Col6a1+/CD201+ mesenchymal cells regulate intestinal morphogenesis and homeostasis.

Intestinal mesenchymal cells encompass multiple subsets, whose origins, functions, and pathophysiological importance are still not clear. Here, we used the Col6a1Cre mouse, which targets distinct fibroblast subsets and perivascular cells that can be further distinguished by the combination of the CD201, PDGFRα and αSMA markers. Developmental studies revealed that the Col6a1Cre mouse also targets mesenchymal aggregates that are crucial for intestinal morphogenesis and patterning, suggesting an ontogenic relationship between them and homeostatic PDGFRαhi telocytes. Cell depletion experiments in adulthood showed that Col6a1+/CD201+ mesenchymal cells regulate homeostatic enteroendocrine cell differentiation and epithelial proliferation. During acute colitis, they expressed an inflammatory and extracellular matrix remodelling gene signature, but they also retained their properties and topology. Notably, both in homeostasis and tissue regeneration, they were dispensable for normal organ architecture, while CD34+ mesenchymal cells expanded, localised at the top of the crypts, and showed increased expression of villous-associated morphogenetic factors, providing thus evidence for the plasticity potential of intestinal mesenchymal cells. Our results provide a comprehensive analysis of the identities, origin, and functional significance of distinct mesenchymal populations in the intestine.

Smyd3-associated regulatory pathways in cancer.

SMYD3 is a member of the SET and MYND-domain family of methyl-transferases, the increased expression of which correlates with poor prognosis in various types of cancer. In liver and colon tumors, SMYD3 is localized in the nucleus, where it interacts with RNA Pol II and H3K4me3 and functions as a selective transcriptional amplifier of oncogenes and genes that control cell proliferation and metastatic spread. Smyd3 expression has a high discriminative power for the characterization of liver tumors and positively correlates with poor prognosis. In lung and pancreatic cancer, SMYD3 acts in the cytoplasm, potentiating oncogenic Ras/ERK signaling through the methylation of the MAP3K2 kinase and the subsequent release from its inhibitor. A clinico-pathological analysis of lung cancer patients uncovers prognostic significance of SMYD3 only for first progression survival. However, stratification of patients according to their smoking history significantly expands the prognostic value of SMYD3 to overall survival and other features, suggesting that smoking-related effects saturate the clinical analysis and mask the function of SMYD3 as an oncogenic potentiator.

R-spondin 1 and noggin facilitate expansion of resident stem cells from non-damaged gallbladders.

Pioneering studies within the last few years have allowed the in vitro expansion of tissue-specific adult stem cells from a variety of endoderm-derived organs, including the stomach, small intestine, and colon. Expansion of these cells requires activation of the receptor Lgr5 by its ligand R-spondin 1 and is likely facilitated by the fact that in healthy adults the stem cells in these organs are highly proliferative. In many other adult organs, such as the liver, proliferating cells are normally not abundant in adulthood. However, upon injury, the liver has a strong regenerative potential that is accompanied by the emergence of Lgr5-positive stem cells; these cells can be isolated and expanded in vitro as organoids. In an effort to isolate stem cells from non-regenerating mouse livers, we discovered that healthy gallbladders are a rich source of stem/progenitor cells that can be propagated in culture as organoids for more than a year. Growth of these organoids was stimulated by R-spondin 1 and noggin, whereas in the absence of these growth factors, the organoids differentiated partially toward the hepatocyte fate. When transplanted under the liver capsule, gallbladder-derived organoids maintained their architecture for 2 weeks. Furthermore, single cells prepared from dissociated organoids and injected into the mesenteric vein populated the liver parenchyma of carbon tetrachloride-treated mice. Human gallbladders were also a source of organoid-forming stem cells. Thus, under specific growth conditions, stem cells can be isolated from healthy gallbladders, expanded almost indefinitely in vitro, and induced to differentiate toward the hepatocyte lineage.

Significant improvement in the survival of patients with multiple myeloma presenting with severe renal impairment after the introduction of novel agents.

BACKGROUND: Renal impairment (RI) is a common presenting complication of multiple myeloma (MM); the availability of new treatments has improved the outcomes of patients with MM; however, their impact on the survival of patients who present with RI has not been extensively studied. PATIENTS AND METHODS: We analyzed the characteristics and outcomes of 1773 consecutive unselected patients who were treated for symptomatic myeloma since January 1990. RESULTS: Although there was a significant increase in the proportion of patients of advanced age in the more recent periods, the frequency of RI as well as the proportion of patients who presented with severe RI (eGFR < 30 ml/min/1.73 m(2)) remained unchanged around 18%. Thus, after adjustment for age, there was a decrease in the risk of severe RI at presentation after 2000. Myeloma response rates (≥PR) to frontline therapy have substantially increased, and this was translated in a significant increase in the median survival. Specifically for patients with severe RI, the median OS has improved from 18 and 19.5 months in the 1990-1994 and 1995-1999 to 29 and 32 months for the periods 2000-2004 and after 2005 (P = 0.005). Severe RI was associated with a high risk of early death (12% versus 7% for patients with moderate RI versus 3% for patients with mild or no RI (P < 0.001), especially among older patients, and has remained unchanged over time. CONCLUSIONS: There has been a major improvement in the survival of patients with severe RI in the past decade, despite the increasing numbers of patients of advanced age. However, the risk of early death remains high in patients with severe RI, especially in the elderly.

Mathematical modelling of spatio-temporal glioma evolution.

BACKGROUND: Gliomas are the most common types of brain cancer, well known for their aggressive proliferation and the invasive behavior leading to a high mortality rate. Several mathematical models have been developed for identifying the interactions between glioma cells and tissue microenvironment, which play an important role in the mechanism of the tumor formation and progression. METHODS: Building and expanding on existing approaches, this paper develops a continuous three-dimensional model of avascular glioma spatio-temporal evolution. The proposed spherical model incorporates the interactions between the populations of four different glioma cell phenotypes (proliferative, hypoxic, hypoglychemic and necrotic) and their tissue microenvironment, in order to investigate how they affect tumor growth and invasion in an isotropic and homogeneous medium. The model includes two key variables involved in the proliferation and invasion processes of cancer cells; i.e. the extracellular matrix and the matrix-degradative enzymes concentrations inside the tumor and its surroundings. Additionally, the proposed model focuses on innovative features, such as the separate and independent impact of two vital nutrients, namely oxygen and glucose, in tumor growth, leading to the formation of cell populations with different metabolic profiles. The model implementation takes under consideration the variations of particular factors, such as the local cell proliferation rate, the variable conversion rates of cells from one category to another and the nutrient-dependent thresholds of conversion. All model variables (cell densities, ingredients concentrations) are continuous and described by reaction-diffusion equations. RESULTS: Several simulations were performed using combinations of growth and invasion rates, for different evolution times. The model results were evaluated by medical experts and validated on experimental glioma models available in the literature, revealing high agreement between simulated and experimental results. CONCLUSIONS: Based on the experimental validation, as well as the evaluation by clinical experts, the proposed model may provide an essential tool for the patient-specific simulation of different tumor evolution scenarios and reliable prognosis of glioma spatio-temporal progression.

The International Scoring System (ISS) for multiple myeloma remains a robust prognostic tool independently of patients' renal function.

BACKGROUND: The International Staging System (ISS) is the most widely used staging system for patients with multiple myeloma (MM). However, serum ß2-microglobulin increases in renal impairment (RI) and there have been concerns that ISS-3 stage may include 'up-staged' MM patients in whom elevated ß2-microglobulin reflects the degree of renal dysfunction rather than tumor load. PATIENTS AND METHODS: In order to assess the impact of RI on the prognostic value of ISS, we analyzed 1516 patients with symptomatic MM and the degree of RI was classified according to the Kidney Disease Outcomes Quality Initiative-Chronic Kidney Disease (CKD) criteria. RESULTS: Forty-eight percent patients had stages 3-5 CKD while 29% of patients had ISS-1, 38% had ISS-2 and 33% ISS-3. The frequency and severity of RI were more common in ISS-3 patients. RI was associated with inferior survival in univariate but not in multivariate analysis. When analyzed separately, ISS-1 and ISS-2 patients with RI had inferior survival in univariate but not in multivariate analysis. In ISS-3 MM patients, RI had no prognostic impact either in univariate or multivariate analysis. Results were similar, when we analyzed only patients with Bence-Jones >200 mg/day. CONCLUSIONS: ISS remains unaffected by the degree of RI, even in patients with ISS-3, which includes most patients with renal dysfunction.

Fibroblast Reprogramming in Gastrointestinal Cancer.

Gastrointestinal cancers are a significant cause of cancer mortality worldwide and have been strongly linked with chronic inflammation. Current therapies focus on epithelial/cancer cells; however, the importance of the tumor microenvironment in the development and treatment of the disease is also now well established. Cancer-associated fibroblasts (CAFs) are a major component of the tumor microenvironment, and are actively participating in tumor initiation, promotion and metastasis. They structurally and functionally affect cancer cell proliferation, tumor immunity, angiogenesis, extracellular matrix remodeling and metastasis through a variety of signaling pathways. CAFs originate predominantly from resident mesenchymal cells, which are activated and reprogrammed in response to cues from cancer cells. In recent years, chronic inflammation of the gastrointestinal tract has also proven an important driver of mesenchymal cell activation and subsequent CAF development, which in turn are capable of regulating the transition from acute to chronic inflammation and cancer. In this review, we will provide a concise overview of the mechanisms that drive fibroblast reprogramming in cancer and the recent advances on the downstream signaling pathways that regulate the functional properties of the activated mesenchyme. This new mechanistic insight could pave the way for new therapeutic strategies and better prognosis for cancer patients.

Outcome prediction based on microarray analysis: a critical perspective on methods.

BACKGROUND: Information extraction from microarrays has not yet been widely used in diagnostic or prognostic decision-support systems, due to the diversity of results produced by the available techniques, their instability on different data sets and the inability to relate statistical significance with biological relevance. Thus, there is an urgent need to address the statistical framework of microarray analysis and identify its drawbacks and limitations, which will enable us to thoroughly compare methodologies under the same experimental set-up and associate results with confidence intervals meaningful to clinicians. In this study we consider gene-selection algorithms with the aim to reveal inefficiencies in performance evaluation and address aspects that can reduce uncertainty in algorithmic validation. RESULTS: A computational study is performed related to the performance of several gene selection methodologies on publicly available microarray data. Three basic types of experimental scenarios are evaluated, i.e. the independent test-set and the 10-fold cross-validation (CV) using maximum and average performance measures. Feature selection methods behave differently under different validation strategies. The performance results from CV do not mach well those from the independent test-set, except for the support vector machines (SVM) and the least squares SVM methods. However, these wrapper methods achieve variable (often low) performance, whereas the hybrid methods attain consistently higher accuracies. The use of an independent test-set within CV is important for the evaluation of the predictive power of algorithms. The optimal size of the selected gene-set also appears to be dependent on the evaluation scheme. The consistency of selected genes over variation of the training-set is another aspect important in reducing uncertainty in the evaluation of the derived gene signature. In all cases the presence of outlier samples can seriously affect algorithmic performance. CONCLUSION: Multiple parameters can influence the selection of a gene-signature and its predictive power, thus possible biases in validation methods must always be accounted for. This paper illustrates that independent test-set evaluation reduces the bias of CV, and case-specific measures reveal stability characteristics of the gene-signature over changes of the training set. Moreover, frequency measures on gene selection address the algorithmic consistency in selecting the same gene signature under different training conditions. These issues contribute to the development of an objective evaluation framework and aid the derivation of statistically consistent gene signatures that could eventually be correlated with biological relevance. The benefits of the proposed framework are supported by the evaluation results and methodological comparisons performed for several gene-selection algorithms on three publicly available datasets.

The linear neuron as marker selector and clinical predictor in cancer gene analysis.

OBJECTIVE: The problem of gene selection has been extensively studied in a number of scientific works using various kinds of methods. However, the application of a linear neuron is a novel approach possessing several advantages. In this work we propose to study the behavior of such a linear neuron, appropriately adapted and trained to the problem of gene selection in the DNA microarray experiment. METHODS AND MATERIALS: We explore the proposed approach in terms of an accuracy evaluation criterion, which is used to assess the performance of the proposed methodology, but we also evaluate the produced results in terms of cluster quality and survival prediction. Cluster quality reflects the ability of the method to select differentially expressed genes, which in turn leads to better clustering and survival prediction. RESULTS: We directly compare the proposed methodology with RFE-SVM, a well known and broadly accepted method demonstrating remarkable performance on various data sets of clinical interest. CONCLUSIONS: Conducted computational experiments show that the proposed approach can be efficiently used within the field of gene selection producing high-quality results in terms of accuracy and robustness.

Wrapper filtering criteria via linear neuron and kernel approaches.

OBJECTIVE: The problem of marker selection in DNA microarray analysis has been addressed so far by two basic types of approaches, the so-called filter and wrapper methods. Wrapper methods operate in a recursive fashion where feature (gene) weights are re-evaluated and dynamically changing from iteration to iteration, while in filter methods feature weights remain fixed. Our objective in this study is to show that the application of filter criteria in a recursive fashion, where weights are potentially adjusted from cycle to cycle, produces noticeable improvement on the generalization performance measured on independent test sets. METHODS AND MATERIALS: Toward this direction we explore the behavior of two well known and broadly accepted pattern recognition approaches namely the support vector machines (SVM) and a single linear neuron (LN), properly adapted to the problem of marker selection. Within this context we also show how the kernel ability of SVM could be employed in a practical manner to provide alternative ways to approach the problem of reliable marker selection. RESULTS: We explore how the proposed approaches behave in two application domains (breast cancer and leukemia), achieving comparable or even better results than those reported in the related bibliography. An important advantage of these approaches is their ability to derive stable performance without deteriorating due to the complexity of the application domain. Validation is performed using internal leave one out (ILOO) and 10-fold cross validation as well as independent test set evaluation. CONCLUSIONS: Results show that the proposed methodologies achieve remarkable performance and indicate that applying filter criteria in a wrapper fashion ('wrapper filtering criteria') provides a useful tool for marker selection. The contribution of this study is threefold. First it provides a methodology to apply filter criteria in a wrapper way (which is a new approach), second it introduces a fundamental pattern recognition component namely the single neuron (which is a linear estimator) and explores its behavior on marker selection and third it demonstrates an approach to exploit the kernel ability of SVMs in a practical and effective manner.

Glioma growth modeling based on the effect of vital nutrients and metabolic products.

Glioma brain tumors exhibit considerably aggressive behavior leading to high mortality rates. Mathematical modeling of tumor growth aims to explore the interactions between glioma cells and tissue microenvironment, which affect tumor evolution. Leveraging this concept, we present a three-dimensional model of glioma spatio-temporal evolution based on existing continuum approaches, yet incorporating novel factors of the phenomenon. The proposed model involves the interactions between different tumor cell phenotypes and their microenvironment, investigating how tumor growth is affected by complex biological exchanges. It focuses on the separate and combined effect of vital nutrients and cellular wastes on tumor expansion, leading to the formation of cell populations with different metabolic, proliferative, and diffusive profiles. Several simulations were performed on a virtual and a real glioma, using combinations of proliferation and diffusion rates for different evolution times. The model results were validated on a glioma model available in the literature and a real case of tumor progression. The experimental observations indicate that our model estimates quite satisfactorily the expansion of each region and the overall tumor growth. Based on the individual results, the proposed model may provide an important research tool for patient-specific simulation of different tumor evolution scenarios and reliable estimation of glioma evolution. Graphical Abstract Outline of the mathematical model functionality and application to glioma growth with indicative results.

A huge posteromedial mediastinal cyst complicated with vertebral dislodgment.

BACKGROUND: Mediastinal cysts compromise almost 20% of all mediastinal masses with bronchogenic subtype accounting for 60% of all cystic lesions. Although compression of adjoining soft tissues is usual, spinal complications and neurological symptoms are outmost rare and tend to characterize almost exclusively the neuroenteric cysts. CASE PRESENTATION: A young patient with intermittent, dull pain in his back and free medical history presented in the orthopaedic department of our hospital. There, the initial clinical and radiologic evaluation revealed a mediastinal mass and the patient was referred to the thoracic surgery department for further exploration. The following computed tomography (CT) and magnetic resonance imaging (MRI) shown a huge mediastinal cyst compressing the T4-T6 vertebral bodies. The neurological symptoms of the patient were attributed to this specific pathology due to the complete agreement between the location of the cyst and the nervous rule area of the compressed thoracic vertebrae. Despite our strongly suggestions for surgery the patient denied any treatment. CONCLUSION: In controversy with the common faith that the spine plays the role of the natural barrier to the further expansion of cystic lesions, our case clearly indicates that, exceptionally, mediastinal cysts may cause severe vertebral complications. Therefore, early excision should be considered especially in young patients or where close follow up is uncertain.

Smyd3 Is a Transcriptional Potentiator of Multiple Cancer-Promoting Genes and Required for Liver and Colon Cancer Development.

Smyd3 is a protein methyltransferase implicated in cancer development. Here we show that Smyd3 expression in mice is required for chemically induced liver and colon cancer formation. In these organs Smyd3 functions in the nucleus, stimulating the transcription of several key regulators involved in cell proliferation, epithelial-mesenchymal transition, the JAK/Stat3 oncogenic pathway, as well as the Myc and Ctnnb1 oncogenes. Smyd3 interacts with H3K4Me3-modified histone tails, which facilitates its recruitment to the core promoter regions of most active genes. Smyd3 binding density on target genes positively correlates with increased RNA polymerase-II density and transcriptional outputs. Despite its widespread distribution, the transcription-potentiating function of Smyd3 is restricted to a particular set of genes, whose expression is induced specifically during carcinogenesis.

A Glycolysis-Based In Silico Model for the Solid Tumor Growth.

Cancer-tumor growth is a complex process depending on several biological factors, such as the chemical microenvironment of the tumor, the cellular metabolic profile, and its proliferation rate. Several mathematical models have been developed for identifying the interactions between tumor cells and tissue microenvironment, since they play an important role in tumor formation and progression. Toward this direction we propose a new continuum model of avascular glioma-tumor growth, which incorporates a new factor, namely, the glycolytic potential of cancer cells, to express the interactions of three different tumor-cell populations (proliferative, hypoxic, and necrotic) with their tissue microenvironment. The glycolytic potential engages three vital nutrients, i.e., oxygen, glucose, and lactate, which provide cells with the necessary energy for their survival and proliferation. Extensive simulations are performed for different evolution times and various proliferation rates, in order to investigate how the tumor growth is affected. According to medical experts, the experimental observations indicate that the model predicts quite satisfactorily the overall tumor growth as well as the expansion of each region separately. Following extensive evaluation, the proposed model may provide an essential tool for patient-specific tumor simulation and reliable prediction of glioma spatiotemporal expansion.

Treatment of patients with myelodysplastic syndrome with amifostine.

The efficacy and toxicity of amifostine (300 mg/m(2) three times a week for three consecutive weeks for a maximum of six courses) was evaluated in 12 patients with primary myelodysplastic syndromes. Dose escalation up to 400 mg/m(2) was allowed to patients who failed to respond. Hemoglobin concentration was increased > or = 1.5 g/dl in two (18%) of the 11 anemic patients. These two patients obtained transfusion independence for 20 weeks. Reticulocyte counts and ANC increased > or = 50% of baseline in four (44%) of the nine patients with reticulocytopenia and in three (25%) of the 12 neutropenic patients. Platelet count increased in three (50%) of the six patients with thrombocytopenia. Progenitor growth of CFU-GMs and BFU-Es improved in 8/12 patients. No major side effects were observed. In conclusion amifostine is well tolerated and can promote the growth of primitive hematopoietic progenitors and ameliorate the cytopenias in MDS patients.

Primary renal lymphoma presenting with chronic low-grade fever.

Primary renal lymphoma (PRL) is a rare form of extranodal non-Hodgkin's lymphoma often diagnosed and treated by oncologists and urologists. Pathophysiological and clinical data on PRL are sparse, but the limited reported experience suggests the disease usually has an ominous outcome. As in other renal tumors, comprehensive radiological investigations have a central role in the recognition and final diagnosis of PRL. We describe the presenting features and clinical course of an elderly woman who was found to have PRL after evaluation for persistent low-grade fever. Diagnostic and therapeutic caveats are discussed on the basis of a critical literature review of case reports and descriptions of small series of patients.

Preserved levels of uninvolved immunoglobulins are independently associated with favorable outcome in patients with symptomatic multiple myeloma.

Suppression of uninvolved immunoglobulins is common in multiple myeloma (MM) but the prognostic significance of this phenomenon has not been assessed. We evaluated the prognostic significance of the preservation of uninvolved immunoglobulins in 1755 consecutive, unselected, patients with newly diagnosed, symptomatic MM with pre-therapy immunoglobulin levels measured by nephelometry. Suppression of at least one uninvolved immunoglobulin was observed in 87% of patients and was more common in patients with immunoglobulin A myeloma, those aged over 65 years, in patients with advanced-International Staging System (ISS) stage, extensive-bone marrow infiltration, anemia, low platelet counts, high levels of serum M-monoclonal protein or renal dysfunction. Patients with preserved immunoglobulins had a better survival than patients with suppressed immunoglobulins (median survival 55 vs 41.5 months, P<0.001). In multivariate analysis, preservation of uninvolved immunoglobulins was independently associated with better survival (hazard ratio: 0.781, 95% confidence interval: 0.618-0.987, P=0.039); irrespective of the treatment. In a subset of 500 patients, which were strictly followed for disease progression, preservation of uninvolved immunoglobulins was associated with a significantly longer progression-free survival (60 vs 25 months, P<0.001), independently of other common prognostic factors. In conclusion, preservation of uninvolved immunoglobulins in newly diagnosed patients with symptomatic MM was independently associated with long term disease control and improved survival.

In-depth analysis and evaluation of diffusive glioma models.

Glioma is one of the most aggressive types of brain tumor. Several mathematical models have been developed during the past two decades, toward simulating the mechanisms that govern the development of glioma. The most common models use the diffusion-reaction equation (DRE) for simulating the spatiotemporal variation of tumor cell concentration. Nevertheless, despite the applications presented, there has been little work on studying the details of the mathematical solution and implementation of the 3-D diffusion model and presenting a qualitative analysis of the algorithmic results. This paper presents a complete mathematical framework on the solution of the DRE using different numerical schemes. This framework takes into account all characteristics of the latest models, such as brain tissue heterogeneity, anisotropic tumor cell migration, chemotherapy, and resection modeling. The different numerical schemes presented have been evaluated based upon the degree to which the DRE exact solution is approximated. Experiments have been conducted both on real datasets and a test case for which there is a known algebraic expression of the solution. Thus, it is possible to calculate the accuracy of the different models.

High-grade glioma diffusive modeling using statistical tissue information and diffusion tensors extracted from atlases.

Glioma, especially glioblastoma, is a leading cause of brain cancer fatality involving highly invasive and neoplastic growth. Diffusive models of glioma growth use variations of the diffusion-reaction equation in order to simulate the invasive patterns of glioma cells by approximating the spatiotemporal change of glioma cell concentration. The most advanced diffusive models take into consideration the heterogeneous velocity of glioma in gray and white matter, by using two different discrete diffusion coefficients in these areas. Moreover, by using diffusion tensor imaging (DTI), they simulate the anisotropic migration of glioma cells, which is facilitated along white fibers, assuming diffusion tensors with different diffusion coefficients along each candidate direction of growth. Our study extends this concept by fully exploiting the proportions of white and gray matter extracted by normal brain atlases, rather than discretizing diffusion coefficients. Moreover, the proportions of white and gray matter, as well as the diffusion tensors, are extracted by the respective atlases; thus, no DTI processing is needed. Finally, we applied this novel glioma growth model on real data and the results indicate that prognostication rates can be improved.

Integration of gene signatures using biological knowledge.

OBJECTIVE: Gene expression patterns that distinguish clinically significant disease subclasses may not only play a prominent role in diagnosis, but also lead to the therapeutic strategies tailoring the treatment to the particular biology of each disease. Nevertheless, gene expression signatures derived through statistical feature-extraction procedures on population datasets have received rightful criticism, since they share few genes in common, even when derived from the same dataset. We focus on knowledge complementarities conveyed by two or more gene-expression signatures by means of embedded biological processes and pathways, which alternatively form a meta-knowledge platform of analysis towards a more global, robust and powerful solution. METHODS: The main contribution of this work is the introduction and study of an approach for integrating different gene signatures based on the underlying biological knowledge, in an attempt to derive a unified global solution. It is further recognized that one group's signature does not perform well on another group's data, due to incompatibilities of microarray technologies and the experimental design. We assess this cross-platform aspect, showing that a unified solution derived on the basis of both statistical and biological validation may also help in overcoming such inconsistencies. RESULTS: Based on the proposed approach we derived a unified 69-gene signature, which outperforms significantly the performance of the initial signatures succeeding a 0.73 accuracy metric on 234 new patients with 81% sensitivity and 64% specificity. The same signature manages to reveal the two prognostic groups on an additional dataset of 286 new patients obtained through a different experimental protocol and microarray platform. Furthermore, it manages to derive two clusters in a dataset from a different platform, showing remarkable difference on both gene-expression and survival-prediction levels.