This paper describes an automated method for finding clusters of interconnected proteins in protein interaction networks and retrieving protein annotations associated with these clusters.Protein interaction graphs were separated into subgraphs of interconnected proteins, using the JUNG implementation of Girvan and Newman's Edge-Betweenness algorithm. Functions were sought for these subgraphs by detecting significant correlations with the distribution of Gene Ontology terms which had been used to annotate the proteins within each cluster. The method was implemented using freely available software . Protein clusters with significant correlations to functional annotations could be identified and included groups of proteins know to cooperate in cell metabolism. The method appears to be resilient against the presence of false positive interactions.This method provides a useful tool for rapid screening of small to medium size protein interaction datasets. Protein interaction datasets are typically presented as graphs (or networks), in which the nodes are proteins and the edges represent the interactions between the proteins. These graphs can be used to investigate the functions of unannotated proteins through their interactions with neighbouring annotated proteins. Protein interaction datasets frequently contain many false positives and false negatives,  Range of cluster sizes: To decide what a sensible distribution of cluster sizes would be, the range of sizes of clusters found by affinity purification was used as a guide. Gavin  reportedOther results from Tables (i)distribution cluster size Gavin 25% Uetz 13% Lehner 14% edges(ii) significant GO terms For all datasets, the more edges that are removed the more terms become significant down to the smallest cluster sizes investigated(iii)average cluster 5–20 Gavin 25% Uetz 2–13% Lehner 7–25% edges(iv)biggest cluster < 20% Gavin 25% Uetz 13% Lehner 14% edges(v)single nodes < 10 % Gavin 25% Uetz 27% Lehner 25% edgesThe data above shows that most of these criteria give similar results and suggest that the method used to produce the data (Y2H or affinity purification) will be a major determinant of the proportion of edges to remove. To summarise, for Y2H, useful results are obtained by removing 10%–15% of edges whereas for affinity purification, removing 25% edges gives better results. Newman and Girvan  have devSize of cluster is important, because the quantity of significant annotation information i.e. the average number of significant GO terms per protein, Table  increaseThe utility of this approach is currently restricted by the size of the dataset being analysed, especially when a large number of edges are being removed. For the Gavin dataset, when 57 edges were removed the total time to cluster was 1 h 25 min but when removing 1500 edges it took 10 h 10 min. According to the software documentation  and as dAfter performing the Chi Squared tests and checking them against a random reallocation of GO terms across the network, all the significant GO cluster correlations remained significant. In no case were more than 5% of the lowest p values of the randomly reallocated GO terms lower than the lowest p value in the original dataset.In almost every case the significant annotations were informative about a potential function for the clusters  were created Table . From TaOne important question is whether the functions identified for these protein clusters are confirmed by biological experimentation. The Lsm complex is mentioned by the authors of all 3 papers -15, It hIn the Lehner dataset two GO terms, GO:6371 "mRNA splicing" and GO:8380 "RNA splicing", were always associated with only one cluster in the dataset. This was a good candidate for the Lsm complex.Of the 8 Lsm proteins examined in , all eigGO:15980 energy derivation by oxidation of organic compounds (2/19)GO:5837 26S proteosome (2/16)GO:6350 transcription (2/3)For the Lehner data, when 15, 30 and 57 edges were removed, the clusters labeled as being associated with RNA splicing are large containing 190, 143 and 60 proteins respectively (see below). The cluster with 5/8 Lsm proteins (100 edges removed) had only 17 proteins. In addition to the Lsm proteins the large clusters contained other proteins known (i.e. having GO labels) to be involved in RNA splicing. The proportions are shown in Table This data clearly shows that as the cluster size gets smaller, the cluster is more focused round the RNA splicing function. Larger clusters must have sub-clusters related to other functions. The last column in the table above shows that many of the RNA splicing proteins grouped in these clusters were the prey of the Lsm proteins in the original experiments , which iTherefore for the Lehner data, the cluster identified by Edge-Betweenness clustering as the "RNA splicing" cluster, did contain the proteins expected to be associated with this process. However this is a small dataset focused around a specific biological process. A more stringent test of this method is provided by the yeast proteome datasets where screening was not functionally focused.Gavin et al  and UetzIn Gavin et al , the LsmTherefore in a dataset not focused round RNA metabolism, the edge-betweenness algorithm successfully clustered the Lsm proteins with a number of other proteins that were co-purified in the TAP-C128 complex and a cluster produced using the graph topology was shown to correspond to a cluster of known function.In Uetz et al 2000 , the LsmTherefore in the Uetz dataset although all the Lsm proteins clustered together, it was only once more than 10% of edges had been removed that it was possible to get a significant association with the relevant GO term. Finding the correct number of edges to remove is obviously essential to extracting the required information.Overall it can be seen that the method is capable of finding clusters of proteins with known biological function and of correctly assigning a relevant annotation to a particular group.In Gavin et al  the authThe distribution of these two complexes between the clusters generated by edge-betweenness clustering, was compared at different levels of clustering,  the same clusters are still being found 75% of the time. In other words the presence of false positives and false negatives in the dataset does not seem to distort the composition of the clusters created by the Edge-Betweenness method in a way that obliterates cluster identity. But false negatives do appear to have a slightly more detrimental effect than false positives.Looking at the edges which were removed during clustering, when 57 edges were removed  3/57 (5%) had false positive nodes at one or both ends. When clustering was done by removing 100 edges 15/100(15%) were attached to false positive nodes. This compares with 68/465(14.6%) edges attached to false positive nodes in the whole dataset. There is no obvious bias in the presence of false positive edges between or within clusters.Overall it appears that the clustering is fairly robust to the presence of false positives and also to the random removal of edges i.e. false negatives.With the Ito et al  dataset Edge-Betweenness clustering can be used to separate protein interaction networks into clusters which have correlations with annotated gene functions. This can be done in an automated fashion and thus can provide a means of rapidly screening the results of protein interaction experiments. Clusters produced by this method contain groups of proteins which are known to cooperate to perform common functions, described by the correlating annotations. Therefore the clusters detected by this method correspond to active protein complexes found in the cell. Moreover the method worked for different types of dataset (Y2H and affinity purification) different organisms (yeast and human) and for datasets with a 5× difference in the number of edges.The smaller the clusters generated by this method, the higher the average number of significant annotations. The preliminary results presented here suggest that, in general, useful information was obtained once approximately 10% of edges were removed from Y2H datasets and a slightly higher proportion (25%) from affinity purification data. This method is particularly good at detecting "stable" clusters. The method is also flexible and can be adjusted according to the nature of the dataset and to the function being studied. Currently scaling to very large datasets when large numbers of edges need to be removed is problematic, but this may soon be alleviated by new developments of the algorithm . The levDrosophila protein interaction networks, found by Giot et al [Spirin and Mirny  have demot et al .Giot et al  also fouThe hierarchical nature of the Gene Ontology made this a very useful system of annotation to exploit in this method. It allows proteins to be grouped according to the most detailed shared level of annotation but also enables higher level (less informative) annotation to be used when this is all that is available. The very high level terms which apply to almost all proteins are usually ignored as they are not concentrated in a particular cluster, although these terms occasionally appear as significant, in clusters with higher than average levels of annotation.Edge-Betweenness clustering provides a quick way of picking out functionally interesting areas of protein interaction datasets. It also appears to be robust against false positives and negatives. As such this approach can be applied to any quality of data. It also deals effectively with poorly connected nodes, such as the many prey with single connections found in Y2H graphs. Because the Edge-Betweenness algorithm does not scale well to larger graphs, this method is currently most appropriate for studies focused on specific areas of the proteome. However, modifications of the algorithm are being developed and these should allow it to be applied to larger datasets in the future . The impH. sapiens protein interactions, detected using the Y2H method [The datasets used for analysis are described in Table H method . The othH method  and UetzH method , are larThe Ito dataset , an evenThe Gene Ontology (GO)  was usedThe steps of our method to cluster the graph and assign functions to the clusters, were as follows:-1. Transform the protein interaction data to GraphML (an XML format for graphs ), removi2. Use the JUNG graph analysis framework  to clust3. Find GO terms and the parents of those GO terms for each GO annotated protein in every cluster.4. Test the association between each GO term and each cluster, from a 2 by 2 contingency table.5. Correct the association tests for multiple comparisons, using a permutation test with random re-allocation of GO terms to proteins.6. Generate reports on cluster size and significant GO terms.Perl scripts were used to perform most of these steps, the other software used is described below. Details of the steps listed above are as follows:JUNG version 1.3  was usedThe number of edges to remove was supplied as a parameter. Removing a larger number of edges reduced the size of the clusters produced. The number of edges removed was varied to see whether (a), clusters of certain sizes gave better correlations with GO terms and (b), whether datasets of different types cluster in different ways .GO terms available for each of the proteins in the graph were retrieved. In the case of the Lehner dataset these were taken from the RefSeq records , for theThe Gene Ontology "termdb" release from December 2003 was used as the source of the parent GO terms . Tables The 'R' statistical package  (versionThe GO terms (significant and non-significant) were redistributed across the clustered network at random. The p value was recalculated for each GO/cluster combination. This randomisation was repeated 1000 times. The overall significance was calculated as the proportion of randomisations in which the smallest p value for a GO-cluster association was less than or equal to the smallest p value in the original data. We considered the GO numbers to be significantly associated with the clusters if the overall significance was less than 5% .In order to compare the informativeness of the GO/cluster associations, the following ratios were calculated (a), the average number of GO terms per node in the clusters and (b), the average depth of the GO terms per node per cluster. These provided an indication of the 'quantity' and 'quality' of the GO information. A GO at a greater depth in the GO hierarchy provides more detailed information than one higher in the GO hierarchy.In our original experiments ,13 thereIf these nodes were disconnected this removed 68 edges, so nine of the edges connected to false positives were part of the original data. In a control experiment 68 edges were removed at random , this dataset was clustered. This was repeated 100 times and the results were compared to the clusters obtained from the dataset which had false positive edges removed.Gavin 2002 Supplementary Information Table S2  providedAll false positive datasets  formed when the Gavin, Uetz and Lehner datasets were clustered. Each file name begins with the name of the dataset used in clustering, followed by the number of edges removed to perform the clustering. The files with a name ending 'clusters.txt' list all the members of each cluster and all the GO terms which were found to be significant for that cluster. Files ending with 'proteins-per-GO.txt', give further detail, showing the transcripts in the cluster which had the significant GO annotations. The examples provided show each dataset with the 'best' number of edges removed (see Results section: Difference in clustering between the datasets). For the smallest (Lehner) dataset, examples with greater and fewer numbers of edges removed are also provided.Click here for fileThese files provide information on all the clusters (of size > 1) formed when the Gavin, Uetz and Lehner datasets were clustered. Each file name begins with the name of the dataset used in clustering, followed by the number of edges removed to perform the clustering. The files with a name ending 'clusters.txt' list all the members of each cluster and all the GO terms which were found to be significant for that cluster. Files ending with 'proteins-per-GO.txt', give further detail, showing the transcripts in the cluster which had the significant GO annotations. The examples provided show each dataset with the 'best' number of edges removed (see Results section: Difference in clustering between the datasets). For the smallest (Lehner) dataset, examples with greater and fewer numbers of edges removed are also provided.Click here for fileThese files provide information on all the clusters (of size > 1) formed when the Gavin, Uetz and Lehner datasets were clustered. Each file name begins with the name of the dataset used in clustering, followed by the number of edges removed to perform the clustering. The files with a name ending 'clusters.txt' list all the members of each cluster and all the GO terms which were found to be significant for that cluster. Files ending with 'proteins-per-GO.txt', give further detail, showing the transcripts in the cluster which had the significant GO annotations. The examples provided show each dataset with the 'best' number of edges removed (see Results section: Difference in clustering between the datasets). For the smallest (Lehner) dataset, examples with greater and fewer numbers of edges removed are also provided.Click here for fileThese files provide information on all the clusters (of size > 1) formed when the Gavin, Uetz and Lehner datasets were clustered. Each file name begins with the name of the dataset used in clustering, followed by the number of edges removed to perform the clustering. The files with a name ending 'clusters.txt' list all the members of each cluster and all the GO terms which were found to be significant for that cluster. Files ending with 'proteins-per-GO.txt', give further detail, showing the transcripts in the cluster which had the significant GO annotations. The examples provided show each dataset with the 'best' number of edges removed (see Results section: Difference in clustering between the datasets). For the smallest (Lehner) dataset, examples with greater and fewer numbers of edges removed are also provided.Click here for fileThese files provide information on all the clusters (of size > 1) formed when the Gavin, Uetz and Lehner datasets were clustered. Each file name begins with the name of the dataset used in clustering, followed by the number of edges removed to perform the clustering. The files with a name ending 'clusters.txt' list all the members of each cluster and all the GO terms which were found to be significant for that cluster. Files ending with 'proteins-per-GO.txt', give further detail, showing the transcripts in the cluster which had the significant GO annotations. The examples provided show each dataset with the 'best' number of edges removed (see Results section: Difference in clustering between the datasets). For the smallest (Lehner) dataset, examples with greater and fewer numbers of edges removed are also provided.Click here for fileThese files provide information on all the clusters (of size > 1) formed when the Gavin, Uetz and Lehner datasets were clustered. Each file name begins with the name of the dataset used in clustering, followed by the number of edges removed to perform the clustering. The files with a name ending 'clusters.txt' list all the members of each cluster and all the GO terms which were found to be significant for that cluster. Files ending with 'proteins-per-GO.txt', give further detail, showing the transcripts in the cluster which had the significant GO annotations. The examples provided show each dataset with the 'best' number of edges removed (see Results section: Difference in clustering between the datasets). For the smallest (Lehner) dataset, examples with greater and fewer numbers of edges removed are also provided.Click here for fileThese files provide information on all the clusters (of size > 1) formed when the Gavin, Uetz and Lehner datasets were clustered. Each file name begins with the name of the dataset used in clustering, followed by the number of edges removed to perform the clustering. The files with a name ending 'clusters.txt' list all the members of each cluster and all the GO terms which were found to be significant for that cluster. Files ending with 'proteins-per-GO.txt', give further detail, showing the transcripts in the cluster which had the significant GO annotations. The examples provided show each dataset with the 'best' number of edges removed (see Results section: Difference in clustering between the datasets). For the smallest (Lehner) dataset, examples with greater and fewer numbers of edges removed are also provided.Click here for fileThese files provide information on all the clusters (of size > 1) formed when the Gavin, Uetz and Lehner datasets were clustered. Each file name begins with the name of the dataset used in clustering, followed by the number of edges removed to perform the clustering. The files with a name ending 'clusters.txt' list all the members of each cluster and all the GO terms which were found to be significant for that cluster. Files ending with 'proteins-per-GO.txt', give further detail, showing the transcripts in the cluster which had the significant GO annotations. The examples provided show each dataset with the 'best' number of edges removed (see Results section: Difference in clustering between the datasets). For the smallest (Lehner) dataset, examples with greater and fewer numbers of edges removed are also provided.Click here for fileThese files provide information on all the clusters (of size > 1) formed when the Gavin, Uetz and Lehner datasets were clustered. Each file name begins with the name of the dataset used in clustering, followed by the number of edges removed to perform the clustering. The files with a name ending 'clusters.txt' list all the members of each cluster and all the GO terms which were found to be significant for that cluster. Files ending with 'proteins-per-GO.txt', give further detail, showing the transcripts in the cluster which had the significant GO annotations. The examples provided show each dataset with the 'best' number of edges removed (see Results section: Difference in clustering between the datasets). For the smallest (Lehner) dataset, examples with greater and fewer numbers of edges removed are also provided.Click here for fileThese files provide information on all the clusters (of size > 1) formed when the Gavin, Uetz and Lehner datasets were clustered. Each file name begins with the name of the dataset used in clustering, followed by the number of edges removed to perform the clustering. The files with a name ending 'clusters.txt' list all the members of each cluster and all the GO terms which were found to be significant for that cluster. Files ending with 'proteins-per-GO.txt', give further detail, showing the transcripts in the cluster which had the significant GO annotations. The examples provided show each dataset with the 'best' number of edges removed (see Results section: Difference in clustering between the datasets). For the smallest (Lehner) dataset, examples with greater and fewer numbers of edges removed are also provided.Click here for fileClick here for file


The new "Developmental lineage classification of neoplasms" was described in a prior publication. The classification is simple (the entire hierarchy is described with just 39 classifiers), comprehensive (providing a place for every tumor of man), and consistent with recent attempts to characterize tumors by cytogenetic and molecular features. A taxonomy is a list of the instances that populate a classification. The taxonomy of neoplasia attempts to list every known term for every known tumor of man.The taxonomy provides each concept with a unique code and groups synonymous terms under the same concept. A Perl script validated successive drafts of the taxonomy ensuring that: 1) each term occurs only once in the taxonomy; 2) each term occurs in only one tumor class; 3) each concept code occurs in one and only one hierarchical position in the classification; and 4) the file containing the classification and taxonomy is a well-formed XML (eXtensible Markup Language) document.The taxonomy currently contains 122,632 different terms encompassing 5,376 neoplasm concepts. Each concept has, on average, 23 synonyms. The taxonomy populates "The developmental lineage classification of neoplasms," and is available as an XML file, currently 9+ Megabytes in length. A representation of the classification/taxonomy listing each term followed by its code, followed by its full ancestry, is available as a flat-file, 19+ Megabytes in length.The taxonomy is the largest nomenclature of neoplasms, with more than twice the number of neoplasm names found in other medical nomenclatures, including the 2004 version of the Unified Medical Language System, the Systematized Nomenclature of Medicine Clinical Terminology, the National Cancer Institute's Thesaurus, and the International Classification of Diseases Oncolology version.This manuscript describes a comprehensive taxonomy of neoplasia that collects synonymous terms under a unique code number and assigns each tumor to a single class within the tumor hierarchy. The entire classification and taxonomy are available as open access files (in XML and flat-file formats) with this article. On March 19, 2004, the author published a new classification for neoplasms, now called "The developmental lineage classification of neoplasms".,13.12,13The developmental lineage classification of neoplasms was described in a prior publication . The claTerms were grouped by concept and examined for completeness, with software written in the Perl programming language. Perl is a free, open source language, available for virtually every computer operating system, and widely used in the bioinformaitcs community ,17. NomeAs the taxonomy enlarged, inadvertent duplications of terms and codes were unavoidable. In addition, duplicate terms were occasionally placed into different classes within the hierarchy. Much of the value of a classification comes from the parsimonious deployment of taxons (i.e. no instance can appear in more than one class). A Perl script was prepared that was executed after each modification to the classification/taxonomy. The Perl script parsed through the updated XML classification/taxonomy file, validating that: 1) each term occurs only once in the taxonomy; 2) each term occurs in only one tumor class; 3) each concept code occurs in only one hierarchical position in the classification; and 4) the file containing the classification and taxonomy is a well-formed XML document. The script finds anomalous records, permitting facile repair of the taxonomy file. The validating Perl script, xmlvocab.pl is included as a supplemental file with this manuscript see A perl script transformed the taxonomy XML file into a flat-file consisting of line-records for each term in the taxonomy . The transforming Perl script is neoself.pl and is distributed with this article see .The taxonomy was compared with the neoplasm terms and concepts included in the UMLS . UMLS isThe UMLS is curated and distributed by the U.S. National Library of Medicine . AlthougAll terms from the UMLS Metathesaurus that have a neoplasm relationship were obtained through the use of a Perl script see . The 200MRCON , containing the terms for each UMLS concept (CUI).MRCXT , containing the relationships for every UMLS code.All MRCXT records with a SNOMED-CT derivation were extracted using a Perl script see . All MRCThe taxonomy currently contains 122,632 different terms encompassing 5,376 neoplasm concepts (neocl.xml). Each concept has, on average, 23 synonymous terms. The second-largest source of tumor names is contained in the licensed 2004 version of the Unified Medical Language System Metathesaurus (UMLS), which draws neoplasm terms from the National Cancer Institute Thesaurus and SNOMED-CT. SNOMED-CT incorporates the International Classification of Disease – Oncology) ,19,20.The UMLS contains 24,593 unique English neoplasm terms with a specific "neoplasms" relationship, about one fifth the number of terms contained in the taxonomy. However, when one counts the terms in UMLS that have ANY type of relationship to neoplasia, the number UMLS-derived neoplasia terms expands to 64,601, about half of the number of terms contained in the taxonomy. It is difficult, if not impossible, to determine a correct number of neoplasm names contained in UMLS. The reason is that in UMLS, a concept may have many different relationships. For instance, the UMLS concept for "abdominal pain" has 805 relationships. Among these are: colic, constipation, diarrhea, influenza-like symptoms, malaise, multiple organ failure AND GI neoplasm benign, GI neoplasm malignant. The last two items are relationships to neoplasms. These relationships are valid because abdominal pain can be associated with benign or malignant neoplasms. Although the Perl script ca_mrrec.pl see  outputs Similarly, the number of SNOMED-CT terms with a neoplasm relationship is 35,920. This is the number of UMLS records with a SNOMED-CT derivation and with any type of neoplasm relationship see . This shIn general, taxonomy concepts that were highly generic (such as adenocarcinoma of lung) and the concepts that were the most highly pre-coordinated (i.e. multi-word terms with modifiers) had the greatest numbers of synonymous representations.For example, consider these 48 synonyms for adenocarcinoma of the lung:adenoca arising from lung, adenoca arising in lung, adenoca of lung, adenocarcinoma arising from lung, adenocarcinoma arising from pulmonary, adenocarcinoma arising from the lung, adenocarcinoma arising from the lungs, adenocarcinoma arising in lung, adenocarcinoma arising in pulmonary, adenocarcinoma arising in the lung, adenocarcinoma arising in the lungs, adenocarcinoma of lung, adenocarcinoma of pulmonary, ca arising from lung, ca arising from lungs, ca arising in lung, ca arising in lungs, ca of lung, ca of lungs, cancer arising from lung, cancer arising from lungs, cancer arising in lung, cancer arising in lungs, cancer of lung, cancer of lungs, carcinoma arising from lung, carcinoma arising from lungs, carcinoma arising in lung, carcinoma arising in lungs, carcinoma of lung, carcinoma of lungs, lung adenoca, lung adenocarcinoma, lung ca, lung cancer, lung with adenoca, lung with adenocarcinoma, lung with ca, lung with cancer, lung with carcinoma, lungs with ca, lungs with cancer, lungs with carcinoma, pulmonary adenoca, pulmonary adenocarcinoma, pulmonary ca, pulmonary cancer, pulmonary carcinomaCommonly occurring lesions have many different representations. The taxonomy contains closely-related but non-synonymous concepts as separate entries Other items in the taxonomy that have multiple term-variants are the so-called pre-coordinated terms characterized by modifying phrases. These terms are the hardest to capture in a taxonomy, and seem to return the smallest value on the effort. For instance, the taxonomy contains 118 synonyms for "testis with mixed embryonal carcinoma and endodermal sinus neoplasm with seminoma." The large number of synonyms are the direct result of the many different ways that modifying phrases can be ordered and combined to create the same terms. A few examples of the 118 synonyms for this term are:mixed embryonal cancer and endodermal sinus neoplasm with seminoma arising in testismixed embryonal cancer and endodermal sinus neoplasm with seminoma of testistestis with mixed embryonal cancer and endodermal sinus tumor with seminomamixed embryonal cancer and endodermal sinus tumor with seminoma arising in testistestis with mixed embryonal cancer and yolk sac neoplasm with seminomamixed embryonal cancer and yolk sac neoplasm with seminoma arising in testismixed embryonal cancer and yolk sac neoplasm with seminoma arising from testismixed embryonal cancer and yolk sac neoplasm with seminoma of testistestis with mixed embryonal cancer and yolk sac tumor with seminomaThe taxonomy database is distributed as an XML or as a flat-file. A short excerpt from the XML file is shown:<mesoderm><name nci-code = "C3731000">mesoblastic nephroma</name><name nci-code = "C3731100">cellular mesoblastic nephroma</name><mesoderm> indicates a class tag. Beneath it are two different terms and concepts. Each is given a unique concept number. The second term is similar to the first term, but not identical. Both code numbers share the first 4 digits.The flat-file version of the taxonomy lists these two terms as line-records. Each line record contains the term name, the term code, and the ancestry of the term within the developmental lineage classification. Each ancestor is separated by its predecessor by an arrow character.mesoblastic nephroma|C3731000|mesoderm>non_primitive>embryonic>neoplasms>tumor_classification>cellular mesoblastic nephroma|C3731100|mesoderm>non_primitive>embryonic>neoplasms>tumor_classification>Medical informaticians dream of the day when all medical data will be captured by computers in a highly structured format that ensures data uniformity. In this utopian vision, only canonical forms of medical terms will be used. Medical reports will have a uniform format, and will be computer parsable and human readable. Taxonomies will be small. Unfortunately, the current trend in medical reporting seems to favor unstructured narrative data entry. Personally, I can remember the early days of computers when data storage and memory constraints were at a premium. Years were entered as two-digit values (nobody worried about Y2K back in the 60s), and entry-words were selected from lists and typically represented by a single digit. Today, the storage and transmission of textual data are non-issues. Large vocabularies of millions of terms can reside in active memory. Physicians prefer narrative text over structured text , and mosThe neoplasm taxonomy was created to collect all ways of expressing the names of all human tumors. The purpose of the taxonomy is to make it possible for computer algorithms to index textual information about all tumors regardless of the terms used to describe particular tumors. At first inspection, this may seem like a hopelessly complex and ultimately futile endeavor. Can anyone seriously hope to make sense of narrative text? Won't the taxonomy become larger and more complex as additional clinical, genomic, and proteomic modifiers split tumors into incomprehensible subcategories?Actually, the purpose of a taxonomy is to reduce the complexity of the knowledge domain. By focusing efforts on a relatively small area of medicine, it is feasible to create a product that does not exceed the limits of a single expert's mental capacity. The large number of terms contained in the taxonomy 122,632), is encompassed by just 5,376 concepts. We can parse through text, replacing the many different variants of a term with either a single concept code or with a preferred  synonym. This means that the taxonomy gives us a method of reducing any document index of neoplastic terms down to a maximum of 5,376 canonical terms. In addition, the 5,376 concepts in the taxonomy are represented by 39 different ancestral classes. The developmental lineage classification of neoplasms is constructed using strict rules: no multi-class inheritance; each subclass endowed by the properties of its ancestor. This means that once we have identified a term, we can easily determine its place among just a few dozen classes, and its ancestry should tell us basic information about the biology of the tumor [22,632, iThe taxonomy for the developmental lineage classification of neoplams is the largest neoplasm taxonomy. It should come as no surprise that large nomenclatures provide better coverage of textual terms than smaller nomenclatures. A 1997 study by Humphreys et al., showed that by combining controlled vocabularies, the UMLS provided substantially more exact matches to free-text terms than any individual vocabulary in the nomenclature . FindingThis manuscript describes a comprehensive taxonomy of neoplasia that collects synonymous terms under a unique code number and assigns each tumor to a single class within a tumor hierarchy. The entire classification and taxonomy are available as open access documents (in XML and flat-file formats) with this article. The taxonomy will be merged into the U.S. National Cancer Institute's Thesaurus, a curated, publicly available nomenclature and ontology that includes neoplasms and cancer-related terminology.The author(s) declare that they have no competing interests.This work represents the opinions of the author and does not represent the policy of the NIH or of any other U.S. Federal Agency.The pre-publication history for this paper can be accessed here:Neoplasia classification structure (XML version) Neoclxml.gz is a compressed (gzipped) XML file. The downloaded file should be renamed neoclxml.gz so that the .gz suffix can be recognized by unzip utilities. Unzip the file . Once unzipped, the file should be renamed neocl.xml, so that it will have an .xml suffix. If the file is too large for viewing on your web browser, it can be viewed on plain-text word processors.Click here for fileNeoplasia classification with taxonomy (flat-file plain-text version) Neoself.gz is a compressed (gzipped) ascii flat-file. If the filename is changed during download, it should be renamed neoself.gz so that the .gz suffix can be recognized by unzip utilities. Unzip the file . Once unzipped, the file is 19+ Mbytes in length. The expanded file should be renamed neoself.txt.Click here for fileTaxonomy validating Perl script The validating Perl script is xmlvocab.pl. Perl scripts will execute on any computer with a Perl interpreter. It requires the external taxonomy file named "neocl.xml" residing in the same subdirectory as xmlvocab.pl.Click here for filePerl script for transforming taxonomy XML file to a plain-text flat file The Perl script neoself.pl transforms the XML database file (neocl.xml) to a flat file (neoself.txt).Click here for filePerl script for extracting neoplasm codes and terms from UMLS The Perl script ca_mrrec.pl produces a file (neomrcxt.txt) containing all UMLS codes and terms with a neoplasm relationship. This script requires the external files MRCXT and MRCON  to reside in the same directory as ca_mrrec.pl. This script may take more than one-half hour to execute.Click here for filePerl script for extracting UMLS codes for SNOMED-derived terms The Perl script snomout.pl produces a file (snomout.txt) containing all UMLS terms with a SNOMED-CT vocabulary relationship It requires the external file MRCXT  to reside in the same directory as snomout.txt. It produces an output file, snomout.txt that has a size of 1,895,054,040 bytes. This script may take more than one-half hour to execute.Click here for filePerl script for extracting neoplasm concepts and terms from the SNOMED-CT subset of UMLS The Perl script ca_snrec.pl produces a file (neosnom.txt) containing all UMLS terms derived from SNOMED-CT having a neoplasm relationship. It requires the external file MRCON  and snomout.txt (produced by snomout.pl) [see .pl) see , both reClick here for file


It aims to reduce the intra-operative dissemination of colorectal cancer cells. Recently, the detection of circulating tumour cells has been enhanced by molecular biology techniques. This paper reviews the evidence for the no-touch isolation technique in light of the recent developments in circulating tumour cell detection.Lymphovascular ligation before tumour manipulation during colorectal cancer resection is termed the 'Studies investigating the effect of colorectal cancer surgery on circulating tumour cells were identified by a Medline search using the subject headings colorectal neoplasms and neoplasm circulating cells together with the map term 'no-touch isolation technique'. Further references were obtained from key articles.Molecular biological techniques have improved the detection of circulating colorectal cancer cells. There is a trend towards reduced tumour cell dissemination with the no-touch technique compared with the conventional method. However the benefit in terms of improved patient survival remains unproven.The no-touch isolation technique reduces circulating tumour cell dissemination but further work is needed to determine the significance of this with regards to patient survival. This was proposed by Barnes [Of the patients with colorectal cancer (CRC) undergoing surgery for resectable disease, 30–50% will subsequently develop metastases . Dissemiy Barnes  as a wayy Barnes .Animal studies suggest tumour cells are shed into the circulation during resection of a primary tumour, increasing the likelihood of metastases . However7 normal cells [In recent years, the question of tumour cell dissemination during surgical resection has been re-examined following the introduction of molecular biology techniques to detect CTC. These are much more sensitive, and it is now possible to detect one tumour cell in a sample of 10This paper reviews the evidence for the no-touch isolation technique for the resection of a CRC in light of the recent developments in molecular biological techniques for CTC detection.In performing this review, the studies looking at the effect of CRC surgery on CTC were identified by a Medline search using the subject headings colorectal neoplasms and neoplasm circulating cells together with the map term 'no-touch isolation technique' -16. FurtWe decided to review this topic by addressing three areas of uncertainty. Firstly, does the surgical manipulation of a CRC increase the level of tumour cells within the blood? Secondly, what is the biological significance of CTC in CRC? Thirdly, is the no-touch isolation technique of CRC resection associated with an improved patient survival?To investigate the effect of tumour manipulation on CTC, we have reported the conversion, for each reviewed article, of a negative preoperative blood sample (for CTC) to a positive intra- or post-operative sample within the same patient. We have only noted conversions within the same sample source (systemic venous (SV) or portal venous (PV) blood). Where conversion rates cannot be determined, as data for individual patients are not provided, a comparison between overall preoperative and intra/post-operative positivity for CTC is given. Unless otherwise stated in the reviewed article, we have presumed the conventional method was utilised (tumour manipulation before lymphovascular ligation).A number of experimental models have been used to investigate the effect of surgical manipulation on CTC. Nishitaki et al  studied Romsdahl et al  measuredIn order for the no-touch technique to be effective in humans, early ligation of the main pedicle must eliminate passage of liberated tumour cells into the portal and systemic circulations. However, Salsbury et al  found thOn theoretical grounds, the no-touch technique would appear unsuitable for low rectal cancers, as it is necessary to mobilise tumours in the rectum before all the draining veins are controlled. In addition, a large proportion of the venous drainage occurs into the iliac system, thereby negating the effect of mesenteric pedicle ligation.There have been numerous studies investigating the effect of tumour manipulation on CTC in human tumours. This review focuses on studies involving CRC. The overall conversion rates for each study  are listed in Additional Files The only study to compare directly the conventional and no-touch techniques for the resection of a CRC involved the non-random assignment of 27 patients into conventional and no-touch groups . The tecThe other studies involving CRC have used either the conventional or no-touch surgical techniques, but have not directly compared both. Weitz et al  and SaleStudies employing the conventional technique of CRC resection have found overall conversion rates of 0 – 80% in SV samples -15,18,19As a number of patients were positive for CTC before surgery, a more direct assessment of the effect of tumour manipulation might be to analyse the conversion rates only for those patients negative for CTC preoperatively. This reveals a much greater conversion rate for most studies  involves detection and amplification of certain DNA mutations. A copy of the DNA containing the target sequence is made and the process is repeated many times, producing multiple copies and thereby increasing the sensitivity of detection. Mutations in the k-ras gene have often been used for detection of CTC in CRC.However, the detection of these sequences within the blood does not necessarily imply there are circulating tumour cells, as DNA is relatively stable and may represent fragments of tumour DNA released by cell necrosis or apoptosis and persisting in the blood for some time . Also, t7 normal cells [An alternative technique, now more commonly employed, is reverse transcriptase-PCR (RT-PCR). This detects messenger RNA (mRNA), which is used as an indicator of tissue-specific gene transcription. The technique is rapid, more easily automated than immunocytochemistry, and is capable of detecting one tumour cell in 10al cells . RNA is al cells .in-vitro sensitivity reported in the reviewed articles will be higher than the true in-vivo sensitivity, as the cell lines used to determine the sensitivity are known to express strongly the marker of interest and are not affected by the presence of in-vivo PCR inhibitors.However, there is a lack of standardisation of RT-PCR techniques, resulting in different sensitivities and specificities between centres . IllegitMost of the reviewed articles that state the specificity of their technique claim a rate of 100%, indicating all the tumour samples were positive for the marker of interest, whereas none of the control blood samples  were positive. However, Patel et al  examinedThere are no tumour-specific genes for CRC, and it is known malignant cells continue to express markers that are characteristic of their tissue of origin. Therefore, a range of epithelial cell markers, which are normally absent from the blood, have been used as RT-PCR targets for cancer cells derived from epithelial tissues.a and sialyl Lex, are associated with CRC formation and progression [The cytokeratin (CK) polypeptides are found within the cytoplasm of epithelial cells, and several CK markers have been used to detect CTC. The most common of these is CK 20, which is expressed in gastrointestinal epithelia, urothelium, Merkel cells, and tumours derived from these tissues . Carcinogression , and havConcerns regarding marker specificity have been raised. CK 8, 18 and 19 have all been shown to be expressed in normal blood . CK 20 aThe source of blood for analysis appears to be important. Most studies have investigated systemic venous samples for the presence of CTC. Animal studies suggest tumour cells become trapped in the capillary bed of the first organ encountered ,40, and Due to the discrepancy in sample sources, we have only noted patients who converted within the same venous compartment. Several studies detected CTC in draining vein blood intra-operatively, and compared it against a reference SV sample taken before manipulation. It is difficult to be certain of conversion in these patients, however, as it is possible the PV samples were positive preoperatively. It is important for subsequent studies to measure CTC in both systemic venous and draining vein blood, before, during and after tumour manipulation to investigate further the discrepancy in CTC detection between the venous compartments.The importance of multiple sampling should be considered. Glaves et al  suggestsWeitz et al  suggest The detachment of malignant cells from the primary tumour is an early step in the formation of metastases . The relEarly studies suggested fewer than 0.1% of circulating tumour cells survive in the circulation , and onlThe metastatic process may be enhanced by the surgical procedure itself. It is known the entrapment of tumour cells in the microcirculation of a target organ is facilitated by the presence of fibrin and platelets ,49. The The prognostic value of CTC in CRC has yet to be fully determined. Fujita et al  found paThere are few studies directly comparing the outcomes following conventional and no-touch techniques. Turnbull et al  retrospeWiggers et al  randomlyThe largest series was reported recently by Slanetz , who retAnother component of the no-touch isolation technique investigated by Slanetz is the control of intraluminal spread of malignant cells by applying bowel clamps or ligatures before tumour manipulation. He reviewed the results of 599 CRC resections in which bowel ligation prior to tumour mobilisation was used, and compared the data against 1416 resections in which bowel ligation was not performed. He found the application of bowel ligatures before tumour mobilisation significantly improved the 5-year cancer-related death rate for colon cancer , but not for rectal cancer. When considering colon and rectal cancer combined, bowel ligation prior to mobilisation significantly reduced the local  and distant  recurrence rates compared to resection without prior bowel ligation. These findings support the earlier work by Cole et al , who shoIt is difficult to draw any firm conclusions from the articles studied due to the lack of standardisation of sample source, CTC detection method and the small sample sizes. However, the only study directly comparing conventional and no-touch surgical techniques found a conversion rate of 73% for conventional surgery and 14% for the no-touch technique, suggesting a benefit of vascular clamping before tumour mobilisation. The overall conversion rates for the other studies employing the no-touch technique were 0–16% see , compareDue to the lack of consensus regarding the best technique for detection, the biological importance of CTC has not been fully determined. With the introduction of molecular biological techniques, the sensitivity for detection has improved considerably. The hope is that accurate detection of occult circulating malignant cells would help to stage the disease and predict prognosis, as well as monitor the response to therapy and highlight early the possibility of recurrence. However due to the differences in methodology and conclusions of studies investigating this area, the detection of CTC cannot, at present, be used to dictate therapeutic strategy.Despite the methodological heterogeneity of the articles reviewed, we believe the introduction of highly sensitive methods of CTC detection has forced a re-analysis of the role of the no-touch isolation technique in CRC surgery, and the stages of CRC resection using the no-touch technique would appear to offer a sensible, systematic approach to the surgical management of large bowel tumours. However, further work needs to be done to investigate the remaining areas of uncertainty. In particular, future developments in CTC detection must ensure more automation and greater standardisation of techniques between centres. RT-PCR appears to offer the greatest sensitivity, and techniques detecting multiple markers that are more tumour-specific should be investigated ,58. The The author(s) declare that they have no competing interests.GA researched the topic and prepared the manuscript, IM conceived the article and AC critically analysed and formatted the manuscript.Conversion rates for studies employing the no-touch isolation technique of CRC resectionClick here for fileConversion rates for studies employing the conventional technique of CRC resectionClick here for file


Activation of the epidermal growth factor receptor (EGFR) triggers downstream signaling pathways that regulate many cellular processes involved in tumour survival and growth. Gefitinib ('Iressa') is an orally active tyrosine kinase inhibitor (TKI) targeted to the ATP-binding domain of EGFR .SUM was calculated based on the percentage tumour growth inhibition (TGI) at each test drug concentration (TDC). Gefitinib was tested at concentrations ranging from 0.0625–2 microM (TDC = 0.446 microg/ml). This study represents the first use of a TKI in the assay.In this study we have used a standardised ATP-based tumour chemosensitivity assay (ATP-TCA) to measure the activity of gefitinib alone or in combination with different cytotoxic drugs  against a variety of solid tumours (n = 86), including breast, colorectal, oesophageal and ovarian cancer, carcinoma of unknown primary site, cutaneous and uveal melanoma, non-small cell lung cancer (NSCLC) and sarcoma. The IC50 and IC90 were calculated for each single agent or combination. To allow comparison between samples the IndexSUM). In 38% of tumours (29/76), the TGI was decreased when the combination of gefitinib + cytotoxic was used in comparison to the cytotoxic alone. In the remaining 3% (2/76) there was no change observed.There was heterogeneity in the degree of TGI observed when tumours were tested against single agent gefitinib. 7% (6/86) of tumours exhibited considerable inhibition, but most showed a more modest response resulting in a low TGI. The median IC50 value for single agent gefitinib in all tumours tested was 3.98 microM. Interestingly, gefitinib had both positive and negative effects when used in combination with different cytotoxics. In 59% (45/76) of tumours tested, the addition of gefitinib appeared to potentiate the effect of the cytotoxic agent or combination  had a >50% decrease in their Indexin vitro model suggests that gefitinib may have differential effects in response to concomitant cytotoxic chemotherapy with the agents tested during this study. The mechanism involved may relate to the effect of TKIs on growth rate versus their effect on the ability of the cell to survive the stimulus to apoptosis produced by chemotherapy.The  The epidermal growth factor receptor (EGFR) is involved in many cellular processes including cell proliferation, motility, adhesion and angiogenesis via the activation of three pathways: phosphatidylinositol-3 kinase (PI3)/Akt pathway, the Jak/STAT pathway and the ras/raf pathway. EGFR is expressed or highly expressed in a variety of human tumours including non-small-cell lung cancer (NSCLC), breast, bladder, ovarian and head and neck  and is tin vitro growth inhibition against a variety of human cell lines including NSCLC, ovarian, breast, colon and head and neck and is active in a range of xenograft models, including breast, colon and prostate [Gefitinib ('Iressa') is an EGFR-tyrosine kinase inhibitor (EGFR-TKI) that competitively inhibits binding of ATP at the ATP site on EGFR. It also displays remarkable selectivity for EGFR (IC50 = 0.033 microM) compared with other receptor tyrosine kinases (RTKs) that share sequence homology in the ATP binding domain . In pre-prostate ,5. Resulprostate  and gefiprostate  in NSCLCAt present, there is conflicting evidence relating the activity of gefitinib directly to the levels of EGFR expression. One group found that the concentration of gefitinib required to inhibit ligand-independent growth by 50% (IC50) in four bladder cancer cell lines ranged from 1.8–9.7 microM and correlated with EGFR protein and transcript level . AlternaWe have previously shown that the ATP-based tumour chemosensitivity assay (ATP-TCA) can be used to measure the effects of cytotoxic agents and antibodies against human tumour-derived cells, and that this matches clinical outcome in a number of tumour types ,15. Use EGF and TGF-alpha, ligands of EGFR, act as survival factors for many cells as well as growth factors. As many cytotoxic agents induce apoptosis, gefitinib may be able to potentiate their effects by reducing survival stimuli. The current pilot study was undertaken to assess the effect of gefitinib in combination with existing chemotherapeutic agents  against a wide range of tumour types.A total of 86 tumours  were tested in this study, with a median age of 59 years (range 21–90). The samples tested consisted of the following tumour types; breast adenocarcinoma (n = 8), colorectal carcinoma (n = 18), cutaneous melanoma (n = 7), NSCLC (n = 1), oesophageal adenocarcinoma (n = 4), ovarian carcinoma (n = 26), sarcoma (n = 2), squamous cell carcinoma (n = 2), sweat gland carcinoma (n = 1), uveal melanoma (n = 12) and carcinoma of unknown primary site (n = 5). The 26 ovarian carcinomas were all recurrent stage 3/4 cancers and 25/26 were pre-treated (11 with carboplatin and 14 with carboplatin + paclitaxel). Of the remaining samples, 10/86 had been treated with a variety of chemotherapy regimens and some patients had more than one treatment; epirubicin + cisplatin + 5-Fluorouracil (5-FU) (n = 3), epirubicin + cyclophosphamide (4-HC) (n = 1), 4-HC+methotrexate+5-FU (CMF) (n = 2), cisplatin + vinorelbine (n = 1), mitomycin C + 5-FU (n = 1), mitoxantrone + paclitaxel (n = 1), chlorambucil (n = 1), 4-HC (n = 1) and irinotecan (n = 1). The remaining 51 patients had no previous treatment. In each case only tumour material not required for diagnosis was sent for ATP-TCA, and in all cases consent had been obtained and permission had been granted by the local ethics committee.SUM) calculated by direct addition of the percentage survival at each concentration tested  provides a better indication of sensitivity or resistance to different drugs in different tumour types [AUC) was calculated from the data using the trapezoidal rule.The ATP-TCA was performed as previously published ,21. Soliur types . The totur types . Area unThe results were entered into an Access 2000 database for further analysis. Statistical tests were performed using non-parametric methods.The cytotoxic drugs used in the assay were obtained as vials for injection and made up according to manufacturers' instructions. Gemcitabine, oxaliplatin and treosulfan were stored in aliquots at -20°C, while cisplatin was stored at room temperature. Table Tissue was available for EGFR immunohistochemical staining in 31/86 (36%) cases comprising of 4 breast carcinomas, 12 colon carcinomas, 2 oesophageal carcinomas, 2 ovarian carcinomas, 1 sarcoma, 4 skin melanomas, 5 uveal melanomas and 1 carcinoma of unknown primary site. Paraffin embedded sections of 4 μm thick were dewaxed and rehydrated in preparation for immunohistochemical staining. Endogenous peroxidase was blocked using 3% hydrogen peroxide in methanol. The sections were pretreated with 0.1% Trypsin (CaCl2/Tris buffer pH8.0) for 10 minutes at 37°C for antigen retrieval. Immunohistochemical studies were performed according to manufacturer's instructions of the Vectastain Universal ABC-AP kit , which uses an avidin-biotin complex method and Vector red as the chromogen. Monoclonal antibody for EGFR, Clone E30  was used at a dilution of 1:20 and incubated with sections for 18 hours at 4°C. Positive (squamous cell carcinoma tissue) and negative controls were included in each staining procedure. Samples were assessed by a pathologist using the H-score. Intensity was graded on a scale ranging between 0, 1+, 2+ or 3+,  and the proportion of cells stained at the highest intensity. The two values were then multiplied together to give the final value.The same tissue available for EGFR staining was also available for pAkt staining. Paraffin embedded sections of 4 μm thick were dewaxed and rehydrated in preparation for immunohistochemical staining. Endogenous peroxidase was blocked using 3% hydrogen peroxide in methanol. The sections were pretreated with 0.1 M citrate buffer in a pressure cooker for 2.5 minutes for antigen retrieval. Immunohistochemical studies were performed according to manufacturer's instructions of the Vectastain Universal ABC-AP kit , which uses an avidin-biotin complex method and Fuchsin as the chromogen. Phospho-Akt, Ser473  was used at a dilution of 1:50 and incubated with sections for 18 hours at 4°C. Positive (prostate cancer tissue) and negative controls were included in each staining procedure. Samples were assessed as described previously.SUM >300) across the range of concentrations tested in the ATP-TCA, with little evidence of increasing inhibition with increasing drug concentration. 7% (6/86) of tumours exhibited considerable inhibition (>50% inhibition at 100% TDC), but most showed a more modest response resulting in a low maximum percentage inhibition  of samples, comprising 1 colorectal tumour, 1 ovarian tumour, 1 uveal melanoma and 1 unknown primary carcinoma. In 88% (76/86) of samples there was sufficient material to test gefitinib in combination with different cytotoxics.There was heterogeneity in the degree of inhibition observed when tumours were tested against single agent gefitinib Figure . To compSUM), compared to when cisplatin was used alone. The remaining 67% (4/6) showed increased resistance (i.e. an increase in their IndexSUM). This compares with gefitinib in combination with oxaliplatin (n = 10) where 90% (9/10) of samples showed an increase in sensitivity with the combination, with 1 sample showing a >50% decrease in the IndexSUM. When gefitinib was combined with gemcitabine (n = 2), both samples showed an increase in their sensitivity.Table SUM. 31% (4/13) showed increased resistance with the combination in comparison with treosulfan alone and 1 sample showed no change  showed an increase in resistance.Of the tumours tested with treosulfan + gefitinib, 38% (13/34) were of ovarian origin. Of these, 62% (8/13) showed potentiation, with 1 sample showing a >50% decrease in Indexe Figure  and an oe Figure . When geSUM. In 38% of tumours (29/76), the combination of gefitinib + cytotoxic caused the IndexSUM to increase thereby increasing resistance. In the remaining 3% (2/76) there was no change observed.In summary, the addition of gefitinib appeared to potentiate the effect of the cytotoxic agent or combination in 59% (45/76) of tumours tested; of these 11% (5/45) had a >50% decrease in their IndexSUM value available for comparison. In all cases tested there was no relationship with gefitinib activity and EGFR or pAkt staining.Immunostaining for EGFR was positive in 32% (10/31) of samples comprising of 1 breast carcinomas, 5 colon carcinomas, 1 ovarian carcinoma, 1 sarcoma, 1 skin melanoma and 1 carcinoma of unknown primary site. Immunostaining for pAkt was positive in 81% (25/31) of samples comprising of 3 breast carcinomas, 10 colon carcinomas, 2 oesophageal carcinomas, 2 ovarian carcinomas, 4 skin melanomas, 3 uveal melanomas and 1 carcinoma of unknown primary site. Of the positive samples, 8 were positive for both antibodies . In 74% (23/31) of samples that were stained for EGFR and pAkt, there was an IC50, IC90 and IndexThis is the first study in which a TKI has been successfully tested in the ATP-TCA. ATP-TCA has potential to assist drug development for TKIs and possibly to direct therapy for individual patients. It represents one possible answer to the need for predictive oncology testing of these agents, and could be performed alongside clinical trials to obtain correlation data with outcome in patients treated with gefitinib. However, it is difficult to ascertain whether these were specific or non-specific effects of gefitinib and whether similar outcomes would be seen in the clinical setting. This would need to be determined before using this test for routine screening. Gefitinib showed activity in the assay and even though cytotoxic effects were not expected, in some cases the diminution in ATP levels suggests that these may occur. In general, flat concentration – activity curves were observed which are consistent with a cytostatic rather than a cytotoxic effect. Gefitinib alone showed activity in lung, ovarian and colon carcinomas. These results were consistent with previous findings in cell lines .When gefitinib was tested in combination with a limited number of cytotoxic drugs, increases and decreases in the activity of the cytotoxic agent were observed. For example, gefitinib in combination with cisplatin caused 67% of samples to have a decrease in the activity of the cytotoxic. This compares with gefitinib in combination with a second platinum-containing agent, oxaliplatin, where 91% of samples showed an increase in the activity of the cytotoxic. However, it should be noted that oxaliplatin was virtually ineffective against the cells tested and this is therefore likely to reflect the effect of the gefitinib alone Figure . Decreaset al. [et al., [Although there was heterogeneity in the response of tumours to single agent gefitinib, there was no relationship between immunostaining for EGFR and gefitinib activity, consistent with other published studies . Sirotnaet al.  showed t[et al.,  whose daSUM of <300 and the 6 samples that demonstrated >50% inhibition at 100% TDC when tested with single agent gefitinib, 2 samples  had material available for immunohistochemical staining with EGFR and pAkt. The uveal melanoma was negative for EGFR and positive for pAkt compared to the unknown primary, which was positive for both EGFR and pAkt. However, there were samples with similar IHC results that did not show sensitivity to gefitinib. As the EGFR (HER1) dimerizes with the other HER molecules and mediates greater activity as a heterodimer, it is likely that the expression of these molecules is also important to the activity of gefitinib [Of the 4 samples that had an Indexefitinib . SensitiIn this study we have found that gefitinib in combination with different cytotoxic agents  is a double-edged sword: their effect on growth rate may make some tumours more resistant to concomitant cytotoxic chemotherapy, while their effect on cytokine-mediated cell survival (anti-apoptotic) mechanisms may potentiate sensitivity to the same drugs in tumours from other individuals.IAC is director of Cantech Ltd.LAK drafted the manuscript and carried out ATP-TCA assays. FDN, PW, SM, SS and SG also carried out ATP-TCA assays. PJ carried out the immunohistochemical work and IAC conceived the study and participated in its co-ordination.The pre-publication history for this paper can be accessed here:


Identifying the factors underlying the origin and maintenance of the latitudinal diversity gradient is a central problem in ecology, but no consensus has emerged on which processes might generate this broad pattern. Interestingly, the vast majority of studies exploring the gradient have focused on free-living organisms, ignoring parasitic and infectious disease (PID) species. Here, we address the influence of environmental factors on the biological diversity of human pathogens and their global spatial organization. Using generalized linear multivariate models and Monte Carlo simulations, we conducted a series of comparative analyses to test the hypothesis that human PIDs exhibit the same global patterns of distribution as other taxonomic groups. We found a significant negative relationship between latitude and PID species richness, and a nested spatial organization, i.e., the accumulation of PID species with latitude, over large spatial scales. Additionally, our results show that climatic factors are of primary importance in explaining the link between latitude and the spatial pattern of human pathogens. Based on our findings, we propose that the global latitudinal species diversity gradient might be generated in large part by biotic interactions, providing strong support for the idea that current estimates of species diversity are substantially underestimated. When parasites and pathogens are included, estimates of total species diversity may increase by more than an order of magnitude. Comparative analyses reveal that human pathogens increase towards the equator and that the relationship is linked to climate - this has important implications for global biodiversity, public health and environmental epidemiology Generally, the number of plant and animal species declines as one moves away from the equator . This paThe vast majority of studies exploring the latitudinal species diversity gradient have focused on free-living organisms, such as herbivores, mammals, and angiosperms, and with rare exception , none haIn recent years, research into nonrandom organization in parasite communities has turned, e.g., to the possible existence of nestedness. Nested structure is a hierarchical organization of species composition in which assemblages with successively lower species richness tend to be nonrandom subsets of richer assemblages . Some spWe examine the global spatial distribution of species richness for human PIDs, and test the hypothesis that human diseases follow a latitudinal species richness gradient, with low latitudes being the richest zones in pathogen species diversity. We then test two additional propositions: (i) PID assemblages show nested species patterns along latitudinal gradients, i.e., PIDs present at northern latitudes are also present in larger PID assemblages of equatorial zones, and (ii) PID assemblages may be strongly influenced by environmental climatic forces.After correcting for cofactors  that could influence the relationships between latitude and PID species richness, we still found that species richness in human pathogens is strongly correlated with latitude . On aversN = 2,481.4, R0 and R1 procedures, p < 0.0001) and showed diversity to be strongly nested, with some anecdotal differences across the different groups of etiological agents . When considering the Northern and Southern hemispheres separately, both were highly nested . This was confirmed by the R00 procedure used by the Nestedness Temperature Calculator program . The occurrence boundary lines  were fitted by nonlinear regression . Results from Monte Carlo simulations confirmed that our nested matrix was nonrandomly generated (p < 0.0001)  C. The sp, with a progression of species richness from polar regions to the equator, indicating that nestedness is strongly associated with latitude , viruses directly transmitted from person to person , viruses indirectly transmitted via a vector , fungi , protozoa , and helminths . On the other hand, the relationship between PID species richness and monthly temperature range was only significant for three groups of pathogens: bacteria , directly transmitted viruses , and helminths . In contrast to previous results . Very little difference was observed among hemispheres concerning these relationships (data not shown).Latitude is a proxy variable for a wide range of covarying bio-climatic factors and in itself has no meaning regarding factors potentially affecting species diversity. We therefore investigated the relationship between pathogen diversity and individual climatic variables reflected in the composite variable “latitude” . Results results , we founTaken together, these findings indicate that the species richness of human pathogens, their spatial distribution and organization on a large scale, the maximum range of precipitation, and, to a lesser extent, monthly temperature might be intimately connected in generating the observed pattern of disease diversity.To our knowledge, this is the most comprehensive report of how PID species richness varies with latitude and the ecological factors behind observed trends. Our results support previous studies in showing that species diversity increases as one proceeds from the poles to the equator . This siAll three nestedness models see  explaineIn addition, our results suggest that total species diversity on the planet might be substantially underestimated, especially because inventories generally focus attention on the most charismatic groups , and litThe demonstration that parasitic and infectious organisms in humans do not constitute random assemblages at large spatial scales, but rather that many types of microorganisms show a predictable geographical distribution over the planet, could have important implications for public health policies. Our results show that climatic factors are of primary importance in explaining the occurrence and diversity of human pathogens, suggesting that global climate change might have cascading effects regarding the risks of PIDs. For instance, if specific temperate areas were to become more tropical, our results suggest that PID species and their associated vectors/reservoirs would be likely to colonize these changed areas. This would imply a progressive dissolution of the latitudinal effect and of the nested hierarchical structure as observed in the present study as pathogen species became more globally distributed. There is some recent evidence for this hypothesis see .Other variables are indeed important in explaining global-scale patterns of human pathogens . Thus, we do not mean to imply that latitude and surrogate variables are the only ones affecting PID species richness. Nevertheless, our results challenge the conventional wisdom that socio-economic conditions are of preponderant importance in controlling or eradicating diseases. These considerations indicate that a better understanding of PID species diversity and community dynamics in a changing world will be one of the major challenges in environmental epidemiology in the future.http://www.cyinfo.com).The presence/absence matrix for the 229 distinct PIDs  across the Northern and Southern hemispheres was organised employing the Nestedness Temperature Calculator  human population size, (2) human population density (persons per square kilometer), (3) human birth rate , (4) human death rate , and (5) annual population growth rate .http://www.cia.gov/cia/publications/factbook) and from the appendix of We employed the gross national product (per capita in United States dollars) as the economic factor, which is the value of all final goods and services produced within a nation in a given year, plus income earned by its citizens abroad, minus income earned by foreigners from domestic production. We also selected a few other variables linked to particular landscape practices , which were supposed to interact with the production of the nation. Data were collected from The World Factbook 2001 on the Internet  between the matrix of PID species richness and the matrix of distances across the 224 countries , suggesting that the close similarities between PID species richness and composition observed between neighboring countries conforms to the latitudinal diversity gradient.When data suggested nonlinear trends, explanatory variables were transformed and fitted again to improve their contribution to the models. Since close geographical neighbors  probably also share common PID species, simple cross-country comparisons could include spatial autocorrelation artefacts . To testountries . The I v0) or with a probability proportional to their incidence (R1)  , whereas(R1) (00 . Nestedn(R1) (00 .According to the procedure adopted by the Nestedness Temperature Calculator see , the mat


The multiple drug resistance protein (MDR1/P-glycoprotein) is overexpressed in glia and blood-brain barrier (BBB) endothelium in drug refractory human epileptic tissue. Since various antiepileptic drugs (AEDs) can act as substrates for MDR1, the enhanced expression/function of this protein may increase their active extrusion from the brain, resulting in decreased responsiveness to AEDs.Human drug resistant epileptic brain tissues were collected after surgical resection. Astrocyte cell cultures were established from these tissues, and commercially available normal human astrocytes were used as controls. Uptake of fluorescent doxorubicin and radioactive-labeled Phenytoin was measured in the two cell populations, and the effect of MDR1 blockers was evaluated.Frozen human epileptic brain tissue slices were double immunostained to locate MDR1 in neurons and glia. Other slices were exposed to toxic concentrations of Phenytoin to study cell viability in the presence or absence of a specific MDR1 blocker.MDR1 was overexpressed in blood vessels, astrocytes and neurons in human epileptic drug-resistant brain. In addition, MDR1-mediated cellular drug extrusion was increased in human 'epileptic' astrocytes compared to 'normal' ones. Concomitantly, cell viability in the presence of cytotoxic compounds was increased.Overexpression of MDR1 in different cell types in drug-resistant epileptic human brain leads to functional alterations, not all of which are linked to drug pharmacokinetics. In particular, the modulation of glioneuronal MDR1 function in epileptic brain in the presence of toxic concentrations of xenobiotics may constitute a novel cytoprotective mechanism. Failure to respond to therapeutic concentrations of antiepileptic drugs (AEDs) is the usual basis for defining multiple drug resistant epilepsy, but the mechanisms underlying resistance to AEDs are still largely unknown. It is generally believed to be a multifactorial phenomenon, depending on both pharmacodynamic and pharmacokinetic mechanisms. The electrical and synaptic properties of neurons in epileptic human tissue are reported to undergo changes that may result in decreased susceptibility to some AEDs . MultidrRecent findings suggest that the molecular mechanisms of clinically defined multiple drug resistance involve drug-efflux transporters such as the ATP-binding cassette subfamily B member 1 (ABCB1), also known as MDR1 or P-glycoprotein (P-gp) -4. SpeciThe link between MDR1 overexpression and drug resistance in epilepsy is still poorly understood ,11. ThusIn this study we have evaluated the relationship between neuronal and astrocytic MDR1 expression and the capacity of the cells to survive cytotoxic insults, using drug resistant epileptic human specimens. Our results suggest that overexpression of MDR1 leads to functional alterations in the CNS that may be linked to both drug pharmacokinetics and neuroglial survival in injured brain.Human subjects were used as donors of cortical tissue samples. The investigation conformed to the principles outlined in the Declaration of Helsinki. Patient consent was obtained as per Institutional Review Board instructions before collection of the specimens. All the experiments involved small portions of human neocortical or hippocampal tissue, which were excised for therapeutic reasons from patients with pharmacoresistant epilepsy .To investigate MDR1 protein expression and cellular distribution in human epileptic tissue, slide-mounted sections (10 μm thickness) from frozen brain tissue  were treated overnight with 1μM XR9576, a specific MDR1 blocker [2) were randomly analyzed and optical density measurements were averaged to obtain representative data. To quantify the doxorubicin in the cells, the fluorescence in each sample was compared with standard solutions of doxorubicin (10 nM-10 μM). Data were analyzed using Origin Lab 7 software.Astrocytes from epileptic tissue  and incubated at 37°C with 1 ml of T3 cell buffer  containing 10 μM 14C-Phenytoin (Specific activity = 9.57 μCi/μmol). The incubation was terminated after 10 s, 30 s, 1 min, 5 min or 10 min by adding 1 ml of ice-cold T3 buffer. The cells were washed with 1 ml of ice-cold T3 buffer and solubilized with 1% (w/v) Triton X-100 for 1 h at 37°C. Intracellular radioactivity was measured using a scintillation cocktail .in vitro as previously described [2 1; MgCl2 1.4; NaHCO3- 26; KH2PO4 1.25; glucose 10, with or without 3 μM XR9576. A toxic concentration (375 μM) of Phenytoin (PHE) was then added for 5 h [2) within each slice. These values were averaged for each slice and expressed as percentages of the average number of DAPI-positive cells in the chosen fields.Brain slices (500 μm) were cut using a vibratome from dysplastic human cortex and temporal lobe epilepsy brain specimens , 10 μm slices from 11 patients . Differences with p < 0.05 were considered significant.Results were obtained from 14 patients  affected by intractable seizures. The mean patient age was 16.4 years . For details, see Table thin arrows in A) were observed in epileptic cortex, as well as immunopositive parenchymal and perivascular astrocytes (arrowheads in A) double-labeled with GFAP, a specific glial marker. Out of 107 GFAP-positive astrocytes, 91 (85%) showed MDR1 staining . The difference was abolished in the presence of 1 μM XR9576, indicating MDR1-mediated efflux of PHE in epileptic astrocytes. The MDR1 blockers did not affect doxorubicin or Phenytoin uptake in control astrocytes (not shown). Neither Phenytoin nor doxorubicin treatment induced toxicity in cell cultures under our experimental conditions.Fig. MDR1 is involved in detoxification mechanisms that protect cells from xenobiotics , apoptosFigure The main finding was that MDR1 might have different roles depending on the location in the brain where it is expressed. Thus, in addition to overexpression in endothelial cells, which is likely to affect the penetration of AEDs into the brain, MDR1 is also overexpressed in the parenchyma, where it might have a cytoprotective role, extruding otherwise toxic concentrations of xenobiotics from the intracellular compartment.In agreement with previous findings, we report here that MDR1 is highly expressed in vessels of the BBB and in parenchymal cells  in drug refractory epilepsy of different etiologies -9,11,27.perivascular astrocytes impinging on blood vessels may act as an additional barrier to drug penetration into the brain in regions where BBB permeability is transiently altered, for example during epileptic activity [We found that PHE and doxorubicin uptake by astrocytes from human epileptic tissue is reduced in comparison with normal astrocytes. The difference is abolished when MDR1 function is blocked, indicating that these drugs are efficiently extruded by "epileptic" astrocytes by a P-gp mediated mechanism. We suggest that this might contribute to decreasing the AED brain levels only if it occurs in perivascular astrocytes. Thus, activity ,33 (Fig.We show in this paper that MDR1 is expressed in immunocytochemically identifiable neurons and astrocytes in brain slices from refractory human epilepsy patients. We have previously reported that basic apoptotic mechanisms may be defective in glia from epileptic tissue, since the pro-apoptotic proteins p53 and p21 could not be detected in "epileptic" astrocytes ,9,16. ThFinally, we found a positive correlation between neuronal and astrocytic expression of MDR1 and lack of nuclear condensation, a marker of apoptosis and irreversible cell damage. Thus, expression of MDR1 in glia and neurons may protect the cells against toxic xenobiotics or against endogenous compounds that enter the brain in pathological conditions.In conclusion, as summarized in figure AEDs: anti-epileptic drugsBBB: blood-brain barrierDOX: doxorubicinGFAP: glial fibrillary acidic proteinMDR1: multiple drug resistance proteinNeuN: neuronal nucleiNF: neurofilamentPHE: PhenytoinThe authors declare that they have no competing interests.NM and LC carried out the pharmacological experiments and data analysis.KLH and KMK obtained surgical resections, established astrocytic cell cultures and performed the immunohistochemical and cell counting studies.GM performed the acute in vitro toxicity experiments.WB provided the surgical specimens.GD provided additional input and editing.AV and DJ designed and co-ordinated some of the experiments.They also contributed equally to the preparation of the manuscript.DJ, the PI of the study, co-ordinated and supervised most of the personnel involved in this project.The pre-publication history for this paper can be accessed here:


The CXCL1 chemokines, macrophage inflammatory protein-2 (MIP-2) and cytokine-induced neutrophil chemoattractant (KC), have been shown to play a role in a number of pathophysiological disease states including endotoxin-induced inflammation and bacterial meningitis. While the expression of these chemokines has been identified in a variety of cell types in the mouse, little is known about their expression with murine B-lymphocytes.Here, we demonstrate that highly purified murine splenic B cells are capable of expressing both MIP-2 and KC protein and mRNA upon activation with lipopolysaccharide (LPS) but not in response to anti-μ and anti-CD40 in combination with interleukin-4 (IL-4) stimulation. Moreover, these chemokines are expressed at higher levels in B cells derived from young (4 m) compared to old (24–29 m) mice. Upon fractionation into distinct B-cell subsets, we found that the expression of MIP-2 and KC by aged follicular (FO) B cells is significantly decreased when compared to the same cells from younger mice, while only MIP-2 production was found to be diminished in aged marginal zone (MZ) B cells. Interestingly, MIP-2 and KC production by newly formed (NF) B cells did not significantly differ with age. Moreover, the potential relevance of these findings is supported by the poor ability of LPS-activated aged B cells to specifically mediate CXCL1-dependent leukocyte recruitment when compared to younger B cells.Overall, the decreased expression of CXCL1 chemokines by aged B cells in response to LPS may have potential implications on the secondary recruitment of leukocytes to sites of microbial infections and inflammation possibly contributing to the increased susceptibility of older subjects to pathogen challenge. Cells were harvested on fiberglass paper. [3H] thymidine uptake was measured in a liquid scintillation counter .MACS-purified splenic B cells , penicillin (100 U/ml), streptomycin (100 μg/ml) were cultured in the presence and in the absence of LPS (10 μg/ml) and incubated at 37°C in 5% in CO2 for 24 hr. After incubation, the cells and supernatants in the lower chambers were assessed for chemotactic activity using young splenocyte responder cells. These splenocytes were isolated from 4 month old C57Bl/6 mice after which they were preincubated with 10 μM Hoechst 33342  in RPMI 1640 supplemented with 10% FCS for 30 min at 37°C. Subsequently, the Hoechst-stained splenocytes (1 × 106) were washed with RPMI 1640 supplemented with 2% FCS, resuspended in 100 mL of medium, and then added to the upper chamber, containing a 6.5-mm diameter polycarbonate Transwell culture insert with 5 μm size pore. Each expected group was performed in duplicates with supernatants in the lower chambers for 8 to 10 h at 37°C. In certain assays, neutralizing antibodies specific for MIP-2 and KC at (1 μg/ml) were added to the low chamber containing the LPS-stimulated B cell supernatants. The transmigration of the Hoechst-labeled cells into the lower chamber were measured in a fluorescent spectrophotometer at 350 nm (excitation)/460 nm (emission). The results are expressed as fluorescent units or as the percentage inhibition of Hoechst-labeled splenocyte migration.In the lower chambers of Transwell plates , 1.5 × 10Statistical evaluation of significance between the experimental groups was determined by Student's t test.Ag, antigen; MIP-2, Macrophage inflammatory protein-2; ELR, glutamate-leucine-arginine; GRO, growth-related oncogene; HSV-1, herpes simplex virus type 1; KC, cytokine-induced neutrophil chemoattractant; MZ, marginal zone; FO, follicular; NF, newly formed; TI, Thymus-independent; TD, Thymus-dependentLH, VMC, and VDD performed the experiments. LH and DDT prepared the figures and wrote the paper. DDT also supervised the work and edited the manuscript. All authors have read and approved the final manuscript.


The Science and Development Network (SciDev.Net) is a source of online news and analysis about the role of science and technology in meeting the needs of the developing world Developing countries today face a wide range of needs, from more secure food supplies to cheap and effective medicines. One factor that almost all these needs have in common is that satisfying them adequately will not occur without the use of modern science. These same countries also face a range of political dilemmas—such as whether to accept the growing of genetically modified crops or how to adapt to the impact of climate change—that also require a knowledge of science, albeit of a slightly different nature. From both points of view, development can be characterised as the process of putting scientific and technical knowledge into practice. Conversely, it is important that building the capacity to absorb and make use of scientific and technical knowledge must be placed at the heart of the development aid efforts if these are to be successful in achieving their goals.But knowledge will not reach those who can benefit from it unless it has been effectively communicated to individuals with the power and skills to put it into practice, whether those are government officials and decision-makers, community groups and their representatives, or even nongovernmental organisations. Formal education, of course, has a key function here. But so does the informal education provided through the media. Furthermore, information and communications technologies (ICTs) have an important role to play in this process by reducing or eliminating the transactional (nonproduction) costs of communicating knowledge about science and technology.At the same time, it is important that the practice of science communication reflects the fact that it takes place in social context. In other words, it is not just a question of conveying information, but also of engaging the potential users of that information. The need is to encourage dialogue and eventually to empower those to whom the information is being provided so that this information can be applied in a practical and useful way.www.scidev.net), of online news and analysis about the role of science and technology in meeting the needs of the developing world.It was with this in mind that the Science and Development Network (SciDev.Net) was launched in December 2001 as a source, through its Web site  makes up 20%.For those with an interest in science activities in a particular geographical region, we link together coverage into so-called regional gateways on the Web site. At present, there are four of these—covering Latin America, the Middle East, South and East Asia, and sub-Saharan Africa. Each gateway therefore has a strong regional identity, and in some cases this is reinforced by the translation of material—either in full text or as a summary—into local languages. The Latin American gateway, for example, carries three languages, and material can appear in one, two, or all three of these, with summaries in the others.Reflecting a commitment to the idea that scientific knowledge will only be effectively communicated if individuals are adequately trained to carry out this task, we are engaged not only in presenting information on our Web site, but also in helping to generate a capacity within developing countries to improve channels of communication for this knowledge.One way in which we do this is through workshops on science communication. Some of these have a broad focus, seeking to help stimulate a national or regional debate on the importance of science communication, how it can be best carried out, and the challenges it faces. Such, for example, was the goal of two roundtables on the theme of “science communication and sustainable development” organised in August 2002 during the World Summit on Sustainable Development in Johannesburg, South Africa. Similarly, a meeting launching our activities in Latin America was held in Sao Paulo in May this year under the title “Science Communication and Development in Latin America.”Other events to encourage scientific communication have been more specific. In particular, we organised  a five-day workshop for African women communicators in Kampala, Uganda, in April 2003 on the use of ICTs to report on the science of HIV/AIDS . A similBuilding the communication channels that will allow science to be fully integrated into the development process at all levels—from governments down to communities—is a major challenge that involves many participants. SciDev.Net does not pretend to be more than one actor and is keen to establish partnerships with others in this field, convinced both of the synergies that can be gained through collaboration and of the contribution that ICTs can make to this .One of the major challenges that we face is to build up our role as a platform for the voice of the developing world on the issues that we cover. We already do this to a certain degree through our correspondents, our regional networks of contributors, and the participation of scientists from developing countries who are on the advisory panels for our dossiers. But we are very aware that more effort is needed. Despite this, we already feel that we are beginning to make a mark. A staff member of the United States' National Academy of Sciences recently informed us: “We hosted a group of visitors from seven African scientific academies here in Washington last week, and they spoke highly of the SciDev project … you are becoming a truly global newsletter!”