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PubMed2005

Neuropeptide and calcium-binding protein gene expression profiles predict neuronal anatomical type in the juvenile rat.

Neocortical neurones can be classified according to several independent criteria: morphological, physiological, and molecular expression (neuropeptides (NPs) and/or calcium-binding proteins (CaBPs)). While it has been suggested that particular NPs and CaBPs characterize certain anatomical subtypes of neurones, there is also considerable overlap in their expression, and little is known about simultaneous expression of multiple NPs and CaBPs in morphologically characterized neocortical neurones. Here we determined the gene expression profiles of calbindin (CB), parvalbumin (PV), calretinin (CR), neuropeptide Y (NPY), vasoactive intestinal peptide (VIP), somatostatin (SOM) and cholecystokinin (CCK) in 268 morphologically identified neurones located in layers 2-6 in the juvenile rat somatosensory neocortex. We used patch-clamp electrodes to label neurones with biocytin and harvest the cytoplasm to perform single-cell RT-multiplex PCR. Quality threshold clustering, an unsupervised algorithm that clustered neurones according to their entire profile of expressed genes, revealed seven distinct clusters. Surprisingly, each cluster preferentially contained one anatomical class. Artificial neural networks using softmax regression predicted anatomical types at nearly optimal statistical levels. Classification tree-splitting (CART), a simple binary neuropeptide decision tree algorithm, revealed the manner in which expression of the multiple mRNAs relates to different anatomical classes. Pruning the CART tree revealed the key predictors of anatomical class (in order of importance: SOM, PV, VIP, and NPY). We reveal here, for the first time, a strong relationship between specific combinations of NP and CaBP gene expressions and the anatomical class of neocortical neurones.

Animals↗
PubMed2001

Resampling method for unsupervised estimation of cluster validity.

We introduce a method for validation of results obtained by clustering analysis of data. The method is based on resampling the available data. A figure of merit that measures the stability of clustering solutions against resampling is introduced. Clusters that are stable against resampling give rise to local maxima of this figure of merit. This is presented first for a one-dimensional data set, for which an analytic approximation for the figure of merit is derived and compared with numerical measurements. Next, the applicability of the method is demonstrated for higher-dimensional data, including gene microarray expression data.

Cluster Analysis↗
PubMed2006

Unsupervised image-set clustering using an information theoretic framework.

In this paper, we combine discrete and continuous image models with information-theoretic-based criteria for unsupervised hierarchical image-set clustering. The continuous image modeling is based on mixture of Gaussian densities. The unsupervised image-set clustering is based on a generalized version of a recently introduced information-theoretic principle, the information bottleneck principle. Images are clustered such that the mutual information between the clusters and the image content is maximally preserved. Experimental results demonstrate the performance of the proposed framework for image clustering on a large image set. Information theoretic tools are used to evaluate cluster quality. Particular emphasis is placed on the application of the clustering for efficient image search and retrieval.

Algorithms↗
PubMed2004

Hierarchical clustering analysis of tissue microarray immunostaining data identifies prognostically significant groups of breast carcinoma.

Prognostically relevant cluster groups, based on gene expression profiles, have been recently identified for breast cancers, lung cancers, and lymphoma. Our aim was to determine whether hierarchical clustering analysis of multiple immunomarkers (protein expression profiles) improves prognostication in patients with invasive breast cancer. A cohort of 438 sequential cases of invasive breast cancer with median follow-up of 15.4 years was selected for tissue microarray construction. A total of 31 biomarkers were tested by immunohistochemistry on these tissue arrays. The prognostic significance of individual markers was assessed by using Kaplan-Meier survival estimates and log-rank tests. Seventeen of 31 markers showed prognostic significance in univariate analysis (P < or = 0.05) and 4 markers showed a trend toward significance (P < or = 0.2). Unsupervised hierarchical clustering analysis was done by using these 21 immunomarkers, and this resulted in identification of three cluster groups with significant differences in clinical outcome. chi2 analysis showed that expression of 11 markers significantly correlated with membership in one of the three cluster groups. Unsupervised hierarchical clustering analysis with this set of 11 markers reproduced the same three prognostically significant cluster groups identified by using the larger set of markers. These cluster groups were of prognostic significance independent of lymph node metastasis, tumor size, and tumor grade in multivariate analysis (P=0.0001). The cluster groups were as powerful a prognostic indicator as lymph node status. This work demonstrates that hierarchical clustering of immunostaining data by using multiple markers can group breast cancers into classes with clinical relevance and is superior to the use of individual prognostic markers.

Adult↗
PubMed2005

Gene expression profiling allows distinction between primary and metastatic squamous cell carcinomas in the lung.

Lung neoplasms commonly develop in patients previously treated for head and neck carcinomas. The derivation of these tumors, either as new primary lung cancers or as metastatic head and neck cancers, is difficult to establish based on clinical or histopathologic criteria since both are squamous cell carcinomas and have identical features under light microscopy. However, this distinction has significant treatment and prognostic implications. Gene expression profiling was performed on a panel of 52 sequentially collected patients with either primary lung (n = 21) or primary head and neck (n = 31) carcinomas using the Affymetrix HG_U95Av2 high-density oligonucleotide microarray. Unsupervised hierarchical clustering with Ward linkage and the Pearson correlation metric was performed. To assess robustness, bootstrap resampling was performed with 1,000 iterations. A t test of the normalized values for each gene was used to determine the genes responsible for segregating head and neck from lung primary carcinomas, and those with the most differential expression were used for later analyses. In the absence of a large "test" set of tumors, we used a supervised leave-one-out cross-validation to test how well we could predict the tumor origin. Once a gene expression profile was established, 12 lung lesions taken from patients with previously treated head and neck cancers were similarly analyzed by gene expression profiling to determine their sites of origin. Unsupervised clustering analysis separated the study cohort into two distinct groups which reliably remained segregated with bootstrap resampling. Group 1 consisted of 30 tongue carcinomas. Group 2 consisted of 21 lung cancers and 1 tongue carcinoma. The clustering was not changed even when normal lung or tongue profiles were subtracted from the corresponding carcinomatous lesions, and a leave-one-out cross-validation showed a 98% correct prediction (see Supplementary Data 1). A minimum set of 500 genes required to distinguish these groups was established. Given the ability to segregate these lesions using molecular profiling, we analyzed the lung tumors of undetermined origin. All cases clearly clustered with either lung or tongue tumor subsets, strongly supporting our hypothesis that this technique could elucidate the tissue of origin of metastatic lesions. Although histologically similar, squamous cell carcinomas have distinct gene expression profiles based on their anatomic sites of origin. Accordingly, the application of gene expression profiling may be useful in identifying the derivation of lung nodules and consequently enhances treatment planning.

Algorithms↗
PubMed2002

An unsupervised self-optimizing gene clustering algorithm.

We have devised a gene-clustering algorithm that is completely unsupervised in that no parameters need be set by the user, and the clustering of genes is self-optimizing to yield the set of clusters that minimizes within-cluster distance and maximizes between-cluster distance. This algorithm was implemented in Java, and tested on a randomly selected 200-gene subset of 3000 genes from cell-cycle data in S. cerevisiae. AlignACE was used to evaluate the resulting optimized cluster set for upstream cis-regulons. The optimized cluster set was found to be of comparable quality to cluster sets obtained by two established methods (complete linkage and k-means), even when provided with only a small, randomly selected subset of the data (200 vs 3000 genes), and with absolutely no supervision. MAP and specificity scores of the highest ranking motifs identified in the largest clusters were comparable.

Algorithms↗
PubMed2005

Expression profiling of serous low malignant potential, low-grade, and high-grade tumors of the ovary.

Papillary serous low malignant potential (LMP) tumors are characterized by malignant features and metastatic potential yet display a benign clinical course. The role of LMP tumors in the development of invasive epithelial cancer of the ovary is not clearly defined. The aim of this study is to determine the relationships among LMP tumors and invasive ovarian cancers and identify genes contributing to their phenotypes. Affymetrix U133 Plus 2.0 microarrays (Santa Clara, CA) were used to interrogate 80 microdissected serous LMP tumors and invasive ovarian malignancies along with 10 ovarian surface epithelium (OSE) brushings. Gene expression profiles for each tumor class were used to complete unsupervised hierarchical clustering analyses and identify differentially expressed genes contributing to these associations. Unsupervised hierarchical clustering analysis revealed a distinct separation between clusters containing borderline and high-grade lesions. The majority of low-grade tumors clustered with LMP tumors. Comparing OSE with high-grade and LMP expression profiles revealed enhanced expression of genes linked to cell proliferation, chromosomal instability, and epigenetic silencing in high-grade cancers, whereas LMP tumors displayed activated p53 signaling. The expression profiles of LMP, low-grade, and high-grade papillary serous ovarian carcinomas suggest that LMP tumors are distinct from high-grade cancers; however, they are remarkably similar to low-grade cancers. Prominent expression of p53 pathway members may play an important role in the LMP tumor phenotype.

Carcinoma, Papillary↗
PubMed2005

Identification of cell-of-origin breast tumor subtypes in inflammatory breast cancer by gene expression profiling.

Inflammatory breast cancer (IBC) is an aggressive form of locally advanced breast cancer with high metastatic potential. Most patients have lymph node involvement at the time of diagnosis and 1/3 of the patients have distant metastases. In a previous study, we demonstrated that IBC is a distinct form of breast cancer in comparison with non-IBC. The aim of this study was to investigate the presence of the different molecular subtypes in our data set of 16 IBC and 18 non-IBC specimen. Therefore, we selected an 'intrinsic gene set' of 144 genes, present on our cDNA chips and common to the 'intrinsic gene set' described by Sorlie et al. [PNAS, 2003]. This set of genes was tested for performance in the Norway/Stanford data set by unsupervised hierarchical clustering. Expression centroids were then calculated for the core members of each of the five subclasses in the Norway/Stanford data set and used to classify our own specimens by calculating Spearman correlations between each sample and each centroid. We identified the same cell-of-origin subtypes in IBC as those already described in non-IBC. The classification was in good agreement with immunohistochemical data for estrogen receptor protein expression and cytokeratin 5/6 protein expression. Confirmation was done by an alternative unsupervised hierarchical clustering method. The robustness of this classification was assessed by an unsupervised hierarchical clustering with an alternative gene set of 141 genes related to the cell-of-origin subtypes, selected using a discriminating score and iterative random permutation testing. The contribution of the different cell-of-origin subtypes to the IBC phenotype was investigated by principal component analysis. Generally, the combined ErbB2-overexpressing and basal-like cluster was more expressed in IBC compared to non-IBC, whereas the combined luminal A, luminal B and normal-like cluster was more pronounced in non-IBC compared to IBC. The presence of the same molecular cell-of-origin subtypes in IBC as in non-IBC does not exclude the specific molecular nature of IBC, since gene lists that characterize IBC and non-IBC are entirely different from gene lists that define the different cell-of-origin subtypes, as evidenced by principal component analysis.

Adenocarcinoma↗
PubMed2004

A functional hierarchical organization of the protein sequence space.

BACKGROUND: It is a major challenge of computational biology to provide a comprehensive functional classification of all known proteins. Most existing methods seek recurrent patterns in known proteins based on manually-validated alignments of known protein families. Such methods can achieve high sensitivity, but are limited by the necessary manual labor. This makes our current view of the protein world incomplete and biased. This paper concerns ProtoNet, a automatic unsupervised global clustering system that generates a hierarchical tree of over 1,000,000 proteins, based solely on sequence similarity. RESULTS: In this paper we show that ProtoNet correctly captures functional and structural aspects of the protein world. Furthermore, a novel feature is an automatic procedure that reduces the tree to 12% its original size. This procedure utilizes only parameters intrinsic to the clustering process. Despite the substantial reduction in size, the system's predictive power concerning biological functions is hardly affected. We then carry out an automatic comparison with existing functional protein annotations. Consequently, 78% of the clusters in the compressed tree (5,300 clusters) get assigned a biological function with a high confidence. The clustering and compression processes are unsupervised, and robust. CONCLUSIONS: We present an automatically generated unbiased method that provides a hierarchical classification of all currently known proteins.

Algorithms↗
PubMed2005

Expression analysis of juvenile pilocytic astrocytomas by oligonucleotide microarray reveals two potential subgroups.

Juvenile pilocytic astrocytoma (JPA) is one of the most common brain tumors in children. The expression profiles of 21 JPAs, determined using Affymetrix GeneChip U133A, were compared with subjects with normal cerebella. The genes involved in neurogenesis, cell adhesion, synaptic transmission, central nervous system development, potassium ion transport, protein dephosphorylation, and cell differentiation were found to be significantly deregulated in JPA. These 21 JPAs were further clustered into two major groups by unsupervised hierarchical clustering using a set of 848 genes with high covariance (0.5-10). Supervised analysis with Significance Analysis of Microarrays software between these two potential subgroups identified a list of significant differentially expressed genes involved in cell adhesion, regulation of cell growth, cell motility, nerve ensheathment, and angiogenesis. Immunostaining of myelin basic protein on paraffin sections derived from 18 incompletely resected JPAs suggests that JPA without myelin basic protein-positively stained tumor cells may have a higher tendency to progress.

Adolescent↗
PubMed2004

Two prognostically significant subtypes of high-grade lung neuroendocrine tumours independent of small-cell and large-cell neuroendocrine carcinomas identified by gene expression profiles.

BACKGROUND: Classification of high-grade neuroendocrine tumours (HGNT) of the lung currently recognises large-cell neuroendocrine carcinoma (LCNEC) and small-cell lung carcinoma (SCLC) as distinct groups. However, a similarity in histology for these two carcinomas and uncertain clinical course have led to suggestions that a single HGNT classification would be more appropriate. Gene expression profiling, which can reproduce histopathological classification, and often defines new subclasses with prognostic significance, can be used to resolve HGNT classification. METHODS: We used cDNA microarrays with 40?386 elements to analyse the gene expression profiles of 38 surgically resected samples of lung neuroendocrine tumours and 11 SCLC cell lines. Samples of large-cell carcinoma, adenocarcinoma, and normal lung were also included to give a total of 105 samples analysed. The data were subjected to filtering to yield informative genes before unsupervised hierarchical clustering that identified relatedness of tumour samples. FINDINGS: Distinct groups for carcinoids, large-cell carcinoma, adenocarcinoma, and normal lung were readily identified. However, we were unable to distinguish LCNEC from SCLC by gene expression profiling. Three independent rounds of unsupervised hierarchical clustering consistently divided SCLC samples into two main groups with LCNEC samples largely integrated with these groups. Furthermore, patients in one of the groups identified by clustering had a significantly better clinical outcome than the other (83% vs 12% survived for 5 years; p=0.0094. None of the highly proliferative SCLC cell lines subsequently analysed clustered with this good-prognosis group. INTERPRETATION: Our findings show that HGNT of the lung can be classified into two groups independent of SCLC and LCNEC. To this end, we have identified many genes, some of which encode well-characterised markers of cancer that distinguish the HGNT groups. These results have implications for the diagnosis, classification, and treatment of lung neuroendocrine tumours, and provide important insights into their underlying biology.

Biomarkers, Tumor↗
PubMed2004

Assessment of clonal relationships in ipsilateral and bilateral multiple breast carcinomas by comparative genomic hybridisation and hierarchical clustering analysis.

The issue of whether multiple, ipsilateral or bilateral, breast carcinomas represent multiple primary tumours or dissemination of a single carcinomatous process has been difficult to resolve, especially for individual patients. We have addressed the problem by comparative genomic hybridisation analysis of 26 tumours from 12 breast cancer patients with multiple ipsilateral and/or bilateral carcinoma lesions. Genomic imbalances were detected in 25 of the 26 (96%) tumours. Using the genomic imbalances detected in these 26 lesions as well as those previously found by us in an independent series of 35 unifocal breast carcinomas, we compared a probabilistic model for likelihood of independence with unsupervised hierarchical clustering methodologies to determine the clonal relatedness of multiple tumours in breast cancer patients. We conclude that CGH analysis of multiple breast carcinomas followed by unsupervised hierarchical clustering of the genomic imbalances is more reliable than previous criteria to determine the tumours' clonal relationship in individual patients, that most ipsilateral breast carcinomas arise through intramammary spreading of a single breast cancer, and that most patients with bilateral breast carcinomas have two different diseases.

Adult↗
PubMed2004

Common denominator genes that distinguish colorectal carcinoma from normal mucosa.

PURPOSE: Microarray technology has been used by a growing number of investigators and several studies have been published that list hundreds of genes differentially expressed by colorectal carcinoma (CRC) and normal mucosa (MC). On the basis of our own and other investigators' microarray data, our goal was to identify a common denominator gene cluster distinguishing CRC from MC. METHODS: Thirty GeneChips (HG-U133A, Affymetrix) were hybridized, 20 with RNA of CRC stages I-IV (UICC) and 10 with MC. Expression signals showing at least a 4-fold difference between CRC and MC (p<0.01) were identified as differentially expressed. In addition, in our integrative data analysis approach only those genes whose expression was altered simultaneously in at least 2 of 5 recently published studies were subjected to an unsupervised hierarchical cluster analysis. RESULTS: We detected 168 up- and 283 down-regulated genes in CRC relative to MC. Twenty-three genes were filtered from the five articles reviewed. An unsupervised hierarchical cluster analysis of these 23 genes confirmed the high specificity of these genes to differentiate between CRC and MC in our microarray data. CONCLUSIONS: Colorectal cancer and mucosa could be clearly separated by 23 genes selected for being differentially expressed more than once in a recent literature review. These genes represent a common denominator gene cluster that can be used to distinguish colorectal MC from CRC.

Carcinoma↗
PubMed2004

Interactively optimizing signal-to-noise ratios in expression profiling: project-specific algorithm selection and detection p-value weighting in Affymetrix microarrays.

MOTIVATION: The most commonly utilized microarrays for mRNA profiling (Affymetrix) include 'probe sets' of a series of perfect match and mismatch probes (typically 22 oligonucleotides per probe set). There are an increasing number of reported 'probe set algorithms' that differ in their interpretation of a probe set to derive a single normalized 'signal' representative of expression of each mRNA. These algorithms are known to differ in accuracy and sensitivity, and optimization has been done using a small set of standardized control microarray data. We hypothesized that different mRNA profiling projects have varying sources and degrees of confounding noise, and that these should alter the choice of a specific probe set algorithm. Also, we hypothesized that use of the Microarray Suite (MAS) 5.0 probe set detection p-value as a weighting function would improve the performance of all probe set algorithms. RESULTS: We built an interactive visual analysis software tool (HCE2W) to test and define parameters in Affymetrix analyses that optimize the ratio of signal (desired biological variable) versus noise (confounding uncontrolled variables). Five probe set algorithms were studied with and without statistical weighting of probe sets using the MAS 5.0 probe set detection p-values. The signal-to-noise ratio optimization method was tested in two large novel microarray datasets with different levels of confounding noise, a 105 sample U133A human muscle biopsy dataset (11 groups: mutation-defined, extensive noise), and a 40 sample U74A inbred mouse lung dataset (8 groups: little noise). Performance was measured by the ability of the specific probe set algorithm, with and without detection p-value weighting, to cluster samples into the appropriate biological groups (unsupervised agglomerative clustering with F-measure values). Of the total random sampling analyses, 50% showed a highly statistically significant difference between probe set algorithms by ANOVA [F(4,10) > 14, p < 0.0001], with weighting by MAS 5.0 detection p-value showing significance in the mouse data by ANOVA [F(1,10) > 9, p < 0.013] and paired t-test [t(9) = -3.675, p = 0.005]. Probe set detection p-value weighting had the greatest positive effect on performance of dChip difference model, ProbeProfiler and RMA algorithms. Importantly, probe set algorithms did indeed perform differently depending on the specific project, most probably due to the degree of confounding noise. Our data indicate that significantly improved data analysis of mRNA profile projects can be achieved by optimizing the choice of probe set algorithm with the noise levels intrinsic to a project, with dChip difference model with MAS 5.0 detection p-value continuous weighting showing the best overall performance in both projects. Furthermore, both existing and newly developed probe set algorithms should incorporate a detection p-value weighting to improve performance. AVAILABILITY: The Hierarchical Clustering Explorer 2.0 is available at http://www.cs.umd.edu/hcil/hce/ Murine arrays (40 samples) are publicly available at the PEPR resource (http://microarray.cnmcresearch.org/pgadatatable.asp http://pepr.cnmcresearch.org Chen et al., 2004).

Cluster Analysis↗
PubMed2005

Gene expression patterns and profile changes pre- and post-erlotinib treatment in patients with metastatic breast cancer.

PURPOSE: To delineate gene expression patterns and profile changes in metastatic tumor biopsies at baseline and 1 month after treatment with the epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor erlotinib in patients with metastatic breast cancer. EXPERIMENTAL DESIGN: Patients were treated with 150 mg of oral erlotinib daily. Gene expression profiles were measured with Affymetrix U133A GeneChip and immunohistochemistry was used to validate microarray findings. RESULTS: Estrogen receptor (ER) status by immunohistochemistry is nearly coincided with the two major expression clusters determined by expression of genes using unsupervised hierarchical clustering analysis. One of 10 patients had an EGFR-positive tumor detected by both microarray and immunohistochemistry. In this tumor, tissue inhibitor of metalloproteinases-3 and collagen type 1 alpha 2, which are the EGF-down-regulated growth repressors, were significantly increased by erlotinib. Gene changes in EGFR-negative tumors are those of G-protein-linked and cell surface receptor-linked signaling. Gene ontology comparison analysis pretreatment and posttreatment in EGFR-negative tumors revealed biological process categories that have more genes differentially expressed than expected by chance. Among 495 gene ontology categories, the significant differed gene ontology groups include G-protein-coupled receptor protein signaling (34 genes, P = 0.002) and cell surface receptor-linked signal transduction (74 genes, P = 0.007). CONCLUSIONS: ER status reflects the major difference in gene expression pattern in metastatic breast cancer. Erlotinib had effects on genes of EGFR signaling pathway in the EGFR-positive tumor and on gene ontology biological process categories or genes that have function in signal transduction in EGFR-negative tumors.

Biomarkers, Tumor↗
PubMed2005

Using unsupervised learning with independent component analysis to identify patterns of glaucomatous visual field defects.

PURPOSE: Clustering by unsupervised learning with machine learning classifiers was shown to segment clusters of patterns in standard automated perimetry (SAP) for glaucoma in previous publications. In this study, unsupervised learning by independent component analysis decomposed SAP field patterns into axes, and the information represented by these axes was evaluated. METHODS: SAP fields were used that were obtained with the Humphrey Visual Field Analyzer (Carl Zeiss Meditec, Dublin, CA) from 189 normal eyes and 156 eyes with glaucomatous optic neuropathy (GON) determined by masked review with stereoscopic optic disc photographs. The variational Bayesian independent component analysis mixture model (vB-ICA-mm) partitioned the SAP fields into the most informative number of clusters. Simultaneously, the model learned an optimal number of maximally independent axes for each cluster. RESULTS: The most informative number of clusters in the SAP set was two. vB-ICA-mm placed 68.6% of the eyes with GON in a cluster labeled G and 98.4% of the eyes with normal optic discs in a cluster labeled N. Cluster G optimally contained six axes. Post hoc analysis of patterns generated at -1 SD and +2 SD from the cluster G mean on the six axes revealed defects similar to those identified by experts as indicative of glaucoma. SAP fields associated with an axis showed increasing severity, as they were located farther in the positive direction from the cluster G mean. CONCLUSIONS: vB-ICA-mm represented the SAP fields with patterns that were meaningful for glaucoma experts. This process also captured severity in the patterns uncovered. These findings should validate vB-ICA-mm as a data-mining technique for new and unfamiliar complex tests.

Artificial Intelligence↗
PubMed2006

High-resolution genomic profiling reveals association of chromosomal aberrations on 1q and 16p with histologic and genetic subgroups of invasive breast cancer.

PURPOSE: Invasive ductal carcinoma and invasive lobular carcinoma (ILC) represent the major histologic subtypes of invasive breast cancer. They differ with regard to presentation, metastatic spread, and epidemiologic features. To elucidate the genetic basis of these differences, we analyzed copy number imbalances that differentiate the histologic subtypes. EXPERIMENTAL DESIGN: High-resolution genomic profiling of 40 invasive breast cancers using matrix-comparative genomic hybridization with an average resolution of 0.5 Mb was conducted on bacterial artificial chromosome microarrays. The data were subjected to classification and unsupervised hierarchical cluster analyses. Expression of candidate genes was analyzed in tumor samples. RESULTS: The highest discriminating power was achieved when combining the aberration patterns of chromosome arms 1q and 16p, which were significantly more often gained in ILC. These regions were further narrowed down to subregions 1q24.2-25.1, 1q25.3-q31.3, and 16p11.2. Located within the candidate gains on 1q are two genes, FMO2 and PTGS2, known to be overexpressed in ILC relative to invasive ductal carcinoma. Assessment of four candidate genes on 16p11.2 by real-time quantitative PCR revealed significant overexpression of FUS and ITGAX in ILC with 16p copy number gain. Unsupervised hierarchical cluster analysis identified three molecular subgroups that are characterized by different aberration patterns, in particular concerning gain of MYC (8q24) and the identified candidate regions on 1q24.2-25.1, 1q25.3-q31.3, and 16p11.2. These genetic subgroups differed with regard to histology, tumor grading, frequency of alterations, and estrogen receptor expression. CONCLUSIONS: Molecular profiling using bacterial artificial chromosome arrays identified DNA copy number imbalances on 1q and 16p as significant classifiers of histologic and molecular subgroups.

Biomarkers, Tumor↗
PubMed2004

Fourier transform infrared (FTIR) spectral mapping of the cervical transformation zone, and dysplastic squamous epithelium.

OBJECTIVES: This paper is aimed at establishing infrared spectral patterns for the different tissue types found in, and for different stages of disease of squamous cervical epithelium. Methods for the unsupervised distinction of these tissue types are discussed. METHODS: Fourier transform infrared (FTIR) maps of the squamous and glandular cervical epithelium, and of the cervical transformation zone, were obtained and analyzed by multivariate unsupervised hierarchical cluster methods. The resulting clusters are correlated to the corresponding stained histopathological features in the tissue sections. RESULTS: Multivariate statistical analysis of FTIR spectra collected for tissue sections permit an unsupervised method of distinguishing tissue types, and of differentiating between normal and diseased tissue. By analyzing different spectral windows and comparing the results with histology, we found the amide I and II region (1740-1470 cm(-1)) to be very important in correlating anatomical and histopathological features in tissue to spectral clusters. Since an unsupervised, rather than a diagnostic, algorithm was used in these efforts, no statistical analysis of false-positive/false-negative results is reported at this time. CONCLUSIONS: The combination of FTIR micro-spectroscopy and multivariate spectral processing provides important insights into the fundamental spectral signatures of individual cells and consequently shows potential as a diagnostic tool for cervical cancer.

Cell Transformation, Neoplastic↗