Gene expression profiling of breast cancer

A neural network computational structure for the fractional order breast cancer model

The current study provides the numerical performances of the fractional kind of breast cancer (FKBC) model, which are based on five different classes including cancer stem cells, healthy cells, tumor cells, excess estrogen, and immune cells. The motive to introduce the fractional order derivatives is to present more precise solutions as compared to integer order. A stochastic computing reliable scheme based on the Levenberg Marquardt backpropagation neural networks (LMBNNS) is proposed to solve three different cases of the fractional order values of the FKBC model. A designed dataset is constructed by using the Adam solver in order to reduce the mean square error by taking the data performances as 9% for both testing and validation, while 82% is used for training. The correctness of the solver is approved through the negligible absolute error and matching of the solutions for each model's case. To validates the accuracy, and consistency of the solver, the performances based on the error histogram, transition state, and regression for solving the FKBC model.

Molecular Classification of Breast Cancer: Relevance and Challenges

CONTEXT.—

Appropriate patient management requires precise and meaningful tumor classification. Breast cancer classification continues to evolve from traditional morphologic evaluation to more sophisticated systems with the integration of new knowledge from research being translated into practice. Breast cancer is heterogeneous at the molecular level, with diversified patterns of gene expression, which is presumably responsible for the difference in tumor behavior and prognosis. Since the beginning of this century, new molecular technology has been gradually applied to breast cancer research on issues pertinent to prognosis (prognostic signature) and therapeutic prediction (predictive signature), and much progress has been made.

OBJECTIVE.—

To summarize the current state and the prospective future of molecular classification of breast cancer.

DATA SOURCES.—

Sources include recent medical literature on molecular classification of breast cancer.

CONCLUSIONS.—

Identification of intrinsic tumor subtypes has set a foundation for refining the breast cancer molecular classification. Studies have explored the genetic features within the intrinsic cancer subtypes and have identified novel molecular targets that led to the innovation of clinical assays to predict a patient's prognosis and to provide specific guidelines for therapeutic decisions. With the development and implication of these molecular tools, we have remarkably advanced our knowledge and enhanced our power to provide optimal management to patients. However, challenges still exist. Besides accurate prediction of prognosis, we are still in urgent need of more molecular predictors for tumor response to therapeutic regimes. Further exploration along this path will be critical for improving a patient's prognosis.

Pathways Involved in Remyelination after Cerebral Ischemia

Brain ischemia, also known as ischemic stroke, occurs when there is a lack of blood supply into the brain. When an ischemic insult appears, both neurons and glial cells can react in several ways that will determine the severity and prognosis. This high heterogeneity of responses has been a major obstacle in developing effective treatments or preventive methods for stroke. Although white matter pathophysiology has not been deeply assessed in stroke, its remodelling can greatly influence the clinical outcome and the disability degree. Oligodendrocytes, the unique cell type implied in CNS myelination, are sensible to ischemic damage. Loss of myelin sheaths can compromise axon survival, so new Oligodendrocyte Precursor Cells are required to restore brain function. Stroke can, therefore, enhance oligodendrogenesis to regenerate those new oligodendrocytes that will ensheath the damaged axons. Given that myelination is a highly complex process that requires coordination of multiple pathways such as Sonic Hedgehog, RTKs or Wnt/β-catenin, we will analyse new research highlighting their importance after brain ischemia. In addition, oligodendrocytes are not isolated cells inside the brain, but rather form part of a dynamic environment of interactions between neurons and glial cells. For this reason, we will put some context into how microglia and astrocytes react against stroke and influence oligodendrogenesis to highlight the relevance of remyelination in the ischemic brain. This will help to guide future studies to develop treatments focused on potentiating the ability of the brain to repair the damage.

Blood Circulating Non-Coding RNAs for the Clinical Management of Triple-Negative Breast Cancer

Triple negative breast cancer (TNBC) represents the most aggressive subtype of breast cancer, and is related to unfavorable prognosis and limited treatment strategies. Currently, there is a lack of reliable biomarkers allowing for the clinical management of TNBC. This is probably caused by a complex molecular background, leading to the development and establishment of a unique tumor phenotype. Recent studies have reported non-coding RNAs (ncRNAs) not only as the most promising class of molecular agents with a high applicability to manage human cancers, including TNBC, but also as robust and non-invasive biomarkers that are able to be monitored in blood circulation, with the application of liquid biopsy. There is a lack of papers discussing the role of blood-circulating ncRNAs as diagnostic, predictive, and prognostic biomarkers for TNBC. In this paper, we summarized the available literature reports on the utility of blood-circulating ncRNAs for TNBC management. Additionally, we supplemented this review by bioinformatics analysis, for better understanding of the role of ncRNAs' machinery in the development of a unique TNBC phenotype.

A Clinicogenetic Prognostic Classifier for Prediction of Recurrence and Survival in Asian Breast Cancer Patients

Background

Several prognostic factors affect the recurrence of breast cancer in patients who undergo mastectomy. Assays of the expression profiles of multiple genes increase the probability of overexpression of certain genes and thus can potentially characterize the risk of metastasis.

Methods

We propose a 20-gene classifier for predicting patients with high/low risk of recurrence within 5 years. Gene expression levels from a quantitative PCR assay were used to screen 473 luminal breast cancer patients treated at Taiwan Hospital (positive for estrogen and progesterone receptors, negative for human epidermal growth factor receptor 2). Gene expression scores, along with clinical information (age, tumor stage, and nodal stage), were evaluated for risk prediction. The classifier could correctly predict patients with and without relapse (logistic regression, P<0.05).

Results

A Cox proportional hazards regression analysis showed that the 20-gene panel was prognostic with hazard ratios of 5.63 (95% confidence interval 2.77-11.5, univariate) and 5.56 (2.62-11.8, multivariate) for the “genetic” model, and of 8.02 (3.52-18.3, univariate) and 19.8 (5.96-65.87, multivariate) for the “clinicogenetic” model during a 5-year follow-up.

Conclusions

The proposed 20-gene classifier can successfully separate the patients into two risk groups, and the two risk group had significantly different relapse rate and prognosis. This 20-gene classifier can provide better estimation of prognosis, which can help physicians to make better personalized treatment plans.

Entropy-driven amplification strategy-assisted lateral flow assay biosensor for ultrasensitive and convenient detection of nucleic acids

Accurate, sensitive and rapid nucleic acid tests are important to implement timely treatment measures and control the spread of disease. Herein, we developed a novel portable platform for highly sensitive and specific detection of nucleic acids by integrating an entropy-driven amplification strategy into lateral flow assay (LFA) biosensor. We find that introducing an entropy-driven amplification strategy yields bright intensities on the test line of LFA stirp, which results in improved sensitivity for targeted nucleic acid detection. The developed LFA biosensor showed good reproducibility, specificity and sensitivity for target DNA and H1N1-RNA detection with a low detection limit of 1.43 pM and 2.02 pM, respectively. Its practical potential was also verified by detecting the target nucleic acid in human serum. More importantly, the design of an entropy-driven amplification strategy in this portable platform retained the convenient, rapid and low-cost characterizations of LFA biosensor due to the compact amplification principle and the elimination of enzyme use. Thus, we believe that this assay biosensor will certainly report its own position in the timely detection of nucleic acid, especially when the medical environment and resources are fewer.

Gene expression signatures: A tool for analysis of breast cancer prognosis and therapy

Breast Cancer is the most predominant female cancer in developed as well as developing countries. The treatment strategies of breast cancers depends on an array of factors like age at diagnosis, menstrual status, dietary pattern, immunological response, genetic variations of the cancer cells etc. Recent technological advancements in cancer diagnosis lead to the emergence of gene expression pattern for better understanding of the tumor behavior. It has not only bolstered the prognosis, but also the early diagnosis and therapy. The accuracy in disease prognosis can be boosted when gene expression signatures are combined with traditional clinicopathological features. This review explains how the evolution of gene expression signatures for breast cancers, its advantages and future prospects. In addition, an overview of currently available gene expression signature analysis tools and consolidated information on their current status and specific benefits, that can be availed for breast cancer diagnosis are also discussed.

IGF1R constitutive activation expands luminal progenitors and influences lineage differentiation during breast tumorigenesis

Breast tumors display tremendous heterogeneity in part due to varying molecular alterations, divergent cells of origin, and differentiation. Understanding where and how this heterogeneity develops is likely important for effective breast cancer eradication. Insulin-like growth factor (IGF) signaling is critical for normal mammary gland development and function, and has an established role in tumor development and resistance to therapy. Here we demonstrate that constitutive activation of the IGF1 receptor (IGF1R) influences lineage differentiation during mammary tumorigenesis. Transgenic IGF1R constitutive activation promotes tumors with mixed histologies, multiple cell lineages and an expanded bi-progenitor population. In these tumors, IGF1R expands the luminal-progenitor population while influencing myoepithelial differentiation. Mammary gland transplantation with IGF1R-infected mammary epithelial cells (MECs) resulted in hyperplastic, highly differentiated outgrowths and attenuated reconstitution. Restricting IGF1R constitutive activation to luminal versus myoepithelial lineage-sorted MECs resulted in ductal reconstitutions co-expressing high IGF1R levels in the opposite lineage of origin. Using in vitro models, IGF1R constitutively activated MCF10A cells showed increased mammosphere formation and CD44+/CD24-population, which was dependent upon Snail and NFκB signaling. These results suggest that IGF1R expands luminal progenitor populations while also stimulating myoepithelial cell differentiation. This ability to influence lineage differentiation may promote heterogeneous mammary tumors, and have implications for clinical treatment.

Influencing the Tumor Microenvironment: A Phase II Study of Copper Depletion Using Tetrathiomolybdate in Patients with Breast Cancer at High Risk for Recurrence and in Preclinical Models of Lung Metastases

PURPOSE

Bone marrow-derived progenitor cells, including VEGFR2⁺ endothelial progenitor cells (EPCs) and copper-dependent pathways, model the tumor microenvironment. We hypothesized that copper depletion using tetrathiomolybdate would reduce EPCs in high risk for patients with breast cancer who have relapsed. We investigated the effect of tetrathiomolybdate on the tumor microenvironment in preclinical models.

EXPERIMENTAL DESIGN

Patients with stage II triple-negative breast cancer (TNBC), stage III and stage IV without any evidence of disease (NED), received oral tetrathiomolybdate to maintain ceruloplasmin (Cp) between 8 and 17 mg/dL for 2 years or until relapse. Endpoints were effect on EPCs and other biomarkers, safety, event-free (EFS), and overall survival (OS). For laboratory studies, MDA-LM2-luciferase cells were implanted into CB17-SCID mice and treated with tetrathiomolybdate or water. Tumor progression was quantified by bioluminescence imaging (BLI), copper depletion status by Cp oxidase levels, lysyl oxidase (LOX) activity by ELISA, and collagen deposition.

RESULTS

Seventy-five patients enrolled; 51 patients completed 2 years (1,396 cycles). Most common grade 3/4 toxicity was neutropenia (3.7%). Lower Cp levels correlated with reduced EPCs (P = 0.002) and LOXL-2 (P < 0.001). Two-year EFS for patients with stage II-III and stage IV NED was 91% and 67%, respectively. For patients with TNBC, EFS was 90% (adjuvant patients) and 69% (stage IV NED patients) at a median follow-up of 6.3 years, respectively. In preclinical models, tetrathiomolybdate decreased metastases to lungs (P = 0.04), LOX activity (P = 0.03), and collagen crosslinking (P = 0.012).

CONCLUSIONS

Tetrathiomolybdate is safe, well tolerated, and affects copper-dependent components of the tumor microenvironment. Biomarker-driven clinical trials in high risk for patients with recurrent breast cancer are warranted. Clin Cancer Res; 23(3); 666-76. ©2016 AACR.

Automated Ex Situ Assays of Amyloid Formation on a Microfluidic Platform

Increasingly prevalent neurodegenerative diseases are associated with the formation of nanoscale amyloid aggregates from normally soluble peptides and proteins. A widely used strategy for following the aggregation process and defining its kinetics involves the use of extrinsic dyes that undergo a spectral shift when bound to β-sheet-rich aggregates. An attractive route to carry out such studies is to perform ex situ assays, where the dye molecules are not present in the reaction mixture, but instead are only introduced into aliquots taken from the reaction at regular time intervals to avoid the possibility that the dye molecules interfere with the aggregation process. However, such ex situ measurements are time-consuming to perform, require large sample volumes, and do not provide for real-time observation of aggregation phenomena. To overcome these limitations, here we have designed and fabricated microfluidic devices that offer continuous and automated real-time ex situ tracking of the protein aggregation process. This device allows us to improve the time resolution of ex situ aggregation assays relative to conventional assays by more than one order of magnitude. The availability of an automated system for tracking the progress of protein aggregation reactions without the presence of marker molecules in the reaction mixtures opens up the possibility of routine noninvasive study of protein aggregation phenomena.

Decoding the usefulness of non-coding RNAs as breast cancer markers

Although important advances in the management of breast cancer (BC) have been recently accomplished, it still constitutes the leading cause of cancer death in women worldwide. BC is a heterogeneous and complex disease, making clinical prediction of outcome a very challenging task. In recent years, gene expression profiling emerged as a tool to assist in clinical decision, enabling the identification of genetic signatures that better predict prognosis and response to therapy. Nevertheless, translation to routine practice has been limited by economical and technical reasons and, thus, novel biomarkers, especially those requiring non-invasive or minimally invasive collection procedures, while retaining high sensitivity and specificity might represent a significant development in this field. An increasing amount of evidence demonstrates that non-coding RNAs (ncRNAs), particularly microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), are aberrantly expressed in several cancers, including BC. miRNAs are of particular interest as new, easily accessible, cost-effective and non-invasive tools for precise management of BC patients because they circulate in bodily fluids (e.g., serum and plasma) in a very stable manner, enabling BC assessment and monitoring through liquid biopsies. This review focus on how ncRNAs have the potential to answer present clinical needs in the personalized management of patients with BC and comprehensively describes the state of the art on the role of ncRNAs in the diagnosis, prognosis and prediction of response to therapy in BC.

Smyd3-associated regulatory pathways in cancer

SMYD3 is a member of the SET and MYND-domain family of methyl-transferases, the increased expression of which correlates with poor prognosis in various types of cancer. In liver and colon tumors, SMYD3 is localized in the nucleus, where it interacts with RNA Pol II and H3K4me3 and functions as a selective transcriptional amplifier of oncogenes and genes that control cell proliferation and metastatic spread. Smyd3 expression has a high discriminative power for the characterization of liver tumors and positively correlates with poor prognosis. In lung and pancreatic cancer, SMYD3 acts in the cytoplasm, potentiating oncogenic Ras/ERK signaling through the methylation of the MAP3K2 kinase and the subsequent release from its inhibitor. A clinico-pathological analysis of lung cancer patients uncovers prognostic significance of SMYD3 only for first progression survival. However, stratification of patients according to their smoking history significantly expands the prognostic value of SMYD3 to overall survival and other features, suggesting that smoking-related effects saturate the clinical analysis and mask the function of SMYD3 as an oncogenic potentiator.

Identification of a Low-Risk Luminal A Breast Cancer Cohort That May Not Benefit From Breast Radiotherapy

PURPOSE

To determine the prognostic and predictive value of intrinsic subtyping by using immunohistochemical (IHC) biomarkers for ipsilateral breast relapse (IBR) in participants in an early breast cancer randomized trial of tamoxifen with or without breast radiotherapy (RT).

PATIENTS AND METHODS

IHC analysis of estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2 (HER2), cytokeratin 5/6, epidermal growth factor receptor, and Ki-67 was conducted on 501 of 769 available blocks. Patients were classified as luminal A (n = 265), luminal B (n = 165), or high-risk subtype (luminal HER2, n = 22; HER2 enriched, n = 13; basal like, n = 30; or triple-negative nonbasal, n = 6). Median follow-up was 10 years.

RESULTS

Classification by subtype was prognostic for IBR (10-year estimates: luminal A, 5.2%; luminal B, 10.5%; high-risk subtypes, 21.3%; P < .001). Luminal subtypes seemed to derive less benefit from RT (luminal A hazard ratio [HR], 0.40; luminal B HR, 0.51) than high-risk subtypes (HR, 0.13); however, the overall subtype-treatment interaction term was not significant (P = .26). In an exploratory analysis of women with clinical low-risk (age older than 60 years, T1, grade 1 or 2) luminal A tumors (n = 151), 10-year IBR was 3.1% versus 11.8% for the high-risk cohort (n = 341; P = .0063). Clinical low-risk luminal A patients had a 10-year IBR of 1.3% with tamoxifen versus 5.0% with tamoxifen plus RT (P = .42). Multivariable analysis showed that RT (HR, 0.31; P < .001), clinical risk group (HR, 2.2; P = .025), and luminal A subtype (HR, 0.25; P < .001) were significantly associated with IBR.

CONCLUSION

IHC subtyping was prognostic for IBR but was not predictive of benefit from RT. Further studies may validate the exploratory finding of a low-risk luminal A group who may be spared breast RT.

Role of IGF1R in Breast Cancer Subtypes, Stemness, and Lineage Differentiation

Insulin-like growth factor (IGF) signaling is fundamental for growth and survival. A large body of evidence (laboratory, epidemiological, and clinical) implicates the exploitation of this pathway in cancer. Up to 50% of breast tumors express the activated form of the type 1 insulin-like growth factor receptor (IGF1R). Breast cancers are categorized into subtypes based upon hormone and ERRB2 receptor expression and/or gene expression profiling. Even though IGF1R influences tumorigenic phenotypes and drug resistance across all breast cancer subtypes, it has specific expression and function in each. In some subtypes, IGF1R levels correlate with a favorable prognosis, while in others it is associated with recurrence and poor prognosis, suggesting different actions based upon cellular and molecular contexts. In this review, we examine IGF1R expression and function as it relates to breast cancer subtype and therapy-acquired resistance. Additionally, we discuss the role of IGF1R in stem cell maintenance and lineage differentiation and how these cell fate influences may alter the differentiation potential and cellular composition of breast tumors.

Measures for the degree of overlap of gene signatures and applications to TCGA

For cancer and many other complex diseases, a large number of gene signatures have been generated. In this study, we use cancer as an example and note that other diseases can be analyzed in a similar manner. For signatures generated in multiple independent studies on the same cancer type and outcome, and for signatures on different cancer types, it is of interest to evaluate their degree of overlap. Many of the existing studies simply count the number (or percentage) of overlapped genes shared by two signatures. Such an approach has serious limitations. In this study, as a demonstrating example, we consider cancer prognosis data under the Cox model. Lasso, which is representative of a large number of regularization methods, is adopted for generating gene signatures. We examine two families of measures for quantifying the degree of overlap. The first family is based on the Cox-Lasso estimates at the optimal tunings, and the second family is based on estimates across the whole solution paths. Within each family, multiple measures, which describe the overlap from different perspectives, are introduced. The analysis of TCGA (The Cancer Genome Atlas) data on five cancer types shows that the degree of overlap varies across measures, cancer types and types of (epi)genetic measurements. More investigations are needed to better describe and understand the overlaps among gene signatures.

Nuclear CSPP1 expression defined subtypes of basal-like breast cancer

BACKGROUND

The multi-exon CSPP1 gene, encoding for centrosome and microtubule-associated proteins involved in ciliogenesis and cell division, is a candidate oncogene in luminal breast cancer but expression of CSPP1 proteins remained unexplored.

METHODS

CSPP1 gene and protein expression was examined in normal mammary tissue, human breast cancer cell lines, and primary breast cancer biopsies from two patient cohorts. Cell type and epitope-dependent subcellular-specific CSPP1 staining pattern in normal mammary gland epithelium and cancer biopsies were correlated to molecular and clinical parameters.

RESULTS

A novel, nuclear localised CSPP1 isoform was exclusively detected in luminal epithelial cells, whereas cytoplasmic CSPP-L was generally expressed in normal mammary epithelium. Luminal cell-related nuclear CSPP1 expression was preserved in type-matched cell lines and carcinomas, and correlated to gene copy number and mRNA expression. In contrast, basal-like carcinomas displayed generally lower CSPP1 mRNA expression. Yet, a subgroup of basal-like breast carcinomas depicted nuclear CSPP1 expression, displayed luminal traits, and differed from nuclear CSPP1 devoid counterparts in expression of eight genes. Eight-gene signature defined groups of basal-like tumours from an independent cohort showed significant differences in survival.

CONCLUSIONS

Differential expression of a nuclear CSPP1 isoform identified biologically and clinically distinct subgroups of basal-like breast carcinoma.

Sparse group penalized integrative analysis of multiple cancer prognosis datasets

In cancer research, high-throughput profiling studies have been extensively conducted, searching for markers associated with prognosis. Owing to the 'large d, small n' characteristic, results generated from the analysis of a single dataset can be unsatisfactory. Recent studies have shown that integrative analysis, which simultaneously analyses multiple datasets, can be more effective than single-dataset analysis and classic meta-analysis. In most of existing integrative analysis, the homogeneity model has been assumed, which postulates that different datasets share the same set of markers. Several approaches have been designed to reinforce this assumption. In practice, different datasets may differ in terms of patient selection criteria, profiling techniques, and many other aspects. Such differences may make the homogeneity model too restricted. In this study, we assume the heterogeneity model, under which different datasets are allowed to have different sets of markers. With multiple cancer prognosis datasets, we adopt the accelerated failure time model to describe survival. This model may have the lowest computational cost among popular semiparametric survival models. For marker selection, we adopt a sparse group minimax concave penalty approach. This approach has an intuitive formulation and can be computed using an effective group coordinate descent algorithm. Simulation study shows that it outperforms the existing approaches under both the homogeneity and heterogeneity models. Data analysis further demonstrates the merit of heterogeneity model and proposed approach.

Application of microarray in breast cancer: An overview

There are more than 1.15 million cases of breast cancer diagnosed worldwide annually. At present, only small numbers of accurate prognostic and predictive factors are used clinically for managing the patients with breast cancer. DNA microarrays have the potential to assess the expression of thousands of genes simultaneously. Recent preliminary researches indicate that gene expression profiling based on DNA microarray can offer potential and independent prognostic information in patients with newly diagnosed breast cancer. In this paper, an overview upon the applications of microarray techniques in breast cancer is presented.

Risk assessment, disease prevention and personalised treatments in breast cancer: is clinically qualified integrative approach in the horizon?

Breast cancer is a multifactorial disease. A spectrum of internal and external factors contributes to the disease promotion such as a genetic predisposition, chronic inflammatory processes, exposure to toxic compounds, abundant stress factors, a shift-worker job, etc. The cumulative effects lead to high incidence of breast cancer in populations worldwide. Breast cancer in the USA is currently registered with the highest incidence rates amongst all cancer related patient cohorts. Currently applied diagnostic approaches are frequently unable to recognise early stages in tumour development that impairs individual outcomes. Early diagnosis has been demonstrated to be highly beneficial for significantly enhanced therapy efficacy and possibly full recovery. Actual paper shows that the elaboration of an integrative diagnostic approach combining several levels of examinations creates a robust platform for the reliable risk assessment, targeted preventive measures and more effective treatments tailored to the person in the overall task of breast cancer management. The levels of examinations are proposed, and innovative technological approaches are described in the paper. The absolute necessity to create individual patient profiles and extended medical records is justified for the utilising by routine medical services. Expert recommendations are provided to promote further developments in the field.

Biclustering reveals breast cancer tumour subgroups with common clinical features and improves prediction of disease recurrence

BACKGROUND

Many studies have revealed correlations between breast tumour phenotypes, variations in gene expression, and patient survival outcomes. The molecular heterogeneity between breast tumours revealed by these studies has allowed prediction of prognosis and has underpinned stratified therapy, where groups of patients with particular tumour types receive specific treatments. The molecular tests used to predict prognosis and stratify treatment usually utilise fixed sets of genomic biomarkers, with the same biomarker sets being used to test all patients. In this paper we suggest that instead of fixed sets of genomic biomarkers, it may be more effective to use a stratified biomarker approach, where optimal biomarker sets are automatically chosen for particular patient groups, analogous to the choice of optimal treatments for groups of similar patients in stratified therapy. We illustrate the effectiveness of a biclustering approach to select optimal gene sets for determining the prognosis of specific strata of patients, based on potentially overlapping, non-discrete molecular characteristics of tumours.

RESULTS

Biclustering identified tightly co-expressed gene sets in the tumours of restricted subgroups of breast cancer patients. The co-expressed genes in these biclusters were significantly enriched for particular biological annotations and gene regulatory modules associated with breast cancer biology. Tumours identified within the same bicluster were more likely to present with similar clinical features. Bicluster membership combined with clinical information could predict patient prognosis in conditional inference tree and ridge regression class prediction models.

CONCLUSIONS

The increasing clinical use of genomic profiling demands identification of more effective methods to segregate patients into prognostic and treatment groups. We have shown that biclustering can be used to select optimal gene sets for determining the prognosis of specific strata of patients.

Tissue biomarkers of breast cancer and their association with conventional pathologic features

Background:

Tissue protein expression profiling has the potential to detect new biomarkers to improve breast cancer (BC) diagnosis, staging, and prognostication. This study aimed to identify tissue proteins that differentiate breast cancer tissue from healthy breast tissue using protein chip mass spectrometry and to examine associations with conventional pathological features.

Methods:

To develop a training model, 82 BC and 82 adjacent unaffected tissue (AT) samples were analysed on cation-exchange protein chips by time-of-flight mass spectrometry. For validation, 89 independent BC and AT sample pairs were analysed.

Results:

From the protein peaks that were differentially expressed between BC and AT by univariate analysis, binary logistic regression yielded two peaks that together classified BC and AT with a ROC area under the curve of 0.92. Two proteins, ubiquitin and S100P (in a novel truncated form), were identified by liquid chromatography/tandem mass spectrometry and validated by immunoblotting and reactive-surface protein chip immunocapture. The combined marker panel was positively associated with high histologic grade, larger tumour size, lymphovascular invasion, ER and PR positivity, and HER2 overexpression, suggesting that it may be associated with a HER2-enriched molecular subtype of breast cancer.

Conclusion:

This independently validated protein panel may be valuable in the classification and prognostication of breast cancer patients.

Evaluation of PDE5 and PDE9 expression in benign and malignant breast tumors

BACKGROUND AND AIMS

Phosphodiesterases 5 and 9 (PDE5, PDE9) are enzymes responsible for regulating second messenger signaling by hydrolyzing 3',5' cyclic guanosine monophosphate (cGMP). PDE isoforms are deregulated in some types of human cancer. The present study was carried out to evaluate the expression of phosphodiesterase isoenzymes, PDE5 and PDE9, in benign and malignant breast tumors.

METHODS

The expression levels of PDE5 and PDE9 were assayed in malignant and benign breast tumors and corresponding normal breast tissues using quantitative real-time RT-PCR. Moreover, the correlation between PDE5, PDE9 relative expression and clinicopathological characteristics were analyzed.

RESULTS

The relative expressions of PDE5 and PDE9 in malignant tumors were significantly higher than those of respective normal breast tissues and benign tumors (5.5-fold, p <0.001 and 6-fold, p <0.001, respectively). Furthermore, a significant positive correlation was found between PDE5 and PDE9 overexpression and tumor grade, stage, and lymph node involvement. However, a negative correlation was observed with age.

CONCLUSIONS

Based on the present results, it is concluded that assessment of PDE5 and PDE9 expression may be useful in the differential diagnosis of benign and malignant breast disease and successful treatment of breast cancer. To the best of our knowledge, this is the first study to show that PDE5 and PDE9 expression levels are higher in malignant breast tumors than those of normal and benign breast tissue.

Identification of Breast Cancer Prognosis Markers via Integrative Analysis

In breast cancer research, it is of great interest to identify genomic markers associated with prognosis. Multiple gene profiling studies have been conducted for such a purpose. Genomic markers identified from the analysis of single datasets often do not have satisfactory reproducibility. Among the multiple possible reasons, the most important one is the small sample sizes of individual studies. A cost-effective solution is to pool data from multiple comparable studies and conduct integrative analysis. In this study, we collect four breast cancer prognosis studies with gene expression measurements. We describe the relationship between prognosis and gene expressions using the accelerated failure time (AFT) models. We adopt a 2-norm group bridge penalization approach for marker identification. This integrative analysis approach can effectively identify markers with consistent effects across multiple datasets and naturally accommodate the heterogeneity among studies. Statistical and simulation studies demonstrate satisfactory performance of this approach. Breast cancer prognosis markers identified using this approach have sound biological implications and satisfactory prediction performance.

Identification of breast cancer prognosis markers using integrative sparse boosting

OBJECTIVES

In breast cancer research, it is important to identify genomic markers associated with prognosis. Multiple microarray gene expression profiling studies have been conducted, searching for prognosis markers. Genomic markers identified from the analysis of single datasets often suffer a lack of reproducibility because of small sample sizes. Integrative analysis of data from multiple independent studies has a larger sample size and may provide a cost-effective solution.

METHODS

We collect four breast cancer prognosis studies with gene expression measurements. An accelerated failure time (AFT) model with an unknown error distribution is adopted to describe survival. An integrative sparse boosting approach is employed for marker selection. The proposed model and boosting approach can effectively accommodate heterogeneity across multiple studies and identify genes with consistent effects.

RESULTS

Simulation study shows that the proposed approach outperforms alternatives including meta-analysis and intensity approaches by identifying the majority or all of the true positives, while having a low false positive rate. In the analysis of breast cancer data, 44 genes are identified as associated with prognosis. Many of the identified genes have been previously suggested as associated with tumorigenesis and cancer prognosis. The identified genes and corresponding predicted risk scores differ from those using alternative approaches. Monte Carlo-based prediction evaluation suggests that the proposed approach has the best prediction performance.

CONCLUSIONS

Integrative analysis may provide an effective way of identifying breast cancer prognosis markers. Markers identified using the integrative sparse boosting analysis have sound biological implications and satisfactory prediction performance.

Performance comparison of multiple microarray platforms for gene expression profiling

With genome-wide gene expression microarrays being increasingly applied in various areas of biomedical research, the diversity of platforms and analytical methods has made comparison of data from multiple platforms very challenging. In this chapter, we describe a generalized framework for systematic comparisons across gene expression profiling platforms, which could accommodate both the available commercial arrays and "in-house" platforms, with both one-dye and two-dye platforms. It includes experimental design, data preprocessing protocols, cross-platform gene matching approaches, measures of data consistency comparisons, and considerations in biological validation. In the design of this framework, we considered the variety of platforms available, the need for uniform quality control procedures, real-world practical limitations, statistical validity, and the need for flexibility and extensibility of the framework. Using this framework, we studied ten diverse microarray platforms, and we conclude that using probe sequences matched at the exon level is important to improve cross-platform data consistency compared to annotation-based matches. Generally, consistency was good for highly expressed genes, and variable for genes with lower expression values, as confirmed by QRT-PCR. After stringent preprocessing, commercial arrays were more consistent than "in-house" arrays, and by most measures, one-dye platforms were more consistent than two-dye platforms.

Incremental increase in VEGFR1⁺ hematopoietic progenitor cells and VEGFR2⁺ endothelial progenitor cells predicts relapse and lack of tumor response in breast cancer patients

Animal models have demonstrated the critical role of bone marrow-derived VEGFR1(+) hematopoietic progenitor cells (HPCs) and VEGFR2(+) endothelial progenitor cells (EPCs) in metastatic progression. We explored whether these cells could predict relapse and response in breast cancer (BC) patients. One hundred and thirty-two patients with stages 1-4 BC were enrolled on 2 studies. Circulating CD45(+)/CD34(+)/VEGFR1(+) HPCs and CD45(dim)/CD133(+)/VEGFR2(+) EPCs were assessed from peripheral blood mononuclear cells using flow cytometry. Changes in HPCs and EPCs were analyzed in (1) patients without overt disease that relapsed and (2) metastatic patients according to response by RECIST. At study entry, 102 patients were without evidence of disease and 30 patients had metastatic BC. Seven patients without evidence of BC by exam, labs, and imaging developed recurrence while on study. Median HPC/ml (range) increased from 645.8 (23.5-1,914) to 2,899 (1,176-37,336), P = 0.016, followed by an increase in median EPC/ml from 21.3 (4.7-42.5) to 94.7 (28.2-201.3), P = 0.016, prior to clinical relapse. In metastatic patients with progressive disease, median HPC/ml increased from 1,696 (10-16,470) to 5,124 (374-77,605), P = 0.0009, and median EPC/ml increased from 26 (0-560) to 71 (0-615) prior to progression, P = 0.10. In patients with responding disease, median HPC/ml decreased from 6,147 (912-85,070) to 633 (47-18,065), P = 0.05, and EPC/ml decreased from 46 (0-197) to 23 (0-105), P = 0.41, at response. There were no significant changes in these cells over time in patients with stable disease. Circulating bone marrow-derived HPCs and EPCs predict relapse and disease progression in BC patients.

Integrative analysis of multiple cancer prognosis studies with gene expression measurements

Although in cancer research microarray gene profiling studies have been successful in identifying genetic variants predisposing to the development and progression of cancer, the identified markers from analysis of single datasets often suffer low reproducibility. Among multiple possible causes, the most important one is the small sample size hence the lack of power of single studies. Integrative analysis jointly considers multiple heterogeneous studies, has a significantly larger sample size, and can improve reproducibility. In this article, we focus on cancer prognosis studies, where the response variables are progression-free, overall, or other types of survival. A group minimax concave penalty (GMCP) penalized integrative analysis approach is proposed for analyzing multiple heterogeneous cancer prognosis studies with microarray gene expression measurements. An efficient group coordinate descent algorithm is developed. The GMCP can automatically accommodate the heterogeneity across multiple datasets, and the identified markers have consistent effects across multiple studies. Simulation studies show that the GMCP provides significantly improved selection results as compared with the existing meta-analysis approaches, intensity approaches, and group Lasso penalized integrative analysis. We apply the GMCP to four microarray studies and identify genes associated with the prognosis of breast cancer.

Prognostic value of an RNA expression signature derived from cell cycle proliferation genes in patients with prostate cancer: a retrospective study

BACKGROUND

Optimum management of clinically localised prostate cancer presents unique challenges because of the highly variable and often indolent natural history of the disease. To predict disease aggressiveness, clinicians combine clinical variables to create prognostic models, but the models have limited accuracy. We assessed the prognostic value of a predefined cell cycle progression (CCP) score in two cohorts of patients with prostate cancer.

METHODS

We measured the expression of 31 genes involved in CCP with quantitative RT-PCR on RNA extracted from formalin-fixed paraffin-embedded tumour samples, and created a predefined score and assessed its usefulness in the prediction of disease outcome. The signature was assessed retrospectively in a cohort of patients from the USA who had undergone radical prostatectomy, and in a cohort of randomly selected men with clinically localised prostate cancer diagnosed by use of a transurethral resection of the prostate (TURP) in the UK who were managed conservatively. The primary endpoint was time to biochemical recurrence for the cohort of patients who had radical prostatectomy, and time to death from prostate cancer for the TURP cohort.

FINDINGS

After prostatectomy, the CCP score was useful for predicting biochemical recurrence in the univariate analysis (hazard ratio for a 1-unit change [doubling] in CCP 1·89; 95% CI 1·54-2·31; p=5·6×10(-9)) and the best multivariate analysis (1·77, 1·40-2·22; p=4·3×10(-6)). In the best predictive model (final multivariate analysis), the CCP score and prostate-specific antigen (PSA) concentration were the most important variables and were more significant than any other clinical variable. In the TURP cohort, the CCP score was the most important variable for prediction of time to death from prostate cancer in both univariate analysis (2·92, 2·38-3·57, p=6·1×10(-22)) and the final multivariate analysis (2·57, 1·93-3·43; p=8·2×10(-11)), and was stronger than all other prognostic factors, although PSA concentration also added useful information. Heterogeneity in the hazard ratio for the CCP score was not noted in any case for any clinical variables.

INTERPRETATION

The results of this study provide strong evidence that the CCP score is a robust prognostic marker, which, after additional validation, could have an essential role in determining the appropriate treatment for patients with prostate cancer.

FUNDING

Cancer Research UK, Queen Mary University of London, Orchid Appeal, US National Institutes of Health, and Koch Foundation.

A disease annotation study of gene signatures in a breast cancer microarray dataset

Breast cancer is a complex disease with heterogeneity between patients regarding prognosis and treatment response. Recent progress in advanced molecular biology techniques and the development of efficient methods for database mining lead to the discovery of promising novel biomarkers for prognosis and prediction of breast cancer. In this paper, we applied three computational algorithms (RFE-LNW, Lasso and FSMLP) to one microarray dataset for breast cancer and compared the obtained gene signatures with a recently described disease-agnostic tool, the Genotator. We identified a panel of 152 genes as a potential prognostic signature and the ERRFI1 gene as possible biomarker of breast cancer disease.

Discovery and preclinical validation of salivary transcriptomic and proteomic biomarkers for the non-invasive detection of breast cancer

Background

A sensitive assay to identify biomarkers using non-invasively collected clinical specimens is ideal for breast cancer detection. While there are other studies showing disease biomarkers in saliva for breast cancer, our study tests the hypothesis that there are breast cancer discriminatory biomarkers in saliva using de novo discovery and validation approaches. This is the first study of this kind and no other study has engaged a de novo biomarker discovery approach in saliva for breast cancer detection. In this study, a case-control discovery and independent preclinical validations were conducted to evaluate the performance and translational utilities of salivary transcriptomic and proteomic biomarkers for breast cancer detection.

Methodology/Principal Findings

Salivary transcriptomes and proteomes of 10 breast cancer patients and 10 matched controls were profiled using Affymetrix HG-U133-Plus-2.0 Array and two-dimensional difference gel electrophoresis (2D-DIGE), respectively. Preclinical validations were performed to evaluate the discovered biomarkers in an independent sample cohort of 30 breast cancer patients and 63 controls using RT-qPCR (transcriptomic biomarkers) and quantitative protein immunoblot (proteomic biomarkers). Transcriptomic and proteomic profiling revealed significant variations in salivary molecular biomarkers between breast cancer patients and matched controls. Eight mRNA biomarkers and one protein biomarker, which were not affected by the confounding factors, were pre-validated, yielding an accuracy of 92% (83% sensitive, 97% specific) on the preclinical validation sample set.

Conclusions

Our findings support that transcriptomic and proteomic signatures in saliva can serve as biomarkers for the non-invasive detection of breast cancer. The salivary biomarkers possess discriminatory power for the detection of breast cancer, with high specificity and sensitivity, which paves the way for prediction model validation study followed by pivotal clinical validation.

A decade of systems biology

Systems biology provides a framework for assembling models of biological systems from systematic measurements. Since the field was first introduced a decade ago, considerable progress has been made in technologies for global cell measurement and in computational analyses of these data to map and model cell function. It has also greatly expanded into the translational sciences, with approaches pioneered in yeast now being applied to elucidate human development and disease. Here, we review the state of the field with a focus on four emerging applications of systems biology that are likely to be of particular importance during the decade to follow: (a) pathway-based biomarkers, (b) global genetic interaction maps, (c) systems approaches to identify disease genes, and (d) stem cell systems biology. We also cover recent advances in software tools that allow biologists to explore system-wide models and to formulate new hypotheses. The applications and methods covered in this review provide a set of prime exemplars useful to cell and developmental biologists wishing to apply systems approaches to areas of interest.

Enumerating the gene sets in breast cancer, a "direct" alternative to hierarchical clustering

Background

Two-way hierarchical clustering, with results visualized as heatmaps, has served as the method of choice for exploring structure in large matrices of expression data since the advent of microarrays. While it has delivered important insights, including a typology of breast cancer subtypes, it suffers from instability in the face of gene or sample selection, and an inability to detect small sets that may be dominated by larger sets such as the estrogen-related genes in breast cancer. The rank-based partitioning algorithm introduced in this paper addresses several of these limitations. It delivers results comparable to two-way hierarchical clustering, and much more. Applied systematically across a range of parameter settings, it enumerates all the partition-inducing gene sets in a matrix of expression values.

Results

Applied to four large breast cancer datasets, this alternative exploratory method detects more than thirty sets of co-regulated genes, many of which are conserved across experiments and across platforms. Many of these sets are readily identified in biological terms, e.g., "estrogen", "erbb2", and 8p11-12, and several are clinically significant as prognostic of either increased survival ("adipose", "stromal"...) or diminished survival ("proliferation", "immune/interferon", "histone",...). Of special interest are the sets that effectively factor "immune response" and "stromal signalling".

Conclusion

The gene sets induced by the enumeration include many of the sets reported in the literature. In this regard these inventories confirm and consolidate findings from microarray-based work on breast cancer over the last decade. But, the enumerations also identify gene sets that have not been studied as of yet, some of which are prognostic of survival. The sets induced are robust, biologically meaningful, and serve to reveal a finer structure in existing breast cancer microarrays.

Prediction and prognosis: impact of gene expression profiling in personalized treatment of breast cancer patients

Breast cancer is a complex disease, whose heterogeneity is increasingly recognized. Despite considerable improvement in breast cancer treatment and survival, a significant proportion of patients seems to be over- or undertreated. To date, single clinicopathological parameters show limited success in predicting the likelihood of survival or response to endocrine therapy and chemotherapy. Consequently, new gene expression based prognostic and predictive tests are emerging that promise an improvement in predicting survival and therapy response. Initial evidence has emerged that this leads to allocation of fewer patients into high-risk groups allowing a reduction of chemotherapy treatment. Moreover, pattern-based approaches have also been developed to predict response to endocrine therapy or particular chemotherapy regimens. Irrespective of current pitfalls such as lack of validation and standardization, these pattern-based biomarkers will prove useful for clinical decision making in the near future, especially if more patients get access to this form of personalized medicine.

Towards elucidation of functional molecular signatures of the adhesive-migratory phenotype of malignant cells

Over the years, malignant transformation has been investigated on multiple levels, ranging from clinical pathology to the underlying molecular mechanisms. In "zooming in" on this process, cancer biologists have focused their attention on the molecular and cellular manifestations of the "transformed phenotype", including the genomic instability of cancer cells, their deregulated transcriptional activity, their aberrant morphology and dynamics, and the altered signaling networks activated in them. Attempts to elucidate the mechanisms underlying malignant and metastatic transformation are primarily motivated by the desire to identify specific molecules and signaling pathways that can serve as targets for novel therapies. In recent years, such studies were reinforced by major technological and conceptual developments: novel and powerful tools for genomic and proteomic analysis have been developed, and advanced computational approaches offer "systems-level" integration of rich and complex biological datasets into meaningful functional networks. In this article, we consider the current and potential impact of these new experimental approaches and, in particular, the recent progress made in quantitative proteomics, to elucidate the mechanisms underlying the "transformed phenotype". We will primarily focus on the adhesion and migration of cancer cells, and their relationships to the deregulated growth, metastatic dissemination, and anchorage independence associated with malignant transformation.

The pathophysiology, diagnosis and prognosis of adrenocortical tumors revisited by transcriptome analyses

Analyzing gene expression (transcriptome) in tissue is now reliable using industrial pangenomic microarrays. Accumulating data on adrenal cortex and adrenocortical tumor transcriptomes have already identified striking transcriptome differences not only between adenoma and carcinoma but also between two sets of carcinoma, which have very different prognoses. These findings result in the development of diagnostic and prognostic molecular predictors, which improve the outcome determination compared with standard clinical and pathological tools. These transcriptome data observing adrenocortical tumor phenotype in great but complex detail, combined with genomic and proteomic information, will function for future research investigating the pathophysiology of their tumorigenesis and hormonal secretion.

Cytoplasmic localization of beta-catenin is a marker of poor outcome in breast cancer patients

Beta-catenin is involved in cell adhesion through catenin-cadherin complexes and as a transcriptional regulator in the Wnt signaling pathway. Its deregulation is important in the genesis of a number of human malignancies, particularly colorectal cancer. A range of studies has been undertaken in breast cancer, with contradictory associations reported among beta-catenin expression, clinicopathologic variables, and disease outcome. We undertook an immunohistochemical study measuring the levels and subcellular localization of beta-catenin in 292 invasive ductal breast cancers with known treatment and outcome. No association with breast cancer-specific death was observed for cytoplasmic or membrane expression alone; however, a continuous score representing both locations (membrane minus cytoplasmic expression: MTC score) was associated with a worse outcome in univariate analysis (P = 0.004), and approached significance in a multivariate analysis model that included lymph node, progesterone receptor (PR), and HER2 status (P = 0.054). Therefore, the MTC score was used for further statistical analyses due to the importance of both the subcellular location and the levels of expression of beta-catenin. An association was identified between high cytoplasmic expression (low MTC score), and high tumor grade (P = 0.004), positive Ki67 (P = 0.005), negative estrogen receptor (ER) (P = 0.005), positive HER2 (P = 0.04) status, and an active phosphoinositide 3-kinase pathway (P = 0.005), measured as PIK3CA mutations (P = 0.05) or PTEN loss (P = 0.05). Low cytoplasmic expression (high MTC score) was associated with the luminal A subtype (P = 0.004). In conclusion, a low beta-catenin MTC score is associated with an adverse outcome in breast cancer, which may be of mechanistic significance in the disease process.

Detection of gene pathways with predictive power for breast cancer prognosis

Background

Prognosis is of critical interest in breast cancer research. Biomedical studies suggest that genomic measurements may have independent predictive power for prognosis. Gene profiling studies have been conducted to search for predictive genomic measurements. Genes have the inherent pathway structure, where pathways are composed of multiple genes with coordinated functions. The goal of this study is to identify gene pathways with predictive power for breast cancer prognosis. Since our goal is fundamentally different from that of existing studies, a new pathway analysis method is proposed.

Results

The new method advances beyond existing alternatives along the following aspects. First, it can assess the predictive power of gene pathways, whereas existing methods tend to focus on model fitting accuracy only. Second, it can account for the joint effects of multiple genes in a pathway, whereas existing methods tend to focus on the marginal effects of genes. Third, it can accommodate multiple heterogeneous datasets, whereas existing methods analyze a single dataset only. We analyze four breast cancer prognosis studies and identify 97 pathways with significant predictive power for prognosis. Important pathways missed by alternative methods are identified.

Conclusions

The proposed method provides a useful alternative to existing pathway analysis methods. Identified pathways can provide further insights into breast cancer prognosis.

Identification of genes associated with multiple cancers via integrative analysis

Background

Advancement in gene profiling techniques makes it possible to measure expressions of thousands of genes and identify genes associated with development and progression of cancer. The identified cancer-associated genes can be used for diagnosis, prognosis prediction, and treatment selection. Most existing cancer microarray studies have been focusing on the identification of genes associated with a specific type of cancer. Recent biomedical studies suggest that different cancers may share common susceptibility genes. A comprehensive description of the associations between genes and cancers requires identification of not only multiple genes associated with a specific type of cancer but also genes associated with multiple cancers.

Results

In this article, we propose the Mc.TGD (Multi-cancer Threshold Gradient Descent), an integrative analysis approach capable of analyzing multiple microarray studies on different cancers. The Mc.TGD is the first regularized approach to conduct "two-dimensional" selection of genes with joint effects on cancer development. Simulation studies show that the Mc.TGD can more accurately identify genes associated with multiple cancers than meta analysis based on "one-dimensional" methods. As a byproduct, identification accuracy of genes associated with only one type of cancer may also be improved. We use the Mc.TGD to analyze seven microarray studies investigating development of seven different types of cancers. We identify one gene associated with six types of cancers and four genes associated with five types of cancers. In addition, we also identify 11, 9, 18, and 17 genes associated with 4 to 1 types of cancers, respectively. We evaluate prediction performance using a Leave-One-Out cross validation approach and find that only 4 (out of 570) subjects cannot be properly predicted.

Conclusion

The Mc.TGD can identify a short list of genes associated with one or multiple types of cancers. The identified genes are considerably different from those identified using meta analysis or analysis of marginal effects.

Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer

Background

Gene expression profiling of breast cancer has identified two biologically distinct estrogen receptor (ER)-positive subtypes of breast cancer: luminal A and luminal B. Luminal B tumors have higher proliferation and poorer prognosis than luminal A tumors. In this study, we developed a clinically practical immunohistochemistry assay to distinguish luminal B from luminal A tumors and investigated its ability to separate tumors according to breast cancer recurrence-free and disease-specific survival.

Methods

Tumors from a cohort of 357 patients with invasive breast carcinomas were subtyped by gene expression profile. Hormone receptor status, HER2 status, and the Ki67 index (percentage of Ki67-positive cancer nuclei) were determined immunohistochemically. Receiver operating characteristic curves were used to determine the Ki67 cut point to distinguish luminal B from luminal A tumors. The prognostic value of the immunohistochemical assignment for breast cancer recurrence-free and disease-specific survival was investigated with an independent tissue microarray series of 4046 breast cancers by use of Kaplan–Meier curves and multivariable Cox regression.

Results

Gene expression profiling classified 101 (28%) of the 357 tumors as luminal A and 69 (19%) as luminal B. The best Ki67 index cut point to distinguish luminal B from luminal A tumors was 13.25%. In an independent cohort of 4046 patients with breast cancer, 2847 had hormone receptor–positive tumors. When HER2 immunohistochemistry and the Ki67 index were used to subtype these 2847 tumors, we classified 1530 (59%, 95% confidence interval [CI] = 57% to 61%) as luminal A, 846 (33%, 95% CI = 31% to 34%) as luminal B, and 222 (9%, 95% CI = 7% to 10%) as luminal–HER2 positive. Luminal B and luminal–HER2-positive breast cancers were statistically significantly associated with poor breast cancer recurrence-free and disease-specific survival in all adjuvant systemic treatment categories. Of particular relevance are women who received tamoxifen as their sole adjuvant systemic therapy, among whom the 10-year breast cancer–specific survival was 79% (95% CI = 76% to 83%) for luminal A, 64% (95% CI = 59% to 70%) for luminal B, and 57% (95% CI = 47% to 69%) for luminal–HER2 subtypes.

Conclusion

Expression of ER, progesterone receptor, and HER2 proteins and the Ki67 index appear to distinguish luminal A from luminal B breast cancer subtypes.

Prediction of breast cancer metastasis by genomic profiling: where do we stand?

Current concepts conceive “breast cancer” as a complex disease that comprises several very different types of neoplasms. Nonetheless, breast cancer treatment has considerably improved through early diagnosis, adjuvant chemotherapy, and endocrine treatments. The limited prognostic power of classical classifiers determines considerable over-treatment of women who either do not benefit from, or do not at all need, chemotherapy. Several gene expression based molecular classifiers (signatures) have been developed for a more reliable prognostication. Gene expression profiling identifies profound differences in breast cancers, most probably as a consequence of different cellular origin and different driving mutations and can therefore distinguish the intrinsic propensity to metastasize. Existing signatures have been shown to be useful for treatment decisions, although they have been developed using relatively small sample numbers. Major improvements are expected from the use of large datasets, subtype specific signatures and from the re-introduction of functional information. We show that molecular signatures encounter clear limitations given by the intrinsic probabilistic nature of breast cancer metastasis. Already today, signatures are, however, useful for clinical decisions in specific cases, in particular if the personal inclination of the patient towards different treatment strategies is taken into account.

Improving decision-making in early breast cancer: who to treat and how?

Recent advances in primary and adjuvant treatment for early-stage breast cancer have reduced mortality rates, and improved the overall prognosis. Many patients can be cured, while others may survive for 10 years or more beyond diagnosis, thanks to a combination of preoperative therapy, surgery, radiotherapy, and systemic adjuvant therapy. Minimally invasive procedures, more effective drugs, and improved treatment regimens are helping to reduce breast cancer recurrences and deaths, while minimizing side effects and maintaining quality of life. Despite such improvements, a significant number of patients with early disease will relapse, including those who are clinically disease-free after primary and adjuvant therapy. Advances in breast tumor biology have led to the discovery of many different tumor types, and a uniform approach to treatment is no longer appropriate. Potential markers have been identified for the risk of relapse and responsiveness to a given therapy, thus treatment decisions and clinical guidelines, previously based on data from large patient populations, are changing to reflect a movement towards individually tailored treatment. Refinements in clinical practice will help physicians to identify the patients who will benefit the most from a particular approach, reducing overtreatment, and sparing patients unnecessary therapy. Genetic studies are helping to increase our understanding of the metastatic potential of tumors, leading to the development of adjuvant therapies for the prevention of metastases in selected patients. This article reviews the latest advances in treatment for early breast cancer, and explores how research and clinical practice are evolving to improve therapies and treatment decision-making, allowing physicians to optimize patient care.

Identification of alternative splicing markers for breast cancer

Breast cancer is the most common cause of cancer death among women under age 50 years, so it is imperative to identify molecular markers to improve diagnosis and prognosis of this disease. Here, we present a new approach for the identification of breast cancer markers that does not measure gene expression but instead uses the ratio of alternatively spliced mRNAs as its indicator. Using a high-throughput reverse transcription-PCR-based system for splicing annotation, we monitored the alternative splicing profiles of 600 cancer-associated genes in a panel of 21 normal and 26 cancerous breast tissues. We validated 41 alternative splicing events that significantly differed in breast tumors relative to normal breast tissues. Most cancer-specific changes in splicing that disrupt known protein domains support an increase in cell proliferation or survival consistent with a functional role for alternative splicing in cancer. In a blind screen, a classifier based on the 12 best cancer-associated splicing events correctly identified cancer tissues with 96% accuracy. Moreover, a subset of these alternative splicing events could order tissues according to histopathologic grade, and 5 markers were validated in a further blind set of 19 grade 1 and 19 grade 3 tumor samples. These results provide a simple alternative for the classification of normal and cancerous breast tumor tissues and underscore the putative role of alternative splicing in the biology of cancer.