Association of p53 codon 72 polymorphism with advanced lung cancer: the Arg allele is preferentially retained in tumours arising in Arg/Pro germline heterozygotes

The association of p53 codon 72 polymorphism with cancer has been investigated by several scientific groups with controversial results. In the present study, we examined the genotypic frequency of this polymorphism in 54 patients with advanced lung cancer and 99 normal controls from the geographical region of Greece. Sputum and bronchial washing samples from each patient were assayed for the presence of human papillomavirus. Codon 72 heterozygous (Arg/Pro) patients were also analysed for loss of heterozygosity at the TP53 locus, in order to determine the lost p53 allele (Arg or Pro). p53 Arg/Arg genotype was significantly increased in lung cancer patients compared to normal controls (50% vs 24.2%, P<0.002). Human papillomavirus was detected only in two patients (3.7%). Loss of heterozygosity at the TP53 locus was found in 14 out of 27 Arg/Pro patients (51.85%). The Pro allele was lost in 11 cases (78.6%), while the Arg allele was lost in three (21.4%). Our results suggest that p53 codon 72 Arg homozygosity is associated with advanced lung cancer, and that the Arg allele is preferentially retained in patients heterozygous for this polymorphism. On the other hand, human papillomavirus infection does not seem to play an important role in lung carcinogenesis

Haplotype analysis of TP53 polymorphisms, Arg72Pro and Ins16, in BRCA1 and BRCA2 mutation carriers of French Canadian descent

Research articl

Characterization of Novel and Uncharacterized p53 SNPs in the Chinese Population – Intron 2 SNP Co-Segregates with the Common Codon 72 Polymorphism

Multiple single nucleotide polymorphisms (SNPs) have been identified in the tumor suppressor gene p53, though the relevance of many of them is unclear. Some of them are also differentially distributed in various ethnic populations, suggesting selective functionality. We have therefore sequenced all exons and flanking regions of p53 from the Singaporean Chinese population and report here the characterization of some novel and uncharacterized SNPs - four in intron 1 (nucleotide positions 8759/10361/10506/11130), three in intron 3 (11968/11969/11974) and two in the 3′UTR (19168/19514). Allelic frequencies were determined for all these and some known SNPs, and were compared in a limited scale to leukemia and lung cancer patient samples. Intron 2 (11827) and 7 (14181/14201) SNPs were found to have a high minor allele frequency of between 26–47%, in contrast to the lower frequencies found in the US population, but similar in trend to the codon 72 polymorphism (SNP12139) that shows a distribution pattern correlative with latitude. Several of the SNPs were linked, such as those in introns 1, 3 and 7. Most interestingly, we noticed the co-segregation of the intron 2 and the codon 72 SNPs, the latter which has been shown to be expressed in an allele-specific manner, suggesting possible regulatory cross-talk. Association analysis indicated that the T/G alleles in both the co-segregating intron 7 SNPs and a 4tagSNP haplotype was strongly associated increased susceptibility to lung cancer in non-smoker females [OR: 1.97 (1.32, 3.394)]. These data together demonstrate high SNP diversity in p53 gene between different populations, highlighting ethnicity-based differences, and their association with cancer risk

Polymorphisms of TP53 codon 72 with breast carcinoma risk: evidence from 12226 cases and 10782 controls

<p>Abstract</p> <p>Background</p> <p>Previously, TP53 codon 72 polymorphisms have been implicated as risk factors for various cancers. A number of studies have conducted on the association of TP53 codon 72 polymorphisms with susceptibility to breast carcinoma and have yielded inconclusive results. The aim of the present study was to derive a more precise estimation of the relationship.</p> <p>Methods</p> <p>We conducted a search in the Medline, EMBASE, OVID, Sciencedirect, and Chinese National Knowledge Infrastructure (CNKI) without a language limitation, covering all papers published up to Jan 2009. The associated literature was acquired through deliberate searching and selected based on the established inclusion criteria for publications.</p> <p>Results</p> <p>A total of seventeen case-control studies, including 12226 cases and 10782 controls, met the included criteria and thus were selected. Ultimately, the relevant data were extracted and further analyzed using systematic meta-analyses. Overall, no associations of TP53 codon 72 polymorphisms with breast carcinoma were observed (for Arg/Arg vs Pro/Pro: OR = 1.20; 95%CI = 0.96–1.50; for dominant model: OR = 1.12; 95%CI = 0.96–1.32; for recessive model: OR = 1.13; 95%CI = 0.98–1.31). In the subgroup analysis by ethnicity, statistically similar results were obtained when the data were stratified as Asians, Caucasians and Africans.</p> <p>Conclusion</p> <p>Collectively, the results of the present study suggest that <it>TP53 codon 72 </it>polymorphisms might not be a low-penetrant risk factor for developing breast carcinoma.</p

HRS/EHRA/APHRS Expert Consensus Statement on the Diagnosis and Management of Patients with Inherited Primary Arrhythmia Syndromes

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Enhanced pose normalization and matching of non-rigid objects based on support vector machine modelling

International audienceThe estimation of 3D surface correspondence constitutes a fundamental problem in shape matching and analysis applications. In the presence of non-rigid shape deformations, the ambiguity of surface correspondence increases together with the complexity of registration algorithms. In this paper, we alleviate this problem by means of 3D pose normalization using One-Class Support Vector Machines (OCSVM). In detail, we show how OCSVM are employed in order to increase the consistency of translation and scale normalization under articulations, extrusions or the presence of outliers. To estimate the relative translation and scale of an object, we use the 3D distribution of points that is modelled by employing OCSVM to estimate the decision surface corresponding to the surface points of the object under a preset tolerance to outliers. By discarding the outliers in the estimation of the object's center and size we compute the canonical pose of the core distribution that is less sensitive to intra-class shape variations. The effectiveness of the proposed method is demonstrated through the increased stability of translation and scale normalization and further justified by improving the precision of content-based 3D object retrieval