DNA microarray and cancer

Identification of Differentially Expressed Genes in COVID-19 and Integrated Bioinformatics Analysis of Signaling Pathways

Coronavirus disease 2019 (COVID-19) is acutely infectious pneumonia. Currently, the specific causes and treatment targets of COVID-19 are still unclear. Herein, comprehensive bioinformatics methods were employed to analyze the hub genes in COVID-19 and tried to reveal its potential mechanisms. First of all, 34 groups of COVID-19 lung tissues and 17 other diseases' lung tissues were selected from the GSE151764 gene expression profile for research. According to the analysis of the DEGs (differentially expressed genes) in the samples using the limma software package, 84 upregulated DEGs and 46 downregulated DEGs were obtained. Later, by the Database for Annotation, Visualization, and Integrated Discovery (DAVID), they were enriched in the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. It was found that the upregulated DEGs were enriched in the type I interferon signaling pathway, AGE-RAGE signaling pathway in diabetic complications, coronavirus disease, etc. Downregulated DEGs were in cellular response to cytokine stimulus, IL-17 signaling pathway, FoxO signaling pathway, etc. Then, based on GSEA, the enrichment of the gene set in the sample was analyzed in the GO terms, and the gene set was enriched in the positive regulation of myeloid leukocyte cytokine production involved in immune response, programmed necrotic cell death, translesion synthesis, necroptotic process, and condensed nuclear chromosome. Finally, with the help of STRING tools, the PPI (protein-protein interaction) network diagrams of DEGs were constructed. With degree ≥13 as the cutoff degree, 3 upregulated hub genes (ISG15, FN1, and HLA-G) and 4 downregulated hub genes (FOXP3, CXCR4, MMP9, and CD69) were screened out for high degree. All these findings will help us to understand the potential molecular mechanisms of COVID-19, which is also of great significance for its diagnosis and prevention.

Identification of core genes in ovarian cancer by an integrative meta-analysis

BACKGROUND

Epithelial ovarian cancer is one of the most severe public health threats in women. Since it is still challenging to screen in early stages, identification of core genes that play an essential role in epithelial ovarian cancer initiation and progression is of vital importance.

RESULTS

Seven gene expression datasets (GSE6008, GSE18520, GSE26712, GSE27651, GSE29450, GSE36668, and GSE52037) containing 396 ovarian cancer samples and 54 healthy control samples were analyzed to identify the significant differentially expressed genes (DEGs). We identified 563 DEGs, including 245 upregulated and 318 downregulated genes. Enrichment analysis based on the gene ontology (GO) functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways showed that the upregulated genes were significantly enriched in cell division, cell cycle, tight junction, and oocyte meiosis, while the downregulated genes were associated with response to endogenous stimuli, complement and coagulation cascades, the cGMP-PKG signaling pathway, and serotonergic synapse. Two significant modules were identified from a protein-protein interaction network by using the Molecular Complex Detection (MCODE) software. Moreover, 12 hub genes with degree centrality more than 29 were selected from the protein-protein interaction network, and module analysis illustrated that these 12 hub genes belong to module 1. Furthermore, Kaplan-Meier analysis for overall survival indicated that 9 of these hub genes was correlated with poor prognosis of epithelial ovarian cancer patients.

CONCLUSION

The present study systematically validates the results of previous studies and fills the gap regarding a large-scale meta-analysis in the field of epithelial ovarian cancer. Furthermore, hub genes that could be used as a novel biomarkers to facilitate early diagnosis and therapeutic approaches are evaluated, providing compelling evidence for future genomic-based individualized treatment of epithelial ovarian cancer.

Meta-analysis of mRNA expression profiles to identify differentially expressed genes in lung adenocarcinoma tissue from smokers and non-smokers

Compared to other types of lung cancer, lung adenocarcinoma patients with a history of smoking have a poor prognosis during the treatment of lung cancer. How lung adenocarcinoma-related genes are differentially expressed between smoker and non-smoker patients has yet to be fully elucidated. We performed a meta-analysis of four publicly available microarray datasets related to lung adenocarcinoma tissue in patients with a history of smoking using R statistical software. The top 50 differentially expressed genes (DEGs) in smoking vs. non‑smoking patients are shown using heat maps. Additionally, we conducted KEGG and GO analyses. In addition, we performed a PPI network analysis for 8 genes that were selected during a previous analysis. We identified a total of 2,932 DEGs (1,806 upregulated, 1,126 downregulated) and five genes (CDC45, CDC20, ANAPC7, CDC6, ESPL1) that may link lung adenocarcinoma to smoking history. Our study may provide new insights into the complex mechanisms of lung adenocarcinoma in smoking patients, and our novel gene expression signatures will be useful for future clinical studies.

Application of a New Probabilistic Model for Mining Implicit Associated Cancer Genes from OMIM and Medline

An important issue in current medical science research is to find the genes that are strongly related to an inherited disease. A particular focus is placed on cancer-gene relations, since some types of cancers are inherited. As bio-medical databases have grown speedily in recent years, an informatics approach to predict such relations from currently available databases should be developed. Our objective is to find implicit associated cancer-genes from biomedical databases including the literature database. Co-occurrence of biological entities has been shown to be a popular and efficient technique in biomedical text mining. We have applied a new probabilistic model, called mixture aspect model (MAM) [ 48 ], to combine different types of co-occurrences of genes and cancer derived from Medline and OMIM (Online Mendelian Inheritance in Man). We trained the probability parameters of MAM using a learning method based on an EM (Expectation and Maximization) algorithm. We examined the performance of MAM by predicting associated cancer gene pairs. Through cross-validation, prediction accuracy was shown to be improved by adding gene-gene co-occurrences from Medline to cancer-gene co-occurrences in OMIM. Further experiments showed that MAM found new cancer-gene relations which are unknown in the literature. Supplementary information can be found at http://www.bic.kyotou.ac.jp/pathway/zhusf/CancerInformatics/Supplemental2006.html

Tubulin Inhibitor-Based Antibody-Drug Conjugates for Cancer Therapy

Antibody-drug conjugates (ADCs) are a class of highly potent biopharmaceutical drugs generated by conjugating cytotoxic drugs with specific monoclonal antibodies through appropriate linkers. Specific antibodies used to guide potent warheads to tumor tissues can effectively reduce undesired side effects of the cytotoxic drugs. An in-depth understanding of antibodies, linkers, conjugation strategies, cytotoxic drugs, and their molecular targets has led to the successful development of several approved ADCs. These ADCs are powerful therapeutics for cancer treatment, enabling wider therapeutic windows, improved pharmacokinetic/pharmacodynamic properties, and enhanced efficacy. Since tubulin inhibitors are one of the most successful cytotoxic drugs in the ADC armamentarium, this review focuses on the progress in tubulin inhibitor-based ADCs, as well as lessons learned from the unsuccessful ADCs containing tubulin inhibitors. This review should be helpful to facilitate future development of new generations of tubulin inhibitor-based ADCs for cancer therapy.

Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles

Background. Precisely predicting cancer is crucial for cancer treatment. Gene expression profiles make it possible to analyze patterns between genes and cancers on the genome-wide scale. Gene expression data analysis, however, is confronted with enormous challenges for its characteristics, such as high dimensionality, small sample size, and low Signal-to-Noise Ratio. Results. This paper proposes a method, termed RS_SVM, to predict gene expression profiles via aggregating SVM trained on random subspaces. After choosing gene features through statistical analysis, RS_SVM randomly selects feature subsets to yield random subspaces and training SVM classifiers accordingly and then aggregates SVM classifiers to capture the advantage of ensemble learning. Experiments on eight real gene expression datasets are performed to validate the RS_SVM method. Experimental results show that RS_SVM achieved better classification accuracy and generalization performance in contrast with single SVM, K-nearest neighbor, decision tree, Bagging, AdaBoost, and the state-of-the-art methods. Experiments also explored the effect of subspace size on prediction performance. Conclusions. The proposed RS_SVM method yielded superior performance in analyzing gene expression profiles, which demonstrates that RS_SVM provides a good channel for such biological data.

Computational networks of activating transcription factor 3 gene in Huh7 cell lines and hepatitis C virus-infected Huh7 cell lines

Activating transcription factor 3 (ATF3) is an adaptive‑response gene of the ATF family. ATF3 activity may be induced in response to a number of different stress-associated signals and ATF3 is involved in a variety of cellular processes. However, the functions of ATF3 and its molecular networks in human hepatoma cells lines and hepatitis C virus-infected Huh7 (HCV-Huh7) cells are not well understood. In the present study, ATF3 regulatory networks in Huh7 and HCV-Huh7 cell lines were established using the linear programming-based GRNinfer software and molecule annotation system 3.0 software. The gene expression omnibus dataset, GSE20948, was analyzed. The resulting network consisted of clusters located upstream and downstream of ATF3 in Huh7 and HCV-Huh7 cell lines. Using the annotation, visualization and integrated discovery (DAVID) software, 10 activation and 2 inhibition enriched functional annotation clusters were identified downstream of ATF3 in HCV-Huh7 cells. However, there were no enriched functional annotation clusters identified upstream of ATF3 in HCV-Huh7 cells. Furthermore, no clusters were identified downstream nor upstream of ATF3 in Huh7 cells. Gene ontology term and Kyoto encyclopedia of genes and genomes pathway analyses demonstrated that ATF3 may be involved in a number of biological processes, in particular, in metabolism regulation in HCV-Huh7 cells. It is hypothesized that the ATF3 pathway may be activated in Huh7 cells following HCV infection and that it is a potential 'hub' in the network of HCV-Huh7 cells.

The next generation of antibody drug conjugates

Antibody-drug conjugates (ADCs) represent a promising therapeutic modality for the clinical management of cancer. The recent approvals of brentuximab vedotin and ado-trastuzumab emtansine plus emerging data for many molecules in clinical trials highlight the potential for ADCs to offer new therapeutic options for patients. Currently, more than 30 ADCs are being evaluated in early- or late-stage clinical trials. Accordingly, much has been done to refine and transform the early-generation ADCs to the highly effective products that we now have in clinical development. These changes include a better understanding of optimal target selection, advances in antibody engineering, improvements in linker/payload conjugation strategies, and the generation of highly potent ADC payloads. In this review, we detail the current status of ADCs in both preclinical and clinical development, highlight technological advancements in ADC development, and speculate towards the future of this targeted therapeutic platform.

Transcriptome network analysis reveals potential candidate genes for esophageal squamous cell carcinoma

The esophageal squamous cell carcinoma (ESCC) is an aggressive tumor with a poor prognosis. Understanding molecular changes in ESCC should improve identification of risk factors with different molecular subtypes and provide potential targets for early detection and therapy. Our study aimed to obtain a molecular signature of ESCC through the regulation network based on differentially expressed genes (DEGs). We used the GSE23400 series to identify potential genes related to ESCC. Based on bioinformatics we constructed a regulation network. From the results, we could establish that many transcription factors and pathways closely related with ESCC were linked by our method. STAT1 also arose as a hub node in our transcriptome network, along with some transcription factors like CCNB1, TAP1, RARG and IFITM1 proven to be related with ESCC by previous studies. In conclusion, our regulation network provided information on important genes which might be useful in investigating the complex interacting mechanisms underlying the disease.

Computational oncology

Oncology research has traditionally been conducted using techniques from the biological sciences. The new field of computational oncology has forged a new relationship between the physical sciences and oncology to further advance research. By applying physics and mathematics to oncologic problems, new insights will emerge into the pathogenesis and treatment of malignancies. One major area of investigation in computational oncology centers around the acquisition and analysis of data, using improved computing hardware and software. Large databases of cellular pathways are being analyzed to understand the interrelationship among complex biological processes. Computer-aided detection is being applied to the analysis of routine imaging data including mammography and chest imaging to improve the accuracy and detection rate for population screening. The second major area of investigation uses computers to construct sophisticated mathematical models of individual cancer cells as well as larger systems using partial differential equations. These models are further refined with clinically available information to more accurately reflect living systems. One of the major obstacles in the partnership between physical scientists and the oncology community is communications. Standard ways to convey information must be developed. Future progress in computational oncology will depend on close collaboration between clinicians and investigators to further the understanding of cancer using these new approaches.

Extensive increase of microarray signals in cancers calls for novel normalization assumptions

When using microarray data for studying a complex disease such as cancer, it is a common practice to normalize data to force all arrays to have the same distribution of probe intensities regardless of the biological groups of samples. The assumption underlying such normalization is that in a disease the majority of genes are not differentially expressed genes (DE genes) and the numbers of up- and down-regulated genes are roughly equal. However, accumulated evidences suggest gene expressions could be widely altered in cancer, so we need to evaluate the sensitivities of biological discoveries to violation of the normalization assumption. Here, we analyzed 7 large Affymetrix datasets of pair-matched normal and cancer samples for cancers collected in the NCBI GEO database. We showed that in 6 of these 7 datasets, the medians of perfect match (PM) probe intensities increased in cancer state and the increases were significant in three datasets, suggesting the assumption that all arrays have the same median probe intensities regardless of the biological groups of samples might be misleading. Then, we evaluated the effects of three currently most widely used normalization algorithms (RMA, MAS5.0 and dChip) on the selection of DE genes by comparing them with LVS which relies less on the above-mentioned assumption. The results showed using RMA, MAS5.0 and dChip may produce lots of false results of down-regulated DE genes while missing many up-regulated DE genes. At least for cancer study, normalizing all arrays to have the same distribution of probe intensities regardless of the biological groups of samples might be misleading. Thus, most current normalizations based on unreliable assumptions may distort biological differences between normal and cancer samples. The LVS algorithm might perform relatively well due to that it relies less on the above-mentioned assumption. Also, our results indicate that genes may be widely up-regulated in most human cancer.

Expression analysis of mitotic spindle checkpoint genes in breast carcinoma: role of NDC80/HEC1 in early breast tumorigenicity, and a two-gene signature for aneuploidy

Background

Aneuploidy and chromosomal instability (CIN) are common abnormalities in human cancer. Alterations of the mitotic spindle checkpoint are likely to contribute to these phenotypes, but little is known about somatic alterations of mitotic spindle checkpoint genes in breast cancer.

Methods

To obtain further insight into the molecular mechanisms underlying aneuploidy in breast cancer, we used real-time quantitative RT-PCR to quantify the mRNA expression of 76 selected mitotic spindle checkpoint genes in a large panel of breast tumor samples.

Results

The expression of 49 (64.5%) of the 76 genes was significantly dysregulated in breast tumors compared to normal breast tissues: 40 genes were upregulated and 9 were downregulated. Most of these changes in gene expression during malignant transformation were observed in epithelial cells.

Alterations of nine of these genes, and particularly NDC80, were also detected in benign breast tumors, indicating that they may be involved in pre-neoplastic processes.

We also identified a two-gene expression signature (PLK1 + AURKA) which discriminated between DNA aneuploid and DNA diploid breast tumor samples. Interestingly, some DNA tetraploid tumor samples failed to cluster with DNA aneuploid breast tumors.

Conclusion

This study confirms the importance of previously characterized genes and identifies novel candidate genes that could be activated for aneuploidy to occur. Further functional analyses are required to clearly confirm the role of these new identified genes in the molecular mechanisms involved in breast cancer aneuploidy. The novel genes identified here, and/or the two-gene expression signature, might serve as diagnostic or prognostic markers and form the basis for novel therapeutic strategies.

Simultaneous detection of multiple biomarkers with over three orders of concentration difference using phase change nanoparticles

A big challenge for multiplexed detection of cancer biomarkers is that biomarker concentrations in body fluid differs several orders of magnitude. Existing techniques are not suitable to detect low- and high-concentration biomarkers (protein and DNA) at the same time, and liquid chromatography or electrophoresis is used to separate or purify target biomarkers before analysis. This paper describes a new broad-range biomarker assay using solid to liquid phase change nanoparticles, where a panel of metallic nanoparticles (i.e., metals and eutectic alloys) are modified with a panel of ligands to establish a one-to-one correspondence and attached onto ligand-modified substrates by forming sandwiched complexes. The melting peak and fusion enthalpy of phase change nanoparticles during thermal analysis reflect the type and concentration of biomarkers, respectively. The thermal readout condition can be adjusted in such a way that multiple biomarkers with concentration difference over 3 orders of magnitude have been simultaneously detected under the same condition.

Combined analysis of transcriptome and proteome data as a tool for the identification of candidate biomarkers in renal cell carcinoma

Results obtained from expression profilings of renal cell carcinoma using different "ome"-based approaches and comprehensive data analysis demonstrated that proteome-based technologies and cDNA microarray analyses complement each other during the discovery phase for disease-related candidate biomarkers. The integration of the respective data revealed the uniqueness and complementarities of the different technologies. While comparative cDNA microarray analyses though restricted to up-regulated targets largely revealed genes involved in controlling gene/protein expression (19%) and signal transduction processes (13%), proteomics/PROTEOMEX-defined candidate biomarkers include enzymes of the cellular metabolism (36%), transport proteins (12%), and cell motility/structural molecules (10%). Candidate biomarkers defined by proteomics and PROTEOMEX are frequently shared, whereas the sharing rate between cDNA microarray and proteome-based profilings is limited. Putative candidate biomarkers provide insights into their cellular (dys)function and their diagnostic/prognostic value but still warrant further validation in larger patient numbers. Based on the fact that merely three candidate biomarkers were shared by all applied technologies, namely annexin A4, tubulin alpha-1A chain, and ubiquitin carboxyl-terminal hydrolase L1, the analysis at a single hierarchical level of biological regulation seems to provide only limited results thus emphasizing the importance and benefit of performing rather combinatorial screenings which can complement the standard clinical predictors.

Development of differential diagnosis for benign and malignant pheochromocytomas

Unlike common malignant tumors, malignant pheochromocytomas cannot be definitely diagnosed using histological features. This unique nature of pheochromocytomas provides a valuable model that may promote the investigation of the mechanism of other common malignant tumors where similar frameworks are not available. Studies on malignant pheochromocytomas should benefit not only the individuals with pheochromocytomas but those with other tumors. A review on the development of differentiating diagnosis between malignant and benign pheochromocytomas in imaging studies, biological fluid examinations, pathological examinations, molecular markers and genome studies, was updated in the hopes of guiding the next studies of pheochromcytomas.

The 4-methylthio-1-butyl group for phosphate/thiophosphate protection in oligodeoxyribonucleotide synthesis

The detailed preparation of deoxyribonucleoside phosphoramidites functionalized with a 4-methylthio-1-butyl group for P(III) protection is described, along with the incorporation of these phosphoramidites into DNA oligonucleotides via solid-phase techniques. The versatility of the thermolabile 4-methylthio-1-butyl phosphate/thiophosphate-protecting group is exemplified through its facile removal from oligonucleotides under neutral conditions or under standard basic conditions. The sulfonium salt that is produced during the thermolytic deprotection of oligonucleotides did not alter DNA nucleobases or desulfurize phosphorothioate diesters to a significant extent.

Tissue handling for genome-wide expression analysis: a review of the issues, evidence, and opportunities

CONTEXT

Molecular diagnostic applications that use microarrays to analyze large numbers of genes simultaneously require high-quality mRNA. As these genome-wide expression assays become more commonly used in medical practice, pathologists and oncologists will benefit from understanding the importance of obtaining high-quality RNA in order to generate reliable diagnostic and prognostic information, especially as these relate to cancer.

OBJECTIVE

To review the effects that different tissue preservation techniques have on RNA quality and to provide practical advice on changes in tissue acquisition and handling that may soon be needed for certain clinical situations.

DATA SOURCES

A review of recent literature on RNA quality, tissue fixation, cancer diagnosis, and gene expression analysis.

CONCLUSIONS

Studies have consistently shown that frozen tissue yields more intact RNA than formalin-fixed, paraffin-embedded tissue. The chemical modification, cross-linking, and fragmentation caused by formalin fixation often render RNA unsuitable for microarray analysis. Thus, when expression analysis involving hundreds or more than 1000 gene markers is contemplated, pathologists should consider freezing a specimen within half an hour (preferably within minutes) of surgical resection and storing it at -80 degrees C or below. In coming years, pathologists will need to work closely with oncologists and other clinicians to determine when saving frozen tissue for microarray expression analysis is both practical and necessary. In select cases, the benefit of implementing a few extra tissue-handling steps may improve diagnostic and prognostic capability.

Preparation and use of metal surface-immobilized DNA hairpins for the detection of oligonucleotides

The following protocol describes the experimental steps used to prepare arrayable and label-free biological sensors that are based on the fluorescence unquenching of DNA hairpins immobilized on metal surfaces. This two-part protocol describes both the creation of gold-coated substrates and the oligonucleotide surface self-assembly process that transforms the substrates into reporters for detecting DNA. Using this procedure, one can create sensors for oligonucleotides that are highly sensitive and have demonstrated an exceptional specificity to single nucleotide polymorphisms. From start to finish, the entire procedure can be accomplished in 24-30 h.

Expression of lipoprotein lipase associated with lung adenocarcinoma tissues

Lipoprotein lipase (LPL) plays a key role in the lipid metabolism and transporting. It can catalyze the hydrolysis of chylomicron and very low-density lipoprotein triglyceride. Moreover, the abnormality of LPL associates with many pathophysiological conditions. Herein cDNA microarray and Northern blots analysis were used to study the expression of lipoprotein lipase in lung adenocarcinoma tissues. There were 113 genes of all tested blots in cDNA microarray expressed lowly. LPL gene is expressed lowly at the average ratio 0.26 (Cy5/Cy3) in lung adenocarcinoma tissues over controls. Northern blots confirmed those changes detected from the cDNA microarray and suggested that low expression of LPL may play an important role in the lung adenocarcinoma development.

Microarray-based identification of novel biomarkers in asthma

Bronchial asthma is a complicated and diverse disorder affected by genetic and environmental factors. It is widely accepted that it is a Th2-type inflammation originating in lung and caused by inhalation of ubiquitous allergens. The complicated and diverse pathogenesis of this disease yet to be clarified. Functional genomics is the analysis of whole gene expression profiling under given condition, and microarray technology is now the most powerful tool for functional genomics. Several attempts to clarify the pathogenesis of bronchial asthma have been carried out using microarray technology, providing us some novel biomarkers for diagnosis, therapeutic targets or understanding pathogenic mechanisms of bronchial asthma. In this article, we review the outcomes of these analyses by the microarray approach as applied to this disease by focusing on the identification of novel biomarkers.

Toward genomic cell culture engineering

Genomic and proteomic based global gene expression profiling has altered the landscape of biological research in the past few years. Its potential impact on cell culture bioprocessing has only begun to emanate, partly due to the lack of genomic sequence information for the most widely used industrial cells, Chinese hamster ovary (CHO) cells. Transcriptome and proteome profiling work for species lacking extensive genomic resources must rely on information for other related species or on data obtained from expressed sequence tag (EST) sequencing projects, for which burgeoning efforts have only recently begun. This article discusses the aspects of EST sequencing in those industrially important, genomic resources-poor cell lines, articulates some of the unique features in employing microarray in the study of cultured cells, and highlights the infrastructural needs in establishing a platform for genomics based cell culture research. Recent experience has revealed that generally, most changes in culture conditions only elicit a moderate level of alteration in gene expression. Nevertheless, by broadening the conventional scope of microarray analysis to consider estimated levels of transcript abundance, much physiological insight can be gained. Examples of the application of microarray in cell culture are discussed, and the utility of pattern identification and process diagnosis are highlighted. As genomic resources continue to expand, the power of genomic tools in cell culture processing research will be amply evident. The key to harnessing the immense benefit of these genomic resources resides in the development of physiological understanding from their application.

Activation of IFN pathways and plasmacytoid dendritic cell recruitment in target organs of primary Sjögren's syndrome

Gene expression analysis of target organs might help provide new insights into the pathogenesis of autoimmune diseases. We used global gene expression profiling of minor salivary glands to identify patterns of gene expression in patients with primary Sjögren's syndrome (pSS), a common and prototypic systemic autoimmune disease. Gene expression analysis allowed for differentiating most patients with pSS from controls. The expression of 23 genes in the IFN pathways, including two Toll-like receptors (TLR8 and TLR9), was significantly different between patients and controls. Furthermore, the increased expression of IFN-inducible genes, BAFF and IFN-induced transmembrane protein 1, was also demonstrated in ocular epithelial cells by quantitative RT-PCR. In vitro activation showed that these genes were effectively modulated by IFNs in salivary gland epithelial cells, the target cells of autoimmunity in pSS. The activation of IFN pathways led us to investigate whether plasmacytoid dendritic cells were recruited in salivary glands. These IFN-producing cells were detected by immunohistochemistry in all patients with pSS, whereas none was observed in controls. In conclusion, our results support the pathogenic interaction between the innate and adaptive immune system in pSS. The persistence of the IFN signature might be related to a vicious circle, in which the environment interacts with genetic factors to drive the stimulation of salivary TLRs.

Individualized cancer chemotherapy integrating drug sensitivity tests, pathological profile analysis and computational coordination – An effective strategy to improve clinical treatment

BACKGROUND

Most current cancer chemotherapy is unsatisfactory. There is a trend towards changing the norm for drug selection; one approach is to seek individualized cancer chemotherapy (ICC).

METHODS AND RESULTS

ICC is an approach to maximizing the efficacy of chemotherapy and reducing its adverse effects to a minimum. It involves choosing anticancer drugs through the following critical steps: (i) performing drug sensitivity tests in vivo and/or in vitro; (ii) analyzing pathogenic information from morphology, histology and bioinformatics, so that targeted therapy can be offered to disrupt the escalating tumorigenic molecules and pathways; (iii) introducing mathematical and computational systems to assist in improving the quality of decision-making.

CONCLUSION

Increasing clinical evidence indicates that drug sensitivity tests, pathological profile analyses and computational coordination are ways to improve therapeutic quality. In future, each patient should have his own unique chemotherapy protocol.

Identification of Clinical Prognostic Factors in Patients with Unknown Primary Tumors Treated with a Platinum-Based Combination

OBJECTIVE

The aim of this study was to evaluate patient and tumor characteristics in 102 patients with unknown primary tumors (UPT) prospectively treated with a combination of carboplatin, doxorubicin, and etoposide, to identify clinical variables predictive of response and survival.

PATIENTS AND METHODS

The association between clinical characteristics and outcome was evaluated by univariate and multivariate analysis: chi(2) test and logistic regression analysis were used to study variables predictive of response, and survival analysis, comparison of survival curves and Cox multiple regression analysis to study variables predictive of survival.

RESULTS

We obtained 26.5% objective responses (95% confidence interval: 18.2-36.1%) and a median survival of 9 months (95% confidence interval: 7-11 months). Several variables were associated with response to treatment and survival at univariate analysis. At multivariate analysis the number of tumor sites, bone/visceral involvement and epithelial tumor markers were significantly predictive of response; presence of pain, serum alkaline phosphatase, carboplatin AUC and response to treatment were significantly associated with survival.

CONCLUSIONS

The identification of variables that can predict prognosis and response to treatment in patients with UPT may be useful to offer aggressive treatment to sensitive subsets of patients and provide therapeutic alternatives to those with a low probability of benefiting from standard treatment. In our patients the use of carboplatin AUC higher than 6 and response to treatment were the most important factors associated with prognosis, together with presence of pain and serum alkaline phosphatase. However, larger series and identification of new disease markers are necessary to better define predictive and prognostic variables in UPT patients.

Moving toward whole-genome analysis: A technology perspective

PURPOSE

New, highly efficient technologies used in genomic analysis are described, and their implications for health care are discussed.

SUMMARY

The availability of the human genome sequence, in confluence with the ability to affordably package it for analysis, is opening new frontiers in biomedical research. On the horizon, personalized medicine--driven by molecular characterization of disease, genetic analysis of the patient, and information technologies designed to enable health care professionals to leverage these tools--promises to fundamentally transform health care. New genetics technologies, such as high-density microarrays, will fuel this research by providing researchers with the ability to comprehensively access the human genome in all its complexity. Some of the most promising areas for application of genetic information are those where society's current needs are greatest: complex, common disorders, such as cancer and cardiovascular disease; drug interactions; inherited genetic disorders that afflict children; and late-onset conditions for which no cure currently exists. The barriers to using genetic information widely in health care are in many cases not technological or economic, but social and political.

CONCLUSION

New technology enables efficient, large-scale analysis of the whole genome, genetic variations, and gene expression. Genomic analysis has profound clinical, economic, and social implications for health care.

Identification of a three-gene expression signature of poor-prognosis breast carcinoma

BACKGROUND

The clinical course of breast cancer is difficult to predict on the basis of established clinical and pathological prognostic criteria. Given the genetic complexity of breast carcinomas, it is not surprising that correlations with individual genetic abnormalities have also been disappointing. The use of gene expression profiles could result in more accurate and objective prognostication.

RESULTS

To this end, we used real-time quantitative RT-PCR assays to quantify the mRNA expression of a large panel (n = 47) of genes previously identified as candidate prognostic molecular markers in a series of 100 ERalpha-positive breast tumor samples from patients with known long-term follow-up. We identified a three-gene expression signature (BRCA2, DNMT3B and CCNE1) as an independent prognostic marker (P = 0.007 by univariate analysis; P = 0.006 by multivariate analysis). This "poor prognosis" signature was then tested on an independent panel of ERalpha-positive breast tumors from a well-defined cohort of 104 postmenopausal breast cancer patients treated with primary surgery followed by adjuvant tamoxifen alone: although this "poor prognosis" signature was associated with shorter relapse-free survival in univariate analysis (P = 0.029), it did not persist as an independent prognostic factor in multivariate analysis (P = 0.27).

CONCLUSION

Our results confirm the value of gene expression signatures in predicting the outcome of breast cancer.

Gene expression analysis in response to lung toxicants: I. Sequencing and microarray development

A key challenge in measuring gene expression changes in the lung in response to site-selective toxicants is differentiating between target and nontarget areas. The toxicity for the cytotoxicant 1-nitronaphthalene is highly localized in the airway epithelium. Target cells comprise but a fraction of the total lung cell mass; measurements from whole lung homogenates are not likely to reflect what occurs at the target site. Additionally, the use of generic microarrays to measure expression in airway epithelium may not provide a good representation of transcripts present at the site of toxic action. cDNA libraries from airway and alveolar subcompartments of rat lung were sequenced for the development of a custom microarray representative of these lung regions. We identified 7,460 nonredundant rat lung sequences. Nearly 30% of the sequences on this array are not present on the Affymetrix Rat GeneChip 230. A 20,000-element microarray was developed that delineates differences in gene expression between subcompartments. This is the first in a series of articles employing this microarray for detecting gene expression changes during acute injury produced by 1-nitronaphthalene and subsequent repair.

Discovery and immunologic validation of new antigens for therapeutic cancer vaccines

Immunotherapy using both active and passive approaches is increasingly being used as a modality to treat human cancer. The last decade has seen a tremendous burst of activity in antigen discovery in cancer, and many new targets have now been identified for both monoclonal antibody therapy and active immunization. In addition, advances have been made in our understanding of the immune response against cancer and how new vaccine vectors, such as poxviruses, alphaviruses and bacterial vectors, can be used to overcome some of the traditional hurdles (e.g. self-tolerance and immune suppression in cancer patients) that have hindered the generation of effective antitumor immune responses. Improvements in genomics technology in the area of DNA microarrays and differential display and subtractive hybridization together with a new wave of mass spectrometry-based proteomics tools, as well as more sensitive assays to validate the immunoreactivity of new antigens, have all accelerated the rate of new antigen discovery in cancer. This rapid progress should initiate a major paradigm shift in how we treat cancer within the next 10 years, where, instead of being a novelty, the combination of targeted T cell-based vaccines and antiangiogenesis therapies will be routinely combined with traditional chemotherapy. The successful combination of these approaches will change the face of cancer from a relatively acute, life-threatening disease to a manageable chronic disorder with long survival times.

Dynamical and integrative cell signaling: challenges for the new biology

Years of careful experimental analysis have revealed that signaling molecules are organized into complex networks of biochemical reactions exquisitely regulated in time and space to provide a cell with high-fidelity information about an extremely noisy and volatile environment. A new view of signaling networks as systems consisting of multiple complex elements interacting in a multifarious fashion is emerging, a view that conflicts with the single-gene or protein-centric approach common in biological research. The postgenomic era has brought about a different, network-centric methodology of analysis, suddenly forcing researchers toward the opposite extreme of complexity, where the networks being explored are, to a certain extent, intractable and uninterpretable. Both the cartoons of simple pathways and the very large "hair-ball" diagrams of large intracellular networks are also representations of static worlds, superficially devoid of dynamics and chemistry. These representations are often viewed as being analogous to stably linked computer and neural networks rather than dynamically changing networks of chemical interactions, where the notions of concentration, compartmentalization, and diffusion may be the primary determinants of connectivity. Arguably, the systems biology approach, relying on computational modeling coupled with various experimental techniques and methodologies, will be an essential component of analysis of the behavior of signal transduction pathways. Combining the dynamical view of rapidly evolving responses and the structural view arising from high-throughput analyses of the interacting species will be the best approach toward efforts toward greater understanding of intracellular signaling processes.