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PubMed2006

Gene expression signatures for predicting prognosis of squamous cell and adenocarcinomas of the lung.

Non-small-cell lung cancers (NSCLC) compose 80% of all lung carcinomas with squamous cell carcinomas (SCC) and adenocarcinoma representing the majority of these tumors. Although patients with early-stage NSCLC typically have a better outcome, 35% to 50% will relapse within 5 years after surgical treatment. We have profiled primary squamous cell lung carcinomas from 129 patients using Affymetrix U133A gene chips. Unsupervised analysis revealed two clusters of SCC that had no correlation with tumor stage but had significantly different overall patient survival (P = 0.036). The high-risk cluster was most significantly associated with down-regulation of epidermal development genes. Cox proportional hazard models identified an optimal set of 50 prognostic mRNA transcripts using a 5-fold cross-validation procedure. Quantitative reverse transcription-PCR and immunohistochemistry using tissue microarrays were used to validate individual gene candidates. This signature was tested in an independent set of 36 SCC samples and achieved 84% specificity and 41% sensitivity with an overall predictive accuracy of 68%. Kaplan-Meier analysis showed clear stratification of high-risk and low-risk patients [log-rank P = 0.04; hazard ratio (HR), 2.66; 95% confidence interval (95% CI), 1.01-7.05]. Finally, we combined the SCC classifier with our previously identified adenocarcinoma prognostic signature and showed that the combined classifier had a predictive accuracy of 71% in 72 NSCLC samples also showing significant differences in overall survival (log-rank P = 0.0002; HR, 3.54; 95% CI, 1.74-7.19). This prognostic signature could be used to identify patients with early-stage high-risk NSCLC who might benefit from adjuvant therapy following surgery.

Adenocarcinoma↗

PubMed2006

Microarray identifies ADAM family members as key responders to TGF-beta1 in alveolar epithelial cells.

The molecular mechanisms of Idiopathic Pulmonary Fibrosis (IPF) remain elusive. Transforming Growth Factor beta 1(TGF-beta1) is a key effector cytokine in the development of lung fibrosis. We used microarray and computational biology strategies to identify genes whose expression is significantly altered in alveolar epithelial cells (A549) in response to TGF-beta1, IL-4 and IL-13 and Epstein Barr virus. A549 cells were exposed to 10 ng/ml TGF-beta1, IL-4 and IL-13 at serial time points. Total RNA was used for hybridisation to Affymetrix Human Genome U133A microarrays. Each in vitro time-point was studied in duplicate and an average RMA value computed. Expression data for each time point was compared to control and a signal log ratio of 0.6 or greater taken to identify significant differential regulation. Using normalised RMA values and unsupervised Average Linkage Hierarchical Cluster Analysis, a list of 312 extracellular matrix (ECM) proteins or modulators of matrix turnover was curated via Onto-Compare and Gene-Ontology (GO) databases for baited cluster analysis of ECM associated genes. Interrogation of the dataset using ontological classification focused cluster analysis revealed coordinate differential expression of a large cohort of extracellular matrix associated genes. Of this grouping members of the ADAM (A disintegrin and Metalloproteinase domain containing) family of genes were differentially expressed. ADAM gene expression was also identified in EBV infected A549 cells as well as IL-13 and IL-4 stimulated cells. We probed pathologenomic activities (activation and functional activity) of ADAM19 and ADAMTS9 using siRNA and collagen assays. Knockdown of these genes resulted in diminished production of collagen in A549 cells exposed to TGF-beta1, suggesting a potential role for these molecules in ECM accumulation in IPF.

ADAM Proteins↗

PubMed2026

Coagulation activation is associated with genomic-instability-related features in TP53-mutated AML and MDS: routine laboratory patterns beyond classical disseminated intravascular coagulation.

BACKGROUND: Disseminated intravascular coagulation (DIC) is a serious complication of acute myeloid leukemia (AML) associated with poor prognosis. In TP53-mutated AML and myelodysplastic syndrome (MDS), however, the classical ISTH criteria rarely identify overt DIC, although bleeding and thrombotic complications are well documented in acute leukaemia. We hypothesized that these patients exhibit a lower-grade, subclinical coagulation activation that is associated with the underlying genomic-instability-related features of TP53-mutant disease. METHODS: We retrospectively analyzed 107 consecutive patients with TP53-mutated AML (n = 52) or high-risk MDS (MDS, n = 55), median age 65 years, diagnosed and initially evaluated at our centre between 2018 and 2025. Seven routine coagulation markers and 46 co-mutated genes were evaluated for associations with overall survival (OS) using univariate and multivariable Cox regression, continuous dose-response modeling, and unsupervised k-means clustering. Internal validity was assessed by 1000 bootstrap resamples. RESULTS: Overt DIC according to ISTH criteria was rare (15%). Subclinical activation was common: 50% of patients had a D-dimer ≥1 μg/mL, 41% a fibrinogen ≥4 g/L, and 29% an INR ≥1.2. In univariate analysis, D-dimer, fibrinogen, INR, prothrombin time, and activated partial thromboplastin time were each associated with OS (HR 1.33-1.38 per SD; all p < 0.05). Complex karyotype correlated with higher D-dimer (median 1.39 vs. 0.60 μg/mL, p = 0.022) and fibrinogen (3.91 vs. 2.53 g/L, p = 0.007), while TP53 variant allele frequency (VAF) showed modest positive correlations with D-dimer (ρ = 0.21), INR (ρ = 0.27), and PT (ρ = 0.27; all p < 0.05). Clustering identified three coagulation phenotypes: Silent (51%), Thrombo-inflammatory (31%), and Consumption-like (18%), showing a graded but statistically non-significant gradient in molecular features and a stepwise decline in median OS (14, 10 and 8 months; log-rank p = 0.041). After adjustment for complex karyotype, TP53 VAF, and favorable co-mutation count, the Consumption-like phenotype was associated with a non-significant increased risk (HR 1.83, 95% CI 0.92-3.65, p = 0.084), whereas favorable co-mutation pathways remained independently protective (HR 0.56, 95% CI 0.35-0.90, p = 0.016). CONCLUSION: In TP53-mutated AML/MDS, coagulation activation intensity is associated with the degree of genomic instability. The three phenotypes may add biological resolution beyond classical DIC and cytogenetic risk groups, but represent laboratory patterns rather than validated bleeding or thrombosis prediction tools. However, after accounting for genomic features, phenotypes were not independent predictors of outcome, with complex karyotype, TP53 VAF, and favorable co-mutation count driving prognosis. Because treatment intensity and other clinical confounders were not available, these survival associations are hypothesis-generating. Coagulation profiling remains inexpensive, widely accessible, and offers a practical window into disease biology that warrants prospective validation.

TP53↗

PubMedDate not supplied

Expression of the G2-M checkpoint regulators cyclin B1 and P34CDC2 in breast cancer: a correlation with cellular kinetics.

In this study, the expression of cyclin B1 and p34cdc2 in neoplastic and non-neoplastic breast lesions was evaluated by immunohistochemistry and quantitative analysis in relation to cellular kinetic parameters such as Mitotic Index (MI), Anatelophase Index (ATI), and Apoptotic Index (AI). The percentage of cyclin B1 and p34cdc2-positive cells was significantly higher in neoplastic glands than in their normal counterparts. This finding was paralleled by significantly higher values of MI, ATI, and AI in breast cancer than in normal glands. Furthermore, two groups with different cytokinetic characteristics were identified among infiltrating ductal carcinomas by an unsupervised learning technique of cluster analysis using the percentages of cyclin B1 and p34cdc2 positive cells and the cellular kinetic parameters (MI, ATI and AI) as variables. The final clusters, groups I and II, consisted of 42 and 13 cases respectively. The first cluster (group I) was characterized by a significantly linear correlation between the percentages of cyclin B1 and p34cdc2-positive cells. On the contrary, the second cluster (group II) revealed no correlation between these two proteins and was characterized by values of p34cdc2 largely exceeding those of cyclin B1. A positive correlation between the expression of these two proteins and the cellular kinetic parameters (MI, ATI and AI) was also found in group I but not in group II. These observations suggest that a disturbed nuclear translocation of Mitosis Promoting Factor (MPF) components is present in group II cases, resulting in a defective cellular division cycle. In fact, group I cases showed lymph node metastasis more frequently than group II cases. Our results suggest that the analysis of the cell cycle "machinery" components, such as the cyclins and their dependent kinases, can identify tumors with different levels of aggressiveness.

Adult↗

PubMed2005

Computational cluster validation in post-genomic data analysis.

MOTIVATION: The discovery of novel biological knowledge from the ab initio analysis of post-genomic data relies upon the use of unsupervised processing methods, in particular clustering techniques. Much recent research in bioinformatics has therefore been focused on the transfer of clustering methods introduced in other scientific fields and on the development of novel algorithms specifically designed to tackle the challenges posed by post-genomic data. The partitions returned by a clustering algorithm are commonly validated using visual inspection and concordance with prior biological knowledge--whether the clusters actually correspond to the real structure in the data is somewhat less frequently considered. Suitable computational cluster validation techniques are available in the general data-mining literature, but have been given only a fraction of the same attention in bioinformatics. RESULTS: This review paper aims to familiarize the reader with the battery of techniques available for the validation of clustering results, with a particular focus on their application to post-genomic data analysis. Synthetic and real biological datasets are used to demonstrate the benefits, and also some of the perils, of analytical clustervalidation. AVAILABILITY: The software used in the experiments is available at http://dbkweb.ch.umist.ac.uk/handl/clustervalidation/. SUPPLEMENTARY INFORMATION: Enlarged colour plots are provided in the Supplementary Material, which is available at http://dbkweb.ch.umist.ac.uk/handl/clustervalidation/.

Algorithms↗

PubMed2000

Mapping and fuzzy classification of macromolecular images using self-organizing neural networks.

In this work the effectiveness of the fuzzy kohonen clustering network (FKCN) in the unsupervised classification of electron microscopic images of biological macromolecules is studied. The algorithm combines Kohonen's self-organizing feature maps (SOFM) and Fuzzy c-means (FCM) in order to obtain a powerful clustering technique with the best properties inherited from both. Exploratory data analysis using SOFM is also presented as a step previous to final clustering. Two different data sets obtained from the G40P helicase from B. Subtilis bacteriophage SPP1 have been used for testing the proposed method, one composed of 2458 rotational power spectra of individual images and the other composed by 338 images from the same macromolecule. Results of FKCN are compared with self-organizing feature maps (SOFM) and manual classification. Experimental results prove that this new technique is suitable for working with large, high-dimensional and noisy data sets and, thus, it is proposed to be used as a classification tool in electron microscopy.

Algorithms↗

PubMed2004

Clustered components analysis for functional MRI.

A common method of increasing hemodynamic response (SNR) in functional magnetic resonance imaging (fMRI) is to average signal timecourses across voxels. This technique is potentially problematic because the hemodynamic response may vary across the brain. Such averaging may destroy significant features in the temporal evolution of the fMRI response that stem from either differences in vascular coupling to neural tissue or actual differences in the neural response between two averaged voxels. Two novel techniques are presented in this paper in order to aid in an improved SNR estimate of the hemodynamic response while preserving statistically significant voxel-wise differences. The first technique is signal subspace estimation for periodic stimulus paradigms that involves a simple thresholding method. This increases SNR via dimensionality reduction. The second technique that we call clustered components analysis is a novel amplitude-independent clustering method based upon an explicit statistical data model. It includes an unsupervised method for estimating the number of clusters. Our methods are applied to simulated data for verification and comparison to other techniques. A human experiment was also designed to stimulate different functional cortices. Our methods separated hemodynamic response signals into clusters that tended to be classified according to tissue characteristics.

Algorithms↗

PubMed2006

Use of unsupervised and supervised artificial neural networks for the identification of lactic acid bacteria on the basis of SDS-PAGE patterns of whole cell proteins.

Conventional multivariate statistical techniques (hierarchical cluster analysis, linear discriminant analysis) and unsupervised (Kohonen Self Organizing Map) and supervised (Bayesian network) artificial neural networks were compared for as tools for the classification and identification of 352 SDS-PAGE patterns of whole cell proteins of lactic acid bacteria belonging to 22 species of the genera Lactobacillus, Leuconostoc, Enterococcus, Lactococcus and Streptococcus including 47 reference strains. Electrophoretic data were pre-treated using the logistic weighting function described by Piraino et al. [Piraino, P., Ricciardi, A., Lanorte, M. T., Malkhazova, I., Parente, E., 2002. A new procedure for data reduction in electrophoretic fingerprints of whole-cell proteins. Biotechnol. Lett. 24, 1477-1482]. Hierarchical cluster analysis provided a satisfactory classification of the patterns but was unable to discriminate some species (Leuconostoc, Lb. sakei/Lb. curvatus, Lb. acidophilus/Lb. helveticus, Lb. plantarum/Lb. paraplantarum, Lc. lactis/Lc. raffinolactis). A 7x7 Kohonen self-organizing map (KSOM), trained with the patterns of the reference strains, provided a satisfactory classification of the patterns and was able to discriminate more species than hierarchical cluster analysis. The map was used in predictive mode to identify unknown strains and provided results which in 85.5% of cases matched the classification obtained by hierarchical cluster analysis. Two supervised tools, linear discriminant analysis and a 23:5:2 Bayesian network were proven to be highly effective in the discrimination of SDS-PAGE patterns of Lc. lactis from those of other species. We conclude that data reduction by logistic weighting coupled to traditional multivariate statistical analysis or artificial neural networks provide an effective tool for the classification and identification of lactic acid bacteria on the basis of SDS-PAGE patterns of whole cell proteins.

Bacterial Proteins↗

PubMed2004

A competitive wavelet network for signal clustering.

This correspondence proposes a novel signal clustering method based on the unsupervised training of a wavelet network. The synaptic weights are parameterized by wavelet basis functions, which are adjusted by a competitive algorithm that makes use of the neighborhood concept proposed by Kohonen. The robustness of the wavelet network with respect to noise is illustrated in a simulated problem, in which dynamic systems are grouped on the basis of their step responses. An example involving clustering of electrocardiographic signals taken from the MIT-BIH database is also presented. In this case, the ability of the proposed network to perform clustering at successive resolution levels is illustrated. The possibility of interpreting the information encoded in the network at the end of training is also discussed.

Letter↗

PubMed2003

Classification of human astrocytic gliomas on the basis of gene expression: a correlated group of genes with angiogenic activity emerges as a strong predictor of subtypes.

The development of targeted treatment strategies adapted to individual patients requires identification of the different tumor classes according to their biology and prognosis. We focus here on the molecular aspects underlying these differences, in terms of sets of genes that control pathogenesis of the different subtypes of astrocytic glioma. By performing cDNA-array analysis of 53 patient biopsies, comprising low-grade astrocytoma, secondary glioblastoma (respective recurrent high-grade tumors), and newly diagnosed primary glioblastoma, we demonstrate that human gliomas can be differentiated according to their gene expression. We found that low-grade astrocytoma have the most specific and similar expression profiles, whereas primary glioblastoma exhibit much larger variation between tumors. Secondary glioblastoma display features of both other groups. We identified several sets of genes with relatively highly correlated expression within groups that: (a). can be associated with specific biological functions; and (b). effectively differentiate tumor class. One prominent gene cluster discriminating primary versus nonprimary glioblastoma comprises mostly genes involved in angiogenesis, including VEGF fms-related tyrosine kinase 1 but also IGFBP2, that has not yet been directly linked to angiogenesis. In situ hybridization demonstrating coexpression of IGFBP2 and VEGF in pseudopalisading cells surrounding tumor necrosis provided further evidence for a possible involvement of IGFBP2 in angiogenesis. The separating groups of genes were found by the unsupervised coupled two-way clustering method, and their classification power was validated by a supervised construction of a nearly perfect glioma classifier.

Adolescent↗

PubMed2026

Molecular subgroups of human malignant peripheral nerve sheath tumors are conserved in canines.

Malignant peripheral nerve sheath tumors (MPNST) are aggressive sarcomas of Schwann cell lineage with poor prognosis in both humans and dogs. While rare in humans, MPNSTs occur more frequently in dogs and share histomorphological and clinical features. Recent methylome and transcriptome analyses have identified two molecular subgroups of human MPNST with distinct oncogenic signaling pathways and prognostic implications; however, it remains unclear if these subgroups also exist in canines. Given their higher incidence and biological similarities to human disease, canine MPNSTs represent a promising comparative model to investigate molecular subtypes and evaluate novel therapeutic strategies. To characterize canine MPNST and assess molecular parallels with the human subgroups, we applied laser-capture microdissection (LCM) followed by RNAsequencing to analyze tumor tissue from 20 canine MPNST. Principle component and differential gene expression analyses identified two clearly distinct transcriptional clusters corresponding to spindle cell and epithelioid MPNST variants, respectively. Unsupervised cross-species comparison aligned the two canine clusters with the human G1 and G2 subgroups. Accordingly, one cluster was characterized by SHH pathway activation and increased cell cycle activity, while the other showed non-canonical WNT pathway, Schwann cell-like features and marked macrophage infiltration. Immunohistochemistry further demonstrated loss of H3K27me3, p-ERK activation and β-catenin signaling by IHC in a subset of tumors. These findings support the value of canine MPNST as clinically amenable model for structured assessment of novel therapeutic approaches to benefit patients of both species.

Canine cancer model↗

PubMed2005

A new algorithm for comparing and visualizing relationships between hierarchical and flat gene expression data clusterings.

MOTIVATION: Clustering is one of the most widely used methods in unsupervised gene expression data analysis. The use of different clustering algorithms or different parameters often produces rather different results on the same data. Biological interpretation of multiple clustering results requires understanding how different clusters relate to each other. It is particularly non-trivial to compare the results of a hierarchical and a flat, e.g. k-means, clustering. RESULTS: We present a new method for comparing and visualizing relationships between different clustering results, either flat versus flat, or flat versus hierarchical. When comparing a flat clustering to a hierarchical clustering, the algorithm cuts different branches in the hierarchical tree at different levels to optimize the correspondence between the clusters. The optimization function is based on graph layout aesthetics or on mutual information. The clusters are displayed using a bipartite graph where the edges are weighted proportionally to the number of common elements in the respective clusters and the weighted number of crossings is minimized. The performance of the algorithm is tested using simulated and real gene expression data. The algorithm is implemented in the online gene expression data analysis tool Expression Profiler. AVAILABILITY: http://www.ebi.ac.uk/expressionprofiler

Algorithms↗

PubMed1997

Unsupervised classification of EEG from subdural seizure recordings.

Whereas the visual EEG-inspection of epileptic seizures draws the attention to the waxing and waning of specific graphoelements in multi-channel recordings, the domain of computerized EEG-analysis for epilepsy diagnosis is detection of transients (i.e., spikes) and the quantification of background activity (i.e., mapping procedures). We present an approach to identify relatively fast changes of background activity by use of an automatic classifier. This algorithm is independent of the occurrence of any specific single type of graphoelement. The EEG is segmentated into short epochs of 0.64 sec duration each. For every segment a set of parameters (Hjorth, spectral power in classical frequency bands) is extracted, which taken together build elements of a vector-space. The elements are clustered in an automatic and unsupervised manner by use of a cosine-classifier, such that every EEG-epoch belongs to one class. Changes of brain activity as seen with the EEG are marked by transitions from one class to another. The class occurrence density is defined as the number of different classes that occur within a pre-defined number of EEG-epochs. It gives a new measure of variability of the EEG-signal. Comparing the epochs when class transitions take place in different channels, the class transitions coincidence between two channels is a measure of functional coupling of brain areas.

Adult↗

PubMed2006

cDNA microarray analysis of non-selected cases of acute myeloid leukemia demonstrates distinct clustering independent of cytogenetic aberrations and consistent with morphological signs of differentiation.

Acute myeloid leukemia (AML) is a heterogeneous disease with respect to biology and clinical course. Until now the basis for prognostic evaluation and therapeutic decision has been the karyotype, genetic FLT3 abnormalities and the initial chemotherapy response. A question that has emerged is if extensive gene expression analysis may supplement or partly replace current diagnostics. In an attempt to address this question, we performed cDNA microarray analysis on peripheral blood samples of 25 patients with newly diagnosed AML with high blast counts. The patients were randomly selected from a large group of consecutive patients. Leave-one-out crossvalidation (LOOCV) showed with high accuracy that gene expression classifiers could predict if leukaemia samples belonged to the FAB AML-M1 or to the FAB AML-M2 groups. An unsupervised two-dimensional hierarchical cluster analysis generated 3 patient subgroups. Except for an accumulation of samples classified as FAB M1 and M2 in cluster 3, there was no evident relationship between the clusters and the FAB classification. Each subgroup displayed clearly distinguished gene expression patterns validated using real-time quantitative PCR analysis. The identification of specific gene expressions that together constitute regulatory modules must complement cluster analyses in order to achieve an accurate basis for prognosis and prediction.

Adult↗

PubMed2004

Evaluation and optimization of clustering in gene expression data analysis.

MOTIVATION: A measurement of cluster quality is needed to choose potential clusters of genes that contain biologically relevant patterns of gene expression. This is strongly desirable when a large number of gene expression profiles have to be analyzed and proper clusters of genes need to be identified for further analysis, such as the search for meaningful patterns, identification of gene functions or gene response analysis. RESULTS: We propose a new cluster quality method, called stability, by which unsupervised learning of gene expression data can be performed efficiently. The method takes into account a cluster's stability on partition. We evaluate this method and demonstrate its performance using four independent, real gene expression and three simulated datasets. We demonstrate that our method outperforms other techniques listed in the literature. The method has applications in evaluating clustering validity as well as identifying stable clusters. AVAILABILITY: Please contact the first author.

Algorithms↗

PubMed2002

Exploring regions of interest with cluster analysis (EROICA) using a spectral peak statistic for selecting and testing the significance of fMRI activation time-series.

Much relevant information about activations and artifacts in a functional magnetic resonance imaging (fMRI) dataset can be obtained from an exploratory cluster analysis. In contrast to testing the significance of the measured experimental effect for a given model, unsupervised pattern recognition techniques, such as fuzzy clustering, often find unexpected behavior in addition to expected activations, allowing the exploitation of this element of surprise. The many artifact clusters often discovered might aid the experimenter in deciding whether the dataset is usable, whether some additional preprocessing step is required, or whether the one used has introduced spurious effects. However, clustering alone does not complete the analysis because the membership values that are generated are not indicative of the level of statistical significance with respect to the cluster activation patterns (centroids). This is of particular importance for fMRI datasets for which most time-series are "noise", with no activation patterns. We propose that an initial partition step should precede the clustering step. Only time-series that meet a certain statistical criterion (using a scaled version of Fisher's g-order statistic) are selected for clustering; this typically represents <5% of the whole brain region. The purpose of clustering is to generate a set of cluster centers that are the possible activation patterns; these are used in forming a linear model of all the time-series. The model parameter is tested for significance in both the time and frequency domains. We present a novel method of conducting these tests, which limits the number of false positives. We call the three-step process of initial partition, clustering and the two-domain significance test as exploring regions of interest with cluster analysis (EROICA).

Brain↗

PubMed2026

The machine-learning classifier ALLCatchR2 identifies 20 T-ALL subtypes across cohorts and age groups.

T-cell acute lymphoblastic leukemia (T-ALL) comprises molecularly diverse subtypes, but robust cross-cohort validations and operational gene-expression definitions are lacking. To establish a gene-expression-anchored framework for T-ALL subtyping, we aggregated 2314 transcriptomes (15 cohorts, age: 0.8-90.8 years). An extended unsupervised approach defined 17 main clusters and 3 subclusters in samples with high blast fractions. Supervised analyses added an overarching immature T-ALL (early T cell precursor [ETP]-like) definition and resolved the LMO2 γδ-like subtype. All clusters contained samples from at least two cohorts. Characteristic genomic driver enrichments were consistent across cohorts, while gene-expression clusters did not correspond exclusively to single driver events but also reflected developmental origins. A machine-learning classifier based on ALLCatchR, our B-cell acute lymphoblastic leukemia (B-ALL) classifier, identified these 20 transcriptomic subtypes and the immature T-ALL (ETP-like) signature with 0.995-1.0 accuracy in a validation set (n = 203). Testing the classifier on a second hold-out data set (n = 265 samples) showed that 92.7% of predictions matched with corresponding driver alterations. Across all samples, 83.2% of cases received high-confidence predictions, 7.3% candidate predictions, and 9.5% remained unclassified, largely because of low blast fractions. We identified a novel gene-expression cluster markedly enriched (P < 0.001) for clonal hematopoiesis mutations (IDH2 R140Q, DNMT3A) and a stem-/progenitor cell-like gene expression. This novel clonal hematopoiesis-related T-ALL subtype was observed in six cohorts and accounted for 8.9% of adults and 39.5% of patients aged >50 years. We extended ALLCatchR into ALLCatchR2, a free R package that now enables B-/T-lineage separation, gene-expression subtyping, blast estimation, and developmental annotation to harmonize T-ALL classification across studies and clinical contexts.

Journal Article↗

PubMed2005

Kinase substrate protein microarray analysis of human colon cancer and hepatic metastasis.

BACKGROUND: Liver metastases represent the major determinant of survival in patients with colorectal cancer (CRC). In cases with unresectable liver disease, more effective agents are needed, since chemotherapy achieves median survival of only 15 months. Protein kinases coordinate complex functions that are often disregulated in cancer and are therefore considered important targets for molecular therapeutics. In this study, we investigated the phosphoproteomic status of different protein kinases in primary CRC and in liver metastases. METHODS: The status of 29 key endpoints was evaluated using reverse phase protein array on laser capture microdissected neoplastic cells from five primary CRCs without metastases, three patient-matched primary CRCs and synchronous liver metastases and five CRC metachronous liver metastases. RESULTS: Unsupervised hierarchical two-way clustering analysis showed an entirely different phosphoproteomic profile in primary CRCs compared to liver metastases. This difference was observed also in primary and metastatic patient-matched lesions. CONCLUSIONS: Our findings of different signaling pathways between primary and metastatic CRC suggest a possible microenvironment effect, and emphasize the need to perform molecular network analysis of metastatic tissue when molecular targeting is considered.

Cluster Analysis↗