Decoding global gene expression programs in liver cancer by noninvasive imaging

Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results

PURPOSE

To identify prognostic imaging biomarkers in non-small cell lung cancer (NSCLC) by means of a radiogenomics strategy that integrates gene expression and medical images in patients for whom survival outcomes are not available by leveraging survival data in public gene expression data sets.

MATERIALS AND METHODS

A radiogenomics strategy for associating image features with clusters of coexpressed genes (metagenes) was defined. First, a radiogenomics correlation map is created for a pairwise association between image features and metagenes. Next, predictive models of metagenes are built in terms of image features by using sparse linear regression. Similarly, predictive models of image features are built in terms of metagenes. Finally, the prognostic significance of the predicted image features are evaluated in a public gene expression data set with survival outcomes. This radiogenomics strategy was applied to a cohort of 26 patients with NSCLC for whom gene expression and 180 image features from computed tomography (CT) and positron emission tomography (PET)/CT were available.

RESULTS

There were 243 statistically significant pairwise correlations between image features and metagenes of NSCLC. Metagenes were predicted in terms of image features with an accuracy of 59%-83%. One hundred fourteen of 180 CT image features and the PET standardized uptake value were predicted in terms of metagenes with an accuracy of 65%-86%. When the predicted image features were mapped to a public gene expression data set with survival outcomes, tumor size, edge shape, and sharpness ranked highest for prognostic significance.

CONCLUSION

This radiogenomics strategy for identifying imaging biomarkers may enable a more rapid evaluation of novel imaging modalities, thereby accelerating their translation to personalized medicine.

Radiomics: extracting more information from medical images using advanced feature analysis

Solid cancers are spatially and temporally heterogeneous. This limits the use of invasive biopsy based molecular assays but gives huge potential for medical imaging, which has the ability to capture intra-tumoural heterogeneity in a non-invasive way. During the past decades, medical imaging innovations with new hardware, new imaging agents and standardised protocols, allows the field to move towards quantitative imaging. Therefore, also the development of automated and reproducible analysis methodologies to extract more information from image-based features is a requirement. Radiomics--the high-throughput extraction of large amounts of image features from radiographic images--addresses this problem and is one of the approaches that hold great promises but need further validation in multi-centric settings and in the laboratory.

DART: Denoising Algorithm based on Relevance network Topology improves molecular pathway activity inference

BACKGROUND

Inferring molecular pathway activity is an important step towards reducing the complexity of genomic data, understanding the heterogeneity in clinical outcome, and obtaining molecular correlates of cancer imaging traits. Increasingly, approaches towards pathway activity inference combine molecular profiles (e.g gene or protein expression) with independent and highly curated structural interaction data (e.g protein interaction networks) or more generally with prior knowledge pathway databases. However, it is unclear how best to use the pathway knowledge information in the context of molecular profiles of any given study.

RESULTS

We present an algorithm called DART (Denoising Algorithm based on Relevance network Topology) which filters out noise before estimating pathway activity. Using simulated and real multidimensional cancer genomic data and by comparing DART to other algorithms which do not assess the relevance of the prior pathway information, we here demonstrate that substantial improvement in pathway activity predictions can be made if prior pathway information is denoised before predictions are made. We also show that genes encoding hubs in expression correlation networks represent more reliable markers of pathway activity. Using the Netpath resource of signalling pathways in the context of breast cancer gene expression data we further demonstrate that DART leads to more robust inferences about pathway activity correlations. Finally, we show that DART identifies a hypothesized association between oestrogen signalling and mammographic density in ER+ breast cancer.

CONCLUSIONS

Evaluating the consistency of prior information of pathway databases in molecular tumour profiles may substantially improve the subsequent inference of pathway activity in clinical tumour specimens. This de-noising strategy should be incorporated in approaches which attempt to infer pathway activity from prior pathway models.

CAM-CM: a signal deconvolution tool for in vivo dynamic contrast-enhanced imaging of complex tissues

SUMMARY

In vivo dynamic contrast-enhanced imaging tools provide non-invasive methods for analyzing various functional changes associated with disease initiation, progression and responses to therapy. The quantitative application of these tools has been hindered by its inability to accurately resolve and characterize targeted tissues due to spatially mixed tissue heterogeneity. Convex Analysis of Mixtures - Compartment Modeling (CAM-CM) signal deconvolution tool has been developed to automatically identify pure-volume pixels located at the corners of the clustered pixel time series scatter simplex and subsequently estimate tissue-specific pharmacokinetic parameters. CAM-CM can dissect complex tissues into regions with differential tracer kinetics at pixel-wise resolution and provide a systems biology tool for defining imaging signatures predictive of phenotypes.

AVAILABILITY

The MATLAB source code can be downloaded at the authors' website www.cbil.ece.vt.edu/software.htm

CONTACT

yuewang@vt.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Integration and analysis of genome-scale data from gliomas

Primary brain tumors are a leading cause of cancer-related mortality among young adults and children. The most common primary malignant brain tumor, glioma, carries a median survival of only 14 months. Two major multi-institutional programs, the Glioma Molecular Diagnostic Initiative and The Cancer Genome Atlas, have pursued a comprehensive genomic characterization of a large number of clinical glioma samples using a variety of technologies to measure gene expression, chromosomal copy number alterations, somatic and germline mutations, DNA methylation, microRNA, and proteomic changes. Classification of gliomas on the basis of gene expression has revealed six major subtypes and provided insights into the underlying biology of each subtype. Integration of genome-wide data from different technologies has been used to identify many potential protein targets in this disease, while increasing the reliability and biological interpretability of results. Mapping genomic changes onto both known and inferred cellular networks represents the next level of analysis, and has yielded proteins with key roles in tumorigenesis. Ultimately, the information gained from these approaches will be used to create customized therapeutic regimens for each patient based on the unique genomic signature of the individual tumor. In this Review, we describe efforts to characterize gliomas using genomic data, and consider how insights gained from these analyses promise to increase understanding of the biological underpinnings of the disease and lead the way to new therapeutic strategies.

Intersectional innovation in biomarker development for patient-centric medicine

The pharmaceutical and healthcare industries are being revolutionized by the use of genomics, proteomics, metabolomics, bioinformatics and molecular imaging. Patient friendly diagnosis, treatment and disease management options that utilize the combination of these technologies are currently in development. New innovations in pharmaceutical advancement are taking place at the intersection of these technologies, and will be coupled with societal changes as we move to a fully networked and individual-centric consumer base. Numerous examples of the combinations of molecular characterization technologies aimed at better preclinical and clinical disease understanding, diagnosis and treatment are highlighted that are ideally situated to generate the intersectional innovation that drives healthcare advancement. The true value in patient-centric medicine will only be realized as the improved molecular characterization of disease provided by these technologies is integrated across platforms that operate directly in the patient and care provider space to provide a comprehensive view of health. Molecular profiling and imaging technologies must become fully integrated and amenable for patient and physician use in a networked environment that can provide a personal health avatar approach to medicine.

β-arrestin deficiency protects against pulmonary fibrosis in mice and prevents fibroblast invasion of extracellular matrix

Idiopathic pulmonary fibrosis is a progressive disease that causes unremitting extracellular matrix deposition with resulting distortion of pulmonary architecture and impaired gas exchange. β-Arrestins regulate G protein (heterotrimeric guanine nucleotide-binding protein)-coupled receptors through receptor desensitization while also acting as signaling scaffolds to facilitate numerous effector pathways. Here, we examine the role of β-arrestin1 and β-arrestin2 in the pathobiology of pulmonary fibrosis. In the bleomycin-induced mouse lung fibrosis model, loss of either β-arrestin1 or β-arrestin2 resulted in protection from mortality, inhibition of matrix deposition, and protected lung function. Fibrosis was prevented despite preserved recruitment of inflammatory cells and fibroblast chemotaxis. However, isolated lung fibroblasts from bleomycin-treated β-arrestin-null mice failed to invade extracellular matrix and displayed altered expression of genes involved in matrix production and degradation. Furthermore, knockdown of β-arrestin2 in fibroblasts from patients with idiopathic pulmonary fibrosis attenuated the invasive phenotype. These data implicate β-arrestins as mediators of fibroblast invasion and the development of pulmonary fibrosis, and as a potential target for therapeutic intervention in patients with idiopathic pulmonary fibrosis.

The challenges of integrating molecular imaging into the optimization of cancer therapy

We review novel, in vivo and tissue-based imaging technologies that monitor and optimize cancer therapeutics. Recent advances in cancer treatment centre around the development of targeted therapies and personalisation of treatment regimes to individual tumour characteristics. However, clinical outcomes have not improved as expected. Further development of the use of molecular imaging to predict or assess treatment response must address spatial heterogeneity of cancer within the body. A combination of different imaging modalities should be used to relate the effect of the drug to dosing regimen or effective drug concentration at the local site of action. Molecular imaging provides a functional and dynamic read-out of cancer therapeutics, from nanometre to whole body scale. At the whole body scale, an increase in the sensitivity and specificity of the imaging probe is required to localise (micro)metastatic foci and/or residual disease that are currently below the limit of detection. The use of image-guided endoscopic biopsy can produce tumour cells or tissues for nanoscopic analysis in a relatively patient-compliant manner, thereby linking clinical imaging to a more precise assessment of molecular mechanisms. This multimodality imaging approach (in combination with genetics/genomic information) could be used to bridge the gap between our knowledge of mechanisms underlying the processes of metastasis, tumour dormancy and routine clinical practice. Treatment regimes could therefore be individually tailored both at diagnosis and throughout treatment, through monitoring of drug pharmacodynamics providing an early read-out of response or resistance.

Use of DNA microarray and small animal positron emission tomography in preclinical drug evaluation of RAF265, a novel B-Raf/VEGFR-2 inhibitor

Positron emission tomography (PET) imaging has become a useful tool for assessing early biologic response to cancer therapy and may be particularly useful in the development of new cancer therapeutics. RAF265, a novel B-Raf/vascular endothelial growth factor receptor-2 inhibitor, was evaluated in the preclinical setting for its ability to inhibit the uptake of PET tracers in the A375M(B-Raf(V600E)) human melanoma cell line. RAF265 inhibited 2-deoxy-2-[(18)F]fluoro-d-glucose (FDG) accumulation in cell culture at 28 hours in a dose-dependent manner. RAF265 also inhibited FDG accumulation in tumor xenografts after 1 day of drug treatment. This decrease persisted for the remaining 2 weeks of treatment. DNA microarray analysis of treated tumor xenografts revealed significantly decreased expression of genes regulating glucose and thymidine metabolism and revealed changes in apoptotic genes, suggesting that the imaging tracers FDG, 3-deoxy-3-[(18)F]fluorothymidine, and annexin V could serve as potential imaging biomarkers for RAF265 therapy monitoring. We concluded that RAF265 is highly efficacious in this xenograft model of human melanoma and decreases glucose metabolism as measured by DNA microarray analysis, cell culture assays, and small animal FDG PET scans as early as 1 day after treatment. Our results support the use of FDG PET in clinical trials with RAF265 to assess early tumor response. DNA microarray analysis and small animal PET studies may be used as complementary technologies in drug development. DNA microarray analysis allows for analysis of drug effects on multiple pathways linked to cancer and can suggest corresponding imaging tracers for further analysis as biomarkers of tumor response.

Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer

(18)F-FDG PET is often used in clinical routine for diagnosis, staging, and response to therapy assessment or prediction. The standardized uptake value (SUV) in the primary or regional area is the most common quantitative measurement derived from PET images used for those purposes. The aim of this study was to propose and evaluate new parameters obtained by textural analysis of baseline PET scans for the prediction of therapy response in esophageal cancer.

METHODS

Forty-one patients with newly diagnosed esophageal cancer treated with combined radiochemotherapy were included in this study. All patients underwent pretreatment whole-body (18)F-FDG PET. Patients were treated with radiotherapy and alkylatinlike agents (5-fluorouracil-cisplatin or 5-fluorouracil-carboplatin). Patients were classified as nonresponders (progressive or stable disease), partial responders, or complete responders according to the Response Evaluation Criteria in Solid Tumors. Different image-derived indices obtained from the pretreatment PET tumor images were considered. These included usual indices such as maximum SUV, peak SUV, and mean SUV and a total of 38 features (such as entropy, size, and magnitude of local and global heterogeneous and homogeneous tumor regions) extracted from the 5 different textures considered. The capacity of each parameter to classify patients with respect to response to therapy was assessed using the Kruskal-Wallis test (P < 0.05). Specificity and sensitivity (including 95% confidence intervals) for each of the studied parameters were derived using receiver-operating-characteristic curves.

RESULTS

Relationships between pairs of voxels, characterizing local tumor metabolic nonuniformities, were able to significantly differentiate all 3 patient groups (P < 0.0006). Regional measures of tumor characteristics, such as size of nonuniform metabolic regions and corresponding intensity nonuniformities within these regions, were also significant factors for prediction of response to therapy (P = 0.0002). Receiver-operating-characteristic curve analysis showed that tumor textural analysis can provide nonresponder, partial-responder, and complete-responder patient identification with higher sensitivity (76%-92%) than any SUV measurement.

CONCLUSION

Textural features of tumor metabolic distribution extracted from baseline (18)F-FDG PET images allow for the best stratification of esophageal carcinoma patients in the context of therapy-response prediction.

Antiangiogenic therapy for primary liver cancer: correlation of changes in dynamic contrast-enhanced magnetic resonance imaging with tissue hypoxia markers and clinical response

BACKGROUND

This study utilized the imaging data of primary liver cancer (PLC) treated with floxuridine (FUDR) and bevacizumab to test the hypothesis that dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters correlate with tissue hypoxia markers and treatment outcome.

METHODS

Seventeen patients with PLC were treated with hepatic artery infusional (HAI) FUDR for 14 days followed by systemic bevacizumab therapy. DCE-MRI images were obtained at baseline and after HAI FUDR and bevacizumab therapy. The parameters (K(trans), AUC) pertaining to perfusion and vascular permeability of the tumor and adjacent liver parenchyma were measured with DCE-MRI. Tissue obtained at baseline was stained for hypoxia markers (anti-hypoxia inducible factor-1α, anti-carbonic anhydrase IX, and vascular endothelial growth factor). Changes in DCE-MRI parameters were correlated with tissue hypoxia and time to progression (TTP).

RESULTS

The median TTP was 8.8 months. Significant decreases in AUC90 (P = 0.004), AUC180 (P = 0.004), and K(trans) (P = 0.05) were noted in tumors after bevacizumab but not in nontumor areas. TTP correlated inversely with changes in AUC90 and AUC180 after bevacizumab (P = 0.002 and P = 0.0001). Reductions in tumor perfusion (AUC90 and AUC180) were greater in tumors expressing anti-hypoxia inducible factor-1α (P = 0.02 and 0.03), vascular endothelial growth factor (P = 0.01 and P = 0.01), and anti-carbonic anhydrase IX (P = 0.009 and P = 0.009).

CONCLUSIONS

In patients with PLC, bevacizumab induces a reduction in tumor perfusion measured by DCE-MRI. These changes correlate with TTP and tissue markers of tumor hypoxia.

The biology underlying molecular imaging in oncology: from genome to anatome and back again

Cancers are complex, evolving, multiscale ecosystems that are characterized by profound spatial and temporal heterogeneity. The interactions in cancer are non-linear in that small changes in one variable can have large changes on another. These multiple interacting phenotypes and spatial scales can best be understood with appropriate mathematical and computational models. Imaging is central to this investigation because it can non-destructively and longitudinally characterize spatial variations in the tumour phenotype and environment so that the system dynamics over time can be captured quantitatively.

Multiparametric imaging of tumor response to therapy

There is an increasing opportunity to perform multifunctional imaging at a variety of organ sites with relatively short examination times. Each technique yields quantitative parameters that reflect specific aspects of the underlying tumor or tissue biology. Many biomarkers have emerged that provide unique information on tumor behavior, including response to treatment. The multiparametric approach combines the information from different functional imaging techniques; this goes beyond what can be achieved by using any single functional technique, thus allowing an improved understanding of biologic processes and of responses to therapeutic interventions. Multiparametric imaging has many potential clinical roles; it is useful for pharmaceutical drug development and for predicting therapeutic efficacy.

Drug discovery in a multidimensional world: systems, patterns, and networks

Despite great strides in revealing and understanding the physiological and molecular bases of cardiovascular disease, efforts to translate this understanding into needed therapeutic interventions continue to lag far behind the initial discoveries. Although pharmaceutical companies continue to increase investments into research and development, the number of drugs gaining federal approval is in decline. Many factors underlie these trends, and a vast number of technological and scientific innovations are being sought through efforts to reinvigorate drug discovery pipelines. Recent advances in molecular profiling technologies and development of sophisticated computational approaches for analyzing these data are providing new, systems-oriented approaches towards drug discovery. Unlike the traditional approach to drug discovery which is typified by a one-drug-one-target mindset, systems-oriented approaches to drug discovery leverage the parallelism and high-dimensionality of the molecular data to construct more comprehensive molecular models that aim to model broader bimolecular systems. These models offer a means to explore complex molecular states (e.g., disease) where thousands to millions of molecular entities comprising multiple molecular data types (e.g., proteomics and gene expression) can be evaluated simultaneously as components of a cohesive biomolecular system. In this paper, we discuss emerging approaches towards systems-oriented drug discovery and contrast these efforts with the traditional, unidimensional approach to drug discovery. We also highlight several applications of these system-oriented approaches across various aspects of drug discovery, including target discovery, drug repositioning and drug toxicity. When available, specific applications to cardiovascular drug discovery are highlighted and discussed.

Regenerative medicine: a surgeon's perspective

More than 200 million incisions are made in the world each year on children and adults. They all end up with a scar unless there is an unusual situation where we are operating on an early gestation fetus. The question is, "why do we not regenerate?" and "why do we always heal with either a 'normal amount of scarring' or, approximately 15% of the time, with a pathologic amount of scarring (hypertrophic scar or keloid)?"

Initial assessment of a model relating intratumoral genetic heterogeneity to radiological morphology

Tumour heterogeneity has major implications for tumour development and response to therapy. Tumour heterogeneity results from mutations in the genes responsible for mismatch repair or maintenance of chromosomal stability. Cells with different genetic properties may grow at different rates and exhibit different resistance to therapeutic interventions. To date, there exists no approach to non-invasively assess tumour heterogeneity. Here we present a biologically inspired model of tumour growth, which relates intratumoral genetic heterogeneity to gross morphology visible on radiological images. The model represents the development of a tumour as a set of expanding spheres, each sphere representing a distinct clonal centre, with the sprouting of new spheres corresponding to new clonal centres. Each clonal centre may possess different characteristics relating to genetic composition, growth rate and response to treatment. We present a clinical example for which the model accurately tracks tumour growth and shows the correspondence to genetic variation (as determined by array comparative genomic hybridisation). One clinical implication of our work is that the assessment of heterogeneous tumours using Response Evaluation Criteria In Solid Tumours (RECIST) or volume measurements may not accurately reflect tumour growth, stability or the response to treatment. We believe that this is the first model linking the macro-scale appearance of tumours to their genetic composition. We anticipate that our model will provide a more informative way to assess the response of heterogeneous tumours to treatment, which is of increasing importance with the development of novel targeted anti-cancer treatments.

Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging

Studies employing high-throughput biological techniques have recently contributed to an improved characterization of human cancers, allowing for novel sub-classification, better diagnostic accuracy, and more precise prognostication. However, requirement of surgical procurement of tissue among other things limits the clinical application of such methods in everyday patient care. Radiographic imaging is routine in clinical practice but is currently histopathology based. The use of routine radiographic imaging provides a potential platform for linking specific imaging traits with specific gene expression patterns that inform the underlying cellular pathophysiology; imaging features could then serve as molecular surrogates that contribute to the diagnosis, prognosis, and likely gene-expression-associated treatment response of various forms of human cancer. This review focuses on high-throughput methods such as microarray analysis of gene expression, their role in cancer research, and in particular, on novel methods of associating gene expression patterns with radiographic imaging phenotypes, known as "radiogenomics." These findings underline a potential future role of both diagnostic and interventional radiologists in genetic assessment of cancer patients with radiographic imaging studies.

From molecular imaging to systems diagnostics: time for another paradigm shift?

The term "Molecular Imaging" has hit the consciousness of radiologists only in the past decade although many of the concepts that molecular imaging encompasses has been practiced in biomedical imaging, especially in nuclear medicine, for many decades. Many new imaging techniques have allowed us to interrogate biologic events at the cellular and molecular level in vivo in four dimensions but the challenge now is to translate these techniques into clinical practice in a way that will enable us to revolutionize healthcare delivery. The purpose of this article is to introduce the term "Systems Diagnostics" and examine how radiologists can become translators of disparate sources of information into medical decisions and therapeutic actions.

Angiogenesis in liver disease

Angiogenesis and disruption of liver vascular architecture have been linked to progression to cirrhosis and liver cancer (HCC) in chronic liver diseases, which contributes both to increased hepatic vascular resistance and portal hypertension and to decreased hepatocyte perfusion. On the other hand, recent evidence shows that angiogenesis modulates the formation of portal-systemic collaterals and the increased splanchnic blood flow which are involved in the life threatening complications of cirrhosis. Finally, angiogenesis plays a key role in the growth of tumours, suggesting that interference with angiogenesis may prevent or delay the development of HCC. This review summarizes current knowledge on the molecular mechanisms of liver angiogenesis and on the consequences of angiogenesis in chronic liver disease. On the other hand, it presents the different strategies that have been used in experimental models to counteract excessive angiogenesis and its potential role in preventing transition to cirrhosis, development of portal hypertension and its consequences, and its application in the treatment of hepatocellular carcinoma.

Noninvasive prediction of tumor responses to gemcitabine using positron emission tomography

Gemcitabine (2',2'-difluorodeoxycytidine, dFdC) and cytosine arabinoside (cytarabine, ara-C) represent a class of nucleoside analogs used in cancer chemotherapy. Administered as prodrugs, dFdC and ara-C are transported across cell membranes and are converted to cytotoxic derivatives through consecutive phosphorylation steps catalyzed by endogenous nucleoside kinases. Deoxycytidine kinase (DCK) controls the rate-limiting step in the activation cascade of dFdC and ara-C. DCK activity varies significantly among individuals and across different tumor types and is a critical determinant of tumor responses to these prodrugs. Current assays to measure DCK expression and activity require biopsy samples and are prone to sampling errors. Noninvasive methods that can detect DCK activity in tumor lesions throughout the body could circumvent these limitations. Here, we demonstrate an approach to detecting DCK activity in vivo by using positron emission tomography (PET) and (18)F-labeled 1-(2'-deoxy-2'-fluoroarabinofuranosyl) cytosine] ([(18)F]FAC), a PET probe recently developed by our group. We show that [(18)F]FAC is a DCK substrate with an affinity similar to that of dFdC. In vitro, accumulation of [(18)F]FAC in murine and human leukemia cell lines is critically dependent on DCK activity and correlates with dFdC sensitivity. In mice, [(18)F]FAC accumulates selectively in DCK-positive vs. DCK-negative tumors, and [(18)F]FAC microPET scans can predict responses to dFdC. We suggest that [(18)F]FAC PET might be useful for guiding treatment decisions in certain cancers by enabling individualized chemotherapy.

Module networks revisited: computational assessment and prioritization of model predictions

MOTIVATION

The solution of high-dimensional inference and prediction problems in computational biology is almost always a compromise between mathematical theory and practical constraints, such as limited computational resources. As time progresses, computational power increases but well-established inference methods often remain locked in their initial suboptimal solution.

RESULTS

We revisit the approach of Segal et al. to infer regulatory modules and their condition-specific regulators from gene expression data. In contrast to their direct optimization-based solution, we use a more representative centroid-like solution extracted from an ensemble of possible statistical models to explain the data. The ensemble method automatically selects a subset of most informative genes and builds a quantitatively better model for them. Genes which cluster together in the majority of models produce functionally more coherent modules. Regulators which are consistently assigned to a module are more often supported by literature, but a single model always contains many regulator assignments not supported by the ensemble. Reliably detecting condition-specific or combinatorial regulation is particularly hard in a single optimum but can be achieved using ensemble averaging.

AVAILABILITY

All software developed for this study is available from http://bioinformatics.psb.ugent.be/software.

An overview of hepatocellular carcinoma study by omics-based methods

Hepatocellular carcinoma (HCC) is one of the most deadly malignancies worldwide. Scientists have been studying the molecular mechanism of HCC for years, but the understanding of it remains incomplete and scattered across the literature at different molecular levels. Chromosomal aberrations, epigenetic abnormality and changes of gene expression have been reported in HCC. High-throughput omics technologies have been widely applied, aiming at the discovery of candidate biomarkers for cancer staging, prediction of recurrence and prognosis, and treatment selection. Large amounts of data on genetic and epigenetic abnormalities, gene expression profiles, microRNA expression profiles and proteomics have been accumulating, and bioinformatics is playing a more and more important role. In this paper, we review the current omics-based studies on HCC at the levels of genomics, transcriptomics and proteomics. Integrating observations from multiple aspects is an essential step toward the systematic understanding of the disease.

Validation of putative reference genes for gene expression studies in human hepatocellular carcinoma using real-time quantitative RT-PCR

BACKGROUND

Reference genes, which are often referred to as housekeeping genes are frequently used to normalize mRNA levels between different samples in quantitative reverse transcription polymerase chain reaction (qRT-PCR). The selection of reference genes is critical for gene expression studies because the expression of these genes may vary among tissues or cells and may change under certain circumstances. Here, a systematic evaluation of six putative reference genes for gene expression studies in human hepatocellular carcinoma (HCC) is presented.

METHODS

Six genes, beta-2-microglobulin (B2M), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), hydroxymethyl-bilane synthase (HMBS), hypoxanthine phosphoribosyl-transferase 1 (HPRT1), succinate dehydrogenase complex, subunit A (SDHA) and ubiquitin C (UBC), with distinct functional characteristics and expression patterns were evaluated by qRT-PCR. Inhibitory substances in RNA samples were quantitatively assessed and controlled using an external RNA control. The stability of selected reference genes was analyzed using both geNorm and NormFinder software.

RESULTS

HMBS and GAPDH were identified as the optimal reference genes for normalizing gene expression data between paired tumoral and adjacent non-tumoral tissues derived from patients with HCC. HMBS, GAPDH and UBC were identified to be suitable for the normalization of gene expression data among tumor tissues; whereas the combination of HMBS, B2M, SDHA and GAPDH was suitable for normalizing gene expression data among five liver cancer cell lines, namely Hep3B, HepG2, HuH7, SK-HEP-1 and SNU-182. The determined gene stability was increased after exclusion of RNA samples containing relatively higher inhibitory substances.

CONCLUSION

Of six genes studied, HMBS was found to be the single best reference gene for gene expression studies in HCC. The appropriate choice of combination of more than one reference gene to improve qRT-PCR accuracy depends on the kind of liver tissues or cells under investigation. Quantitative assessment and control of qRT-PCR inhibitors using an external RNA control can reduce the variation of qRT-PCR assay and facilitate the evaluation of gene stability. Our results may facilitate the choice of reference genes for expression studies in HCC.

Validation of putative reference genes for gene expression studies in human hepatocellular carcinoma using real-time quantitative RT-PCR

BACKGROUND

Reference genes, which are often referred to as housekeeping genes are frequently used to normalize mRNA levels between different samples in quantitative reverse transcription polymerase chain reaction (qRT-PCR). The selection of reference genes is critical for gene expression studies because the expression of these genes may vary among tissues or cells and may change under certain circumstances. Here, a systematic evaluation of six putative reference genes for gene expression studies in human hepatocellular carcinoma (HCC) is presented.

METHODS

Six genes, beta-2-microglobulin (B2M), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), hydroxymethyl-bilane synthase (HMBS), hypoxanthine phosphoribosyl-transferase 1 (HPRT1), succinate dehydrogenase complex, subunit A (SDHA) and ubiquitin C (UBC), with distinct functional characteristics and expression patterns were evaluated by qRT-PCR. Inhibitory substances in RNA samples were quantitatively assessed and controlled using an external RNA control. The stability of selected reference genes was analyzed using both geNorm and NormFinder software.

RESULTS

HMBS and GAPDH were identified as the optimal reference genes for normalizing gene expression data between paired tumoral and adjacent non-tumoral tissues derived from patients with HCC. HMBS, GAPDH and UBC were identified to be suitable for the normalization of gene expression data among tumor tissues; whereas the combination of HMBS, B2M, SDHA and GAPDH was suitable for normalizing gene expression data among five liver cancer cell lines, namely Hep3B, HepG2, HuH7, SK-HEP-1 and SNU-182. The determined gene stability was increased after exclusion of RNA samples containing relatively higher inhibitory substances.

CONCLUSION

Of six genes studied, HMBS was found to be the single best reference gene for gene expression studies in HCC. The appropriate choice of combination of more than one reference gene to improve qRT-PCR accuracy depends on the kind of liver tissues or cells under investigation. Quantitative assessment and control of qRT-PCR inhibitors using an external RNA control can reduce the variation of qRT-PCR assay and facilitate the evaluation of gene stability. Our results may facilitate the choice of reference genes for expression studies in HCC.

An illustration of the potential for mapping MRI/MRS parameters with genetic over-expression profiles in human prostate cancer

INTRODUCTION

Magnetic resonance imaging (MRI) and MR spectroscopy can probe a variety of physiological (e.g. blood vessel permeability) and metabolic characteristics of prostate cancer. However, little is known about the changes in gene expression that underlie the spectral and imaging features observed in prostate cancer. Tumor induced changes in vascular permeability and angiogenesis are thought to contribute to patterns of dynamic contrast enhanced (DCE) MRI images of prostate cancer even though the genetic basis of tumor vasculogenesis is complex and the specific mechanisms underlying these DCEMRI features have not yet been determined.

MATERIALS AND METHODS

In order to identify the changes in gene expression that correspond to MRS and DCEMRI patterns in human prostate cancers, we have utilized tissue print micropeel techniques to generate "whole mount" molecular maps of radical prostatectomy specimens that correspond to pre-surgical MRI/MRS studies. These molecular maps include RNA expression profiles from both Affymetrix GeneChip microarrays and quantitative reverse transcriptase PCR (qrt-PCR) analysis, as well as immunohistochemical studies.

RESULTS

Using these methods on patients with prostate cancer, we found robust over-expression of choline kinase a in the majority of primary tumors. We also observed overexpression of neuropeptide Y (NPY), a newly identified angiogenic factor, in a subset of prostate cancers, visualized on DCEMRI.

CONCLUSION

These studies set the stage for establishing MRI/MRS parameters as validated biomarkers for human prostate cancer.

Molecular subsets in the gene expression signatures of scleroderma skin

BACKGROUND

Scleroderma is a clinically heterogeneous disease with a complex phenotype. The disease is characterized by vascular dysfunction, tissue fibrosis, internal organ dysfunction, and immune dysfunction resulting in autoantibody production.

METHODOLOGY AND FINDINGS

We analyzed the genome-wide patterns of gene expression with DNA microarrays in skin biopsies from distinct scleroderma subsets including 17 patients with systemic sclerosis (SSc) with diffuse scleroderma (dSSc), 7 patients with SSc with limited scleroderma (lSSc), 3 patients with morphea, and 6 healthy controls. 61 skin biopsies were analyzed in a total of 75 microarray hybridizations. Analysis by hierarchical clustering demonstrates nearly identical patterns of gene expression in 17 out of 22 of the forearm and back skin pairs of SSc patients. Using this property of the gene expression, we selected a set of 'intrinsic' genes and analyzed the inherent data-driven groupings. Distinct patterns of gene expression separate patients with dSSc from those with lSSc and both are easily distinguished from normal controls. Our data show three distinct patient groups among the patients with dSSc and two groups among patients with lSSc. Each group can be distinguished by unique gene expression signatures indicative of proliferating cells, immune infiltrates and a fibrotic program. The intrinsic groups are statistically significant (p<0.001) and each has been mapped to clinical covariates of modified Rodnan skin score, interstitial lung disease, gastrointestinal involvement, digital ulcers, Raynaud's phenomenon and disease duration. We report a 177-gene signature that is associated with severity of skin disease in dSSc.

CONCLUSIONS AND SIGNIFICANCE

Genome-wide gene expression profiling of skin biopsies demonstrates that the heterogeneity in scleroderma can be measured quantitatively with DNA microarrays. The diversity in gene expression demonstrates multiple distinct gene expression programs in the skin of patients with scleroderma.

Medicine. The ultimate model organism

A deeper understanding of disease requires a database of human traits and disease states that is integrated with molecular information.

High-dimensional biology to comprehend hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide and is the third leading cause of death from cancer. The diverse etiology, high morbidity/mortality, lack of diagnostic markers for early diagnosis and the highly variable clinical course of HCC have hindered advances in diagnosis and treatment. Microsatellite instability, chromosomal aberrations, mutations in key cell cycle genes and epigenetic changes have been reported in HCC. Availability of modern technologies advance 'high-dimensional biology' (HDB), a term that refers to the simultaneous study of the genetic variants (genome), transcription (mRNA; transcriptome), peptides and proteins (proteomics), and metabolites (metabolomics) for the intermediate products of metabolism of an organ, tissue or organism. The growing interest in omics-based research has enabled the simultaneous examination of thousands of genes, transcripts and proteins of interest, with high-throughput techniques and advanced analytical tools for data analysis. The use of each approach towards functional omics has lead to the classification of HCC into molecular subgroups. Here we review the use of HDB as a tool for the identification of markers for screening, diagnosis, molecular classification and the discovery of new therapeutic drug targets of HCC. With the extensive use of HDB, it may be possible in the near future, to have custom-made therapeutic regimens for HCC based on the molecular subtype, ultimately leading to an improved survival of HCC patients.

Identification of noninvasive imaging surrogates for brain tumor gene-expression modules

Glioblastoma multiforme (GBM) is the most common and lethal primary brain tumor in adults. We combined neuroimaging and DNA microarray analysis to create a multidimensional map of gene-expression patterns in GBM that provided clinically relevant insights into tumor biology. Tumor contrast enhancement and mass effect predicted activation of specific hypoxia and proliferation gene-expression programs, respectively. Overexpression of EGFR, a receptor tyrosine kinase and potential therapeutic target, was also directly inferred by neuroimaging and was validated in an independent set of tumors by immunohistochemistry. Furthermore, imaging provided insights into the intratumoral distribution of gene-expression patterns within GBM. Most notably, an "infiltrative" imaging phenotype was identified that predicted patient outcome. Patients with this imaging phenotype had a greater tendency toward having multiple tumor foci and demonstrated significantly shorter survival than their counterparts. Our findings provide an in vivo portrait of genome-wide gene expression in GBM and offer a potential strategy for noninvasively selecting patients who may be candidates for individualized therapies.

New approaches for imaging tumour responses to treatment

Tumour responses to treatment are still largely assessed from imaging measurements of reductions in tumour size. However, this can take several weeks to become manifest and in some cases may not occur at all, despite a positive response to treatment. There has been considerable interest, therefore, in non-invasive techniques for imaging tissue function that can give early evidence of response. These can be used in clinical trials of new drugs to give an early indication of drug efficacy, and subsequently in the clinic to select the most effective therapy at an early stage of treatment.

Conventional and novel PET tracers for imaging in oncology in the era of molecular therapy

In the last ten years, the development of several novel targeted drugs and the refinement of state of the art technologies such as the genomics and proteomics and their introduction to clinical practice have revolutionized the management of patients affected by cancer. However, everyday practice points out several clinical questions: the difficulty of response assessment to new drugs especially using standard RECIST criteria that do not provide information on biological, vascular or metabolic variations; the inadequate selection of patients who are likely to benefit from a targeted therapy excluding those with breast cancer and gastrointestinal stromal tumours; the need to know the global biological background of diseases especially in metastatic setting using repeatable non-invasive procedures. Molecular imaging could provide information on in vivo distribution of biological markers in response to targeted therapy and could improve the selection of patients before therapies. The aim of this review is to analyze the current role of conventional and innovative positron emission tomography (PET) radiotracers in clinical practice and to explore the promising perspectives of molecular imaging in cancer research.