Review of gene-expression profiling and its clinical use in breast cancer

Lymph node metastasis in cancer: Clearing the clouds to see the dawn

Lymph node metastasis (LNM) is often regarded as an indicator of poor prognosis in various cancers. Despite over three centuries of exploration since its discovery, the molecular mechanisms underlying LNM remain inconclusive. This review summarizes the molecular mechanisms of LNM, using the "PUMP+" principle for clarification. Pathological examination remains the gold standard for LNM diagnosis, yet there is a need to explore early diagnostic strategies that can effectively improve patient outcomes. With the advent of immunotherapy, discussions on the fate of lymph nodes (LN) have emerged, emphasizing the importance of preserving LN integrity prior to immunotherapy. This, in turn, poses higher demands for diagnostic accuracy and precision treatment of LNM. This review comprehensively discusses the molecular mechanisms, diagnostic methods, and treatment strategies for cancer lymph node metastasis, along with current bottlenecks and future directions in this field.

Practical Guide to Adjuncts to Clinical Trials in Surgery

This Guide to Statistics and Methods discusses adjunct outcome parameters that optimize patient-centered, timely, efficient, and equitable components in randomized clinical trials.

Stress-induced EGF receptor signaling through STAT3 and tumor progression in triple-negative breast cancer

Elevated STAT3 activity is a hallmark of many epithelial carcinomas particularly in breast cancers where it is known to contribute to tumor progression through a variety of context-dependent biological responses. However, its role downstream of stress-exposed EGF receptors (EGFR) that are transactivated in endosomes independent of exogenous ligand has not been studied. This review discusses how STAT3 signaling induced by therapeutic stress in EGFR-driven triple-negative breast cancers (TNBC) might override normal epithelial homeostatic mechanisms and provide a survival advantage for tumor cells before they leave the primary tumor and spread to distant sites. Despite continued improvements in breast cancer treatment strategies, TNBC is still associated with poor prognosis and high risk of distant recurrence and death. Understanding EGFR-STAT3 signaling mechanisms regulating the earliest steps of tumor progression is a key to discovery of new targeted therapies against TNBC.

Gene expression profiling of breast cancer in Lebanese women

Breast cancer is commonest cancer in women worldwide. Elucidation of underlying biology and molecular pathways is necessary for improving therapeutic options and clinical outcomes. Molecular alterations in breast cancer are complex and involve cross-talk between multiple signaling pathways. The aim of this study is to extract a unique mRNA fingerprint of breast cancer in Lebanese women using microarray technologies. Gene-expression profiles of 94 fresh breast tissue samples (84 cancerous/10 non-tumor adjacent samples) were analyzed using GeneChip Human Genome U133 Plus 2.0 arrays. Quantitative real-time PCR was employed to validate candidate genes. Differentially expressed genes between breast cancer and non-tumor tissues were screened. Significant differences in gene expression were established for COL11A1/COL10A1/MMP1/COL6A6/DLK1/S100P/CXCL11/SOX11/LEP/ADIPOQ/OXTR/FOSL1/ACSBG1 and C21orf37. Pathways/diseases representing these genes were retrieved and linked using PANTHER^®/Pathway Studio^®. Many of the deregulated genes are associated with extracellular matrix, inflammation, angiogenesis, metastasis, differentiation, cell proliferation and tumorigenesis. Characteristics of breast cancers in Lebanese were compared to those of women from Western populations to explain why breast cancer is more aggressive and presents a decade earlier in Lebanese victims. Delineating molecular mechanisms of breast cancer in Lebanese women led to key genes which could serve as potential biomarkers and/or novel drug targets for breast cancer.

Neural networks and Fuzzy clustering methods for assessing the efficacy of microarray based intrinsic gene signatures in breast cancer classification and the character and relations of identified subtypes

In the classification of breast cancer subtypes using microarray data, hierarchical clustering is commonly used. Although this form of clustering shows basic cluster patterns, more needs to be done to investigate the accuracy of clusters as well as to extract meaningful cluster characteristics and their relations to increase our confidence in their use in a clinical setting. In this study, an in-depth investigation of the efficacy of three reported gene subsets in distinguishing breast cancer subtypes was performed using four advanced computational intelligence methods-Self-Organizing Maps (SOM), Emergent Self-Organizing Maps (ESOM), Fuzzy Clustering by Local Approximation of Memberships (FLAME), and Fuzzy C-means (FCM)-each differing in the way they view data in terms of distance measures and fuzzy or crisp clustering. The gene subsets consisted of 71, 93, and 71 genes reported in the literature from three comprehensive experimental studies for distinguishing Luminal (A and B), Basal, Normal breast-like, and HER2 subtypes. Given the costly procedures involved in clinical studies, the proposed 93-gene set can be used for preliminary classification of breast cancer. Then, as a decision aid, SOM can be used to map the gene signature of a new patient to locate them with respect to all subtypes to get a comprehensive view of the classification. These can be followed by a deeper investigation in the light of the observations made in this study regarding overlapping subtypes. Results from the study could be used as the base for further refining the gene signatures from later experiments and from new experiments designed to separate overlapping clusters as well as to maximally separate all clusters.

A method to measure clinical practice patterns of breast cancer genomic diagnostics in health systems

Personalized genomic diagnostics, often in combination with targeted therapeutics, have become an important component of clinical oncologic practice over the last decade. However, there is a dearth of studies examining utilization of specific genomic diagnostics and their influence on clinical practice patterns in health systems. In order to begin to explore and understand the clinical utility of particular genomic diagnostics in current use, we developed an instrument to collect data on utilization and clinical practice patterns in health systems. We focused our efforts on gene-expression profiling (GEP) assays, particularly on a commonly used GEP in breast cancer: Oncotype DX® (Genomic Health, Inc., CA, USA). This article presents a method we have developed for the retrospective collection of data on the utilization of GEP among breast cancer patients of a real-world, real-time health system.

Effects of aging and anatomic location on gene expression in human retina

OBJECTIVE

To determine the effects of age and topographic location on gene expression in human neural retina.

METHODS

Macular and peripheral neural retina RNA was isolated from human donor eyes for DNA microarray and quantitative RT-PCR analyses.

RESULTS

Total RNA integrity from human donors was preserved. Hierarchical clustering analysis demonstrates that the gene expression profiles of young, old, macula, and peripheral retina cluster into four distinct groups. Genes which are highly expressed in macular, peripheral, young, or old retina were identified, including inhibitors of Wnt Signaling Pathway (DKK1, FZD10, and SFRP2) which are preferably expressed in the periphery.

CONCLUSION

The transcriptome of the human retina is affected by age and topographic location. Wnt pathway inhibitors in the periphery may maintain peripheral retinal cells in an undifferentiated state. Understanding the effects of age and topographic location on gene expression may lead to the development of new therapeutic interventions for age-related eye diseases.

Omics underpins novel clues on VDR chemoprevention target in breast cancer

Breast cancer is the commonest form of female malignancy among women in Western countries. The advent of genomic technologies has enhanced the diagnosis and the biological classification of such pathology. It has been demonstrated that cancer takes many years to be fully established. This long dormancy could represent a potential window for intervening with chemoprevention studies. Cancer chemoprevention is by definition the use of natural, synthetic, or biological chemical agents to reverse, suppress, or delay the genetic or other alterations that culminate in the appearance of the tumor phenotype. An important step for the success of chemoprevention is the identification of molecularly targeted agents to prevent cancer development. Currently, only two chemoprevention agents, raloxifene and tamoxifen, are used in clinical practice to prevent breast cancer. In this review, we will mainly focus on: (1) the application of genomic technologies for the identification and validation of molecular targets for chemoprevention; (2) the role of vitamin D and its cognate receptor VDR (vitamin D receptor) as a model for the molecularly targeted chemoprevention of breast cancer.

Distinct choline metabolic profiles are associated with differences in gene expression for basal-like and luminal-like breast cancer xenograft models

Background

Increased concentrations of choline-containing compounds are frequently observed in breast carcinomas, and may serve as biomarkers for both diagnostic and treatment monitoring purposes. However, underlying mechanisms for the abnormal choline metabolism are poorly understood.

Methods

The concentrations of choline-derived metabolites were determined in xenografted primary human breast carcinomas, representing basal-like and luminal-like subtypes. Quantification of metabolites in fresh frozen tissue was performed using high-resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS).

The expression of genes involved in phosphatidylcholine (PtdCho) metabolism was retrieved from whole genome expression microarray analyses.

The metabolite profiles from xenografts were compared with profiles from human breast cancer, sampled from patients with estrogen/progesterone receptor positive (ER+/PgR+) or triple negative (ER-/PgR-/HER2-) breast cancer.

Results

In basal-like xenografts, glycerophosphocholine (GPC) concentrations were higher than phosphocholine (PCho) concentrations, whereas this pattern was reversed in luminal-like xenografts. These differences may be explained by lower choline kinase (CHKA, CHKB) expression as well as higher PtdCho degradation mediated by higher expression of phospholipase A2 group 4A (PLA2G4A) and phospholipase B1 (PLB1) in the basal-like model. The glycine concentration was higher in the basal-like model. Although glycine could be derived from energy metabolism pathways, the gene expression data suggested a metabolic shift from PtdCho synthesis to glycine formation in basal-like xenografts. In agreement with results from the xenograft models, tissue samples from triple negative breast carcinomas had higher GPC/PCho ratio than samples from ER+/PgR+ carcinomas, suggesting that the choline metabolism in the experimental models is representative for luminal-like and basal-like human breast cancer.

Conclusions

The differences in choline metabolite concentrations corresponded well with differences in gene expression, demonstrating distinct metabolic profiles in the xenograft models representing basal-like and luminal-like breast cancer. The same characteristics of choline metabolite profiles were also observed in patient material from ER+/PgR+ and triple-negative breast cancer, suggesting that the xenografts are relevant model systems for studies of choline metabolism in luminal-like and basal-like breast cancer.

Emerging biomarkers in breast cancer care

Currently, decision-making for breast cancer treatment in the clinical setting is mainly based on clinical data, histomorphological features of the tumor tissue and a few cancer biomarkers such as steroid hormone receptor status (estrogen and progesterone receptors) and oncoprotein HER2 status. Although various therapeutic options were introduced into the clinic in recent decades, with the objective of improving surgery, radiotherapy, biochemotherapy and chemotherapy, varying response of individual patients to certain types of therapy and therapy resistance is still a challenge in breast cancer care. Therefore, since breast cancer treatment should be based on individual features of the patient and her tumor, tailored therapy should be an option by integrating cancer biomarkers to define patients at risk and to reliably predict their course of the disease and/or response to cancer therapy. Recently, candidate-marker approaches and genome-wide transcriptomic and epigenetic screening of different breast cancer tissues and bodily fluids resulted in new promising biomarker panels, allowing breast cancer prognosis, prediction of therapy response and monitoring of therapy efficacy. These biomarkers are now subject of validation in prospective clinical trials.

Dissecting variability in responses to cancer chemotherapy through systems pharmacology

Variability in patient responses to even the most potent and targeted therapeutics is now the primary challenge facing drug discovery and patient care, particularly in oncology and immune therapy. Variability with respect to mechanisms of induced resistance is observed both in drug-naive patients and among those who are initially responsive. Genomics has developed powerful tools for systematic interrogation of disease genotype and transcriptional states (particularly in cancer) and for correlation of these measures with parameters of disease such as histological diagnosis and outcome. In contrast, mechanistic preclinical studies remain relatively narrowly focused, leading to many apparent contradictions and poor understanding of the determinants of response. We describe the emergence of a systems pharmacology approach that is mechanistic, quantitative, probabilistic, and postgenomic and promises to do for mechanistic pharmacology what genomics is doing for correlative studies. We focus on studies in cell lines (which currently dominate mechanism-oriented analysis), but our arguments are equally valid for real tumors studied in short-term culture as xenografts and, perhaps some time in the future, in humans.

DEAD box 1: a novel and independent prognostic marker for early recurrence in breast cancer

Breast cancer is a heterogeneous disease characterized by diverse molecular signatures and a variable response to therapy. Clinical management of breast cancer is guided by the expression of estrogen and progesterone receptors and HER2 amplification. New prognostic and predictive markers, as well as additional targets for therapy, are needed for more effective management of this disease. Gene expression microarrays were probed with RNAs from 176 primary breast cancer samples and tissue microarrays immunostained with anti-DDX1 antibody, an antibody to DEAD box protein DDX1, a putative RNA-RNA and RNA-DNA unwinding protein normally found in the nucleus. Half of the patient cohort had experienced early relapse despite standard adjuvant therapy, but were otherwise matched for estrogen receptor and HER2 status, stage and duration of follow-up. Here, we identify DDX1 RNA overexpression as an independent prognostic marker for early recurrence in primary breast cancer, with a hazard ratio of 4.31 based on logrank analysis of Kaplan-Meier curves. Elevated levels of DDX1 protein in the cytoplasm also independently correlate with early recurrence with a hazard ratio of 1.90. In conclusion, our data indicate a strong and independent association between poor prognosis and deregulation of the DEAD box protein DDX1. We propose that elevated levels of DDX1 RNA or the presence of DDX1 in the cytoplasm could serve as an effective prognostic biomarker for early recurrence in primary breast cancer.

Molecular risk assessment of BIG 1-98 participants by expression profiling using RNA from archival tissue

Background

The purpose of the work reported here is to test reliable molecular profiles using routinely processed formalin-fixed paraffin-embedded (FFPE) tissues from participants of the clinical trial BIG 1-98 with a median follow-up of 60 months.

Methods

RNA from fresh frozen (FF) and FFPE tumor samples of 82 patients were used for quality control, and independent FFPE tissues of 342 postmenopausal participants of BIG 1-98 with ER-positive cancer were analyzed by measuring prospectively selected genes and computing scores representing the functions of the estrogen receptor (eight genes, ER_8), the progesterone receptor (five genes, PGR_5), Her2 (two genes, HER2_2), and proliferation (ten genes, PRO_10) by quantitative reverse transcription PCR (qRT-PCR) on TaqMan Low Density Arrays. Molecular scores were computed for each category and ER_8, PGR_5, HER2_2, and PRO_10 scores were combined into a RISK_25 score.

Results

Pearson correlation coefficients between FF- and FFPE-derived scores were at least 0.94 and high concordance was observed between molecular scores and immunohistochemical data. The HER2_2, PGR_5, PRO_10 and RISK_25 scores were significant predictors of disease free-survival (DFS) in univariate Cox proportional hazard regression. PRO_10 and RISK_25 scores predicted DFS in patients with histological grade II breast cancer and in lymph node positive disease. The PRO_10 and PGR_5 scores were independent predictors of DFS in multivariate Cox regression models incorporating clinical risk indicators; PRO_10 outperformed Ki-67 labeling index in multivariate Cox proportional hazard analyses.

Conclusions

Scores representing the endocrine responsiveness and proliferation status of breast cancers were developed from gene expression analyses based on RNA derived from FFPE tissues. The validation of the molecular scores with tumor samples of participants of the BIG 1-98 trial demonstrates that such scores can serve as independent prognostic factors to estimate disease free survival (DFS) in postmenopausal patients with estrogen receptor positive breast cancer.

Trial Registration

Current Controlled Trials: NCT00004205

Frequent genetic differences between matched primary and metastatic breast cancer provide an approach to identification of biomarkers for disease progression

Breast cancer is a major cause of morbidity and mortality in women and its metastatic spread is the principal reason behind the fatal outcome. Metastasis-related research of breast cancer is however underdeveloped when compared with the abundant literature on primary tumors. We applied an unexplored approach comparing at high resolution the genomic profiles of primary tumors and synchronous axillary lymph node metastases from 13 patients with breast cancer. Overall, primary tumors displayed 20% higher number of aberrations than metastases. In all but two patients, we detected in total 157 statistically significant differences between primary lesions and matched metastases. We further observed differences that can be linked to metastatic disease and there was also an overlapping pattern of changes between different patients. Many of the differences described here have been previously linked to poor patient survival, suggesting that this is a viable approach toward finding biomarkers for disease progression and definition of new targets useful for development of anticancer drugs. Frequent genetic differences between primary tumors and metastases in breast cancer also question, at least to some extent, the role of primary tumors as a surrogate subject of study for the systemic disease.