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PubMed2002

Gene induction studies and toxicity of chemical mixtures.

As part of its mixtures program, the Agency for Toxic Substances and Disease Registry (ATSDR) supports in vitro and limited in vivo toxicity testing to further our understanding of the toxicity and health effects of chemical mixtures. There are increasing concerns that environmental chemicals adversely affect the health of humans and wildlife. These concerns have been augmented by the realization that exposure to chemicals often occurs to mixtures of these chemicals that may exhibit complex synergistic or antagonistic interactions. To address such concerns, we have conducted two studies with techniques that are being used increasingly in experimental toxicology. In the first study, six organochlorine pesticides (4,4 -DDT, 4,4 -DDD, 4,4 -DDE, aldrin, dieldrin, or endrin) were selected from the ATSDR Comprehensive Environmental Response, Compensation and Liability Act of 1980 (or Superfund) priority list and tested for their ability to modulate transcriptional activation of an estrogen-responsive reporter gene in transfected HeLa cells. In these assays, HeLa cells cotransfected with an expression vector encoding estrogen receptor and an estrogen-responsive chloramphenicol acetyltransferase (CAT) reporter plasmid were dosed with and without selected environmental chemicals either individually or in defined combinations. Estradiol consistently elicited 10- to 23-fold dose-dependent inductions in this assay. By contrast, all six of the organochlorine pesticides showed no detectable dose-related response when tested either individually or in binary combinations. Thus, these chemicals as binary mixtures do not exhibit any additional estrogenicity at the levels tested in these assays. In the second study, arsenic [As(V)], cadmium [Cd(II)], chromium [Cr(III, VI)], and lead [Pb(II)] were tested in a commercially developed assay system, CAT-Tox (L), to identify metal-responsive promoters and to determine whether the pattern of gene expression changed with a mixture of these metals. This assay employs a battery of recombinant HepG2 cell lines to test the transcriptional activation capacity of xenobiotics in any of 13 different signal-transduction pathways. Singly, As(V), Cd(II), Cr(III, VI), and Pb(II) produced complex induction profiles in these assays. However, no evidence of synergistic activity was detected with a mixture of Cd(II), Cr(III), and Pb(II). These results have shown metal activation of gene expression through several previously unreported signal-transduction pathways and thus suggest new directions for future studies into their biochemical mechanisms of toxicity. In conclusion, the (italic)in vitro(/italic) methods used in these studies provide insights into complex interactions that occur in cellular systems and could be used to identify biomarkers of exposure to other environmental chemical mixtures.

Biomarkers↗
PubMed2006

Microarray analysis of bleomycin-exposed lymphoblastoid cells for identifying cancer susceptibility genes.

The uncovering of genes involved in susceptibility to the sporadic cancer types is a great challenge. It is well established that the way in which an individual deals with DNA damage is related to the chance to develop cancer. Mutagen sensitivity is a phenotype that reflects an individual's susceptibility to the major sporadic cancer types, including colon, lung, and head and neck cancer. A standard test for mutagen sensitivity is measuring the number of chromatid breaks in lymphocytes after exposure to bleomycin. The aim of the present study was to search for the pathways involved in mutagen sensitivity. Lymphoblastoid cell lines of seven individuals with low mutagen sensitivity were compared with seven individuals with a high score. RNA was isolated from cells exposed to bleomycin (4 hours) and from unexposed cells. Microarray analysis (19K) was used to compare gene expression of insensitive and sensitive cells. The profile of most altered genes after bleomycin exposure, analyzed in all 14 cell lines, included relatively many genes involved in biological processes, such as cell growth and/or maintenance, proliferation, and regulation of cell cycle, as well as some genes involved in DNA repair. When comparing the insensitive and sensitive individuals, other differentially expressed genes were found that are involved in signal transduction and cell growth and/or maintenance (e.g., BUB1 and DUSP4). This difference in expression profiles between mutagen-sensitive and mutagen-insensitive individuals justifies further studies aimed at elucidating the genes responsible for the development of sporadic cancers.

Adolescent↗
PubMed2005

Expression of regulatory genes for pancreas development during murine embryonic stem cell differentiation.

Insulin-producing cells derived from embryonic stem cells could be surrogates for beta cells in diabetes therapy. However, their derivation remains hard to achieve with current protocols which rely on initial embryoid body formation. We assume that factors known to inhibit pancreas development contribute to this limitation in vitro. To evaluate this hypothesis, embryoid bodies were examined after different culture periods by real time RT-PCR to profile the expression of genes known to regulate embryonic pancreas development. Our data indicate that transcripts for pancreas markers (insulin, glucagon and amylase ) were expressed during differentiation, but the highest levels achieved were at least 10(5) times lower than in the adult mouse pancreas. Notch signalling was activated as suggested by Delta, Jagged, Ngn3 and NeuroD1 profiles. However, Sonic hedgehog, a known inhibitor of pancreas induction in vivo drastically increased in day 6 embryoid bodies, while Inhibin betaA and betaB were down-regulated and follistatin up-regulated. Members of the Fibroblast- and Transforming Growth Factor families which pattern the endoderm were expressed at low levels, while those that inhibit pancreas development were highly transcribed. The profile of pancreas regulators expressed in embryoid bodies is therefore not compatible with differentiation of pancreatic and insulin-producing cells. These findings provide an explanation for the limited derivation of such cells to date, in addition to basic information for establishing novel differentiation protocols.

Animals↗
PubMed2006

Microarray analysis reveals differential gene expression patterns in tumors of the pineal region.

Several types of tumors are known to originate from the pineal region, among them pineal parenchymal tumors (PPTs) and papillary tumors of the pineal region (PTPRs), probably derived from the subcommissural organ. As a result of their rarity, their histologic diagnosis remains difficult. To identify molecular markers, using CodeLink oligonucleotide arrays, gene expression was studied in 3 PPTs (2 pineocytomas and one pineoblastoma), 2 PTPRs, and one chordoid glioma, another rare tumor of the third ventricle. Because PTPR and chordoid glioma may present ependymal differentiation, gene expression was also analyzed in 4 ependymomas. The gene patterns of the 3 PPTs fell in the same cluster. The pineocytomas showed high expression of TPH, HIOMT, and genes related to phototransduction in the retina (OPN4, RGS16, and CRB3), whereas the pineoblastoma showed high expression of UBEC2, SOX4, TERT, TEP1, PRAME, CD24, POU4F2, and HOXD13. Using reverse transcriptase-polymerase chain reaction on 13 PPTs, we demonstrated that PRAME, CD24, POU4F2, and HOXD13 might be candidates for grading PPT with intermediate differentiation. PTPRs, classified with chordoid glioma and separately from ependymomas, showed high expression of SPEDF, KRT18, and genes encoding proteins reported to be expressed in the subcommissural organ, namely ZFH4, RFX3, TTR, and CGRP. Our results highlight the usefulness of gene expression profiling for classify tumors of the pineal region and identify genes with potential use as diagnostic markers.

Adolescent↗
PubMed2005

Unique gene expression profile based on pathologic response in epithelial ovarian cancer.

PURPOSE: We investigated whether tumor tissue obtained at diagnosis expresses a specific gene profile that is predictive of findings at second-look surgery in patients with epithelial ovarian cancer (EOC). PATIENTS AND METHODS: Tumor tissue obtained at the time of diagnosis was profiled with oligonucleotide microarrays. Class prediction analysis was performed in a training set of 24 patients who had undergone a second-look procedure. The resultant predictive signature was then tested on an independent validation set comprised of 36 patients. RESULTS: A 93-gene signature referred to as the Chemotherapy Response Profile (CRP) was identified through its association with pathologic complete response. When applied to a separate validation set, the CRP distinguished between patients with unfavorable versus favorable overall survival (median 41 months v not yet reached, respectively, log-rank P = .007), with a median follow-up of 52 months. The signature maintained independent prognostic value in multivariate analysis, controlling for other known prognostic factors such as age, stage, grade, and debulking status. There was no genetic overlap between the CRP and our previously described Ovarian Cancer Prognostic Profile (OCPP), which demonstrated similar prognostic value. The combination of the CRP and OCPP yielded better prognostic discrimination then either profile alone. Genes present in the CRP include BAX, a proapoptotic protein previously associated with chemotherapy response in ovarian cancer. CONCLUSION: Identification of a gene expression profile based on pathologic response in EOC provides independent prognostic information and offers potential insights into the mechanism of drug resistance. Efforts to identify a more tailored profile using selected genes from both the CRP and OCPP are underway.

Adenocarcinoma, Clear Cell↗
PubMed2005

Improving molecular cancer class discovery through sparse non-negative matrix factorization.

MOTIVATION: Identifying different cancer classes or subclasses with similar morphological appearances presents a challenging problem and has important implication in cancer diagnosis and treatment. Clustering based on gene-expression data has been shown to be a powerful method in cancer class discovery. Non-negative matrix factorization is one such method and was shown to be advantageous over other clustering techniques, such as hierarchical clustering or self-organizing maps. In this paper, we investigate the benefit of explicitly enforcing sparseness in the factorization process. RESULTS: We report an improved unsupervised method for cancer classification by the use of gene-expression profile via sparse non-negative matrix factorization. We demonstrate the improvement by direct comparison with classic non-negative matrix factorization on the three well-studied datasets. In addition, we illustrate how to identify a small subset of co-expressed genes that may be directly involved in cancer.

Algorithms↗
PubMed2005

Expression of a novel biomarker, EPCA, in adenocarcinomas and precancerous lesions in the prostate.

PURPOSE: EPCA, a nuclear matrix protein, has been identified as a novel prostate cancer marker through protein profiling analyses comparing prostate cancer and its normal counterpart. A recent study of prostate biopsy specimens revealed that EPCA stained positive in the cancer negative biopsy cores of individuals with cancer at later biopsy. We clarified the relationship between EPCA expression and clinicopathological parameters, including grade and stage. Morphological characteristics of EPCA positive cells in noncancerous tissues were also analyzed. MATERIALS AND METHODS: Prostate tissue specimens were obtained from 50 patients with localized prostate cancer and 10 with invasive bladder cancer. EPCA expression was analyzed with a polyclonal antibody. Anti-P504S/anti-p63/anti-34betaE12 monoclonal antibodies were used for adjunct diagnosis for prostatic intraepithelial neoplasia. A monoclonal antibody, CD45RO, was used to confirm inflammatory infiltrates surrounding focal atrophic glands. RESULTS: EPCA staining was positive in the prostate samples of 94% of patients with prostate cancer but it was completely negative in samples from patients with bladder cancer. There was no correlation of EPCA staining intensity with Gleason grade or pT stage. In noncancerous tissues adjacent to major cancer foci EPCA was positive in 86% of prostate cancers. Most EPCA positive glands adjacent to cancer consisted of prostatic intraepithelial neoplasia and proliferative inflammatory atrophy. CONCLUSIONS: EPCA seems to reflect nuclear matrix alterations that occur in the earlier stage of prostate carcinogenesis. Individuals in whom the prostate is influenced by this field effect may be detected with this new biomarker.

Adenocarcinoma↗
PubMed2002

Gene-Expression - Based Responses to Drug Treatment.

Perfect drugs are potent, specific and nontoxic. Many compounds fail because of unexpected toxicity and lack of efficacy in later stages of clinical development. Therefore, more complete knowledge and understanding of the properties of a drug is needed at an earlier stage of drug development. DNA microarrays can yield gene expression profiles from cells or tissues treated with a compound. Such "expression fingerprints" are used in drug discovery for drug target identification and validation and for elucidating the mode of action of novel compounds during lead identification and optimization. Moreover, during drug development, DNA microarrays help in the discovery of new diagnostic and prognostic biomarkers, as well as in the prediction of resistance and toxic side effects. This review aims to assess to what extent the promise of gene expression profiling has already materialized for the different stages of drug discovery and development. (c) 2002 Prous Science. All rights reserved.

Journal Article↗
PubMed2006

Characterizing disease states from topological properties of transcriptional regulatory networks.

BACKGROUND: High throughput gene expression experiments yield large amounts of data that can augment our understanding of disease processes, in addition to classifying samples. Here we present new paradigms of data Separation based on construction of transcriptional regulatory networks for normal and abnormal cells using sequence predictions, literature based data and gene expression studies. We analyzed expression datasets from a number of diseased and normal cells, including different types of acute leukemia, and breast cancer with variable clinical outcome. RESULTS: We constructed sample-specific regulatory networks to identify links between transcription factors (TFs) and regulated genes that differentiate between healthy and diseased states. This approach carries the advantage of identifying key transcription factor-gene pairs with differential activity between healthy and diseased states rather than merely using gene expression profiles, thus alluding to processes that may be involved in gene deregulation. We then generalized this approach by studying simultaneous changes in functionality of multiple regulatory links pointing to a regulated gene or emanating from one TF (or changes in gene centrality defined by its in-degree or out-degree measures, respectively). We found that samples can often be separated based on these measures of gene centrality more robustly than using individual links. We examined distributions of distances (the number of links needed to traverse the path between each pair of genes) in the transcriptional networks for gene subsets whose collective expression profiles could best separate each dataset into predefined groups. We found that genes that optimally classify samples are concentrated in neighborhoods in the gene regulatory networks. This suggests that genes that are deregulated in diseased states exhibit a remarkable degree of connectivity. CONCLUSION: Transcription factor-regulated gene links and centrality of genes on transcriptional networks can be used to differentiate between cell types. Transcriptional network blueprints can be used as a basis for further research into gene deregulation in diseased states.

Algorithms↗
PubMedDate not supplied

Molecular genetic tumor markers in non-small cell lung cancer.

Not only serum tumor markers, such as carcinoembryonic antigen (CEA), squamous cell carcinoma antigen (SCC) and carbohydrate antigen (CA) 125, but also serum growth factors have been examined to evaluate tumor stages and to predict the recurrence and metastasis in patients with non-small cell lung cancer (NSCLC) (1-5). In recent years, the analysis of the genome and proteome has advanced remarkably. An array of molecular genetic tumor markers (MGTMs) have been identified based on the biological characterization of tumors, such as tumor development, growth, invasion and metastasis. Molecular genetic tumor marker research has also entered a new era, since comprehensive gene profile analysis using cDNA microarrays and comprehensive protein expression analysis using proteomics technology have been developed. On the other hand, the frequency of lung cancer patients with which various tumor markers are associated is increasing in Japan (6-8). This paper reviews MGTMs characteristic of lung cancer and clarifies the clinical usefulness and applications of MGTM for cancer treatment.

Biomarkers, Tumor↗
PubMed2004

Differences between human and mouse embryonic stem cells.

We compared gene expression profiles of mouse and human ES cells by immunocytochemistry, RT-PCR, and membrane-based focused cDNA array analysis. Several markers that in concert could distinguish undifferentiated ES cells from their differentiated progeny were identified. These included known markers such as SSEA antigens, OCT3/4, SOX-2, REX-1 and TERT, as well as additional markers such as UTF-1, TRF1, TRF2, connexin43, and connexin45, FGFR-4, ABCG-2, and Glut-1. A set of negative markers that confirm the absence of differentiation was also developed. These include genes characteristic of trophoectoderm, markers of germ layers, and of more specialized progenitor cells. While the expression of many of the markers was similar in mouse and human cells, significant differences were found in the expression of vimentin, beta-III tubulin, alpha-fetoprotein, eomesodermin, HEB, ARNT, and FoxD3 as well as in the expression of the LIF receptor complex LIFR/IL6ST (gp130). Profound differences in cell cycle regulation, control of apoptosis, and cytokine expression were uncovered using focused microarrays. The profile of gene expression observed in H1 cells was similar to that of two other human ES cell lines tested (line I-6 and clonal line-H9.2) and to feeder-free subclones of H1, H7, and H9, indicating that the observed differences between human and mouse ES cells were species-specific rather than arising from differences in culture conditions.

Animals↗
PubMed2006

Multiple sclerosis therapy monitoring based on gene expression.

Multiple sclerosis (MS) is the most prevalent chronic autoimmune, neurodegenerative disorder of the central nervous system (CNS). Despite substantial progress, treatment of MS and other autoimmune diseases is only moderately effective. It is anticipated that the treatment of autoimmune diseases with single drugs or biological approaches will in the future be complemented, or even replaced, by combination therapies, which include immunomodulation, elimination of infectious triggers and tissue repair. One proclaimed goal of biomedical research and clinical practice is the discovery of sets of genes with expression that correlates with successful outcomes of drug therapy, or with unfortunate side effects. Such information has direct consequences for selection, refinement or development of treatments and will soon be translated into clinical trials. The genome-wide RNA profile of an individual represents one complement to the comprehensive determination of disease- or drug response-related elements; comparable to a 'sentinel' method, it serves as a large-scale approach to MS biology. This work reviews the state of the art in MS research at the transcriptome level applying genomewide screening methods. It discusses implications in understanding disease pathogenicity, diagnostic markers, the identification of new therapeutic targets and a classification of patients towards the advent of tailored therapies.

Biomarkers↗
PubMed2005

Gene profiling in squamous cell carcinoma of the head and neck.

Survival for patients with squamous cell carcinoma of the head and neck (SCCHN) is still poor, despite great technical improvements in radiotherapy and surgery. A possible explanation for this is the lack of individualization in treatment based on biological properties of the tumors, resulting in over- as well as under treatment. Management of SCCHN has mainly been based on TNM classification over the last decades. However, a large amount of studies have shown that biomarkers may add prognostic information, independently of the TNM system, indicating that biological aggressiveness is not entirely reflected by the T- and N-status of the tumor. A conclusion to draw from this is that the present standardized treatment based on macroscopic features of the tumor in many cases will result in suboptimal treatment since important underlaying genetic properties of the tumors are not taken into consideration. A variety of laboratory techniques have been used in studies that investigate the individual biological features, spanning from methods that screen the genome for chromosomal and genetic abnormalities, e.g. cytogenetics, CGH, SKY, cDNA micro array to detailed studies of specific aberrations, e.g. southern, northern and western blotting, PCR based analysis and immunohistochemistry. Dysregulation of genes involved in e.g. cell cycle control, proliferation, drug resistance, and metastasis have been linked to outcome of treatment and survival. The purpose of this review of the literature was to summarize what has been studied so far by cDNA micro array techniques with regards to genetic screening in general and biomarkers that relate to response to therapy and prediction of clinical outcome in particular. We conclude that the majority of investigations that focus on gene profiling have a descriptive character, e.g. comparisons of tumor and normal cells, metastatic and non-metastatic properties, and differences between sub-sites and grades of differentiation. There are just a handful studies that so far have investigated how gene profiling can be used to predict clinical course.

Carcinoma, Squamous Cell↗
PubMed2026

Persistent trauma three decades after the Halabja massacre: psychological symptom profiles and hair cortisol levels in gas attack survivors.

BACKGROUND: Survivors of the 1988 Halabja chemical attack have experienced severe, chronic trauma, yet limited research has examined the long-term psychological and biological consequences of chemical warfare exposure more than three decades later. AIMS: To examine long-term post-traumatic stress disorder (PTSD) symptoms, depression/anxiety symptoms, somatic symptoms and perceived stress among survivors of the Halabja chemical attack, and to investigate the associations of trauma exposure, sociodemographic factors and hair cortisol concentrations (HCCs) with psychological symptom severity and symptom profiles. METHOD: A total of 209 survivors of the Halabja chemical attack participated in structured interviews using culturally adapted and field-tested instruments, including the Post-Traumatic Stress Disorder Checklist for DSM-5, Hopkins Symptom Checklist-25, the Patient Health Questionnaire-13, the Perceived Stress Scale-14 and an adapted War and Adversity Exposure Checklist-26. Hair cortisol and cortisone concentrations were assessed as biomarkers of chronic stress. Data were analysed using correlations, independent-samples t-tests, multiple linear regression analyses, multinomial logistic regression and latent profile analysis (LPA). RESULTS: Survivors demonstrated a substantial long-term psychological symptom burden. Overall, 87.6% of participants exceeded the cut-off for probable PTSD symptoms, and 75.1% exceeded the cut-off for elevated depression/anxiety symptoms. Most participants (81.8%) reported exposure to 11 or more lifetime traumatic events. Men reported significantly greater cumulative trauma exposure, whereas women reported significantly greater somatic symptom severity. Higher educational attainment and employment were consistently associated with lower psychological symptom severity across multiple outcomes. HCCs were not significantly associated with PTSD symptoms or latent symptom-profile membership after adjustment for trauma exposure and psychosocial variables. The LPA identified four symptom-severity classes characterised by differing levels of PTSD and depression/anxiety symptoms. Greater cumulative trauma exposure and lower social support were associated with membership in more severe symptom classes. CONCLUSIONS: Survivors of the Halabja chemical attack continue to experience substantial psychological distress more than three decades after exposure. Long-term symptom severity appears to be more strongly associated with cumulative trauma exposure and psychosocial adversity than with HCCs. The findings highlight substantial heterogeneity in symptom presentation and support the need for long-term, culturally sensitive and trauma-informed mental health interventions for survivors of chemical warfare and mass violence.

Halabja chemical attack↗
PubMed2002

Freshwater pollution biomarker: response of brain acetylcholinesterase activity in two fish species.

The effect of prolonged exposure at two sites along the Reconquista River (Argentina), a highly polluted peri-urban water body, on brain acetylcholinesterase (AChE, EC 3.1.1.7, acetylcholine acetylhydrolase) of two teleosts was examined. Caged Cyprinus carpio and field-captured Cnesterodon decemmaculatus were used as sentinel organisms. Eserine concentration inhibiting 50% of AChE activity (IC50) and inhibition kinetic parameters were also evaluated. Interspecies IC50 differences were found to agree with observed kinetic parameters (KA, ki and kc), indicating that carps were more sensitive to eserine. Data obtained disclosed spatial differences and demonstrated the high sensitivity of AChE activity as an exposure biomarker. Marked species-related differences were detected, showing that enzyme determination of C. decemmaculatus is more effective in highly polluted sites. Considering the river water physicochemical profile, observed changes in AChE activities can be partly attributed to long-lasting raised concentrations of dissolved heavy metals.

Acetylcholinesterase↗
PubMed2003

Mass spectrometry-based clinical proteomics.

In recent years, mass spectrometry (MS) has been recognized as a 'Gold Standard' tool for the identification and analysis of individual proteins in expression proteomics studies. Moreover, MS has proven useful for the analysis of nucleic acids for single nucleotide polymorphism (SNP) genotyping purposes. With the increased usage of MS as a standard tool for life science applications and the advancement of MS instrumentation, sample preparation and bioinformatics, MS technology has entered novel screening and discovery application areas that are beyond the traditional protein identification and characterization applications. The areas of clinical diagnostics and predictive medicine are just two prime examples of these fields. Predictive markers or biomarkers for early diagnosis of diseases are of growing importance for the human healthcare community. The goal of using MS in clinical proteomics is to generate protein profiles (mass to charge [m/z] ratio versus signal intensity) from readily available body fluids like serum, saliva and urine to detect changes in protein levels that reflect changes in the disease states. Whereas the results originating from individual protein markers may be intriguing, data resulting from the analysis of complex, multiple biomarker patterns may be unequivocal. These biomarker patterns are hidden in complex mass spectra and are not always obvious to the human eye. Sophisticated bioinformatics algorithms have to be applied to determine these unique biomarker patterns. Here, we review the latest developments concerning the use of MS for the discovery of biomarker patterns and for the identification of individual biomarkers in the field of clinical proteomics applications.

Biomarkers↗
PubMed2005

Toxicogenomics in the pharmaceutical industry: hollow promises or real benefit?

Almost 10 years ago, microarray technology was established as a new powerful tool for large-scale analysis of gene expression. Soon thereafter the new technology was discovered by toxicologists for the purpose of deciphering the molecular events underlying toxicity, and the term "Toxicogenomics" appeared in scientific literature. Ever since, the toxicology community was fascinated by the multiplicity of sophisticated possibilities toxicogenomics seems to offer: genome-wide analysis of toxicant-induced expression profiles may provide a means for prediction of toxicity prior to classical toxicological endpoints such as histopathology or clinical chemistry. Some researchers even speculated of the classical methods being superfluous before long. It was assumed that by using toxicogenomics it would be possible to classify compounds early in drug development and consequently save animals, time, and money in pre-clinical toxicity studies. Moreover, it seemed within reach to unravel the molecular mechanisms underlying toxicity. The feasibility of bridging data derived from in vitro and in vivo systems, identifying new biomarkers, and comparing toxicological responses "across-species" was also excessively praised. After several years of intensive application of microarray technology in the field of toxicology, not only by the pharmaceutical industry, it is now time to survey its achievements and to question how many of these wishes and promises have really come true.

Animals↗
PubMed2001

Organ-specific molecular classification of primary lung, colon, and ovarian adenocarcinomas using gene expression profiles.

Molecular classification of tumors based on their gene expression profiles promises to significantly refine diagnosis and management of cancer patients. The establishment of organ-specific gene expression patterns represents a crucial first step in the clinical application of the molecular approach. Here, we report on the gene expression profiles of 154 primary adenocarcinomas of the lung, colon, and ovary. Using high-density oligonucleotide arrays with 7129 gene probe sets, comprehensive gene expression profiles of 57 lung, 51 colon, and 46 ovary adenocarcinomas were generated and subjected to principle component analysis and to a cross-validated prediction analysis using nearest neighbor classification. These statistical analyses resulted in the classification of 152 of 154 of the adenocarcinomas in an organ-specific manner and identified genes expressed in a putative tissue-specific manner for each tumor type. Furthermore, two tumors were identified, one in the colon group and another in the ovarian group, that did not conform to their respective organ-specific cohorts. Investigation of these outlier tumors by immunohistochemical profiling revealed the ovarian tumor was consistent with a metastatic adenocarcinoma of colonic origin and the colonic tumor was a pleomorphic mesenchymal tumor, probably a leiomyosarcoma, rather than an epithelial tumor. Our results demonstrate the ability of gene expression profiles to classify tumors and suggest that determination of organ-specific gene expression profiles will play a significant role in a wide variety of clinical settings, including molecular diagnosis and classification.

Adenocarcinoma↗