Cancer classification using the Immunoscore: a worldwide task force

Prediction of clinical outcome in cancer is usually achieved by histopathological evaluation of tissue samples obtained during surgical resection of the primary tumor. Traditional tumor staging (AJCC/UICC-TNM classification) summarizes data on tumor burden (T), presence of cancer cells in draining and regional lymph nodes (N) and evidence for metastases (M). However, it is now recognized that clinical outcome can significantly vary among patients within the same stage. The current classification provides limited prognostic information, and does not predict response to therapy. Recent literature has alluded to the importance of the host immune system in controlling tumor progression. Thus, evidence supports the notion to include immunological biomarkers, implemented as a tool for the prediction of prognosis and response to therapy. Accumulating data, collected from large cohorts of human cancers, has demonstrated the impact of immune-classification, which has a prognostic value that may add to the significance of the AJCC/UICC TNM-classification. It is therefore imperative to begin to incorporate the 'Immunoscore' into traditional classification, thus providing an essential prognostic and potentially predictive tool. Introduction of this parameter as a biomarker to classify cancers, as part of routine diagnostic and prognostic assessment of tumors, will facilitate clinical decision-making including rational stratification of patient treatment. Equally, the inherent complexity of quantitative immunohistochemistry, in conjunction with protocol variation across laboratories, analysis of different immune cell types, inconsistent region selection criteria, and variable ways to quantify immune infiltration, all underline the urgent requirement to reach assay harmonization. In an effort to promote the Immunoscore in routine clinical settings, an international task force was initiated. This review represents a follow-up of the announcement of this initiative, and of the J Transl Med. editorial from January 2012. Immunophenotyping of tumors may provide crucial novel prognostic information. The results of this international validation may result in the implementation of the Immunoscore as a new component for the classification of cancer, designated TNM-I (TNM-Immune).

A Faceted Classification Scheme for Computer-Mediated Discourse

This article describes a classification scheme for computer-mediated discourse that classifies samples in terms of clusters of features, or “facets”. The goal of the scheme is to synthesize and articulate aspects of technical and social context that influence discourse usage in CMC environments. The classification scheme is motivated, presented in detail with support from existing literature, and illustrated through a comparison of two types of weblog (blog) data. In concluding, the advantages and limitations of the scheme are weighed.

The comprehensiveness of the ESHRE/ESGE classification of female genital tract congenital anomalies: a systematic review of cases not classified by the AFS system.

STUDY QUESTION How comprehensive is the recently published European Society of Human Reproduction and Embryology (ESHRE)/European Society for Gynaecological Endoscopy (ESGE) classification system of female genital anomalies? SUMMARY ANSWER The ESHRE/ESGE classification provides a comprehensive description and categorization of almost all of the currently known anomalies that could not be classified properly with the American Fertility Society (AFS) system. WHAT IS KNOWN ALREADY Until now, the more accepted classification system, namely that of the AFS, is associated with serious limitations in effective categorization of female genital anomalies. Many cases published in the literature could not be properly classified using the AFS system, yet a clear and accurate classification is a prerequisite for treatment. STUDY DESIGN, SIZE AND DURATION The CONUTA (CONgenital UTerine Anomalies) ESHRE/ESGE group conducted a systematic review of the literature to examine if those types of anomalies that could not be properly classified with the AFS system could be effectively classified with the use of the new ESHRE/ESGE system. An electronic literature search through Medline, Embase and Cochrane library was carried out from January 1988 to January 2014. Three participants independently screened, selected articles of potential interest and finally extracted data from all the included studies. Any disagreement was discussed and resolved after consultation with a fourth reviewer and the results were assessed independently and approved by all members of the CONUTA group. PARTICIPANTS/MATERIALS, SETTING, METHODS Among the 143 articles assessed in detail, 120 were finally selected reporting 140 cases that could not properly fit into a specific class of the AFS system. Those 140 cases were clustered in 39 different types of anomalies. MAIN RESULTS AND THE ROLE OF CHANCE The congenital anomaly involved a single organ in 12 (30.8%) out of the 39 types of anomalies, while multiple organs and/or segments of Müllerian ducts (complex anomaly) were involved in 27 (69.2%) types. Uterus was the organ most frequently involved (30/39: 76.9%), followed by cervix (26/39: 66.7%) and vagina (23/39: 59%). In all 39 types, the ESHRE/ESGE classification system provided a comprehensive description of each single or complex anomaly. A precise categorization was reached in 38 out of 39 types studied. Only one case of a bizarre uterine anomaly, with no clear embryological defect, could not be categorized and thus was placed in Class 6 (un-classified) of the ESHRE/ESGE system. LIMITATIONS, REASONS FOR CAUTION The review of the literature was thorough but we cannot rule out the possibility that other defects exist which will also require testing in the new ESHRE/ESGE system. These anomalies, however, must be rare. WIDER IMPLICATIONS OF THE FINDINGS The comprehensiveness of the ESHRE/ESGE classification adds objective scientific validity to its use. This may, therefore, promote its further dissemination and acceptance, which will have a positive outcome in clinical care and research. STUDY FUNDING/COMPETING INTERESTS None.

Application of Decision Theory methods for a Community of Madrid Soil classification case

A land classification method was designed for the Community of Madrid (CM), which has lands suitable for either agriculture use or natural spaces. The process started from an extensive previous CM study that contains sets of land attributes with data for 122 types and a minimum-requirements method providing a land quality classification (SQ) for each land. Borrowing some tools from Operations Research (OR) and from Decision Science, that SQ has been complemented by an additive valuation method that involves a more restricted set of 13 representative attributes analysed using Attribute Valuation Functions to obtain a quality index, QI, and by an original composite method that uses a fuzzy set procedure to obtain a combined quality index, CQI, that contains relevant information from both the SQ and the QI methods.

Improving Wishart Classification of Polarimetric SAR Data Using the Hopfield Neural Network Optimization Approach.

This paper proposes the optimization relaxation approach based on the analogue Hopfield Neural Network (HNN) for cluster refinement of pre-classified Polarimetric Synthetic Aperture Radar (PolSAR) image data. We consider the initial classification provided by the maximum-likelihood classifier based on the complex Wishart distribution, which is then supplied to the HNN optimization approach. The goal is to improve the classification results obtained by the Wishart approach. The classification improvement is verified by computing a cluster separability coefficient and a measure of homogeneity within the clusters. During the HNN optimization process, for each iteration and for each pixel, two consistency coefficients are computed, taking into account two types of relations between the pixel under consideration and its corresponding neighbors. Based on these coefficients and on the information coming from the pixel itself, the pixel under study is re-classified. Different experiments are carried out to verify that the proposed approach outperforms other strategies, achieving the best results in terms of separability and a trade-off with the homogeneity preserving relevant structures in the image. The performance is also measured in terms of computational central processing unit (CPU) times.

Discernment of bee pollen loads using computer vision and one-class classification techniques

In this paper, we propose a system for authenticating local bee pollen against fraudulent samples using image processing and classification techniques. Our system is based on the colour properties of bee pollen loads and the use of one-class classifiers to reject unknown pollen samples. The latter classification techniques allow us to tackle the major difficulty of the problem, the existence of many possible fraudulent pollen types. Also presented is a multi-classifier model with an ambiguity discovery process to fuse the output of the one-class classifiers. The method is validated by authenticating Spanish bee pollen types, the overall accuracy of the final system of being 94%. Therefore, the system is able to rapidly reject the non-local pollen samples with inexpensive hardware and without the need to send the product to the laboratory.

Lacunarity of the Spatial Distributions of Soil Types in Europe.

Lacunarity as a means of quantifying textural properties of spatial distributions suggests a classification into three main classes of the most abundant soils that cover 92% of Europe. Soils with a well-defined self-similar structure of the linear class are related to widespread spatial patterns that are nondominant but ubiquitous at continental scale. Fractal techniques have been increasingly and successfully applied to identify and describe spatial patterns in natural sciences. However, objects with the same fractal dimension can show very different optical properties because of their spatial arrangement. This work focuses primary attention on the geometrical structure of the geographical patterns of soils in Europe. We made use of the European Soil Database to estimate lacunarity indexes of the most abundant soils that cover 92% of the surface of Europe and investigated textural properties of their spatial distribution. We observed three main classes corresponding to three different patterns that displayed the graphs of lacunarity functions, that is, linear, convex, and mixed. They correspond respectively to homogeneous or self-similar, heterogeneous or clustered and those in which behavior can change at different ranges of scales. Finally, we discuss the pedological implications of that classification.

Sea Bottom Classification by Means of Bathymetric LIDAR Data

Light Detection and Ranging (LIDAR) provides high horizontal and vertical resolution of spatial data located in point cloud images, and is increasingly being used in a number of applications and disciplines, which have concentrated on the exploit and manipulation of the data using mainly its three dimensional nature. Bathymetric LIDAR systems and data are mainly focused to map depths in shallow and clear waters with a high degree of accuracy. Additionally, the backscattering produced by the different materials distributed over the bottom surface causes that the returned intensity signal contains important information about the reflection properties of these materials. Processing conveniently these values using a Simplified Radiative Transfer Model, allows the identification of different sea bottom types. This paper presents an original method for the classification of sea bottom by means of information processing extracted from the images generated through LIDAR data. The results are validated using a vector database containing benthic information derived by marine surveys.

Understanding replication of experiments in software engineering: a classification

Context: Replication plays an important role in experimental disciplines. There are still many uncertain- ties about how to proceed with replications of SE experiments. Should replicators reuse the baseline experiment materials? How much liaison should there be among the original and replicating experiment- ers, if any? What elements of the experimental configuration can be changed for the experiment to be considered a replication rather than a new experiment? Objective: To improve our understanding of SE experiment replication, in this work we propose a classi- fication which is intend to provide experimenters with guidance about what types of replication they can perform. Method: The research approach followed is structured according to the following activities: (1) a litera- ture review of experiment replication in SE and in other disciplines, (2) identification of typical elements that compose an experimental configuration, (3) identification of different replications purposes and (4) development of a classification of experiment replications for SE. Results: We propose a classification of replications which provides experimenters in SE with guidance about what changes can they make in a replication and, based on these, what verification purposes such a replication can serve. The proposed classification helped to accommodate opposing views within a broader framework, it is capable of accounting for less similar replications to more similar ones regarding the baseline experiment. Conclusion: The aim of replication is to verify results, but different types of replication serve special ver- ification purposes and afford different degrees of change. Each replication type helps to discover partic- ular experimental conditions that might influence the results. The proposed classification can be used to identify changes in a replication and, based on these, understand the level of verification.

Bayesian network modeling of the consensus between experts: an application to neuron classification

Neuronal morphology is hugely variable across brain regions and species, and their classification strategies are a matter of intense debate in neuroscience. GABAergic cortical interneurons have been a challenge because it is difficult to find a set of morphological properties which clearly define neuronal types. A group of 48 neuroscience experts around the world were asked to classify a set of 320 cortical GABAergic interneurons according to the main features of their three-dimensional morphological reconstructions. A methodology for building a model which captures the opinions of all the experts was proposed. First, one Bayesian network was learned for each expert, and we proposed an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts was induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts was built. A thorough analysis of the consensus model identified different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types was defined by performing inference in the Bayesian multinet. These findings were used to validate the model and to gain some insights into neuron morphology.

Classification of mononuclear zinc metal sites in protein structures

Our study of the extended metal environment, particularly of the second shell, focuses in this paper on zinc sites. Key findings include: (i) The second shell of mononuclear zinc centers is generally more polar than hydrophobic and prominently features charged residues engaged in an abundance of hydrogen bonding with histidine ligands. Histidine–acidic or histidine–tyrosine clusters commonly overlap the environment of zinc ions. (ii) Histidine tautomeric metal bonding patterns in ligating zinc ions are mixed. For example, carboxypeptidase A, thermolysin, and sonic hedgehog possess the same ligand group (two histidines, one unibidentate acidic ligand, and a bound water), but their histidine tautomeric geometries markedly differ such that the carboxypeptidase A makes only Nδ1 contacts, thermolysin makes only Nɛ2 contacts, and sonic hedgehog uses one of each. Thus the presence of a similar ligand cohort does not necessarily imply the same topology or function at the active site. (iii) Two close histidine ligands HXmH, m ≤ 5, rarely both coordinate a single metal ion in the Nδ1 tautomeric conformation, presumably to avoid steric conflicts. Mononuclear zinc sites can be classified into six types depending on the ligand composition and geometry. Implications of the results are discussed in terms of divergent and convergent evolution.

A fully automatic evolutionary classification of protein folds: Dali Domain Dictionary version 3

The Dali Domain Dictionary (http://www.ebi.ac.uk/dali/domain) is a numerical taxonomy of all known structures in the Protein Data Bank (PDB). The taxonomy is derived fully automatically from measurements of structural, functional and sequence similarities. Here, we report the extension of the classification to match the traditional four hierarchical levels corresponding to: (i) supersecondary structural motifs (attractors in fold space), (ii) the topology of globular domains (fold types), (iii) remote homologues (functional families) and (iv) homologues with sequence identity above 25% (sequence families). The computational definitions of attractors and functional families are new. In September 2000, the Dali classification contained 10 531 PDB entries comprising 17 101 chains, which were partitioned into five attractor regions, 1375 fold types, 2582 functional families and 3724 domain sequence families. Sequence families were further associated with 99 582 unique homologous sequences in the HSSP database, which increases the number of effectively known structures several-fold. The resulting database contains the description of protein domain architecture, the definition of structural neighbours around each known structure, the definition of structurally conserved cores and a comprehensive library of explicit multiple alignments of distantly related protein families.

Phylogeny of genes for secretion NTPases: Identification of the widespread tadA subfamily and development of a diagnostic key for gene classification

Macromolecular transport systems in bacteria currently are classified by function and sequence comparisons into five basic types. In this classification system, type II and type IV secretion systems both possess members of a superfamily of genes for putative NTP hydrolase (NTPase) proteins that are strikingly similar in structure, function, and sequence. These include VirB11, TrbB, TraG, GspE, PilB, PilT, and ComG1. The predicted protein product of tadA, a recently discovered gene required for tenacious adherence of Actinobacillus actinomycetemcomitans, also has significant sequence similarity to members of this superfamily and to several unclassified and uncharacterized gene products of both Archaea and Bacteria. To understand the relationship of tadA and tadA-like genes to those encoding the putative NTPases of type II/IV secretion, we used a phylogenetic approach to obtain a genealogy of 148 NTPase genes and reconstruct a scenario of gene superfamily evolution. In this phylogeny, clear distinctions can be made between type II and type IV families and their constituent subfamilies. In addition, the subgroup containing tadA constitutes a novel and extremely widespread subfamily of the family encompassing all putative NTPases of type IV secretion systems. We report diagnostic amino acid residue positions for each major monophyletic family and subfamily in the phylogenetic tree, and we propose an easy method for precisely classifying and naming putative NTPase genes based on phylogeny. This molecular key-based method can be applied to other gene superfamilies and represents a valuable tool for genome analysis.