consecutive breast cancer cases (n = 512) to test the immunohistochemical surrogate signature.

RESULTS

Antibody screening revealed 3 candidate prognostic markers: the cell cycle regulator, Anillin (ANLN); the mitogen-activated protein kinase, PDZ-Binding Kinase (PBK); and the estrogen response gene, PDZ-Domain Containing 1 (PDZK1). Increased expression of ANLN and PBK was associated with poor prognosis, whilst increased expression of PDZK1 was associated with good prognosis. A 3-marker signature comprised of high PBK, high ANLN and low PDZK1 expression was associated with decreased recurrence-free survival (p < 0.001) and breast cancer-specific survival (BCSS) (p < 0.001). This novel signature was associated with high tumour grade (p < 0.001), positive nodal status (p = 0.029), ER-negativity (p = 0.006), Her2-positivity (p = 0.036) and high Ki67 status (p < 0.001). However, multivariate Cox regression demonstrated that the signature was not a significant predictor of BCSS (HR = 6.38; 95% CI = 0.79-51.26, p = 0.082).

CONCLUSIONS

We have developed a comprehensive biomarker pathway that extends from discovery through to validation on a TMA platform. This proof-of-concept study has resulted in the identification of a novel 3-protein prognostic panel. Additional biochemical markers, interrogated using this high-throughput platform, may further augment the prognostic accuracy of this panel to a point that may allow implementation into routine clinical practice.

A model of estrogen-related gene expression reveals non-linear effects in transcriptional response to tamoxifen

BACKGROUND

Estrogen receptors alpha (ER) are implicated in many types of female cancers, and are the common target for anti-cancer therapy using selective estrogen receptor modulators (SERMs, such as tamoxifen). However, cell-type specific and patient-to-patient variability in response to SERMs (from suppression to stimulation of cancer growth), as well as frequent emergence of drug resistance, represents a serious problem. The molecular processes behind mixed effects of SERMs remain poorly understood, and this strongly motivates application of systems approaches. In this work, we aimed to establish a mathematical model of ER-dependent gene expression to explore potential mechanisms underlying the variable actions of SERMs.

RESULTS

We developed an equilibrium model of ER binding with 17β-estradiol, tamoxifen and DNA, and linked it to a simple ODE model of ER-induced gene expression. The model was parameterised on the broad range of literature available experimental data, and provided a plausible mechanistic explanation for the dual agonism/antagonism action of tamoxifen in the reference cell line used for model calibration. To extend our conclusions to other cell types we ran global sensitivity analysis and explored model behaviour in the wide range of biologically plausible parameter values, including those found in cancer cells. Our findings suggest that transcriptional response to tamoxifen is controlled in a complex non-linear way by several key parameters, including ER expression level, hormone concentration, amount of ER-responsive genes and the capacity of ER-tamoxifen complexes to stimulate transcription (e.g. by recruiting co-regulators of transcription). The model revealed non-monotonic dependence of ER-induced transcriptional response on the expression level of ER, that was confirmed experimentally in four variants of the MCF-7 breast cancer cell line.

CONCLUSIONS

We established a minimal mechanistic model of ER-dependent gene expression, that predicts complex non-linear effects in transcriptional response to tamoxifen in the broad range of biologically plausible parameter values. Our findings suggest that the outcome of a SERM's action is defined by several key components of cellular micro-environment, that may contribute to cell-type-specific effects of SERMs and justify the need for the development of combinatorial biomarkers for more accurate prediction of the efficacy of SERMs in specific cell types.

The challenge of gene expression profiling in heterogeneous clinical samples

Almost all samples used in tumor biology, such as tissues and bodily fluids, are heterogeneous, i.e., consist of different cell types. Evaluating the degree of heterogeneity in samples can increase our knowledge on processes such as clonal selection and metastasis. In addition, generating expression profiles from specific sub populations of cells can reveal their distinct functions. Tissue heterogeneity also poses a challenge, as it can confound the interpretation of gene expression data. This chapter will (1) give a brief overview on how heterogeneity may influence gene expression profiling data and (2) describe the methods that are currently available to assess transcriptional biomarkers in a heterogeneous cell population.

A five-gene model predicts clinical outcome in ER+/PR+, early-stage breast cancers treated with adjuvant tamoxifen

Primary breast carcinomas expressing both estrogen and progesterone receptors are most likely to respond to tamoxifen therapy, especially in patients with early-stage lesions. However, certain patients exhibit clinicopathologic features suggesting good prognosis relapse within 10 years, justifying a search for biomarkers identifying patients at risk for recurrence. Nine candidate genes associated with estrogen signaling were selected from microarray studies and combined with those for conventional biomarkers (ESR1, PGR, ERBB2). Expression of this 12-gene subset was analyzed by RT-qPCR in frozen tissue specimens from 60 early-stage, estrogen receptor (ER)+/progestin receptor (PR)+ breast cancers from patients treated with adjuvant tamoxifen. A multivariate model was created by Cox regression using a training data set and applied to an independent validation set. A five-gene model was developed from the training set (n = 36) that exhibited significant correlations with both relapse-free and overall survival. Applying this model to Kaplan-Meier regression, patients were separated into low-risk (100% relapse-free at 150 months) and high-risk (60% relapse-free at 150 months) groups (P = 0.03). When this model was applied to the validation set (n = 24), similar risk stratification was achieved for both relapse-free and overall survival (P = 0.01 and 0.04, respectively). We developed a five-gene model composed of PgR, BCL2, ERBB4 JM-a, RERG, and CD34 that identified early-stage, ER+/PR+ breast cancers in patients treated with tamoxifen that relapsed, although they exhibited clinicopathologic features suggesting good prognosis. Within this multivariate model, increased expression of PgR, ERBB4 JM-a, RERG, and CD34 was associated with increased survival, while increased expression of BCL2 was associated with decreased survival.

Targeting Molecular Aberrations in Breast Cancer: Is It about Time?

Breast cancer is not one homogeneous disease; it is a heterogeneous cancer with remarkable genomic variation and variable risk for systemic spread, time to recurrence, and response to treatment. Although it is increasingly clear that a substantial proportion of newly diagnosed patients with breast cancer carry only minimal risk of developing metastatic disease, our inability to accurately prognosticate leads to systemic overtreatment. The recent introduction of multigene predictors for baseline risk assessment and treatment response in breast cancer subsets heralds the beginning of personalized cancer care and the transition to precision medicine. At the same time, rapid clinical adoption of these predictors illustrates the voracious unmet need for better and more finely tuned prognostic and predictive tools. Recent advances in clinical trial designs have enabled the testing of novel agents in combination with standard therapy in the neoadjuvant setting, with a goal of identifying the specific biomarker-drug pairs that should be advanced for confirmatory trials and accelerated approval on the basis of response to therapy.

High miR-26a and low CDC2 levels associate with decreased EZH2 expression and with favorable outcome on tamoxifen in metastatic breast cancer

For patients with metastatic breast cancer, we previously described that increased EZH2 expression levels were associated with an adverse outcome to tamoxifen therapy. Main objective of the present study is to investigate miR-26a and miR-101 levels, which both target EZH2, for their association with molecular pathways and with efficacy of tamoxifen as first-line monotherapy for metastatic breast cancer. Expression levels were measured using quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) in primary breast cancer specimens of 235 estrogen receptor-α (ER)-positive patients. Pathway analysis was performed on microarray data available for 65 of these tumors. Logistic regression and Cox uni- and multivariate analysis were performed to relate expression levels with clinical benefit and time to progression (TTP). Increasing levels of miR-26a were significantly (P < 0.005) associated with both clinical benefit and prolonged TTP, whereas miR-101 was not. Cell cycle regulation and CCNE1 and CDC2 were the only significant overlapping pathway and genes differentially expressed between tumors with high and low levels of miR-26a and EZH2, respectively. In addition, increasing mRNA levels of CCNE1 (P < 0.05) and CDC2 (P < 0.001) were related to poor outcome. Multivariate analysis revealed miR-26a and CDC2 as an optimal set of markers associated with outcome on tamoxifen therapy, independently of traditional predictive factors. To summarize, only miR-26a levels are related with treatment outcome. Cell cycle regulation is the only overlapping pathway linked to miR-26a and EZH2 levels. Low mRNA levels of EZH2, CCNE1, and CDC2, and high levels of miR-26a are associated with favorable outcome on tamoxifen.

A review of the biological and clinical characteristics of luminal-like oestrogen receptor-positive breast cancer

Global gene expression profiling (GEP) studies of breast cancer have identified distinct biological classes with different clinical and therapeutic implications. Oestrogen receptor (ER) has been found to be a central marker of the molecular signature. GEP studies have consistently recognized a molecularly distinct class of tumours that is characterized by high-level expression of ER and other biomarkers recognized to be characteristic of normal luminal cells of the breast. This class is the largest of the GEP-defined molecular subclasses, comprising 60-70% of breast cancer cases. Moreover, it has been proposed that this group of tumours is composed of at least two subclasses distinguished by differing GEP profiles. At present, there is no consensus on the definition of the luminal subclasses and, in clinical practice, luminal-like tumours and ER-positive tumours are frequently considered to be the same. A better understanding of the biological features of luminal tumours could lead to their improved characterization and consistent identification. In this review, we explore the concept and definitions of the luminal-like class of breast carcinoma and their contribution to our understanding of their molecular features, clinical significance and therapeutic implications.

The CYP2C19*2 genotype predicts tamoxifen treatment outcome in advanced breast cancer patients

AIMS

Tamoxifen is metabolized by cytochrome P450s, with an important role for CYP2D6. Recently, we demonstrated in 80 patients that CYP2C19*2 is associated with increased survival in breast cancer patients using tamoxifen. Here, we aimed to confirm this in a large group of 499 patients.

MATERIALS & METHODS

A total of 499 estrogen receptor-positive primary breast tumor specimens of advanced disease patients treated with first-line tamoxifen were genotyped for CYP2C19*2 and *17 variant alleles, with primary end point time-to-treatment failure (TTF). Effects of CYP2C19, independent of treatment, were analyzed in 243 primary systematic untreated patients.

RESULTS

CYP2C19*2 hetero- and homozygote patients combined showed significantly longer TTFs (hazard ratio [HR]: 0.72; 95% CI: 0.57-0.90; p = 0.004). In multivariate analysis, including CYP2D6*4 status, CYP2C19*2 remained independently associated with TTF (HR: 0.73; 95% CI: 0.58-0.91; p = 0.007). In untreated patients, the CYP2C19*17 allele was significantly associated with a longer disease-free interval (HR: 0.66; 95%CI: 0.46-0.95; p = 0.025).

CONCLUSION

CYP2C19 genotyping is potentially important for tamoxifen therapy for advanced disease and for breast cancer prognosis.

Loss of Rho GDIα and resistance to tamoxifen via effects on estrogen receptor α

BACKGROUND

Estrogen receptor (ER) α is a successful therapeutic target in breast cancer, but patients eventually develop resistance to antiestrogens such as tamoxifen.

METHODS

To identify genes whose expression was associated with the development of tamoxifen resistance and metastasis, we used microarrays to compare gene expression in four primary tumors from tamoxifen-treated patients whose breast cancers did not recur vs five metastatic tumors from patients whose cancers progressed during adjuvant tamoxifen treatment. Because Rho guanine dissociation inhibitor (GDI) α was underexpressed in the tamoxifen-resistant group, we stably transfected ERα-positive MCF-7 breast cancer cells with a plasmid encoding a short hairpin (sh) RNA to silence Rho GDIα expression. We used immunoblots and transcription assays to examine the role of Rho GDIα in ER-related signaling and growth of cells in vitro and as xenografts in treated nude mice (n = 8-9 per group) to examine the effects of Rho GDIα blockade on hormone responsiveness and metastatic behavior. The time to tumor tripling as the time in weeks from randomization to a threefold increase in total tumor volume over baseline was examined in treated mice. The associations of Rho GDIα and MTA2 levels with tamoxifen resistance were examined in microarray data from patients. All statistical tests were two-sided.

RESULTS

Rho GDIα was expressed at lower levels in ERα-positive tumors that recurred during tamoxifen treatment than in ERα-positive tamoxifen-sensitive primary tumors. MCF-7 breast cancer cells in which Rho GDIα expression had been silenced were tamoxifen-resistant, had increased Rho GTPase and p21-activated kinase 1 activity, increased phosphorylation of ERα at serine 305, and enhanced tamoxifen-induced ERα transcriptional activity compared with control cells. MCF-7 cells in which Rho GDIα expression was silenced metastasized with high frequency when grown as tumor xenografts. When mice were treated with estrogen or estrogen withdrawal, tripling times for xenografts from cells with Rho GDIα silencing were similar to those from vector-containing control cells; however, tripling times were statistically significantly faster than control when mice were treated with tamoxifen (median tripling time for tumors with Rho GDIα small interfering RNA = 2.34 weeks; for control tumors = not reached, hazard ratio = 4.13, 95% confidence interval = 1.07 to 15.96, P = .040 [adjusted for multiple comparisons, P = .119]). Levels of the metastasis-associated protein MTA2 were also increased upon Rho GDIα silencing, and combined Rho GDIα and MTA2 levels were associated with recurrence in 250 tamoxifen-treated patients.

CONCLUSION

Loss of Rho GDIα enhances metastasis and resistance to tamoxifen via effects on both ERα and MTA2 in models of ERα-positive breast cancer and in tumors of tamoxifen-treated patients.

Rutgers EJ, Wessels LFA, Van't Veer LJ. Gene expression profiles from formalin fixed paraffin embedded breast cancer tissue are largely comparable to fresh frozen matched tissue

Background and Methods

Formalin Fixed Paraffin Embedded (FFPE) samples represent a valuable resource for cancer research. However, the discovery and development of new cancer biomarkers often requires fresh frozen (FF) samples. Recently, the Whole Genome (WG) DASL (cDNA-mediated Annealing, Selection, extension and Ligation) assay was specifically developed to profile FFPE tissue. However, a thorough comparison of data generated from FFPE RNA and Fresh Frozen (FF) RNA using this platform is lacking. To this end we profiled, in duplicate, 20 FFPE tissues and 20 matched FF tissues and evaluated the concordance of the DASL results from FFPE and matched FF material.

Methodology and Principal Findings

We show that after proper normalization, all FFPE and FF pairs exhibit a high level of similarity (Pearson correlation >0.7), significantly larger than the similarity between non-paired samples. Interestingly, the probes showing the highest correlation had a higher percentage G/C content and were enriched for cell cycle genes. Predictions of gene expression signatures developed on frozen material (Intrinsic subtype, Genomic Grade Index, 70 gene signature) showed a high level of concordance between FFPE and FF matched pairs. Interestingly, predictions based on a 60 gene DASL list (best match with the 70 gene signature) showed very high concordance with the MammaPrint® results.

Conclusions and Significance

We demonstrate that data generated from FFPE material with the DASL assay, if properly processed, are comparable to data extracted from the FF counterpart. Specifically, gene expression profiles for a known set of prognostic genes for a specific disease are highly comparable between two conditions. This opens up the possibility of using both FFPE and FF material in gene expressions analyses, leading to a vast increase in the potential resources available for cancer research.

Tamoxifen in early-stage estrogen receptor-positive breast cancer: overview of clinical use and molecular biomarkers for patient selection

Tamoxifen was the first targeted anticancer agent for breast cancer patients and its effects on reduction of breast cancer events and improvement in overall survival are undisputed. Hence, it has long been considered an essential part of patient care. Recent results of several large adjuvant hormonal trials evaluating the use of aromatase inhibitors in comparison with the previous standard of five years of tamoxifen has led to a paradigm shift, ensuring the inclusion of an aromatase inhibitor as part of standard endocrine therapy for most postmenopausal women diagnosed today with estrogen receptor-positive breast cancer. However, one could argue that despite statistically significant improvements in breast cancer events, an overall survival advantage has not been clear. In this review, we discuss recent genomic and molecular data pertaining to estrogen receptor-positive breast cancer and how this knowledge may aid clinicians to prescribe adjuvant hormonal treatment in the future. A combination of gene expression and genetic aberration markers may be most useful in discerning a population that is still appropriate for adjuvant tamoxifen treatment.

Identification of a low-risk subgroup of HER-2-positive breast cancer by the 70-gene prognosis signature

Background:

Overexpression of HER-2 is observed in 15–25% of breast cancers, and is associated with increased risk of recurrence. Current guidelines recommend trastuzumab and chemotherapy for most HER-2-positive patients. However, the majority of patients does not recur and might thus be overtreated with adjuvant systemic therapy. We investigated whether the 70-gene MammaPrint signature identifies HER-2-positive patients with favourable outcome.

Methods:

In all, 168 T1–3, N0–1, HER-2-positive patients were identified from a pooled database, classified by the 70-gene signature as good or poor prognosis, and correlated with long-term outcome. A total of 89 of these patients did not receive adjuvant chemotherapy.

Results:

In the group of 89 chemotherapy-naive patients, after a median follow-up of 7.4 years, 35 (39%) distant recurrences and 29 (33%) breast cancer-specific deaths occurred. The 70-gene signature classified 20 (22%) patients as good prognosis, with 10-year distant disease-free survival (DDFS) of 84%, compared with 69 (78%) poor prognosis patients with 10-year DDFS of 55%. The estimated hazard ratios (HRs) were 4.5 (95% confidence interval (CI) 1.1–18.7, P=0.04) and 3.8 (95% CI 0.9–15.8, P=0.07) for DDFS and breast cancer-specific survival (BCSS), respectively. In multivariate analysis adjusted for known prognostic factors and hormonal therapy, HRs were 5.8 (95% CI 1.3–26.7, P=0.03) and 4.7 (95% CI 1.0–21.7, P=0.05) for DDFS and BCSS, respectively.

Interpretation:

The 70-gene prognosis signature is an independent prognostic indicator that identifies a subgroup of HER-2-positive early breast cancer with a favourable long-term outcome.

Tumor gene expression profiling in women with breast cancer. Test category: prognostic

Differences in the expression of specific genes within breast tumors have been associated with risk of recurrence after treatment. Most women with Stage I or II node-negative breast cancer (especially when estrogen-receptor positive and treated with tamoxifen) remain disease-free at 10 years. Information on risk of recurrence could help identify women most likely to benefit from chemotherapy. Several clinically available gene expression profiles (GEP) provide “recurrence risk scores” that are intended to supplement information used by clinicians and patients in treatment decision-making.

Use of molecular markers for predicting therapy response in cancer patients

Predictive markers are factors that are associated with upfront response or resistance to a particular therapy. Predictive markers are important in oncology as tumors of the same tissue of origin vary widely in their response to most available systemic therapies. Currently recommended oncological predictive markers include both estrogen and progesterone receptors for identifying patients with breast cancers likely to benefit from hormone therapy, HER-2 for the identification of breast cancer patients likely to benefit from trastuzumab, specific K-RAS mutations for the identification of patients with advanced colorectal cancer unlikely to benefit from either cetuximab or panitumumab and specific EGFR mutations for selecting patients with advanced non-small-cell lung cancer for treatment with tyrosine kinase inhibitors such as gefitinib and erlotinib. The availability of predictive markers should increase drug efficacy and decrease toxicity, thus leading to a more personalized approach to cancer treatment.

PKA-induced phosphorylation of ERα at serine 305 and high PAK1 levels is associated with sensitivity to tamoxifen in ER-positive breast cancer

Phosphorylation of estrogen receptor α at serine 305 (ERαS305-P) by protein kinase A (PKA) or p21-activated kinase 1 (PAK1) has experimentally been associated with tamoxifen sensitivity. Here, we investigated the clinical application of this knowledge to predict tamoxifen resistance in ER-positive breast cancer patients. Using immunohistochemistry, a score including PAK1 and co-expression of PKA and ERαS305-P (PKA/ERαS305-P) was developed on a training set consisting of 103 patients treated with tamoxifen for metastatic disease, and validated on 231 patients randomized between adjuvant tamoxifen or no treatment. In the training set, PAK1 levels were associated with tumor progression after tamoxifen (HR 1.57, 95% CI 0.99-2.48), as was co-expression of PKA and ERαS305-P (HR 2.00, 95% CI 1.14-3.52). In the validation set, a significant tamoxifen benefit was found among the 73% patients negative for PAK1 and PKA/ERαS305-P (HR 0.54, 95% CI 0.34-0.87), while others (27%) were likely to have no benefit from tamoxifen (HR 0.88, 95% 0.42-1.82). The test for interaction showed a significant difference in recurrence-free survival between groups defined by PAK1 and PKA/ERαS305-P (P = 0.037). Elevated PAK1 and PKA/ERαS305-P appeared to influence tamoxifen sensitivity. Both PAK1 and PKA/ERαS305-P levels were associated with sensitivity to tamoxifen in breast tumors and the combination of these variables should be considered in predicting tamoxifen benefit.

Gene expression-based diagnosis of efficacy of chemotherapy for breast cancer

Development of a clear index to select drugs, i.e., accurate prediction of drug sensitivity, is important not only to obtain the maximum therapeutic effects of drugs, but also realize personalized medicine (tailor-made medicine). With the recent advancement in genome science represented by microarrays, molecular-level elucidation of many diseases including cancers has been progressing. It has been clarified that molecular information, such as gene expression profiles of cancer cells and gene polymorphisms in individual patients, affects not only cancer development and progression, but also therapeutic and adverse effects. The establishment of a therapeutic method by clinical application of this information has been progressing, in which the therapeutic effects of drugs are accurately predicted, and the maximum effects are obtained corresponding to cancer properties and patients' characteristics.

Prediction of hormone sensitivity for breast cancers

The classic action that leads to transcriptional activation of estrogen response genes mediated through estrogen receptors (ER) and the estrogen complex plays a pivotal role in the development of ER-positive breast cancers. In addition to this pathway, non-classic action and non-genomic action, both estrogen-dependent and estrogen-independent genomic actions have also been found to contribute to ER-positive tumor growth. Although the details of these mechanisms are not well known, participation of the growth factor signaling pathway is likely to be the most significant factor for acquisition of resistance to hormonal therapy. This resistance is mediated not only directly through cell growth promotion by growth factor signaling, but also through enhancement of alternative ER signaling pathways in addition to classic action. The reason why tamoxifen-insensitive ER-positive breast cancers respond to aromatase inhibitors may be explained, at least in part, by the different estrogen-related signaling pathways in which aromatase inhibitors may block estrogen signaling. In this paper we discuss the molecular mechanisms for resistance to hormonal therapy based on an understanding of estrogen signaling pathways.

Biological determinants of endocrine resistance in breast cancer

Endocrine therapies targeting oestrogen action (anti-oestrogens, such as tamoxifen, and aromatase inhibitors) decrease mortality from breast cancer, but their efficacy is limited by intrinsic and acquired therapeutic resistance. Candidate molecular biomarkers and gene expression signatures of tamoxifen response emphasize the importance of deregulation of proliferation and survival signalling in endocrine resistance. However, definition of the specific genetic lesions and molecular processes that determine clinical endocrine resistance is incomplete. The development of large-scale computational and genetic approaches offers the promise of identifying the mediators of endocrine resistance that may be exploited as potential therapeutic targets and biomarkers of response in the clinic.

Mammosphere-derived gene set predicts outcome in patients with ER-positive breast cancer

Tumourigenic subpopulations with stem cell-like features have been identified in breast tumours and breast cancer cell lines. The hormone receptor status, molecular characteristics and clinical significance of these cells are still matters of debate. Enrichment for tumourigenic cells without the requirement of surface markers can be achieved by the in vitro mammosphere culture assay. Here we compared the hormone receptor status and genome-wide gene expression profiles of mammospheres derived from four oestrogen-receptor (ER) positive breast cancer cell lines with those of the respective parental cells. Immunohistochemistry and gene expression profiling revealed a significant reduction in the expression of progesterone receptor, proliferation and cell cycle regulated genes in mammospheres when compared to parental cell lines. The 200 most differentially expressed genes between mammospheres and parental cell lines were used to generate a 'mammosphere-derived' gene set. Hierarchical clustering of gene expression profiles of two independent cohorts of primary ER-positive cancers based on the 'mammosphere-derived' gene set revealed that the subgroup of breast cancers with profiles similar to those of mammospheres has a significantly longer overall survival. In conclusion, tumour-initiating breast cancer cells grown in mammospheres seem to reside in a quiescent state. ER-positive breast cancers with expression profiles similar to those of mammospheres have a better outcome, providing evidence in support of the concept that outcome of patients with ER-positive disease is for a large part determined by cell cycle and proliferation activity.

DNA methylation of polycomb group target genes in cores taken from breast cancer centre and periphery

We previously demonstrated that methylation of neugogenic differentiation 1 (NEUROD1) gene, a polycomb group target (PCGT) gene is a predictor of response to neoadjuvant chemotherapy in breast cancer. Here, we address the question whether NEUROD1 methylation provides clinical information independent from its expression level, and whether PCGT methylation is homogeneous in breast cancer. We examined: (1) NEUROD1 methylation and mRNA expression in 9 breast cancer cell lines and 63 tumour specimens, (2) DNA methylation in a training set of 55 PCGT genes taken from the centre (TUC) and periphery (TUP) of 15 breast cancer specimens, and compared this with 22 non neoplastic controls, and finally, (3) validated statistically significant genes in an independent set of 20 cases versus 18 controls. 8/9 cell lines demonstrated NEUROD1 methylation, whereas, there was only one cell-line that showed NEUROD1 expression. There was no association between methylation and expression in breast tumour specimens, with only 14% exhibiting NEUROD1 expression. Of the 55 PCGT genes analysed, 24% (13/55) were shown to be cancer specific (p < 0.05) with a receiver-operating-characteristic (ROC) area-under-the-curve (AUC) of >0.7 (range 0.71-0.95). DNA methylation accurately predicted the presence of cancer in both TUC and TUP. DNA methylation of PCGT genes predicts the presence of breast cancer and is not subject to tumour heterogeneity. Further work will reveal if methylation of PCGT genes will serve as a robust means for the clinical detection and assessment of breast cancer.