Chromosomal aberrations and gene expression profiles in non-small cell lung cancer.

Alterations in genomic content and changes in gene expression levels are central characteristics of tumors and pivotal to the tumorigenic process. We analyzed 23 non-small cell lung cancer (NSCLC) tumors by array comparative genomic hybridization (array CGH). Aberrant regions identified included well-characterized chromosomal aberrations such as amplifications of 3q and 8q and deletions of 3p21.31. Less frequently identified aberrations such as amplifications of 7q22.3-31.31 and 12p11.23-13.2, and previously unidentified aberrations such as deletion of 11q12.3-13.3 were also detected. To enhance our ability to identify key acting genes residing in these regions, we combined array CGH results with gene expression profiling performed on the same tumor samples. We identified a set of genes with concordant changes in DNA copy number and expression levels, i.e. overexpressed genes located in amplified regions and underexpressed genes located in deleted regions. This set included members of the Wnt/beta-catenin pathway, genes involved in DNA replication, and matrix metalloproteases (MMPs). Functional enrichment analysis of the genes both overexpressed and amplified revealed a significant enrichment for DNA replication and repair, and extracellular matrix component gene ontology annotations. We verified the changes in expressions of MCM2, MCM6, RUVBL1, MMP1, MMP12 by real-time quantitative PCR. Our results provide a high resolution map of copy number changes in non-small cell lung cancer. The joint analysis of array CGH and gene expression analysis highlights genes with concordant changes in expression and copy number that may be critical to lung cancer development and progression.

Multiplexing schemes for generic SNP genotyping assays.

A generic genotyping assay utilizes a fixed set of reagents, which is independent of the actual target sample, to determine all present alleles. An example is the interrogation of several amplicons spanning polymorphic sites using an all k-mer array. Due to the high cost associated with a genotyping experiment, it is desirable to design a set of experiments, which maximizes the number of SNPs that can be genotyped in parallel per assay. In this study we investigate algorithmic approaches for optimally multiplexing SNP genotyping using generic assays. We devise a graph theoretic formulation of the problem and use it to derive an approximation algorithm for the problem, and several practical heuristics. We apply our methods to simulated and real data, for evaluating the multiplexing rates afforded by generic techniques. The results on real human data show the practicality of generic approaches for genotyping, allowing, e.g., the genotyping of 5000 SNPs using four all 7-mer arrays.

Analysis of expression patterns: the scope of the problem, the problem of scope.

Studies of the expression patterns of many genes simultaneously lead to the observation that even in closely related pathologies, there are numerous genes that are differentially expressed in consistent patterns correlated to each sample type. The early uses of the enabling technology, microarrays, was focused on gathering mechanistic biological insights. The early findings now pose another clear challenge, finding ways to effectively use this kind of information to develop diagnostics.

On the complexity of positional sequencing by hybridization.

In sequencing by hybridization (SBH), one has to reconstruct a sequence from its l-long substrings. SBH was proposed as an alternative to gel-based DNA sequencing approaches, but in its original form the method is not competitive. Positional SBH (PSBH) is a recently proposed enhancement of SBH in which one has additional information about the possible positions of each substring along the target sequence. We give a linear time algorithm for solving PSBH when each substring has at most two possible positions. On the other hand, we prove that the problem is NP-complete if each substring has at most three possible positions. We also show that PSBH is NP-complete if the set of allowed positions for each substring is an interval of length k and provide a fast algorithm for the latter problem when k is bounded.

Effects of acyclo-retinoic acid and lycopene on activation of the retinoic acid receptor and proliferation of mammary cancer cells.

The biochemical mechanisms underlying the inhibitory effects of lycopene, the main tomato carotenoid, on the growth of cancer cells are largely unknown. It has been hypothesized that lycopene derivatives may act as ligands for a nuclear receptor in analogy to retinoic acid, the hormone derived from beta-carotene. The inhibition of human mammary cancer (MCF-7) cell growth and the transactivation of the retinoic acid receptor (RAR) reporter gene by synthetic acyclo-retinoic acid, the open chain analog of retinoic acid, was compared to the effects of lycopene and retinoic acid in the same systems. Acyclo-retinoic acid activated the DR-5 retinoic acid response element with a approximately 100-fold lower potency than retinoic acid. This effect was independent of cotransfection with the RARalpha receptor. Lycopene exhibited only very modest activity in this system. In contrast to the results from the transactivation studies, acyclo-retinoic acid, retinoic acid, and lycopene inhibited cell growth with a similar potency. Preincubation with each of the three compounds slowed down cell cycle progression from G1 to S phase. In summary, acyclo-retinoic acid inhibited cancer cell growth and interacted with RAR. However, it exhibited low affinity for RAR and a correspondingly low efficacy in activating this receptor, indicating that RAR does not mediate the growth inhibitory effect of the compound. In addition, the concentrations of acyclo-retinoic acid and of lycopene required for inducing inhibition of cell growth were similar, suggesting that acyclo-retinoic acid is unlikely to be the active metabolite of lycopene.

Gene-expression profiles in hereditary breast cancer.

BACKGROUND: Many cases of hereditary breast cancer are due to mutations in either the BRCA1 or the BRCA2 gene. The histopathological changes in these cancers are often characteristic of the mutant gene. We hypothesized that the genes expressed by these two types of tumors are also distinctive, perhaps allowing us to identify cases of hereditary breast cancer on the basis of gene-expression profiles. METHODS: RNA from samples of primary tumor from seven carriers of the BRCA1 mutation, seven carriers of the BRCA2 mutation, and seven patients with sporadic cases of breast cancer was compared with a microarray of 6512 complementary DNA clones of 5361 genes. Statistical analyses were used to identify a set of genes that could distinguish the BRCA1 genotype from the BRCA2 genotype. RESULTS: Permutation analysis of multivariate classification functions established that the gene-expression profiles of tumors with BRCA1 mutations, tumors with BRCA2 mutations, and sporadic tumors differed significantly from each other. An analysis of variance between the levels of gene expression and the genotype of the samples identified 176 genes that were differentially expressed in tumors with BRCA1 mutations and tumors with BRCA2 mutations. Given the known properties of some of the genes in this panel, our findings indicate that there are functional differences between breast tumors with BRCA1 mutations and those with BRCA2 mutations. CONCLUSIONS: Significantly different groups of genes are expressed by breast cancers with BRCA1 mutations and breast cancers with BRCA2 mutations. Our results suggest that a heritable mutation influences the gene-expression profile of the cancer.

Enhanced-resolution image restoration from a sequence of low-frequency vibrated images by use of convex projections.

An algorithm to increase the spatial resolution of digital video sequences captured with a camera that is subject to mechanical vibration is developed. The blur caused by vibration of the camera is often the primary cause for image degradation. We address the degradation caused by low-frequency vibrations (vibrations for which the exposure time is less than the vibration period). The blur caused by low-frequency vibrations differs from other types by having a random shape and displacement. The different displacement of each frame makes the approach used in superresolution (SR) algorithms suitable for resolution enhancement. However, SR algorithms that were developed for general types of blur should be adapted to the specific characteristics of low-frequency vibration blur. We use the method of projection onto convex sets together with a motion estimation method specially adapted to low-frequency vibration blur characteristics. We also show that the random blur characterizing low-frequency vibration requires selection of the frames prior to processing. The restoration performance as well as the frame selection criteria is dependent mainly on the motion estimation precision.

Universal DNA tag systems: a combinatorial design scheme.

Custom-designed DNA arrays offer the possibility of simultaneously monitoring thousands of hybridization reactions. These arrays show great potential for many medical and scientific applications, such as polymorphism analysis and genotyping. Relatively high costs are associated with the need to specifically design and synthesize problem-specific arrays. Recently, an alternative approach was suggested that utilizes fixed, universal arrays. This approach presents an interesting design problem-the arrays should contain as many probes as possible, while minimizing experimental errors caused by cross-hybridization. We use a simple thermodynamic model to cast this design problem in a formal mathematical framework. Employing new combinatorial ideas, we derive an efficient construction for the design problem and prove that our construction is near-optimal.

Tissue classification with gene expression profiles.

Constantly improving gene expression profiling technologies are expected to provide understanding and insight into cancer-related cellular processes. Gene expression data is also expected to significantly aid in the development of efficient cancer diagnosis and classification platforms. In this work we examine three sets of gene expression data measured across sets of tumor(s) and normal clinical samples: The first set consists of 2,000 genes, measured in 62 epithelial colon samples (Alon et al., 1999). The second consists of approximately equal to 100,000 clones, measured in 32 ovarian samples (unpublished extension of data set described in Schummer et al. (1999)). The third set consists of approximately equal to 7,100 genes, measured in 72 bone marrow and peripheral blood samples (Golub et al, 1999). We examine the use of scoring methods, measuring separation of tissue type (e.g., tumors from normals) using individual gene expression levels. These are then coupled with high-dimensional classification methods to assess the classification power of complete expression profiles. We present results of performing leave-one-out cross validation (LOOCV) experiments on the three data sets, employing nearest neighbor classifier, SVM (Cortes and Vapnik, 1995), AdaBoost (Freund and Schapire, 1997) and a novel clustering-based classification technique. As tumor samples can differ from normal samples in their cell-type composition, we also perform LOOCV experiments using appropriately modified sets of genes, attempting to eliminate the resulting bias. We demonstrate success rate of at least 90% in tumor versus normal classification, using sets of selected genes, with, as well as without, cellular-contamination-related members. These results are insensitive to the exact selection mechanism, over a certain range.

Molecular classification of cutaneous malignant melanoma by gene expression profiling.

The most common human cancers are malignant neoplasms of the skin. Incidence of cutaneous melanoma is rising especially steeply, with minimal progress in non-surgical treatment of advanced disease. Despite significant effort to identify independent predictors of melanoma outcome, no accepted histopathological, molecular or immunohistochemical marker defines subsets of this neoplasm. Accordingly, though melanoma is thought to present with different 'taxonomic' forms, these are considered part of a continuous spectrum rather than discrete entities. Here we report the discovery of a subset of melanomas identified by mathematical analysis of gene expression in a series of samples. Remarkably, many genes underlying the classification of this subset are differentially regulated in invasive melanomas that form primitive tubular networks in vitro, a feature of some highly aggressive metastatic melanomas. Global transcript analysis can identify unrecognized subtypes of cutaneous melanoma and predict experimentally verifiable phenotypic characteristics that may be of importance to disease progression.

RHO--radiation hybrid ordering.

Radiation hybrid (RH) mapping is a somatic cell technique that is used for ordering markers along a chromosome and estimating the physical distances between them. With the advent of this mapping technique, analyzing the experimental data is becoming a challenging and demanding computational task. In this paper we present the software package RHO (radiation hybrid ordering). The package implements a number of heuristics that attempt to order genomic markers along a chromosome, given as input the results of an RH experiment. The heuristics are based on reducing an appropriate optimization problem to the traveling salesman problem (TSP). The reduced optimization problem is either the nonparametric obligate chromosome breaks (OCBs) or the parametric maximum likelihood estimation (MLE). We tested our package on both simulated and publicly available RH data. For synthetic RH data, the reconstructed markers' permutation is very close to the original permutation, even with fairly high error rates. For real data we used the framework markers' data from the Whitehead Institute maps. For most of the chromosomes (18 out of 23), there is a perfect agreement or nearly perfect agreement (reversal of chromosome arm or arms) between our maps and the Whitehead framework maps. For the remaining five chromosomes, our maps improve on the Whitehead framework maps with respect to both optimization criteria, having higher likelihood and fewer breakpoints. For three chromosomes, the results differ significantly (lod score >1.75), with chromosome 2 having the largest improvement (lod score 3.776).

Clustering gene expression patterns.

Recent advances in biotechnology allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. Analysis of data produced by such experiments offers potential insight into gene function and regulatory mechanisms. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. The corresponding algorithmic problem is to cluster multicondition gene expression patterns. In this paper we describe a novel clustering algorithm that was developed for analysis of gene expression data. We define an appropriate stochastic error model on the input, and prove that under the conditions of the model, the algorithm recovers the cluster structure with high probability. The running time of the algorithm on an n-gene dataset is O[n2[log(n)]c]. We also present a practical heuristic based on the same algorithmic ideas. The heuristic was implemented and its performance is demonstrated on simulated data and on real gene expression data, with very promising results.

Constructing phylogenies from quartets: elucidation of eutherian superordinal relationships.

In this work we present two new approaches for constructing phylogenetic trees. The input is a list of weighted quartets over n taxa. Each quartet is a subtree on four taxa, and its weight represents a confidence level for the specific topology. The goal is to construct a binary tree with n leaves such that the total weight of the satisfied quartets is maximized (an NP hard problem). The first approach we present is based on geometric ideas. Using semidefinite programming, we embed the n points on the n-dimensional unit sphere, while maximizing an objective function. This function depends on Euclidean distances between the four points and reflects the quartet topology. Given the embedding, we construct a binary tree by performing geometric clustering. This process is similar to the traditional neighbor joining, with the difference that the update phase retains geometric meaning: When two neighbors are joined together, their common ancestor is taken to be the center of mass of the original points. The geometric algorithm runs in poly(n) time, but there are no guarantees on the quality of its output. In contrast, our second algorithm is based on dynamic programming, and it is guaranteed to find the optimal tree (with respect to the given quartets). Its running time is a modest exponential, so it can be implemented for modest values of n. We have implemented both algorithms and ran them on real data for n = 15 taxa (14 mammalian orders and an outgroup taxon). The two resulting trees improve previously published trees and seem to be of biological relevance. On this dataset, the geometric algorithm produced a tree whose score is 98.2% of the optimal value on this input set (72.1% vs. 73.4%). This gives rise to the hope that the geometric approach will prove viable even for larger cases where the exponential, dynamic programming approach is no longer feasible.

On constructing radiation hybrid maps.

Radiation hybrid (RH) mapping is a somatic cell method for obtaining ordering information of markers on a chromosome, using relatively few experiments. Given the results of a typical RH experiment, finding the true order of the markers is a challenging algorithmic problem. In this work we present several simple algorithms for ordering and mapping the markers, where the input is the genomic data obtained from RH experiments. We provide a rigorous analysis of these algorithms. In particular, we show that under the standard statistical model for RH, our algorithms are "statistically consistent." That is, given enough hybrids, the algorithms do reconstruct the true markers' order (with high probability). We also prove a simple lower bound for the number of hybrids required (by any algorithm) to correctly reconstruct the order. We have implemented these algorithms, and tested them on synthetic and real data. These simulations show that for practical input sizes (number of markers and hybrids) our algorithms produce outputs that are very close to the true ordering. The simulations also indicate that the true ordering of the markers is usually not the one which minimizes the number of obligate chromosome breaks.

Hospital at war: treatment changes in mental patients.

The implications of the chemical war threat and the missile attacks during the Gulf War for a medium-sized psychiatric community are analyzed in terms of psychiatric care and management. Changes in medication, physical restraint, and ward transfer were observed for schizophrenic patients in active psychotic phase (n=50), in residual post-active phase (n = 37), and patients with long-term residual type (n = 167). The variables for the first week of the war (n = 250) were compared to those the same week 1 year before (n = 254). Patients in active phase and patients in residual phase received more supplementary treatment and radical changes in treatment; patients in active phase received more treatment reinforcement, as well as physical restraint, compared to patients in phase and residual type patients. Residual type patients remained unchanged on all variables. Residual type patients remained mostly indifferent, while many severely disturbed patients residing in open wards required only minor tranquilizers. Patients in active phase tended to behave very erratically while denying being affected by the war, and patients in residual phase overtly expressed their anxiety and remained in control.