Intrachromosomal rearrangements fusing L-myc and rlf in small-cell lung cancer.

Chromosomal abnormalities affecting proto-oncogenes are frequently detected in human cancer. Oncogenes of the myc family are activated in several types of tumors as a result of gene amplification or chromosomal translocation. We have recently found the L-myc gene involved in a gene fusion in small-cell lung cancer (SCLC). This results in a chimeric protein with amino-terminal sequences from a novel gene named rif joined to L-myc. Here we present a preliminary structural characterization of the rlf-L-myc fusion gene, which has been found only in cells with an amplified L-myc gene. In addition, we have used somatic cell hybrids to assign the normal rlf locus to the same chromosome (chromosome 1) on which L-myc resides. Finally, we have been able to establish a physical linkage between rif and L-myc with pulsed-field gel electrophoresis. Our results demonstrate that normal rlf and L-myc genes are separated by less than 800 kb of DNA. Thus, the rlf-L-myc gene fusions are due to similar but not identical intrachromosomal rearrangements at 1p32. The presence of independent genetic lesions that cause the formation of identical chimeric rlf-L-myc proteins suggests a role for the fusion protein in the development of these tumors.     

Predicting potential cancer genes by integrating network properties, sequence features and functional annotations

The discovery of novel cancer genes is one of the main goals in cancer research. Bioinformatics methods can be used to accelerate cancer gene discovery, which may help in the understanding of cancer and the development of drug targets. In this paper, we describe a classifier to predict potential cancer genes that we have developed by integrating multiple biological evidence, including protein-protein interaction network properties, and sequence and functional features. We detected 55 features that were significantly different between cancer genes and non-cancer genes. Fourteen cancer-associated features were chosen to train the classifier. Four machine learning methods, logistic regression, support vector machines (SVMs), BayesNet and decision tree, were explored in the classifier models to distinguish cancer genes from non-cancer genes. The prediction power of the different models was evaluated by 5-fold cross-validation. The area under the receiver operating characteristic curve for logistic regression, SVM, Baysnet and J48 tree models was 0.834, 0.740, 0.800 and 0.782, respectively. Finally, the logistic regression classifier with multiple biological features was applied to the genes in the Entrez database, and 1976 cancer gene candidates were identified. We found that the integrated prediction model performed much better than the models based on the individual biological evidence, and the network and functional features had stronger powers than the sequence features in predicting cancer genes.     

Use of covariance analysis for the prediction of structural domain boundaries from multiple protein sequence alignments

Current methods for identification of domains within protein sequences require either structural information or the identification of homologous domain sequences in different sequence contexts. Knowledge of structural domain boundaries is important for fold recognition experiments and structural determination by X-ray crystallography or nuclear magnetic resonance spectroscopy using the divide-and-conquer approach. Here, a new and conceptually simple method for the identification of structural domain boundaries in multiple protein sequence alignments is presented. Analysis of covariance at positions within the alignment is first used to predict 3D contacts. By the nature of the domain as an independent folding unit, inter-domain predicted contacts are fewer than intra-domain predicted contacts. By analysing all possible domain boundaries and constructing a smoothed profile of predicted contact density (PCD), true structural domain boundaries are predicted as local profile minima associated with low PCD. A training data set is constructed from 52 non-homologous two-domain protein sequences of known 3D structure and used to determine optimal parameters for the profile analysis. The alignments in the training data set contained 48 +/- 17 (mean +/- SD) sequences and lengths of 257 +/- 121 residues. Of the 47 alignments yielding predictions, 35% of true domain boundaries are predicted to within 15 amino acids by the local profile minimum with the lowest profile value. Including predictions from the second- and third-lowest local minima increases the correct domain boundary coverage to 60%, whereas the lowest five local minima cover 79% of correct domain boundaries. Through further profile analysis, criteria are presented which reliably identify subsets of more accurate predictions. Retrospective analysis of CASP3 targets shows predictions of sufficient accuracy to enable dramatically improved fold recognition results. Finally, a prediction is made for geminivirus AL1 protein which is in full agreement with biochemical data, yielding a plausible, novel threading result.