A colorectal cancer classification system that associates cellular phenotype and responses to therapy.

Colorectal cancer (CRC) is a major cause of cancer mortality. Whereas some patients respond well to therapy, others do not, and thus more precise, individualized treatment strategies are needed. To that end, we analyzed gene expression profiles from 1,290 CRC tumors using consensus-based unsupervised clustering. The resultant clusters were then associated with therapeutic response data to the epidermal growth factor receptor-targeted drug cetuximab in 80 patients. The results of these studies define six clinically relevant CRC subtypes. Each subtype shares similarities to distinct cell types within the normal colon crypt and shows differing degrees of 'stemness' and Wnt signaling. Subtype-specific gene signatures are proposed to identify these subtypes. Three subtypes have markedly better disease-free survival (DFS) after surgical resection, suggesting these patients might be spared from the adverse effects of chemotherapy when they have localized disease. One of these three subtypes, identified by filamin A expression, does not respond to cetuximab but may respond to cMET receptor tyrosine kinase inhibitors in the metastatic setting. Two other subtypes, with poor and intermediate DFS, associate with improved response to the chemotherapy regimen FOLFIRI in adjuvant or metastatic settings. Development of clinically deployable assays for these subtypes and of subtype-specific therapies may contribute to more effective management of this challenging disease.

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

In vivo evaluation of the anti-tumoral effect of sunitinib eluting beads in the rabbit VX2 tumor model

Purpose: To study the anti-tumoral effect of sunitinib eluting beads in the rabbit VX2 tumor modelMaterials: VX2 tumor were implanted in the left liver lobe of New-Zealand white rabbits. Seven animals received 0.2ml of DC Beads loaded with 6mg of sunitinb (group 1), 6 animals received 0.2ml of DC Beads (group 2) and 6 animals received NaCl 0.9% intra arterially in the left hepatic artery. One animal in each group was sacrificed at 24 hours and the others were left to survive. Liver enzyme were measured daily. In group 1 plasmatic sunitinib concentration were measured daily by LC MS/MS tandem mass spectroscopy. At day 15 all living animals were sacrficed. After sacrifice, or premature euthanasia the livers were harvested for determination of the VEGF receptor tyrosine kinase activity by western blot and histopathological examination.Results: In group 1, no animal died during follow-up. In group 2 and 3, respectively 2 and 3 animals died during follow-up. In group 1 plasmatic sunitinib level remained under therapeutic concentration during the whole experiment. There was an evident lack of phosphorylation of the RTK In group 1 and there was an augmentation of the RTK phosphorylation in group 2 at 24 hours. No difference in RTK activity was noticable at 15 days. From the histopathological point of view it was unpossible to differentiate treatment induced from spontaneous necrosis of tumors.Conclusions: Administration of sunitinib eluting Beads in VX2 carrying rabbits inhibits the activation of RTK's triggered by ischemia. It also seems to prolong survival of the treated animals.

New infectious mammary tumor virus superantigen with V beta-specificity identical to staphylococcal enterotoxin B (SEB).

Only few infectious mouse mammary tumor viruses (MMTV) have been characterized which induce a potent superantigen response in vivo. Here we describe the characterization of an MMTV which was isolated from milk of the highly mammary tumor-prone SHN mouse strain. Exposure of newborn mice to milk-borne MMTV (SHN) results in a very slow deletion of V beta 7, 8.1, 8.2 and 8.3 expressing peripheral T cells. Subcutaneous injection of adult mice with this virus induces a rapid and strong stimulation of all four affected V beta-subsets in vivo. Besides the strong T cell effect we observed an early proliferation and activation of the local B cell pool leading to the initial secretion of IgM followed by preferential secretion of IgG2a by day 6. Sequence comparison of the polymorphic C terminus with known open reading frames revealed high homology to the endogenous provirus Mtv-RCS. This is the first report of a virus having a complete overlap in V beta-specificity with a bacterial superantigen stimulating as many as 35% of the whole CD4+ T cell repertoire including V beta 8.2.

Retrospective feasibility study of simultaneous integrated boost in cervical cancer using Tomotherapy: the impact of organ motion and tumor regression.

BACKGROUND: Whole pelvis intensity modulated radiotherapy (IMRT) is increasingly being used to treat cervical cancer aiming to reduce side effects. Encouraged by this, some groups have proposed the use of simultaneous integrated boost (SIB) to target the tumor, either to get a higher tumoricidal effect or to replace brachytherapy. Nevertheless, physiological organ movement and rapid tumor regression throughout treatment might substantially reduce any benefit of this approach. PURPOSE: To evaluate the clinical target volume - simultaneous integrated boost (CTV-SIB) regression and motion during chemo-radiotherapy (CRT) for cervical cancer, and to monitor treatment progress dosimetrically and volumetrically to ensure treatment goals are met. METHODS AND MATERIALS: Ten patients treated with standard doses of CRT and brachytherapy were retrospectively re-planned using a helical Tomotherapy - SIB technique for the hypothetical scenario of this feasibility study. Target and organs at risk (OAR) were contoured on deformable fused planning-computed tomography and megavoltage computed tomography images. The CTV-SIB volume regression was determined. The center of mass (CM) was used to evaluate the degree of motion. The Dice's similarity coefficient (DSC) was used to assess the spatial overlap of CTV-SIBs between scans. A cumulative dose-volume histogram modeled estimated delivered doses. RESULTS: The CTV-SIB relative reduction was between 31 and 70%. The mean maximum CM change was 12.5, 9, and 3 mm in the superior-inferior, antero-posterior, and right-left dimensions, respectively. The CTV-SIB-DSC approached 1 in the first week of treatment, indicating almost perfect overlap. CTV-SIB-DSC regressed linearly during therapy, and by the end of treatment was 0.5, indicating 50% discordance. Two patients received less than 95% of the prescribed dose. Much higher doses to the OAR were observed. A multiple regression analysis showed a significant interaction between CTV-SIB reduction and OAR dose increase. CONCLUSIONS: The CTV-SIB had important regression and motion during CRT, receiving lower therapeutic doses than expected. The OAR had unpredictable shifts and received higher doses. The use of SIB without frequent adaptation of the treatment plan exposes cervical cancer patients to an unpredictable risk of under-dosing the target and/or overdosing adjacent critical structures. In that scenario, brachytherapy continues to be the gold standard approach.

Induction of macrophage nitric oxide production by interferon-gamma and tumor necrosis factor-alpha is enhanced by interleukin-10.

Interleukin-10 (IL-10) has been reported to inhibit nitric oxide (NO) synthesis and microbicidal activity of interferon-gamma (IFN-gamma)-stimulated macrophages (M phi) by preventing the secretion of tumor necrosis factor-alpha (TNF-alpha) which serves as an autocrine activating signal. We have examined the effects of recombinant IL-10 on the capacity of IFN-gamma together with exogenous TNF-alpha to induce NO synthesis by bone marrow-derived M phi. Under these conditions and in contrast to its reported deactivating potential, IL-10 strongly enhanced NO synthesis measured as nitrite (NO2-) release (half maximal stimulation at approximately 10 U/ml). IL-10 further increased NO2- production by M phi stimulated in the presence of optimal concentrations of prostaglandin E2, a positive modulator of M phi activation by IFN-gamma/TNF-alpha. Increased steady state levels of NO synthase mRNA were observed in 4-h IFN-gamma/TNF-alpha cultures and enhanced NO2(-)-release was evident 24 h but not 48 h after stimulation. These results suggest that the effects of IL-10 on M phi function are more complex than previously recognized.

Myeloid-derived suppressor cells are implicated in regulating permissiveness for tumor metastasis during mouse gestation.

Metastasis depends on the ability of tumor cells to establish a relationship with the newly seeded tissue that is conducive to their survival and proliferation. However, the factors that render tissues permissive for metastatic tumor growth have yet to be fully elucidated. Breast tumors arising during pregnancy display early metastatic proclivity, raising the possibility that pregnancy may constitute a physiological condition of permissiveness for tumor dissemination. Here we have shown that during murine gestation, metastasis is enhanced regardless of tumor type, and that decreased NK cell activity is responsible for the observed increase in experimental metastasis. Gene expression changes in pregnant mouse lung and liver were shown to be similar to those detected in premetastatic sites and indicative of myeloid cell infiltration. Indeed, myeloid-derived suppressor cells (MDSCs) accumulated in pregnant mice and exerted an inhibitory effect on NK cell activity, providing a candidate mechanism for the enhanced metastatic tumor growth observed in gestant mice. Although the functions of MDSCs are not yet understood in the context of pregnancy, our observations suggest that they may represent a shared mechanism of immune suppression occurring during gestation and tumor growth.

Using numerical classification of profiles based on Vis-NIR spectra to distinguish soils from the Piracicaba Region, Brazil

Considering that information from soil reflectance spectra is underutilized in soil classification, this paper aimed to evaluate the relationship of soil physical, chemical properties and their spectra, to identify spectral patterns for soil classes, evaluate the use of numerical classification of profiles combined with spectral data for soil classification. We studied 20 soil profiles from the municipality of Piracicaba, State of São Paulo, Brazil, which were morphologically described and classified up to the 3rd category level of the Brazilian Soil Classification System (SiBCS). Subsequently, soil samples were collected from pedogenetic horizons and subjected to soil particle size and chemical analyses. Their Vis-NIR spectra were measured, followed by principal component analysis. Pearson's linear correlation coefficients were determined among the four principal components and the following soil properties: pH, organic matter, P, K, Ca, Mg, Al, CEC, base saturation, and Al saturation. We also carried out interpretation of the first three principal components and their relationships with soil classes defined by SiBCS. In addition, numerical classification of the profiles based on the OSACA algorithm was performed using spectral data as a basis. We determined the Normalized Mutual Information (NMI) and Uncertainty Coefficient (U). These coefficients represent the similarity between the numerical classification and the soil classes from SiBCS. Pearson's correlation coefficients were significant for the principal components when compared to sand, clay, Al content and soil color. Visual analysis of the principal component scores showed differences in the spectral behavior of the soil classes, mainly among Argissolos and the others soils. The NMI and U similarity coefficients showed values of 0.74 and 0.64, respectively, suggesting good similarity between the numerical and SiBCS classes. For example, numerical classification correctly distinguished Argissolos from Latossolos and Nitossolos. However, this mathematical technique was not able to distinguish Latossolos from Nitossolos Vermelho férricos, but the Cambissolos were well differentiated from other soil classes. The numerical technique proved to be effective and applicable to the soil classification process.

Tumor cell budding and laminin-5 expression in colorectal carcinoma can be modulated by the tissue micro-environment.

Expression of laminin-5 alpha3, beta3 and gamma2 protein subunits was investigated in colorectal adenocarcinomas using immunostaining and confocal microscopy. The laminin-5 heterotrimer was found in basement membranes and as extracellular deposits in tumor stroma. In contrast to the alpha3 subunit, which was under-expressed, the gamma2 and beta3 subunits were detected in the cytoplasm of carcinoma cells dissociating (budding) from neoplastic tubules, suggestive of focal alterations in laminin-5 assembly and secretion. Laminin-5 gamma2 or beta3 subunit-reactive budding carcinoma cells expressed cytokeratins but not vimentin; they did not proliferate and were not apoptotic. Furthermore, expression of laminin-5 gamma2 and beta3 subunits in budding cells was associated with focal under-expression of the E-cadherin-beta-catenin complex. Results from xenograft experiments showed that budding activity in colorectal adenocarcinomas could be suppressed when these tumors grew at ectopic s.c. sites in nude mice. In vitro, cultured colon carcinoma cells, but not adenoma-derived tumor cells, shared the laminin-5 phenotype expressed by carcinoma cells in vivo. Using colon carcinoma cell lines implanted orthotopically and invading the cecum of nude mice, the laminin-5-associated budding was restored, indicating that this phenotype is not only determined by tumor cell properties but also dependent on the tissue micro-environment. Our results indicate that both laminin-5 alpha3 subunit expression and cell-cell cohesiveness are altered in budding carcinoma cells, which we consider to be actively invading. We propose that the local tissue micro-environment contributes to these events.

Dual Blockade of PD-1 and CTLA-4 Combined with Tumor Vaccine Effectively Restores T-Cell Rejection Function in Tumors.

Tumor progression is facilitated by regulatory T cells (Treg) and restricted by effector T cells. In this study, we document parallel regulation of CD8(+) T cells and Foxp3(+) Tregs by programmed death-1 (PD-1, PDCD1). In addition, we identify an additional role of CTL antigen-4 (CTLA-4) inhibitory receptor in further promoting dysfunction of CD8(+) T effector cells in tumor models (CT26 colon carcinoma and ID8-VEGF ovarian carcinoma). Two thirds of CD8(+) tumor-infiltrating lymphocytes (TIL) expressed PD-1, whereas one third to half of CD8(+) TIL coexpressed PD-1 and CTLA-4. Double-positive (PD-1(+)CTLA-4(+)) CD8(+) TIL had characteristics of more severe dysfunction than single-positive (PD-1(+) or CTLA-4(+)) TIL, including an inability to proliferate and secrete effector cytokines. Blockade of both PD-1 and CTLA-4 resulted in reversal of CD8(+) TIL dysfunction and led to tumor rejection in two thirds of mice. Double blockade was associated with increased proliferation of antigen-specific effector CD8(+) and CD4(+) T cells, antigen-specific cytokine release, inhibition of suppressive functions of Tregs, and upregulation of key signaling molecules critical for T-cell function. When used in combination with GVAX vaccination (consisting of granulocyte macrophage colony-stimulating factor-expressing irradiated tumor cells), inhibitory pathway blockade induced rejection of CT26 tumors in 100% of mice and ID8-VEGF tumors in 75% of mice. Our study indicates that PD-1 signaling in tumors is required for both suppressing effector T cells and maintaining tumor Tregs, and that PD-1/PD-L1 pathway (CD274) blockade augments tumor inhibition by increasing effector T-cell activity, thereby attenuating Treg suppression. Cancer Res; 73(12); 3591-603. ©2013 AACR.

Detection of new cross-reacting carcinoembryonic antigen(s) on cultured tumor cells by mixed hemadsorption assay.

Antisera highly specific for carcinoembryonic antigen (CEA) from New Zealand White rabbits and a goat reacted strongly in antibody binding tests with cultured tumor cell lines, irrespective of the ability of the cell lines to produce CEA. The most reactive were colon carcinoma and melanoma cell lines, the former known to produce CEA and the latter not associated with CEA production. The reactivity was not diminished by absorption with perchloric acid extracts of normal lung or spleen, whereas absoprtion with purified CEA preparations abolished the reactivity. Quantitative absorption studies indicated that reactivity against CEA-producing cell lines could be totally removed by absorption with other CEA-producing lines but not with melanoma cell lines. Reactivity against melanoma cell lines could be completely removed by colon carcinoma cells as well as by melanoma cells. Antisera raised against purified CEA, after absorption with extracts of normal lung, still contained two populations of antibodies, one that binds a newly described antigen cross-reacting with CEA which is present on melanoma cells.

Toward synthetic combinatorial peptide libraries in positional scanning format (PS-SCL)-based identification of CD8+ Tumor-reactive T-Cell Ligands: a comparative analysis of PS-SCL recognition by a single tumor-reactive CD8+ cytolytic T-lymphocyte clone.

The use of synthetic combinatorial peptide libraries in positional scanning format (PS-SCL) has emerged recently as an alternative approach for the identification of peptides recognized by T lymphocytes. The choice of both the PS-SCL used for screening experiments and the method used for data analysis are crucial for implementing this approach. With this aim, we tested the recognition of different PS-SCL by a tyrosinase 368-376-specific CTL clone and analyzed the data obtained with a recently developed biometric data analysis based on a model of independent and additive contribution of individual amino acids to peptide antigen recognition. Mixtures defined with amino acids present at the corresponding positions in the native sequence were among the most active for all of the libraries. Somewhat surprisingly, a higher number of native amino acids were identifiable by using amidated COOH-terminal rather than free COOH-terminal PS-SCL. Also, our data clearly indicate that when using PS-SCL longer than optimal, frame shifts occur frequently and should be taken into account. Biometric analysis of the data obtained with the amidated COOH-terminal nonapeptide library allowed the identification of the native ligand as the sequence with the highest score in a public human protein database. However, the adequacy of the PS-SCL data for the identification for the peptide ligand varied depending on the PS-SCL used. Altogether these results provide insight into the potential of PS-SCL for the identification of CTL-defined tumor-derived antigenic sequences and may significantly implement our ability to interpret the results of these analyses.

Advanced Kernel Methods For Remote Sensing Image Classification

Résumé Suite aux recentes avancées technologiques, les archives d'images digitales ont connu une croissance qualitative et quantitative sans précédent. Malgré les énormes possibilités qu'elles offrent, ces avancées posent de nouvelles questions quant au traitement des masses de données saisies. Cette question est à la base de cette Thèse: les problèmes de traitement d'information digitale à très haute résolution spatiale et/ou spectrale y sont considérés en recourant à des approches d'apprentissage statistique, les méthodes à noyau. Cette Thèse étudie des problèmes de classification d'images, c'est à dire de catégorisation de pixels en un nombre réduit de classes refletant les propriétés spectrales et contextuelles des objets qu'elles représentent. L'accent est mis sur l'efficience des algorithmes, ainsi que sur leur simplicité, de manière à augmenter leur potentiel d'implementation pour les utilisateurs. De plus, le défi de cette Thèse est de rester proche des problèmes concrets des utilisateurs d'images satellite sans pour autant perdre de vue l'intéret des méthodes proposées pour le milieu du machine learning dont elles sont issues. En ce sens, ce travail joue la carte de la transdisciplinarité en maintenant un lien fort entre les deux sciences dans tous les développements proposés. Quatre modèles sont proposés: le premier répond au problème de la haute dimensionalité et de la redondance des données par un modèle optimisant les performances en classification en s'adaptant aux particularités de l'image. Ceci est rendu possible par un système de ranking des variables (les bandes) qui est optimisé en même temps que le modèle de base: ce faisant, seules les variables importantes pour résoudre le problème sont utilisées par le classifieur. Le manque d'information étiquétée et l'incertitude quant à sa pertinence pour le problème sont à la source des deux modèles suivants, basés respectivement sur l'apprentissage actif et les méthodes semi-supervisées: le premier permet d'améliorer la qualité d'un ensemble d'entraînement par interaction directe entre l'utilisateur et la machine, alors que le deuxième utilise les pixels non étiquetés pour améliorer la description des données disponibles et la robustesse du modèle. Enfin, le dernier modèle proposé considère la question plus théorique de la structure entre les outputs: l'intègration de cette source d'information, jusqu'à présent jamais considérée en télédétection, ouvre des nouveaux défis de recherche. Advanced kernel methods for remote sensing image classification Devis Tuia Institut de Géomatique et d'Analyse du Risque September 2009 Abstract The technical developments in recent years have brought the quantity and quality of digital information to an unprecedented level, as enormous archives of satellite images are available to the users. However, even if these advances open more and more possibilities in the use of digital imagery, they also rise several problems of storage and treatment. The latter is considered in this Thesis: the processing of very high spatial and spectral resolution images is treated with approaches based on data-driven algorithms relying on kernel methods. In particular, the problem of image classification, i.e. the categorization of the image's pixels into a reduced number of classes reflecting spectral and contextual properties, is studied through the different models presented. The accent is put on algorithmic efficiency and the simplicity of the approaches proposed, to avoid too complex models that would not be used by users. The major challenge of the Thesis is to remain close to concrete remote sensing problems, without losing the methodological interest from the machine learning viewpoint: in this sense, this work aims at building a bridge between the machine learning and remote sensing communities and all the models proposed have been developed keeping in mind the need for such a synergy. Four models are proposed: first, an adaptive model learning the relevant image features has been proposed to solve the problem of high dimensionality and collinearity of the image features. This model provides automatically an accurate classifier and a ranking of the relevance of the single features. The scarcity and unreliability of labeled. information were the common root of the second and third models proposed: when confronted to such problems, the user can either construct the labeled set iteratively by direct interaction with the machine or use the unlabeled data to increase robustness and quality of the description of data. Both solutions have been explored resulting into two methodological contributions, based respectively on active learning and semisupervised learning. Finally, the more theoretical issue of structured outputs has been considered in the last model, which, by integrating outputs similarity into a model, opens new challenges and opportunities for remote sensing image processing.

MAP/ERK kinase kinase 1 (MEKK1) mediates transcriptional repression by interacting with polycystic kidney disease-1 (PKD1) promoter-bound p53 tumor suppressor protein.

Mitogen-activated protein kinase (MAPK) cascades regulate a wide variety of cellular processes that ultimately depend on changes in gene expression. We have found a novel mechanism whereby one of the key MAP3 kinases, Mekk1, regulates transcriptional activity through an interaction with p53. The tumor suppressor protein p53 down-regulates a number of genes, including the gene most frequently mutated in autosomal dominant polycystic kidney disease (PKD1). We have discovered that Mekk1 translocates to the nucleus and acts as a co-repressor with p53 to down-regulate PKD1 transcriptional activity. This repression does not require Mekk1 kinase activity, excluding the need for an Mekk1 phosphorylation cascade. However, this PKD1 repression can also be induced by the stress-pathway stimuli, including TNFα, suggesting that Mekk1 activation induces both JNK-dependent and JNK-independent pathways that target the PKD1 gene. An Mekk1-p53 interaction at the PKD1 promoter suggests a new mechanism by which abnormally elevated stress-pathway stimuli might directly down-regulate the PKD1 gene, possibly causing haploinsufficiency and cyst formation.