Deubiquitination of EGFR by Cezanne-1 contributes to cancer progression.

Once stimulated, the epidermal growth factor receptor (EGFR) undergoes self-phosphorylation, which, on the one hand, instigates signaling cascades, and on the other hand, recruits CBL ubiquitin ligases, which mark EGFRs for degradation. Using RNA interference screens, we identified a deubiquitinating enzyme, Cezanne-1, that opposes receptor degradation and enhances EGFR signaling. These functions require the catalytic- and ubiquitin-binding domains of Cezanne-1, and they involve physical interactions and transphosphorylation of Cezanne-1 by EGFR. In line with the ability of Cezanne-1 to augment EGF-induced growth and migration signals, the enzyme is overexpressed in breast cancer. Congruently, the corresponding gene is amplified in approximately one third of mammary tumors, and high transcript levels predict an aggressive disease course. In conclusion, deubiquitination by Cezanne-1 curtails degradation of growth factor receptors, thereby promotes oncogenic growth signals.

SNPs in genes coding for ROS metabolism and signalling in association with docetaxel clearance.

The dose of docetaxel is currently calculated based on body surface area and does not reflect the pharmacokinetic, metabolic potential or genetic background of the patients. The influence of genetic variation on the clearance of docetaxel was analysed in a two-stage analysis. In step one, 583 single-nucleotide polymorphisms (SNPs) in 203 genes were genotyped on samples from 24 patients with locally advanced non-small cell lung cancer. We found that many of the genes harbour several SNPs associated with clearance of docetaxel. Most notably these were four SNPs in EGF, three SNPs in PRDX4 and XPC, and two SNPs in GSTA4, TGFBR2, TNFAIP2, BCL2, DPYD and EGFR. The multiple SNPs per gene suggested the existence of common haplotypes associated with clearance. These were confirmed with detailed haplotype analysis. On the basis of analysis of variance (ANOVA), quantitative mutual information score (QMIS) and Kruskal-Wallis (KW) analysis SNPs significantly associated with clearance of docetaxel were confirmed for GSTA4, PRDX4, TGFBR2 and XPC and additional putative markers were found in CYP2C8, EPHX1, IGF2, IL1R2, MAPK7, NDUFB4, TGFBR3, TPMT (2 SNPs), (P<0.05 or borderline significant for all three methods, 14 SNPs in total). In step two, these 14 SNPs were genotyped in additional 9 samples and the results combined with the genotyping results from the first step. For 7 of the 14 SNPs, the results are still significant/borderline significant by all three methods: ANOVA, QMIS and KW analysis strengthening our hypothesis that they are associated with the clearance of docetaxel.

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.

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.

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.

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.