Common site of integration of HTLV in cells of three patients with mature T-cell leukaemia-lymphoma

Human T-cell leukaemia-lymphoma virus (HTLV) was first isolated in the United States from a patient with an aggressive form of cutaneous T-cell lymphoma (CTCL) and was later found associated with clusters of adult T-cell leukaemia-lymphomas (ATL) in various parts of the world, including Japan and the Caribbean. Leukaemic cells of the HTLV-positive patients seem to be clonal expansions of single infected cells since the provirus(es) are found at the same sites in a given patient. In avian leukosis virus-induced B-cell lymphomas, the provirus very frequently integrates at several discrete sites in a common domain near the cellular gene, c-myc, that it activates and it has been speculated that the same would hold true for other chronic leukaemia viruses. We report here that cultured cells from two US patients with CTCL and fresh leukaemia cells of a Japanese patient with ATL contained an HTLV provirus integrated at the same site. In addition, a cord blood T-lymphocyte cell line established by co-cultivation with one of the two HTLV-positive CTCL cell lines also contained HTLV provirus contiguous with the same flanking cellular sequences. Ten other HTLV-positive cell samples did not show integration of HTLV at this site, suggesting that there is more than one discrete site of HTLV integration in tumour cells.     

Nucleotide sequence of cloned cDNA of human c-myc oncogene

Like other transforming genes of retroviruses, the v-myc gene of the avian virus, MC29, has a homologue in the genome of normal eukaryotic cells. The human cellular homologue, c-myc, located on human chromosome 8, region q24 leads to qter (refs 1, 2), is translocated into the immunoglobulin heavy-chain locus on human chromosome 14 (ref. 3) in Burkitt's lymphoma, suggesting that c-myc has a primary role in transformation of some human haematopoietic cells. In addition, c-myc is amplified in the human promyelocytic leukaemia cell line, HL60 (refs 6, 7) which also contains high levels of c-myc mRNA. Recently, Colby et al. reported the nucleotide sequence of the human c-myc DNA isolated from a genomic recombinant DNA library derived from human fetal liver. This 4,053-base pair (bp) sequence includes two exons and one intron of the myc gene, and the authors have suggested the existence of a human c-myc mRNA of 2,291 nucleotides that has a coding capacity for a protein of molecular weight (Mr) 48,812. We have approached the problem of accurately defining the characteristics of the human c-myc mRNA and c-myc protein by determining the sequence of the c-myc cDNA isolated from a cDNA library prepared from mRNA of a clone of the K562 human leukaemic cell line. K562 cells are known to contain c-myc mRNA which is similar in size to the c-myc mRNA of other human cell types. We report here the sequence of 2,121 nucleotides of a human c-myc mRNA and demonstrate that its 5' noncoding sequence does not correspond to the sequence of the reported genomic human sequence. However, our data confirm that the intact human c-myc mRNA can encode a 48,812-Mr protein with a sequence identical to that reported by Colby et al.     

Improving Forecast of Binary Rare Events Data: A GAM‐Based Approach

This paper develops a method for modelling binary response data in a regression model with highly unbalanced class sizes. When the class sizes are highly unbalanced and the minority class represents a rare event, conventional regression analysis, i.e. logistic regression models, could underestimate the probability of the rare event. To overcome this drawback, we introduce a flexible skewed link function based on the quantile function of the generalized extreme value (GEV) distribution in a generalized additive model (GAM). The proposed model is known as generalized extreme value additive (GEVA) regression model, and a modified version of the local scoring algorithm is suggested to estimate it. We apply the proposed model to a dataset on Italian small and medium enterprises (SMEs) to estimate the default probability of SMEs. Our proposal performs better than the logistic (linear or additive) model in terms of predictive accuracy. Copyright © 2015 John Wiley & Sons, Ltd.