Gene expression signatures, clinicopathological features, and individualized therapy in breast cancer

Contemporary approaches for identifying individual risk for periodontitis

Key breakthroughs in our understanding of the etiology and principles of predictable treatment of patients with chronic periodontitis first emerged in the late 1960s and carried on into the mid-1980s. Unfortunately, some generalizations of the evidence led many to believe that periodontitis was a predictable result of exposure to bacterial plaque accumulations over time. For a brief period, the initial plaque concept was translated by some to implicate specific bacterial infections, with both concepts (plaque exposure and specific infection) being false assumptions that led to clinical outcomes which were frustrating to both the clinician and the patient. The primary misconceptions were that every individual was equally susceptible to periodontitis, that disease severity was a simple function of magnitude of bacterial exposure over time, and that all patients would respond predictably if treated based on the key principles of bacterial reduction and regular maintenance care. We now know that although bacteria are an essential initiating factor, the clinical severity of periodontitis is a complex multifactorial host response to the microbial challenge. The complexity comes from the permutations of different factors that may interact to alter a single individual's host response to challenge, inflammation resolution and repair, and overall outcome to therapy. Fortunately, although there are many permutations that may influence host response and repair, the pathophysiology of chronic periodontitis is generally limited to mild periodontitis with isolated moderate disease in most individuals. However, approximately 20%-25% of individuals will develop generalized severe periodontitis and probably require more intensive bacterial reduction and different approaches to host modulation of the inflammatory outcomes. This latter group may also have serious systemic implications of their periodontitis. The time appears to be appropriate to use what we know and currently understand to change our approach to clinical care. Our goal would be to increase our likelihood of identifying those patients who have a more biologically disruptive response combined with a more impactful microbial dysbiosis. Current evidence, albeit limited, indicates that for those individuals we should prevent and treat more intensively. This paper discusses what we know and how we might use that information to start individualizing risk and treat some of our patients in a more targeted manner. In my opinion, we are further along than many realize, but we have a great lack of prospective clinical evidence that must be accumulated while we continue to unravel the contributions of specific mechanisms.

Molecular genetics complexity impeding research progress in breast and ovarian cancers

Breast and ovarian cancer are heterogeneous diseases. While breast cancer accounts for 25% of cancers worldwide, ovarian cancer accounts for 3.5% of all cancers and it is considered to be the most lethal type of cancer among women. In Oman, breast cancer accounts for 25% and ovarian cancer for 4.5% of all cancer cases. Various risk factors, including variable biological and clinical traits, are involved in the onset of breast and ovarian cancer. Although highly developed diagnostic and therapeutic methods have paved the way for better management, targeted therapy against specific biomarkers has not yet shown any significant improvement, particularly in triple-negative breast cancer and epithelial ovarian cancer, which are associated with high mortality rates. Thus, elucidating the mechanisms underlying the pathology of these diseases is expected to improve their prevention, prognosis and management. The aim of the present study was to provide a comprehensive review and updated information on genomics and proteomics alterations associated with cancer pathogenesis, as reported by several research groups worldwide. Furthermore, molecular research in our laboratory, aimed at identifying new pathways involved in the pathogenesis of breast and ovarian cancer using microarray and chromatin immunoprecipitation (ChIP), is discussed. Relevant candidate genes were found to be either up- or downregulated in a cohort of breast cancer cases. Similarly, ChIP analysis revealed that relevant candidate genes were regulated by the E2F5 transcription factor in ovarian cancer tissue. An ongoing study aims to validate these genes with a putative role as biological markers that may contribute to the development of targeted therapies for breast and ovarian cancer.

Sequencing-based breast cancer diagnostics as an alternative to routine biomarkers

Sequencing-based breast cancer diagnostics have the potential to replace routine biomarkers and provide molecular characterization that enable personalized precision medicine. Here we investigate the concordance between sequencing-based and routine diagnostic biomarkers and to what extent tumor sequencing contributes clinically actionable information. We applied DNA- and RNA-sequencing to characterize tumors from 307 breast cancer patients with replication in up to 739 patients. We developed models to predict status of routine biomarkers (ER, HER2,Ki-67, histological grade) from sequencing data. Non-routine biomarkers, including mutations in BRCA1, BRCA2 and ERBB2(HER2), and additional clinically actionable somatic alterations were also investigated. Concordance with routine diagnostic biomarkers was high for ER status (AUC = 0.95;AUC(replication) = 0.97) and HER2 status (AUC = 0.97;AUC(replication) = 0.92). The transcriptomic grade model enabled classification of histological grade 1 and histological grade 3 tumors with high accuracy (AUC = 0.98;AUC(replication) = 0.94). Clinically actionable mutations in BRCA1, BRCA2 and ERBB2(HER2) were detected in 5.5% of patients, while 53% had genomic alterations matching ongoing or concluded breast cancer studies. Sequencing-based molecular profiling can be applied as an alternative to histopathology to determine ER and HER2 status, in addition to providing improved tumor grading and clinically actionable mutations and molecular subtypes. Our results suggest that sequencing-based breast cancer diagnostics in a near future can replace routine biomarkers.

Sense-antisense gene-pairs in breast cancer and associated pathological pathways

More than 30% of human protein-coding genes form hereditary complex genome architectures composed of sense-antisense (SA) gene pairs (SAGPs) transcribing their RNAs from both strands of a given locus. Such architectures represent important novel components of genome complexity contributing to gene expression deregulation in cancer cells. Therefore, the architectures might be involved in cancer pathways and, in turn, be used for novel drug targets discovery. However, the global roles of SAGPs in cancer pathways has not been studied. Here we investigated SAGPs associated with breast cancer (BC)-related pathways using systems biology, prognostic survival and experimental methods. Gene expression analysis identified 73 BC-relevant SAGPs that are highly correlated in BC. Survival modelling and metadata analysis of the 1161 BC patients allowed us to develop a novel patient prognostic grouping method selecting the 12 survival-significant SAGPs. The qRT-PCR-validated 12-SAGP prognostic signature reproducibly stratified BC patients into low- and high-risk prognostic subgroups. The 1381 SAGP-defined differentially expressed genes common across three studied cohorts were identified. The functional enrichment analysis of these genes revealed the GABPA gene network, including BC-relevant SAGPs, specific gene sets involved in cell cycle, spliceosomal and proteasomal pathways. The co-regulatory function of GABPA in BC cells was supported using siRNA knockdown studies. Thus, we demonstrated SAGPs as the synergistically functional genome architectures interconnected with cancer-related pathways and associated with BC patient clinical outcomes. Taken together, SAGPs represent an important component of genome complexity which can be used to identify novel aspects of coordinated pathological gene networks in cancers.

Classification Approach for Breast Cancer Detection Using Back Propagation Neural Network

According to the recent surveys, breast cancer has become one of the major causes of mortality rate among women. Breast cancer can be defined as a group of rapidly growing cells that lead to the formation of a lump or an extra mass in the breast tissue which consequently leads to the formation of tumor. Tumors can be classified as malignant (cancerous) or benign (non-cancerous). Feature selection is an important parameter in determining the classification systems. Machine learning methods are the most commonly used methods among researchers for breast cancer diagnosis. This paper proposes to investigate the WBCD (Wisconsin Breast Cancer Dataset) which comprises of 683 patients and implements the chosen features to train the back propagation neural network. The performance is then analyzed on the basis of classification accuracy, sensitivity, specificity, positive and negative predictor values, receiver operating characteristic curves and confusion matrix. A total of 9 features has been used to classify breast cancer with an accuracy of 99.27%. According to the recent surveys, breast cancer has become one of the major causes of mortality rate among women. Breast cancer can be defined as a group of rapidly growing cells that lead to the formation of a lump or an extra mass in the breast tissue which consequently leads to the formation of tumor. Tumors can be classified as malignant (cancerous) or benign (non-cancerous). Feature selection is an important parameter in determining the classification systems. Machine learning methods are the most commonly used methods among researchers for breast cancer diagnosis. This paper proposes to investigate the WBCD (Wisconsin Breast Cancer Dataset) which comprises of 683 patients and implements the chosen features to train the back propagation neural network. The performance is then analyzed on the basis of classification accuracy, sensitivity, specificity, positive and negative predictor values, receiver operating characteristic curves and confusion matrix. A total of 9 features has been used to classify breast cancer with an accuracy of 99.27%.

Comparative study of joint analysis of microarray gene expression data in survival prediction and risk assessment of breast cancer patients

Microarray gene expression data sets are jointly analyzed to increase statistical power. They could either be merged together or analyzed by meta-analysis. For a given ensemble of data sets, it cannot be foreseen which of these paradigms, merging or meta-analysis, works better. In this article, three joint analysis methods, Z -score normalization, ComBat and the inverse normal method (meta-analysis) were selected for survival prognosis and risk assessment of breast cancer patients. The methods were applied to eight microarray gene expression data sets, totaling 1324 patients with two clinical endpoints, overall survival and relapse-free survival. The performance derived from the joint analysis methods was evaluated using Cox regression for survival analysis and independent validation used as bias estimation. Overall, Z -score normalization had a better performance than ComBat and meta-analysis. Higher Area Under the Receiver Operating Characteristic curve and hazard ratio were also obtained when independent validation was used as bias estimation. With a lower time and memory complexity, Z -score normalization is a simple method for joint analysis of microarray gene expression data sets. The derived findings suggest further assessment of this method in future survival prediction and cancer classification applications.

A hybrid approach of gene sets and single genes for the prediction of survival risks with gene expression data

Accumulated biological knowledge is often encoded as gene sets, collections of genes associated with similar biological functions or pathways. The use of gene sets in the analyses of high-throughput gene expression data has been intensively studied and applied in clinical research. However, the main interest remains in finding modules of biological knowledge, or corresponding gene sets, significantly associated with disease conditions. Risk prediction from censored survival times using gene sets hasn’t been well studied. In this work, we propose a hybrid method that uses both single gene and gene set information together to predict patient survival risks from gene expression profiles. In the proposed method, gene sets provide context-level information that is poorly reflected by single genes. Complementarily, single genes help to supplement incomplete information of gene sets due to our imperfect biomedical knowledge. Through the tests over multiple data sets of cancer and trauma injury, the proposed method showed robust and improved performance compared with the conventional approaches with only single genes or gene sets solely. Additionally, we examined the prediction result in the trauma injury data, and showed that the modules of biological knowledge used in the prediction by the proposed method were highly interpretable in biology. A wide range of survival prediction problems in clinical genomics is expected to benefit from the use of biological knowledge.

Prevalence of scientific misconduct among a group of researchers in Nigeria

BACKGROUND

There is a dearth of information on the prevalence of scientific misconduct from Nigeria.

OBJECTIVES

This study aimed at determining the prevalence of scientific misconduct in a group of researchers in Nigeria. Factors associated with the prevalence were ascertained.

METHOD

A descriptive study of researchers who attended a scientific conference in 2010 was conducted using the adapted Scientific Misconduct Questionnaire-Revised (SMQ-R).

RESULTS

Ninety-one researchers (68.9%) admitted having committed at least one of the eight listed forms of scientific misconduct. Disagreement about authorship was the most common form of misconduct committed (36.4%) while plagiarism was the least (9.2%). About 42% of researchers had committed falsification of data or plagiarism. Analysis of specific acts of misconduct showed that committing plagiarism was inversely associated with years in research (Fisher exact p-value = 0.02); falsifying data was related to perceived low effectiveness of the institution's rules and procedures for reducing scientific misconduct (X(2) = 6.44, p-value = 0.01); and succumbing to pressure from study sponsor to engage in unethical practice was related to sex of researcher (Fisher exact p-value = 0.02).

CONCLUSIONS

The emergent data from this study is a cause for serious concern and calls for prompt intervention. The best response to reducing scientific misconduct will proceed from measures that contain both elements of prevention and enforcement. Training on research ethics has to be integrated into the curriculum of undergraduate and postgraduate students while provision should be made for in-service training of researchers. Penalties against acts of scientific misconduct should be enforced at institutional and national levels.

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.

Transcript and protein profiling identifies signaling, growth arrest, apoptosis, and NF-κB survival signatures following GNRH receptor activation

GNRH significantly inhibits proliferation of a proportion of cancer cell lines by activating GNRH receptor (GNRHR)-G protein signaling. Therefore, manipulation of GNRHR signaling may have an under-utilized role in treating certain breast and ovarian cancers. However, the precise signaling pathways necessary for the effect and the features of cellular responses remain poorly defined. We used transcriptomic and proteomic profiling approaches to characterize the effects of GNRHR activation in sensitive cells (HEK293-GNRHR, SCL60) in vitro and in vivo, compared to unresponsive HEK293. Analyses of gene expression demonstrated a dynamic response to the GNRH superagonist Triptorelin. Early and mid-phase changes (0.5-1.0 h) comprised mainly transcription factors. Later changes (8-24 h) included a GNRH target gene, CGA, and up- or downregulation of transcripts encoding signaling and cell division machinery. Pathway analysis identified altered MAPK and cell cycle pathways, consistent with occurrence of G(2)/M arrest and apoptosis. Nuclear factor kappa B (NF-κB) pathway gene transcripts were differentially expressed between control and Triptorelin-treated SCL60 cultures. Reverse-phase protein and phospho-proteomic array analyses profiled responses in cultured cells and SCL60 xenografts in vivo during Triptorelin anti-proliferation. Increased phosphorylated NF-κB (p65) occurred in SCL60 in vitro, and p-NF-κB and IκBε were higher in treated xenografts than controls after 4 days Triptorelin. NF-κB inhibition enhanced the anti-proliferative effect of Triptorelin in SCL60 cultures. This study reveals details of pathways interacting with intense GNRHR signaling, identifies potential anti-proliferative target genes, and implicates the NF-κB survival pathway as a node for enhancing GNRH agonist-induced anti-proliferation.

Challenges of incorporating gene expression data to predict HCC prognosis in the age of systems biology

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. The recurrence of HCC after curative treatments is currently a major hurdle. Identification of subsets of patients with distinct prognosis provides an opportunity to tailor therapeutic approaches as well as to select the patients with specific sub-phenotypes for targeted therapy. Thus, the development of gene expression profiles to improve the prediction of HCC prognosis is important for HCC management. Although several gene signatures have been evaluated for the prediction of HCC prognosis, there is no consensus on the predictive power of these signatures. Using systematic approaches to evaluate these signatures and combine them with clinicopathologic information may provide more accurate prediction of HCC prognosis. Recently, Villanueva et al^[13] developed a composite prognostic model incorporating gene expression patterns in both tumor and adjacent tissues to predict HCC recurrence. In this commentary, we summarize the current progress in using gene signatures to predict HCC prognosis, and discuss the importance, existing issues and future research directions in this field.

Differential expression of ERCC-1 in the primary tumors and metastatic lymph nodes of patients with non-small cell lung cancer adenocarcinoma

About 80 % of lung cancers are carcinomas that are classified histologically as non-small-cell lung carcinoma (NSCLC) and targeted chemotherapy of this cancer is currently based on sensitivity of the primary tumor to specific drugs. The purpose of this study was to compare the levels of four serum markers of cancer and the levels of six molecular markers which are possibly associated with drug selection in the primary tumors and metastatic lymph nodes of 39 consecutive NSCLC patients who were admitted to a single institution in China. Serum markers of cancer (neuron-specific enolase, carcinoembryonic antigen (CEA), cancer antigen 125, cytokeratin fragment 21-1) were measured by an automated electrochemiluminescence system and molecular markers (multidrug resistance protein 1, LDL receptor-related protein, ribonucleotide reductase M1, epidermal growth factor receptor, excision repair cross-complementing gene 1, and breast cancer 1) were measured by immunohistochemistry of the primary tumors and metastatic lymph nodes. The results indicate that the serum level of CEA was higher in NSCLC patients with adenocarcinoma relative to those with squamous cell carcinoma, but no significant differences in the other serum markers. Expression of excision repair cross-complementing gene 1 was significantly different in the primary tumors and metastatic sites of NSCLC patients with adenocarcinoma, but there were no other significant differences. This study provides an initial step toward the development of individualized chemotherapy of NSCLC based on measurement of molecular markers in the primary tumors and metastatic lymph nodes.

Enhancing diagnosis, prognosis, and therapeutic outcome prediction of gliomas using genomics

Malignant gliomas are the most frequent type of primary brain tumors. Patients' outcome has not improved despite new therapeutics, thus underscoring the need for a better understanding of their genetics and a fresh approach to treatment. The lack of reproducibility in the classification of many gliomas presents an opportunity where genomics may be paramount for accurate diagnosis and therefore best for therapeutic decisions. The aim of this work is to identify large and focal copy number abnormalities (CNA) and loss of heterozygosity (LOH) events in a malignant glioma population. We hypothesized that these explorations will allow discovery of genetic markers that may improve diagnosis and predict outcome. DNA from glioma specimens were subjected to CNA and LOH analyses. Our studies revealed more than 4000 CNA and several LOH loci. Losses of chromosomes 1p and/or 19q, 10, 13, 14, and 22 and gains of 7, 19, and 20 were found. Several of these alterations correlated significantly with histology and grade. Further, LOH was detected at numerous chromosomes. Interestingly, several of these loci harbor genes with potential or reported tumor suppressor properties. These novel genetic signatures may lead to critical insights into diagnosis, classification, prognosis, and design of individualized therapies.

Gene interaction enrichment and network analysis to identify dysregulated pathways and their interactions in complex diseases

BACKGROUND

The molecular behavior of biological systems can be described in terms of three fundamental components: (i) the physical entities, (ii) the interactions among these entities, and (iii) the dynamics of these entities and interactions. The mechanisms that drive complex disease can be productively viewed in the context of the perturbations of these components. One challenge in this regard is to identify the pathways altered in specific diseases. To address this challenge, Gene Set Enrichment Analysis (GSEA) and others have been developed, which focus on alterations of individual properties of the entities (such as gene expression). However, the dynamics of the interactions with respect to disease have been less well studied (i.e., properties of components ii and iii).

RESULTS

Here, we present a novel method called Gene Interaction Enrichment and Network Analysis (GIENA) to identify dysregulated gene interactions, i.e., pairs of genes whose relationships differ between disease and control. Four functions are defined to model the biologically relevant gene interactions of cooperation (sum of mRNA expression), competition (difference between mRNA expression), redundancy (maximum of expression), or dependency (minimum of expression) among the expression levels. The proposed framework identifies dysregulated interactions and pathways enriched in dysregulated interactions; points out interactions that are perturbed across pathways; and moreover, based on the biological annotation of each type of dysregulated interaction gives clues about the regulatory logic governing the systems level perturbation. We demonstrated the potential of GIENA using published datasets related to cancer.

CONCLUSIONS

We showed that GIENA identifies dysregulated pathways that are missed by traditional enrichment methods based on the individual gene properties and that use of traditional methods combined with GIENA provides coverage of the largest number of relevant pathways. In addition, using the interactions detected by GIENA, specific gene networks both within and across pathways associated with the relevant phenotypes are constructed and analyzed.

Comparison of data-merging methods with SVM attribute selection and classification in breast cancer gene expression

BACKGROUND

DNA microarray data are used to identify genes which could be considered prognostic markers. However, due to the limited sample size of each study, the signatures are unstable in terms of the composing genes and may be limited in terms of performances. It is therefore of great interest to integrate different studies, thus increasing sample size.

RESULTS

In the past, several studies explored the issue of microarray data merging, but the arrival of new techniques and a focus on SVM based classification needed further investigation. We used distant metastasis prediction based on SVM attribute selection and classification to three breast cancer data sets.

CONCLUSIONS

The results showed that breast cancer classification does not benefit from data merging, confirming the results found by other studies with different techniques.

Single sample expression-anchored mechanisms predict survival in head and neck cancer

Gene expression signatures that are predictive of therapeutic response or prognosis are increasingly useful in clinical care; however, mechanistic (and intuitive) interpretation of expression arrays remains an unmet challenge. Additionally, there is surprisingly little gene overlap among distinct clinically validated expression signatures. These “causality challenges” hinder the adoption of signatures as compared to functionally well-characterized single gene biomarkers. To increase the utility of multi-gene signatures in survival studies, we developed a novel approach to generate “personal mechanism signatures” of molecular pathways and functions from gene expression arrays. FAIME, the Functional Analysis of Individual Microarray Expression, computes mechanism scores using rank-weighted gene expression of an individual sample. By comparing head and neck squamous cell carcinoma (HNSCC) samples with non-tumor control tissues, the precision and recall of deregulated FAIME-derived mechanisms of pathways and molecular functions are comparable to those produced by conventional cohort-wide methods (e.g. GSEA). The overlap of “Oncogenic FAIME Features of HNSCC” (statistically significant and differentially regulated FAIME-derived genesets representing GO functions or KEGG pathways derived from HNSCC tissue) among three distinct HNSCC datasets (pathways:46%, p<0.001) is more significant than the gene overlap (genes:4%). These Oncogenic FAIME Features of HNSCC can accurately discriminate tumors from control tissues in two additional HNSCC datasets (n = 35 and 91, F-accuracy = 100% and 97%, empirical p<0.001, area under the receiver operating characteristic curves = 99% and 92%), and stratify recurrence-free survival in patients from two independent studies (p = 0.0018 and p = 0.032, log-rank). Previous approaches depending on group assignment of individual samples before selecting features or learning a classifier are limited by design to discrete-class prediction. In contrast, FAIME calculates mechanism profiles for individual patients without requiring group assignment in validation sets. FAIME is more amenable for clinical deployment since it translates the gene-level measurements of each given sample into pathways and molecular function profiles that can be applied to analyze continuous phenotypes in clinical outcome studies (e.g. survival time, tumor volume).

Clinical utilization of multi-gene expression signatures that are predictive of therapeutic response has been steadily increasing, however, interpretation of such results remains challenging because multi-gene signatures, generated from analyzing different patient cohorts, tend to be equally predictive but contain minimal overlap. Whereas pathway-level analyses of expression arrays show promise for generating clinically meaningful mechanistic signatures, current approaches do not permit single-patient based analyses that are independent of cross-group calculations. To bridge the gap between deterministic biological mechanisms of single-gene biomarkers and the statistical predictive power of multi-gene signatures that are disconnected from mechanisms, we developed FAIME, a novel method that transforms microarray gene expression data into individualized patient profiles of molecular mechanisms. We have validated its capability for predicting clinical outcomes, including cancer patient samples derived from six different clinical trial cohorts of head and neck cancers. This method provides opportunities to harness an untapped resource for personal genomics: clinical evaluation and testing of individually interpretable mechanistic profiles derived from gene expression arrays.

Predictive and prognostic molecular markers for cancer medicine

Over the last 10 years there has been an explosion of information about the molecular biology of cancer. A challenge in oncology is to translate this information into advances in patient care. While there are well-formed routes for translating new molecular information into drug therapy, the routes for translating new information into sensitive and specific diagnostic, prognostic and predictive tests are still being developed. Similarly, the science of using tumor molecular profiles to select clinical trial participants or to optimize therapy for individual patients is still in its infancy. This review will summarize the current technologies for predicting treatment response and prognosis in cancer medicine, and outline what the future may hold. It will also highlight the potential importance of methods that can integrate molecular, histopathological and clinical information into a synergistic understanding of tumor progression. While these possibilities are without doubt exciting, significant challenges remain if we are to implement them with a strong evidence base in a widely available and cost-effective manner.

Role for transcription factor TFII-I in the suppression of SSeCKS/Gravin/Akap12 transcription by Src

The SSeCKS/Gravin/AKAP12 gene, encoding a kinase scaffolding protein with metastasis-suppressing activity, is transcriptionally downregulated in Src-transformed cells through the recruitment of HDAC1 to a Src-responsive proximal promoter site charged with Sp1, Sp3 and USF1. However, the ectopic expression of these proteins formed a suppressive complex in Src-transformed but not in parental NIH3T3 cells, suggesting the involvement of additional repressor factors. Transcription factor II-I (TFII-I) [general transcription factor 2i (Gtf2i)] was identified by mass spectrometry as being associated with the SSeCKS promoter complex in NIH3T3/Src cells, and moreover, the Src-induced tyrosine phosphorylation of TFII-I significantly increased its binding to the SSeCKS proximal promoter. siRNA-mediated knockdown of TFII-I or the expression of TFII-I(Y248/249F) caused the derepression of SSeCKS in NIH3T3/Src cells. Taken with previous data showing that the tyrosine phosphorylation of TFII-I facilitates its nuclear translocation, these data suggest that Src-family kinase-mediated phosphorylation converts a portion of TFII-I into a transcriptional repressor.

Global transcriptomic profiling using small volumes of whole blood: a cost-effective method for translational genomic biomarker identification in small animals

Blood is an ideal tissue for the identification of novel genomic biomarkers for toxicity or efficacy. However, using blood for transcriptomic profiling presents significant technical challenges due to the transcriptomic changes induced by ex vivo handling and the interference of highly abundant globin mRNA. Most whole blood RNA stabilization and isolation methods also require significant volumes of blood, limiting their effective use in small animal species, such as rodents. To overcome these challenges, a QIAzol-based RNA stabilization and isolation method (QSI) was developed to isolate sufficient amounts of high quality total RNA from 25 to 500 μL of rat whole blood. The method was compared to the standard PAXgene Blood RNA System using blood collected from rats exposed to saline or lipopolysaccharide (LPS). The QSI method yielded an average of 54 ng total RNA per μL of rat whole blood with an average RNA Integrity Number (RIN) of 9, a performance comparable with the standard PAXgene method. Total RNA samples were further processed using the NuGEN Ovation Whole Blood Solution system and cDNA was hybridized to Affymetrix Rat Genome 230 2.0 Arrays. The microarray QC parameters using RNA isolated with the QSI method were within the acceptable range for microarray analysis. The transcriptomic profiles were highly correlated with those using RNA isolated with the PAXgene method and were consistent with expected LPS-induced inflammatory responses. The present study demonstrated that the QSI method coupled with NuGEN Ovation Whole Blood Solution system is cost-effective and particularly suitable for transcriptomic profiling of minimal volumes of whole blood, typical of those obtained with small animal species.

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.

Assessment of kidney organ quality and prediction of outcome at time of transplantation

The critical importance of donor organ quality, i.e., number of surviving nephrons, ability to withstand injury, and capacity for repair in determining short- and long-term outcomes is becoming increasingly clear. This review provides an overview of studies to assess donor kidney quality and subsequent transplant outcomes based on clinical pathology and transcriptome-based variables available at time of transplantation. Prediction scores using clinical variables function when applied to large data sets but perform poorly for the individual patient. Histopathology findings in pre-implantation or post-reperfusion biopsies help to assess structural integrity of the donor kidney, provide information on pre-existing donor disease, and can serve as a baseline for tracking changes over time. However, more validated approaches of analysis and prospective studies are needed to reduce the number of discarded organs, improve allocation, and allow prediction of outcomes. Molecular profiling detects changes not seen by morphology or captured by clinical markers. In particular, molecular profiles provide a quantitative measurement of inflammatory burden or immune activation and reflect coordinated changes in pathways associated with injury and repair. However, description of transcriptome patterns is not an end in itself. The identification of predictive gene sets and the application to an individualized patient management needs the integration of clinical and pathology-based variables, as well as more objective reference markers of transplant function, post-transplant events, and long-term outcomes.

Hyaluronan metabolism in remodeling extracellular matrix: probes for imaging and therapy of breast cancer

Clinical and experimental evidence increasingly support the concept of cancer as a disease that emulates a component of wound healing, in particular abnormal stromal extracellular matrix remodeling. Here we review the biology and function of one remodeling process, hyaluronan (HA) metabolism, which is essential for wound resolution but closely linked to breast cancer (BCA) progression. Components of the HA metabolic cycle (HAS2, SPAM1 and HA receptors CD44, RHAMM/HMMR and TLR2) are discussed in terms of their known functions in wound healing and in breast cancer progression. Finally, we discuss recent advances in the use of HA-based platforms for developing nanoprobes to image areas of active HA metabolism and for therapeutics in breast cancer.

Expression profiling of formalin-fixed paraffin-embedded primary breast tumors using cancer-specific and whole genome gene panels on the DASL® platform

Background

The cDNA-mediated Annealing, extension, Selection and Ligation (DASL) assay has become a suitable gene expression profiling system for degraded RNA from paraffin-embedded tissue. We examined assay characteristics and the performance of the DASL 502-gene Cancer Panel^v1 (1.5K) and 24,526-gene panel (24K) platforms at differentiating nine human epidermal growth factor receptor 2- positive (HER2+) and 11 HER2-negative (HER2-) paraffin-embedded breast tumors.

Methods

Bland-Altman plots and Spearman correlations evaluated intra/inter-panel agreement of normalized expression values. Unequal-variance t-statistics tested for differences in expression levels between HER2 + and HER2 - tumors. Regulatory network analysis was performed using Metacore (GeneGo Inc., St. Joseph, MI).

Results

Technical replicate correlations ranged between 0.815-0.956 and 0.986-0.997 for the 1.5K and 24K panels, respectively. Inter-panel correlations of expression values for the common 498 genes across the two panels ranged between 0.485-0.573. Inter-panel correlations of expression values of 17 probes with base-pair sequence matches between the 1.5K and 24K panels ranged between 0.652-0.899. In both panels, erythroblastic leukemia viral oncogene homolog 2 (ERBB2) was the most differentially expressed gene between the HER2 + and HER2 - tumors and seven additional genes had p-values < 0.05 and log2 -fold changes > |0.5| in expression between HER2 + and HER2 - tumors: topoisomerase II alpha (TOP2A), cyclin a2 (CCNA2), v-fos fbj murine osteosarcoma viral oncogene homolog (FOS), wingless-type mmtv integration site family, member 5a (WNT5A), growth factor receptor-bound protein 7 (GRB7), cell division cycle 2 (CDC2), and baculoviral iap repeat-containing protein 5 (BIRC5). The top 52 discriminating probes from the 24K panel are enriched with genes belonging to the regulatory networks centered around v-myc avian myelocytomatosis viral oncogene homolog (MYC), tumor protein p53 (TP53), and estrogen receptor α (ESR1). Network analysis with a two-step extension also showed that the eight discriminating genes common to the 1.5K and 24K panels are functionally linked together through MYC, TP53, and ESR1.

Conclusions

The relative RNA abundance obtained from two highly differing density gene panels are correlated with eight common genes differentiating HER2 + and HER2 - breast tumors. Network analyses demonstrated biological consistency between the 1.5K and 24K gene panels.

Drosophila as a tool for personalized medicine: a primer

The goal of personalized medicine is to treat each patient with the best drug: optimal therapeutic benefit with minimal side effects. The genomic revolution is rapidly identifying the genetic contribution to the diseased state as well as its contribution to drug efficacy and toxicity. The ability to perform genome-wide studies has led to an overwhelming number of candidate genes and/or their associated variants; however, understanding which are of therapeutic importance is becoming the greatest unmet need in the personalized medicine field. A related issue is the need to improve our methods of identifying and characterizing therapeutic drugs in the context of the complex genomic landscape of the intact body. Drosophila have proven to be a powerful tool for understanding the basic biological mechanisms of human development. This article will review Drosophila as a whole animal tool for gene and drug discovery. We will examine how Drosophila can be used to both sort through the myriad of hits coming from human genome-wide scans and to dramatically improve the early steps in pharmaceutical drug development.

Alterations of microRNAs and their targets are associated with acquired resistance of MCF-7 breast cancer cells to cisplatin

Cancer cells that develop resistance to chemotherapeutic agents are a major clinical obstacle in the successful treatment of breast cancer. Acquired cancer chemoresistance is a multifactorial phenomenon, involving various mechanisms and processes. Recent studies suggest that chemoresistance may be linked to drug-induced dysregulation of microRNA function. Furthermore, mounting evidence indicates the existence of similarities between drug-resistant and metastatic cancer cells in terms of resistance to apoptosis and enhanced invasiveness. We studied the role of miRNA alterations in the acquisition of cisplatin-resistant phenotype in MCF-7 human breast adenocarcinoma cells. We identified a total of 103 miRNAs that were overexpressed or underexpressed (46 upregulated and 57 downregulated) in MCF-7 cells resistant to cisplatin. These differentially expressed miRNAs are involved in the control of cell signaling, cell survival, DNA methylation and invasiveness. The most significantly dysregulated miRNAs were miR-146a, miR-10a, miR-221/222, miR-345, miR-200b and miR-200c. Furthermore, we demonstrated that miR-345 and miR-7 target the human multidrug resistance-associated protein 1. These results suggest that dysregulated miRNA expression may underlie the abnormal functioning of critical cellular processes associated with the cisplatin-resistant phenotype.

Pharmacogenetics in breast cancer: steps toward personalized medicine in breast cancer management

There is wide individual variability in the pharmacokinetics, pharmacodynamics, and tolerance to anticancer drugs within the same ethnic group and even greater variability among different ethnicities. Pharmacogenomics (PG) has the potential to provide personalized therapy based on individual genetic variability in an effort to maximize efficacy and reduce adverse effects. The benefits of PG include improved therapeutic index, improved dose regimen, and selection of optimal types of drug for an individual or set of individuals. Advanced or metastatic breast cancer is typically treated with single or multiple combinations of chemotherapy regimens including anthracyclines, taxanes, antimetabolites, alkylating agents, platinum drugs, vinca alkaloids, and others. In this review, the PG of breast cancer therapeutics, including tamoxifen, which is the most widely used therapeutic for the treatment of hormone-dependent breast cancer, is reviewed. The pharmacological activity of tamoxifen depends on its conversion by cytochrome P450 2D6 (CYP2D6) to its abundant active metabolite, endoxifen. Patients with reduced CYP2D6 activity, as a result of either their genotype or induction by the coadministration of other drugs that inhibit CYP2D6 function, produce little endoxifen and hence derive limited therapeutic benefit from tamoxifen; the same can be said about the different classes of therapeutics in breast cancer. PG studies of breast cancer therapeutics should provide patients with breast cancer with optimal and personalized therapy.

Pharmacogenetics in breast cancer: focus on hormone therapy, taxanes, trastuzumab and bevacizumab

Breast cancer is the most common female cancer, with more than one million new patients diagnosed annually worldwide. The great heterogeneity, in terms of prognosis and outcome, within patients with the same clinical and pathological characteristics may limit the potential for personalized therapy. Most of the cytotoxic agents and new targeted agents have a narrow therapeutic index and the administration of an equal dose may result in a wide range of toxicities as well as to different antitumor efficacy. Inter-subject variability in drug toxicity and response is common during treatment, so that individualization of treatments is an important issue. Pharmacogenetics is the study of how inter-individual variations in the DNA sequence of specific genes may affect drug response and toxicity. This article highlights the clinical use of determination of polymorphisms of important human drug-metabolizing cytochrome P450s, ABCB1, IgG fragment C receptors and vascular endothelial growth factor, which are responsible of the large inter-individual variability in drug metabolism and clearance of the agents commonly used in breast cancer treatment, such as tamoxifen, aromatase inhibitors, taxanes, trastuzumab and bevacizumab.

Triple-negative breast cancer: present challenges and new perspectives

Triple-negative breast cancers (TNBC), characterized by absence of estrogen receptor (ER), progesterone receptor (PR) and lack of overexpression of human epidermal growth factor receptor 2 (HER2), are typically associated with poor prognosis, due to aggressive tumor phenotype(s), only partial response to chemotherapy and present lack of clinically established targeted therapies. Advances in the design of individualized strategies for treatment of TNBC patients require further elucidation, by combined 'omics' approaches, of the molecular mechanisms underlying TNBC phenotypic heterogeneity, and the still poorly understood association of TNBC with BRCA1 mutations. An overview is here presented on TNBC profiling in terms of expression signatures, within the functional genomic breast tumor classification, and ongoing efforts toward identification of new therapy targets and bioimaging markers. Due to the complexity of aberrant molecular patterns involved in expression, pathological progression and biological/clinical heterogeneity, the search for novel TNBC biomarkers and therapy targets requires collection of multi-dimensional data sets, use of robust multivariate data analysis techniques and development of innovative systems biology approaches.

Clinical implications of chemotherapy-induced tumor gene expression in human breast cancers

IMPORTANCE OF THE FIELD

There has been much interest in generating gene signatures to predict treatment response in breast cancer.

AREAS COVERED IN THIS REVIEW

There are at least 15 published studies that describe baseline tumor gene signatures predicting chemotherapy sensitivity. As an extension of these baseline studies, there have been at least 8 published studies evaluating chemotherapy-induced tumor genomic changes over time in human breast cancers.

WHAT THE READER WILL GAIN

Studies on chemotherapy-induced gene expression changes were reviewed in detail. Drug-induced biological changes within the tumor shed light on mechanisms of drug resistance and provided valuable insights regarding genes and pathways that were regulated by different drugs, including therapeutic targets that could be exploited to overcome resistance. One study also suggested post-chemotherapy gene signatures to be more predictive of response and survival than the unchallenged baseline signatures.

TAKE HOME MESSAGE

Studies on chemotherapy-induced changes, although informative, are logistically demanding to execute, often with significant attrition of collected samples resulting in small datasets. They are further limited by heterogeneity of study population, chemotherapy regimens used, timing of the post-therapy sample and definition of response endpoint, making cross-comparisons of studies and data interpretation difficult. Future studies should address these limitations, and should involve larger sample sets and prospective studies for validation.

Use of ER/PR/HER2 subtypes in conjunction with the 2007 St Gallen Consensus Statement for early breast cancer

Background

The 2007 St Gallen international expert consensus statement describes three risk categories and provides recommendations for treatment of early breast cancer. The set of recommendations on how to best treat primary breast cancer is recognized and used by clinicians worldwide. We now examine the variability of five-year survival of the 2007 St Gallen Risk Classifications utilizing the ER/PR/HER2 subtypes.

Methods

Using the population-based California Cancer Registry, 114,786 incident cases of Stages 1-3 invasive breast cancer diagnosed between 2000 and 2006 were identified. Cases were assigned to Low, Intermediate, or High Risk categories. Five-year-relative survival was computed for the three St Gallen risk categories and for the ER/PR/HER2 subtypes for further differentiation.

Results and Discussion

There were 9,124 (13%) cases classified as Low Risk, 44,234 (65%) cases as Intermediate Risk, and 14,340 (21%) as High Risk. Within the Intermediate Risk group, 33,735 (76%) were node-negative (Intermediate Risk 2) and 10,499 (24%) were node-positive (Intermediate Risk 3). For the High Risk group, 6,149 (43%) had 1 to 3 positive axillary lymph nodes (High Risk 4) and 8,191 (57%) had four or more positive lymph nodes (High Risk 5).

Using five-year relative survival as the principal criterion, we found the following: a) There was very little difference between the Low Risk and Intermediate Risk categories; b) Use of the ER/PR/HER2 subtypes within the Intermediate and High Risk categories separated each into a group with better five-year survival (ER-positive) and a group with worse survival (ER-negative), irrespective of HER2-status; c) The heterogeneity of the High Risk category was most evident when one examined the ER/PR/HER2 subtypes with four or more positive axillary lymph nodes; (d) HER2-positivity did not always translate to worse survival, as noted when one compared the triple positive subtype (ER+/PR+/HER2+) to the triple negative subtype (ER-/PR-/HER2-); and (e) ER-negativity appeared to be a stronger predictor of poor survival than HER2-positivity.

Conclusion

The use of ER/PR/HER2 subtype highlights the marked heterogeneity of the Intermediate and High Risk categories of the 2007 St Gallen statements. The use of ER/PR/HER2 subtypes and correlation with molecular classification of breast cancer is recommended.

Analysis of multiple sarcoma expression datasets: implications for classification, oncogenic pathway activation and chemotherapy resistance

Background

Diagnosis of soft tissue sarcomas (STS) is challenging. Many remain unclassified (not-otherwise-specified, NOS) or grouped in controversial categories such as malignant fibrous histiocytoma (MFH), with unclear therapeutic value. We analyzed several independent microarray datasets, to identify a predictor, use it to classify unclassifiable sarcomas, and assess oncogenic pathway activation and chemotherapy response.

Methodology/Principal Findings

We analyzed 5 independent datasets (325 tumor arrays). We developed and validated a predictor, which was used to reclassify MFH and NOS sarcomas. The molecular “match” between MFH and their predicted subtypes was assessed using genome-wide hierarchical clustering and Subclass-Mapping. Findings were validated in 15 paraffin samples profiled on the DASL platform. Bayesian models of oncogenic pathway activation and chemotherapy response were applied to individual STS samples. A 170-gene predictor was developed and independently validated (80-85% accuracy in all datasets). Most MFH and NOS tumors were reclassified as leiomyosarcomas, liposarcomas and fibrosarcomas. “Molecular match” between MFH and their predicted STS subtypes was confirmed both within and across datasets. This classification revealed previously unrecognized tissue differentiation lines (adipocyte, fibroblastic, smooth-muscle) and was reproduced in paraffin specimens. Different sarcoma subtypes demonstrated distinct oncogenic pathway activation patterns, and reclassified MFH tumors shared oncogenic pathway activation patterns with their predicted subtypes. These patterns were associated with predicted resistance to chemotherapeutic agents commonly used in sarcomas.

Conclusions/Significance

STS profiling can aid in diagnosis through a predictor tracking distinct tissue differentiation in unclassified tumors, and in therapeutic management via oncogenic pathway activation and chemotherapy response assessment.

A five-gene signature as a potential predictor of metastasis and survival in colorectal cancer

To understand the molecular mechanisms of metastasis and prognosis of colorectal cancer (CRC), we isolated single cell-derived progenies (SCPs) from SW480 cells in vitro and compared their metastatic potential in an orthotopic CRC tumour model in vivo. Two groups of SCPs with the capability of high and low metastasis, respectively, were obtained. By analysing the gene expression profiles of high (SCP51), low (SCP58) metastatic SCPs, and their parental cell line (SW480/EGFP), we demonstrated that 143 genes were differentially expressed either between SCP51 and SCP58 or between SCP58 and SW480/EGFP. Gene-annotation enrichment analysis of DAVID revealed 80 genes in the top ten clusters of the analysis (gene enrichment score > 1). Of the 80-gene set, 32 genes are potentially involved in metastasis, as revealed by Geneclip. Five putative metastatic genes (LYN, SDCBP, MAP4K4, DKK1, and MID1) were selected for further validations. Immunohistochemical analysis in a cohort of 181 CRC clinical samples showed that the individual expression of LYN, MAP4K4, and MID1, as well as the five-gene signature, was closely correlated with lymph node metastasis in CRC patients. More importantly, the individual expression of LYN, MAP4K4, SDCBP, and MID1, as well as the five-gene signature, was significantly correlated with overall survival in CRC patients. Thus, our five-gene signature may be able to predict metastasis and survival of CRC in the clinic, and opens new perspectives on the biology of CRC.

Predictors of tumor progression during neoadjuvant chemotherapy in breast cancer

PURPOSE Although most breast cancer patients who receive neoadjuvant chemotherapy (NCT) have a tumor response, a small proportion experience progressive disease (PD). Predictors of response have been reported, but predictors for progression have not been identified. We sought to identify predictors of tumor progression during NCT with the ultimate aim of identifying patients who might benefit from a first-line surgical approach or from novel targeted therapies. PATIENTS AND METHODS Data were obtained from reviewing medical records of patients with stage I to III breast cancer who received NCT (anthracycline and/or taxane based). Statistical analysis was performed to compare patients with any response or stable disease with patients with PD. RESULTS One thousand nine hundred twenty-eight patients received NCT; 1,762 patients (91%) had some response, 107 (6%) had stable disease, and 59 (3%) had PD at some point during NCT. Factors predictive of PD included African American race (P = .002), tumor (T) status (P = .002), and American Joint Committee on Cancer clinical stage (P = .02). Histopathologic features of PD were high tumor grade (P = .005), high Ki-67 score (P = .002), and negative estrogen receptor (ER)/progesterone receptor (PR) status (P < .001/P < .001). Pre-NCT T status, race, and ER status were independent predictors of progression in multivariate analysis. Disease progression was a negative predictor of distant disease-free survival and overall survival in multivariate analysis (P < .001). CONCLUSION Factors predictive of PD include race, advanced tumor stage, high nuclear grade, high Ki-67 score, and ER/PR negativity. Because many of these variables are also associated with response to NCT, novel molecular predictors are needed to identify patients at risk for progression on standard NCT.

Interpreting metabolomic profiles using unbiased pathway models

Human disease is heterogeneous, with similar disease phenotypes resulting from distinct combinations of genetic and environmental factors. Small-molecule profiling can address disease heterogeneity by evaluating the underlying biologic state of individuals through non-invasive interrogation of plasma metabolite levels. We analyzed metabolite profiles from an oral glucose tolerance test (OGTT) in 50 individuals, 25 with normal (NGT) and 25 with impaired glucose tolerance (IGT). Our focus was to elucidate underlying biologic processes. Although we initially found little overlap between changed metabolites and preconceived definitions of metabolic pathways, the use of unbiased network approaches identified significant concerted changes. Specifically, we derived a metabolic network with edges drawn between reactant and product nodes in individual reactions and between all substrates of individual enzymes and transporters. We searched for "active modules"--regions of the metabolic network enriched for changes in metabolite levels. Active modules identified relationships among changed metabolites and highlighted the importance of specific solute carriers in metabolite profiles. Furthermore, hierarchical clustering and principal component analysis demonstrated that changed metabolites in OGTT naturally grouped according to the activities of the System A and L amino acid transporters, the osmolyte carrier SLC6A12, and the mitochondrial aspartate-glutamate transporter SLC25A13. Comparison between NGT and IGT groups supported blunted glucose- and/or insulin-stimulated activities in the IGT group. Using unbiased pathway models, we offer evidence supporting the important role of solute carriers in the physiologic response to glucose challenge and conclude that carrier activities are reflected in individual metabolite profiles of perturbation experiments. Given the involvement of transporters in human disease, metabolite profiling may contribute to improved disease classification via the interrogation of specific transporter activities.

Drug treatment of Alzheimer's disease patients leads to expression changes in peripheral blood cells

BACKGROUND

Increasing cholinergic activity has been the primary mechanism for treating dementia due to Alzheimer's disease. However, the effectiveness of cholinesterase inhibitors (ChEIs) is still widely debated. The identification of specific biomarkers capable of identifying patients more likely to respond to these treatments could potentially provide specific evidence to clearly address this controversy through patient stratification. The goal of this study was to determine the feasibility of discovering biomarkers specific for the treatment of Alzheimer's disease.

METHODS

Peripheral blood was collected from a cohort of patients treated with different ChEIs. Total RNA was isolated and profiled on the human Genome-Wide SpliceArray (GWSA) to test the feasibility of discriminating the different treatment subgroups of subjects based on the expression patterns generated from the Genome-Wide SpliceArray.

RESULTS

Specific expression differences were identified for the various treatment groups that lead to a clear separation between patients treated with ChEIs versus naïve patients when Principal Component Analysis was performed on probe sets selected for differential expression. In addition, specific probe sets were identified to be dependent on the inhibitor used among the treated patients.

CONCLUSIONS

Distinct separation between non-treated, galantamine, donepezil, and rivastigmine-treated patients was clearly identified based on small sets of expression probes. The ability to identify drug-specific treatment expression differences strengthens the potential for using peripheral gene signatures for the identification of individuals responding to drug treatment.

Correcting for intra-experiment variation in Illumina BeadChip data is necessary to generate robust gene-expression profiles

Background

Microarray technology is a popular means of producing whole genome transcriptional profiles, however high cost and scarcity of mRNA has led many studies to be conducted based on the analysis of single samples. We exploit the design of the Illumina platform, specifically multiple arrays on each chip, to evaluate intra-experiment technical variation using repeated hybridisations of universal human reference RNA (UHRR) and duplicate hybridisations of primary breast tumour samples from a clinical study.

Results

A clear batch-specific bias was detected in the measured expressions of both the UHRR and clinical samples. This bias was found to persist following standard microarray normalisation techniques. However, when mean-centering or empirical Bayes batch-correction methods (ComBat) were applied to the data, inter-batch variation in the UHRR and clinical samples were greatly reduced. Correlation between replicate UHRR samples improved by two orders of magnitude following batch-correction using ComBat (ranging from 0.9833-0.9991 to 0.9997-0.9999) and increased the consistency of the gene-lists from the duplicate clinical samples, from 11.6% in quantile normalised data to 66.4% in batch-corrected data. The use of UHRR as an inter-batch calibrator provided a small additional benefit when used in conjunction with ComBat, further increasing the agreement between the two gene-lists, up to 74.1%.

Conclusion

In the interests of practicalities and cost, these results suggest that single samples can generate reliable data, but only after careful compensation for technical bias in the experiment. We recommend that investigators appreciate the propensity for such variation in the design stages of a microarray experiment and that the use of suitable correction methods become routine during the statistical analysis of the data.

Biobanking of human pancreas cancer tissue: impact of ex-vivo procurement times on RNA quality

BACKGROUND

Tissue banking has become a major initiative at many oncology centers. The influence of warm ex-vivo ischemia times, storage times, and biobanking protocols on RNA integrity and subsequent microarray data is not well documented.

METHODS

A prospective institutional review board-approved protocol for the banking of abdominal neoplasms was initiated at Memorial Sloan-Kettering Cancer Center in 2001. Sixty-four representative pancreas cancer specimens snap-frozen at various ex-vivo procurement times (< or =10 min, 11-30 min, 31-60 min, >1 h) and banked during three time periods (2001-2004, 2004-2006, 2006-2008) were processed. RNA integrity was determined by microcapillary electrophoresis using the RNA integrity number (RIN) algorithm and by results of laser-capture microdissection (LCM).

RESULTS

Overall, 42% of human pancreas cancer specimens banked under a dedicated protocol yielded RNA with a RIN of > or =7. Limited warm ex-vivo ischemia times did not negatively impact RNA quality (percentage of tissue with total RNA with RIN of > or =7 for < or =10 min, 42%; 11-30 min, 58%; 31-60 min, 33%; >60 min, 42%), and long-term storage of banked pancreas cancer biospecimens did not negatively influence RNA quality (total RNA with RIN of > or =7 banked 2001-2004, 44%; 2004-2006, 38%; 2006-2008, 50%). RNA retrieved from pancreatic cancer samples with RIN of > or =7 subject to LCM yielded RNA suitable for further downstream applications.

CONCLUSIONS

Fresh-frozen pancreas tissue banked within a standardized research protocol yields high-quality RNA in approximately 50% of specimens and can be used for enrichment by LCM. Quality of tissues of the biobank were not adversely impacted by limited variations of warm ischemia times or different storage periods. This study shows the challenges and investments required to initiate and maintain high-quality tissue repositories.

Advances in translational bioinformatics: computational approaches for the hunting of disease genes

Over a 100 years ago, William Bateson provided, through his observations of the transmission of alkaptonuria in first cousin offspring, evidence of the application of Mendelian genetics to certain human traits and diseases. His work was corroborated by Archibald Garrod (Archibald AE. The incidence of alkaptonuria: a study in chemical individuality. Lancert 1902;ii:1616-20) and William Farabee (Farabee WC. Inheritance of digital malformations in man. In: Papers of the Peabody Museum of American Archaeology and Ethnology. Cambridge, Mass: Harvard University, 1905; 65-78), who recorded the familial tendencies of inheritance of malformations of human hands and feet. These were the pioneers of the hunt for disease genes that would continue through the century and result in the discovery of hundreds of genes that can be associated with different diseases. Despite many ground-breaking discoveries during the last century, we are far from having a complete understanding of the intricate network of molecular processes involved in diseases, and we are still searching for the cures for most complex diseases. In the last few years, new genome sequencing and other high-throughput experimental techniques have generated vast amounts of molecular and clinical data that contain crucial information with the potential of leading to the next major biomedical discoveries. The need to mine, visualize and integrate these data has motivated the development of several informatics approaches that can broadly be grouped in the research area of 'translational bioinformatics'. This review highlights the latest advances in the field of translational bioinformatics, focusing on the advances of computational techniques to search for and classify disease genes.

Can survival prediction be improved by merging gene expression data sets?

BACKGROUND

High-throughput gene expression profiling technologies generating a wealth of data, are increasingly used for characterization of tumor biopsies for clinical trials. By applying machine learning algorithms to such clinically documented data sets, one hopes to improve tumor diagnosis, prognosis, as well as prediction of treatment response. However, the limited number of patients enrolled in a single trial study limits the power of machine learning approaches due to over-fitting. One could partially overcome this limitation by merging data from different studies. Nevertheless, such data sets differ from each other with regard to technical biases, patient selection criteria and follow-up treatment. It is therefore not clear at all whether the advantage of increased sample size outweighs the disadvantage of higher heterogeneity of merged data sets. Here, we present a systematic study to answer this question specifically for breast cancer data sets. We use survival prediction based on Cox regression as an assay to measure the added value of merged data sets.

RESULTS

Using time-dependent Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) and hazard ratio as performance measures, we see in overall no significant improvement or deterioration of survival prediction with merged data sets as compared to individual data sets. This apparently was due to the fact that a few genes with strong prognostic power were not available on all microarray platforms and thus were not retained in the merged data sets. Surprisingly, we found that the overall best performance was achieved with a single-gene predictor consisting of CYB5D1.

CONCLUSIONS

Merging did not deteriorate performance on average despite (a) The diversity of microarray platforms used. (b) The heterogeneity of patients cohorts. (c) The heterogeneity of breast cancer disease. (d) Substantial variation of time to death or relapse. (e) The reduced number of genes in the merged data sets. Predictors derived from the merged data sets were more robust, consistent and reproducible across microarray platforms. Moreover, merging data sets from different studies helps to better understand the biases of individual studies and can lead to the identification of strong survival factors like CYB5D1 expression.

State of the art management in spine oncology: a worldwide perspective on its evolution, current state, and future

STUDY DESIGN

A review of the past and current status of the evolving field of spine oncology.

OBJECTIVE

To provide a framework of reference for developments in the field, particularly the rapidly evolving field of molecular biology, as well as contemporary practice in the management of spine tumors.

METHODS

Literature review of the surgical treatment of spine tumors in the past and present, the emerging radiologic and biologic technologies, as well as the field of targeted therapy in cancer and the economic implications of technological advances.

RESULTS

A vast contemporary literature is currently available that provides a clear rational basis for treatment. Most treatment recommendations are currently based on retrospective data and small Phase II prospective studies. Treatment paradigms continue to evolve without their relative merits being evaluated by randomized controlled trials. The current lack of randomized trials in spine oncology reflect both the rarity of spine tumors and strongly held biases based on retrospective studies and institutional bias.

CONCLUSION

Spine oncology is a rapidly evolving field with contributions in surgery, radiation therapy, and targeted chemotherapy resulting in overall improvement in quality of life and survival in patients with spine tumors. However, the economic consequences of these improvements are substantial and need to be kept in proper perspective.

Genomic predictors of outcome and treatment response in breast cancer

Despite advances in breast cancer treatment and outcome over the last two decades, women continue to relapse and die of advanced disease. Historically, estrogen and progesterone receptor expression, HER2 overexpression and clinico-pathologic parameters have guided therapeutic decision making. However, there are limits to the risk estimation provided by these parameters, leading to potential overtreatment of low-risk disease and undertreatment of poor-risk disease. Genomic technologies now provide the opportunity to refine our therapeutic approach by individualizing treatment to patients' individual tumor profiles. Gene profiles or signatures are groupings of genes that are differentially expressed between tumors, reflecting differences in biologic behavior. Prognostic gene signatures stratify breast cancer patients by tumor natural history, regardless of the treatment employed. Currently, there are three commercially available prognostic gene signatures: Oncotype DX (Genomic Health, Inc.), MammaPrint (Agendia BV), and the HOXB13/IL17BR (H/I) ratio; (Theros H/ISM; bioTheranostics). Others under development include the Intrinsic Gene Set, the Rotterdam Signature, the Wound Response Indicator, and the Invasive Gene Signature. Predicative signatures classify patients based on responsiveness to specific therapies. Of the prognostic signatures, Oncotype DX has been shown to have predictive value for the incremental benefit of chemotherapy when added to a hormonal therapy regimen. Additional genetic profiles under development predict response to specific hormonal therapies, anthracyclines, and taxanes. Gene signatures have the potential to transform breast cancer treatment as it becomes tailored to each patient's tumor expression profile and significantly improve the outcomes of this disease.

Breast cancer subtypes as defined by the estrogen receptor (ER), progesterone receptor (PR), and the human epidermal growth factor receptor 2 (HER2) among women with invasive breast cancer in California, 1999-2004

Breast cancer research examining either molecular profiles or biomarker subtypes has focused on the estrogen receptor negative/progesterone receptor negative/human epidermal growth factor receptor 2 negative (ER-/PR-/HER2-) and ER-/PR-/HER2+ subtypes. Less is known about the epidemiology or clinical outcome of the other subtypes. This study examines the eight combinations of ER/PR/HER2 in patients with invasive breast cancer. The 5-year relative survival and the distribution among demographic, socioeconomic, and tumor characteristics of each of the subtypes are examined. Using the California Cancer Registry, 61,309 women with primary invasive breast cancer were classified according to ER/PR/HER2 status. Five-year relative survival was computed for the eight subtypes. Bivariate analyses were used to assess the distribution of cases across all subtypes. Multivariate logistic regression was used to compute the adjusted odds of having one of the five subtypes with the best and worst survival. Survival varied from 96% (ER+/PR+/HER2-) to 76% (ER-/PR-/HER2+ and ER-/PR-/HER2-). The four subtypes with the poorest survival were all ER negative. Women who were younger than age 50, non-Hispanic black or Hispanic, of the lowest SES groups, and had stage IV tumors that were undifferentiated were overrepresented in ER-/PR-/HER2+ and triple negative (ER-/PR-/HER2-) subtypes. Asian Pacific Islanders had increased odds (OR = 1.41; 95% confidence interval [CI] = 1.26-1.57) of having the ER-/PR-/HER2+ subtype. Stage III tumors (OR = 1.25; 95% CI = 1.08-1.44) and stage IV tumors (OR = 1.58; 95% CI = 1.27-1.98) had higher odds than stage I tumors of being ER-/PR-/HER2+. Stage IV tumors (OR = 0.54; 95% CI = 0.44-0.67) strongly decreased the odds of the ER-/PR-/HER2- subtype. Poorly differentiated and undifferentiated tumors were over 20 times as likely as well-differentiated tumors of being ER-/PR-/HER2- or ER-/PR-/HER2+. There are considerable differences in survival, demographics, and tumor characteristics among the eight subtypes. We recommend reporting breast cancer as an ER/PR/HER2 subtype and precisely documenting demographic and tumor characteristics.

Genetic heterogeneity of Myc-induced mammary tumors reflecting diverse phenotypes including metastatic potential

Human cancers result from a complex series of genetic alterations, resulting in heterogeneous disease states. Dissecting this heterogeneity is critical for understanding underlying mechanisms and providing opportunities for therapeutics matching the complexity. Mouse models of cancer have generally been used to reduce this complexity and focus on the role of single genes. Nevertheless, our analysis of tumors arising in the MMTV-Myc model of mammary carcinogenesis reveals substantial heterogeneity, seen in both histological and expression phenotypes. One contribution to this heterogeneity is the substantial frequency of activating Ras mutations. Additionally, we show that these Myc-induced mammary tumors exhibit even greater heterogeneity, revealed by distinct histological subtypes as well as distinct patterns of gene expression, than many other mouse models of tumorigenesis. Two of the major histological subtypes are characterized by differential patterns of cellular signaling pathways, including beta-catenin and Stat3 activities. We also demonstrate that one of the MMTV-Myc mammary tumor subgroups exhibits metastatic capacity and that the signature derived from the subgroup can predict metastatic potential of human breast cancer. Together, these data reveal that a combination of histological and genomic analyses can uncover substantial heterogeneity in mammary tumor formation and therefore highlight aspects of tumor phenotype not evident in the population as a whole.