Gene Expression Profiling Technologies Research Articles

Site-specific gene expression profiling as a novel strategy for unravelling keloid disease pathobiology.

Keloid disease (KD) is a fibroproliferative cutaneous tumour characterised by heterogeneity, excess collagen deposition and aggressive local invasion. Lack of a validated animal model and resistance to a multitude of current therapies has resulted in unsatisfactory clinical outcomes of KD management. In order to address KD from a new perspective, we applied for the first time a site-specific in situ microdissection and gene expression profiling approach, through combined laser capture microdissection and transcriptomic array. The aim here was to analyse the utility of this approach compared with established methods of investigation, including whole tissue biopsy and monolayer cell culture techniques. This study was designed to approach KD from a hypothesis-free and compartment-specific angle, using state-of-the-art microdissection and gene expression profiling technology. We sought to characterise expression differences between specific keloid lesional sites and elucidate potential contributions of significantly dysregulated genes to mechanisms underlying keloid pathobiology, thus informing future explorative research into KD. Here, we highlight the advantages of our in situ microdissection strategy in generating expression data with improved sensitivity and accuracy over traditional methods. This methodological approach supports an active role for the epidermis in the pathogenesis of KD through identification of genes and upstream regulators implicated in epithelial-mesenchymal transition, inflammation and immune modulation. We describe dermal expression patterns crucial to collagen deposition that are associated with TGFβ-mediated signalling, which have not previously been examined in KD. Additionally, this study supports the previously proposed presence of a cancer-like stem cell population in KD and explores the possible contribution of gene dysregulation to the resistance of KD to conventional therapy. Through this innovative in situ microdissection gene profiling approach, we provide better-defined gene signatures of distinct KD regions, thereby addressing KD heterogeneity, facilitating differential diagnosis with other cutaneous fibroses via transcriptional fingerprinting, and highlighting key areas for future KD research.

Genomic assays in breast cancer: Issues yet to settle?

<p>Breast cancer today has emerged as the most commonly diagnosed malignancy in women world wide, accounting for 1 in 4 of every cancer diagnosed in women today. It is the leading cause of cancer death in women in the developing world and second leading cause of cancer (following lung cancer) in the developed world. Introduction of novel high through-output gene expression profiling technologies such as Next Generation Sequencing (NGS) and Genome wide association studies (GWAS) has led to the genetic profiling of breast cancer and to the development of genomic assays that ushered in an paradigm shift in the management of breast cancer from single individual variable to multivariate prediction models encompassing the tumors gross, microscopic and genetic variables. Oncotype DX, MammaPrint assay, MammoStrat assay, & Prosigna kit are some of the commercially available assays in various stages of validation. But various studies have reported discordance in risk stratification when the different tests is applied to the same patient cohort leading to a therapeutic quagmire. Tumor genetic signatures are not concordant but highly variable with each carrying its own unique set of genes dictating its growth, response to chemotherapy and risk of recurrence. Similarly triple negative breast cancers (TNBC), risk of late recurrence (> 5 years), validity of these over different population groups and quality control are some of the other issues which are yet to settle.</p>

Disentangling neural cell diversity using single-cell transcriptomics.

Cellular specialization is particularly prominent in mammalian nervous systems, which are composed of millions to billions of neurons that appear in thousands of different 'flavors' and contribute to a variety of functions. Even in a single brain region, individual neurons differ greatly in their morphology, connectivity and electrophysiological properties. Systematic classification of all mammalian neurons is a key goal towards deconstructing the nervous system into its basic components. With the recent advances in single-cell gene expression profiling technologies, it is now possible to undertake the enormous task of disentangling neuronal heterogeneity. High-throughput single-cell RNA sequencing and multiplexed quantitative RT-PCR have become more accessible, and these technologies enable systematic categorization of individual neurons into groups with similar molecular properties. Here we provide a conceptual and practical guide to classification of neural cell types using single-cell gene expression profiling technologies.

Fetal Bovine Serum RNA Interferes with the Cell Culture derived Extracellular RNA.

Fetal bovine serum (FBS) has been used in eukaryotic cell cultures for decades. However, little attention has been paid to the biological effects associated with RNA content of FBS on cell cultures. Here, using RNA sequencing, we demonstrate that FBS contains a diverse repertoire of protein-coding and regulatory RNA species, including mRNA, miRNA, rRNA, and snoRNA. The majority of them (>70%) are retained even after extended ultracentrifugation in the preparations of vesicle-depleted FBS (vdFBS) commonly utilized in the studies of extracellular vesicles (EV) and intercellular communication. FBS-associated RNA is co-isolated with cell-culture derived extracellular RNA (exRNA) and interferes with the downstream RNA analysis. Many evolutionally conserved FBS-derived RNA species can be falsely annotated as human or mouse transcripts. Notably, specific miRNAs abundant in FBS, such as miR-122, miR-451a and miR-1246, have been previously reported as enriched in cell-culture derived EVs, possibly due to the confounding effect of the FBS. Analysis of publically available exRNA datasets supports the notion of FBS contamination. Furthermore, FBS transcripts can be taken up by cultured cells and affect the results of highly sensitive gene expression profiling technologies. Therefore, precautions for experimental design are warranted to minimize the interference and misinterpretations caused by FBS-derived RNA.

Low-dose oral interferon modulates expression of inflammatory and autoimmune genes in cattle

While the safety and efficacy profiles of orally administered bovine interferon (IFN) alpha have been documented, the mechanism(s) that result in clinical benefits remain elusive. One approach to delineating the molecular pathways of IFN efficacy is through the use of gene expression profiling technologies. In this proof-of-concept study, different (0, 50, 200 and 800 units) oral doses of natural bovine IFN (type I) were tested in cattle to determine if oral IFN altered the expression of genes that may be pivotal to the development of systemic resistance to viral infections such as foot-and-mouth disease (FMD). Oral IFN was administered twice: Time 0 and 8h later. Blood was collected at 0, 8 and 24h after the first IFN administration, and DNA isolated from peripheral blood mononuclear cells (PBMCs) was employed in quantitative polymerase chain reaction (qPCR) microarray assays. Within 8h, 50 and 200 units of oral IFN induced significant (P<0.05) changes in expression of 41 of 92 tested autoimmune and inflammatory response-associated genes. These data suggest that orally administered IFN is a viable approach for providing short-term antiviral immunity to livestock exposed to viruses such as FMD virus (FMDV) until such a time that an effective vaccine can be produced and distributed to producers.

Abstract P4-09-04: Robust 7-gene signature for recurrence prognosis of breast cancer validated in formalin-fixed paraffin-embedded samples

Abstract BACKGROUND: Breast cancer (BC) is a heterogeneous disease with diverse biological-molecular characteristics and clinical behaviors. Traditionally, prediction of recurrence in patients recently diagnosed is a big challenge for oncologists. In the last decade, advances in gene expression profiling technologies have improved the genetic knowledge of breast cancer by reporting several prognostic gene-signatures. However, there is still a need for covering the molecular heterogeneity of this cancer. Integration of public microarray data could be used to identify better and robust predictive gene-signatures. The aim of this study was to investigate prognostic genes that might function as biomarkers to differentiate among all heterogeneous BC patients, into those with high, medium or low recurrence risk. METHODS: We collected nine public datasets, 1574 BC patients with median follow-up time of 10 years. The data were split into train-test, 50% each. Train was subject to univariate and multivariate analysis to generate a signature, which was validated in the test data. Seven genes were identified with high accuracy for recurrence prediction. This signature was validated in 40 formalin-fixed paraffin-embedded (FFPE) Mexican BC tissues with clinical follow up of 3 to 13 years by quantitative RT-PCR assays. RESULTS: We identified a 7-gene signature which showed an accuracy prediction measured by concordance index of 65.62% and Log-rank test p-value: 1 x 10-15. The validation of the gene-signature level expression in 40 FFPE samples in Kaplan Meier analysis showed prediction significance by concordance index of 65.25% and Log-Rank p-value: 0.0005. CONCLUSIONS: This 7-gene signature may provide a powerful tool to guide the adequate treatment protocols in early-diagnosed breast cancer patients. Citation Format: Chacolla RJ, Trevino VM, Scott SP, Guzman EA, Cardona S. Robust 7-gene signature for recurrence prognosis of breast cancer validated in formalin-fixed paraffin-embedded samples. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P4-09-04.

Abstract IA09: Molecular mechanisms controlling the T cell-inflamed tumor microenvironment: Implications for therapy

Abstract Two major categories of melanoma metastases have been observed based on gene expression profiling and confirmatory technologies. One subgroup of patients has T cell-inflamed phenotype that includes expression of chemokines, T cell markers, and a type I IFN gene signature. In contrast, the other major subset lacks this phenotype and these tumors show a behavior of immune “exclusion”. The optimal immunotherapeutic intervention may be distinct for these two general subsets. The T cell-inflamed tumor microenvironment subset contains the highest expression of negative regulatory factors, including PD-L1, IDO, and FoxP3+ Tregs, and evidence for T cell-intrinsic anergy has also emerged. In addition, the mechanism of induction of these inhibitory mechanisms has been elucidated—PD-L1 and IDO are induced by IFN-γ, and Tregs are largely recruited by chemokines such as CCL22, produced by activated CD8+ effector T cells. Preclinical experiments have confirmed a critical role for all 4 of these mechanisms in limiting anti-tumor T cell efficacy in vivo, giving a mechanism base for translation back into the clinic. These include anti-PD-1/PD-L1 mAbs, IDO inhibitors, and approaches to deplete CD25+ Tregs and/or reverse anergy. The presence of multiple inhibitory mechanisms in the same tumor microenvironment argues that combination therapies may be advantageous to overcome compensatory effects. Preclinical data indicated synergy between anti-CTLA-4 +/- anti-PD-L1 +/- IDO inhibition. The mechanism of synergy is striking, as it correlates with a marked improvement of IL-2 production and proliferation of tumor-infiltrating CD8+ T cells. Clinical translation of these combination immunotherapies is promising and ongoing. In contrast to the T cell-inflamed melanomas, a new paradigm may be needed to promote de novo inflammation in cases of the non-T cell-infiltrated tumor microenvironment. Recent work has indicated that successful innate immune sensing of tumors involves the STING pathway and type I IFN production. Therefore, STING agonists are being developed to promote effective innate immune activation and generation of an adaptive immune response. Molecular mechanisms that explain the presence or absence of a T cell-inflamed tumor microenvironment in patients are being elucidated, which is opening up new concepts for therapies that may overcome immune exclusion and render patients responsive to immunotherapies. Citation Format: Thomas Gajewski. Molecular mechanisms controlling the T cell-inflamed tumor microenvironment: Implications for therapy. [abstract]. In: Proceedings of the AACR Special Conference: Tumor Immunology and Immunotherapy: A New Chapter; December 1-4, 2014; Orlando, FL. Philadelphia (PA): AACR; Cancer Immunol Res 2015;3(10 Suppl):Abstract nr IA09.

A computational strategy for predicting lineage specifiers in stem cell subpopulations

Stem cell differentiation is a complex biological event. Our understanding of this process is partly hampered by the co-existence of different cell subpopulations within a given population, which are characterized by different gene expression states driven by different underlying transcriptional regulatory networks (TRNs). Such cellular heterogeneity has been recently explored with the modern single-cell gene expression profiling technologies, such as single-cell RT-PCR and RNA-seq. However, the identification of cell subpopulation-specific TRNs and genes determining specific lineage commitment (i.e., lineage specifiers) remains a challenge due to the slower development of appropriate computational and experimental workflows. Here, we propose a computational method for predicting lineage specifiers for different cell subpopulations in binary-fate differentiation events. Our method first reconstructs subpopulation-specific TRNs, which is more realistic than reconstructing a single TRN representing multiple cell subpopulations. Then, it predicts lineage specifiers based on a model that assumes that each parental stem cell subpopulation is in a stable state maintained by its specific TRN stability core. In addition, this stable state is maintained in the parental cell subpopulation by the balanced gene expression pattern of pairs of opposing lineage specifiers for mutually exclusive different daughter cell subpopulations. To this end, we devised a statistical metric for identifying opposing lineage specifier pairs that show a significant ratio change upon differentiation. Application of this computational method to three different stem cell systems predicted known and putative novel lineage specifiers, which could be experimentally tested. Our method does not require pre-selection of putative candidate genes, and can be applied to any binary-fate differentiation system for which single-cell gene expression data are available. Furthermore, this method is compatible with both single-cell RT-PCR and single-cell RNA-seq data. Given the increasing importance of single-cell gene expression data in stem cell biology and regenerative medicine, approaches like ours would be useful for the identification of lineage specifiers and their associated TRN stability cores.

Roles of noncoding RNAs in metastasis of nonsmall cell lung cancer: A mini review.

Recent advances in genome-wide sequencing and gene expression profiling technologies has facilitated the discovery of a huge amount of long noncoding RNA (lncRNA) transcripts. In general, these lncRNAs have been reported to participate in multiple biological processes and human diseases through transcriptional, posttranscriptional and epigenetic pathway. Furthermore, emerging evidence has suggested that dysregulation of lncRNA contributes to the development and progression of human malignancies, notably lung cancer, which is one of the leading causes of cancer-related death. In this review, we will summarize the functions of lncRNAs from recent reports in human nonsmall cell lung cancer (NSCLC) research, especially in the metastasis of NSCLC, and highlight the future opportunities and challenges in diagnostics and therapy of NSCLC patients.

Characterization of essential genes by topological properties in the perturbation sensitivity network

Genes that are indispensable for survival are called essential genes. In recent years, the analysis of essential genes has become extremely important for understanding the way a cell functions. With the advent of large-scale gene expression profiling technologies, it is now possible to profile transcriptional changes in the entire genome of Saccharomyces cerevisiae. Notwithstanding the accumulation of gene expression profiling in recent years, only a few studies have used these data to construct the network for S. cerevisiae. In this paper, based on the transcriptional profiling of the S. cerevisiae genome in hundreds of different gene disruptions, the perturbation sensitivity (PS) network is constructed. A scale-free topology with node degree following a power-law distribution is shown in the PS network. Twelve topological properties are used to investigate the characteristics of essential and non-essential genes in the PS network. Most of the properties are found to be statistically discriminative between essential and non-essential genes. In addition, the F-score is used to estimate the essentiality of each property, and the core number demonstrates the highest F-score among all properties.

RNA sequencing identifies novel markers of non-small cell lung cancer

IntroductionThe development of reliable gene expression profiling technology increasingly impacts our understanding of lung cancer biology. Here, we used RNA sequencing (RNA-Seq) to compare the transcriptomes of non-small cell lung cancer (NSCLC) and normal lung tissues and to investigate expression in lung cancer tissues. MethodsWe enrolled 88 male patients (mean age, 61.2 years) with NSCLC. RNA-Seq was performed on 88 pairs of NSCLC tumor tissue and non-tumor tissue from 54 patients with adenocarcinoma and 34 patients with squamous cell carcinoma. Immunohistochemistry was performed to validate differential candidate gene expression in a different NSCLC group. ResultsRNA-Seq produced 25.41×106 (±8.90×106) reads in NSCLC tissues and 24.70×106 (±4.70×106) reads in normal lung tissues [mean (±standard deviation)]. Among the genes expressed in both tissues, 335 were upregulated and 728 were downregulated ≥2-fold (p<0.001). Four upregulated genes – CBX3, GJB2, CRABP2, and DSP – not previously reported in lung cancer were studied further. Their altered expression was verified by immunohistochemistry in a different set of NSCLC tissues (n=154). CBX3 was positive in 90.3% (139 cases) of the samples; GJB2, in 22.7% (35 cases); CRABP2, in 72.1% (111 cases); and DSP, in 17.5% (27 cases). The positive rate of CRABP2 was higher in adenocarcinoma than squamous cell carcinoma (p<0.01). ConclusionsCBX3 and CRABP2 expression was markedly increased in lung cancer tissues and especially CRABP2 may be promising candidate genes in lung adenocarcinoma.

Risk stratification in myelodysplastic syndromes: is there a role for gene expression profiling?

Evaluation of: Pellagatti A, Benner A, Mills KI et al. Identification of gene expression-based prognostic markers in the hematopoietic stem cells of patients with myelodysplastic syndromes. J. Clin. Oncol. 31(28), 3557–3564 (2013).Patients with myelodysplastic syndromes (MDS) exhibit wide heterogeneity in clinical outcomes making accurate risk-stratification an integral part of the risk-adaptive management paradigm. Current prognostic schemes for MDS rely on clinicopathological parameters. Despite the increasing knowledge of the genetic landscape of MDS and the prognostic impact of many newly discovered molecular aberrations, none to date has been incorporated formally into the major risk models. Efforts are ongoing to use data generated from genome-wide high-throughput techniques to improve the ‘individualized’ outcome prediction for patients. We here discuss an important paper in which gene expression profiling (GEP) technology was applied to marrow CD34+ cells from 125 MDS patients to generate and validate a standardized GEP-based prognostic signature.

Reverse causal reasoning: applying qualitative causal knowledge to the interpretation of high-throughput data

BackgroundGene expression profiling and other genome-scale measurement technologies provide comprehensive information about molecular changes resulting from a chemical or genetic perturbation, or disease state. A critical challenge is the development of methods to interpret these large-scale data sets to identify specific biological mechanisms that can provide experimentally verifiable hypotheses and lead to the understanding of disease and drug action.ResultsWe present a detailed description of Reverse Causal Reasoning (RCR), a reverse engineering methodology to infer mechanistic hypotheses from molecular profiling data. This methodology requires prior knowledge in the form of small networks that causally link a key upstream controller node representing a biological mechanism to downstream measurable quantities. These small directed networks are generated from a knowledge base of literature-curated qualitative biological cause-and-effect relationships expressed as a network. The small mechanism networks are evaluated as hypotheses to explain observed differential measurements. We provide a simple implementation of this methodology, Whistle, specifically geared towards the analysis of gene expression data and using prior knowledge expressed in Biological Expression Language (BEL). We present the Whistle analyses for three transcriptomic data sets using a publically available knowledge base. The mechanisms inferred by Whistle are consistent with the expected biology for each data set.ConclusionsReverse Causal Reasoning yields mechanistic insights to the interpretation of gene expression profiling data that are distinct from and complementary to the results of analyses using ontology or pathway gene sets. This reverse engineering algorithm provides an evidence-driven approach to the development of models of disease, drug action, and drug toxicity.

Physiology's Impact: Applying Mathematics and Advanced Technologies

How do you measure the mechanical forces generated by single cells? What are the cues that guide cell migration? What are the interactions between cells that account for cooperative behaviors among cells? To answer these and many other basic questions about biological function, we must have the

The classification of invasive carcinoma of the breast

The classification of invasive breast carcinoma assists diagnosis, allows for comparison of different patient groups in clinical trials and facilitates epidemiological analysis. For the individual patient, accurate tumor classification informs clinical decision-making with emphasis on assessment of prognosis and treatment formulation. Tumor grade is an independent prognostic indicator and is calculated by assessing specific tumor characteristics microscopically. The Tumor Node Metastasis staging system, produced by the American Joint Committee on Cancer Union for International Cancer Control, combines information about the primary tumor size, the status of the regional lymph nodes and the presence or absence of distant metastases at diagnosis to classify disease. In recent years, the use of gene expression profiling technology has led to the development of the molecular classification of breast cancer and has highlighted the importance of hormone receptor and HER2 oncogenic pathways, with particular reference to targeted chemotherapy. Tumor typing involves the identification of ‘no special type’ carcinoma with variable clinical, histological and molecular characteristics and ‘special type’ carcinomas that are usually associated with a particular set of prognostic and predictive indices. Some special type carcinomas have unique biological features that influence diagnostic investigation and clinical management.

IPcc: a novel feature extraction method for accurate disease class discovery and prediction

Gene expression profiling has gradually become a routine procedure for disease diagnosis and classification. In the past decade, many computational methods have been proposed, resulting in great improvements on various levels, including feature selection and algorithms for classification and clustering. In this study, we present iPcc, a novel method from the feature extraction perspective to further propel gene expression profiling technologies from bench to bedside. We define ‘correlation feature space’ for samples based on the gene expression profiles by iterative employment of Pearson’s correlation coefficient. Numerical experiments on both simulated and real gene expression data sets demonstrate that iPcc can greatly highlight the latent patterns underlying noisy gene expression data and thus greatly improve the robustness and accuracy of the algorithms currently available for disease diagnosis and classification based on gene expression profiles.

Abstract 1218: Development of comprehensive molecular portraits of lobular breast cancer within the RATHER (Rational Therapy for Breast Cancer) Consortium.

Abstract Background: The RATHER Consortium aims to identify novel kinase targets for therapy in poor-prognosis subtypes of breast cancer for which there are currently no targeted therapies available; namely invasive lobular carcinomas (ILC) which represent 10% of breast tumors. The RATHER approach differs in two fundamental ways from the ongoing cancer genome re-sequencing efforts. Firstly, the major cancer genome re-sequencing efforts focus on DNA sequence analysis of the whole genome. In RATHER, we identify and validate novel kinases as therapy targets in a more comprehensive way by analyzing the DNA sequence and by applying state-of-the-art proteomics, copy number analysis and gene expression profiling technologies to identify activated/altered kinases. Secondly, RATHER also differs by focusing on the lobular subtype of breast cancer. Methods: One hundred and fifty ILC samples (fresh frozen) with &amp;gt;5years follow-up were collected from two institutes (Cambridge, NKI). All samples were processed following one standard operating protocol to isolate RNA, DNA and protein of high quality. We used a five-pronged approach to identify and validate novel kinase targets for therapy in ILC breast cancer, namely i) direct re-sequencing of the kinome of 150 ILC breast tumors, ii) determination of abundance and activation status of kinases in these tumors by reverse phase protein lysate array (RPPA) technology, iii) determination of copy number aberration (CNA) in kinase genes by SNP arrays, iv) mRNA quantitation of both genome and kinome using DNA microarrays and v)RNAseq of a subset of ILC tumors. Results: Data from these independent genome-scale technologies were integrated, yielding a priority list of potential kinase targets for therapy in ILC breast cancer. Deep sequencing of the kinome has revealed somatic mutations characteristic of ILC, which are currently being validated via mass spectrometry-based genotyping technology and their possible effects confirmed with gene expression, protein expression and phosphorylation changes. In addition, a subset of the ILC samples was assessed via RNA sequencing approach to confirm expression of particular mutants. Known gene mutations in ILC such as loss of CDH1 were confirmed; furthermore, a high frequency of PI3K pathway alterations (50%) was observed. Gene expression analysis of such a large set of ILC can reveal subsets of these breast cancers that significantly regulate alternate biological processes. Such biological subsets are currently being validated with clinical and follow-up data. Conclusion: The RATHER project aims to deliver proof-of-concept for novel therapeutic interventions, together with companion molecular diagnostic assays for patient stratification, for up to 10% of breast cancer patients, where current treatment options are unsatisfactory. Citation Format: Justine K. Peeters, Ian Majewski, Leanne de Koning, Yue Fan, Finbarr Tarrant, Darran O'Connor, Jeroen Heijmans, Mireille Snel, Tesa Severson, Astrid Bosma, Magali Michaut, Lorenza Mittempergher, Suet-Feung Chin, Thierry Dubois, William Gallagher, Carlos Caldas, Rene Bernards, Iris Simon, RATHER Consortium. Development of comprehensive molecular portraits of lobular breast cancer within the RATHER (Rational Therapy for Breast Cancer) Consortium. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1218. doi:10.1158/1538-7445.AM2013-1218

Combining differential expression, chromosomal and pathway analyses for the molecular characterization of renal cell carcinoma

Using high-throughput gene-expression profiling technology, we can now gain a better understanding of the complex biology that is taking place in cancer cells. This complexity is largely dictated by the abnormal genetic makeup of the cancer cells. This abnormal genetic makeup can have profound effects on cellular activities such as cell growth, cell survival and other regulatory processes. Based on the pattern of gene expression, or molecular signatures of the tumours, we can distinguish or subclassify different types of cancers according to their cell of origin, behaviour, and the way they respond to therapeutic agents and radiation. These approaches will lead to better molecular subclassification of tumours, the basis of personalized medicine. We have, to date, done whole-genome microarray gene-expression profiling on several hundreds of kidney tumours. We adopt a combined bioinformatic approach, based on an integrative analysis of the gene-expression data. These data are used to identify both cytogenetic abnormalities and molecular pathways that are deregulated in renal cell carcinoma (RCC). For example, we have identified the deregulation of the VHL-hypoxia pathway in clear-cell RCC, as previously known, and the c-Myc pathway in aggressive papillary RCC. Besides the more common clear-cell, papillary and chromophobe RCCs, we are currently characterizing the molecular signatures of rarer forms of renal neoplasia such as carcinoma of the collecting ducts, mixed epithelial and stromal tumours, chromosome Xp11 translocations associated with papillary RCC, renal medullary carcinoma, mucinous tubular and spindle-cell carcinoma, and a group of unclassified tumours. Continued development and improvement in the field of molecular profiling will better characterize cancer and provide more accurate diagnosis, prognosis and prediction of drug response.

Genetics and Environmental Law: Redefining Public Health

The sequencing of the human genome and introduction of high-speed gene expression profiling technologies are revolutionizing our understanding of the interplay of genes and the environment. This Article develops a novel and provocative framework for thinking about the implications of the genetic revolution for the U.S. environmental regulatory system. Professor Grodsky employs the concept of public health as a lens for analyzing emerging issues, and demonstrates how scientific advances may erode the distinction between public and individual health. Future claims of genetically susceptible individuals and groups may challenge the legal definition of the public to be protected by environmental law. Likewise, the Article demonstrates how the meaning of health, and legally significant threats to health, may be questioned as new techniques reveal earlier evidence of toxic harm. These developments strike at the core of the Environmental Protection Agency's public health mission. The Article analyzes and synthesizes diverse elements of the changing scientific landscape, providing a new framework for understanding the science as well as the law. Professor Grodsky examines implications for environmental standard setting, as these potentially beneficial, enormously complex, and increasingly controversial scientific developments may modify estimates of individual and public health risk attributable to toxic exposure. The Article also sounds a cautionary note. It evaluates scientific roadblocks that must be overcome if the new science is to benefit law and regulation rather than generate scientific uncertainty. This new, exciting, and potentially perilous terrain in environmental law has only begun to be perceived by policymakers.

Predicting the response to preoperative radiation or chemoradiation by a microarray analysis of the gene expression profiles in rectal cancer

Preoperative radiotherapy or chemoradiotherapy (CRT) has become a standard treatment for patients with locally advanced rectal cancer. However, there is a wide spectrum of responses to preoperative CRT, ranging from none to complete. There has been intense interest in the identification of molecular biomarkers to predict the response to preoperative CRT, in order to spare potentially non-responsive patients from unnecessary treatment. However, no specific molecular biomarkers have yet been definitively proven to be predictive of the response to CRT. Instead of focusing on specific factors, microarray-based gene expression profiling technology enables the simultaneous analysis of large numbers of genes, and might therefore have immense potential for predicting the response to preoperative CRT. We herein review published studies using a microarray-based analysis to identify gene expression profiles associated with the response of rectal cancer to radiation or CRT. Although some studies have reported gene expression signatures capable of high predictive accuracy, the compositions of these signatures have differed considerably, with little gene overlap. However, considering the promising data regarding gene profiling in breast cancer, the microarray analysis could still have potential to improve the management of locally advanced rectal cancer. Increasing the number of patients analyzed for more accurate prediction and the extensive validation of predictive classifiers in prospective clinical trials are necessary before such profiling can be incorporated into future clinical practice.