Altered tumor formation and evolutionary selection of genetic variants in the human MDM4 oncogene.

A large body of evidence strongly suggests that the p53 tumor suppressor pathway is central in reducing cancer frequency in vertebrates. The protein product of the haploinsufficient mouse double minute 2 (MDM2) oncogene binds to and inhibits the p53 protein. Recent studies of human genetic variants in p53 and MDM2 have shown that single nucleotide polymorphisms (SNPs) can affect p53 signaling, confer cancer risk, and suggest that the pathway is under evolutionary selective pressure (1-4). In this report, we analyze the haplotype structure of MDM4, a structural homolog of MDM2, in several different human populations. Unusual patterns of linkage disequilibrium (LD) in the haplotype distribution of MDM4 indicate the presence of candidate SNPs that may also modify the efficacy of the p53 pathway. Association studies in 5 different patient populations reveal that these SNPs in MDM4 confer an increased risk for, or early onset of, human breast and ovarian cancers in Ashkenazi Jewish and European cohorts, respectively. This report not only implicates MDM4 as a key regulator of tumorigenesis in the human breast and ovary, but also exploits for the first time evolutionary driven linkage disequilibrium as a means to select SNPs of p53 pathway genes that might be clinically relevant.

Dynamic plasticity in coupled avian midbrain maps.

Internal mapping of the external environment is carried out using the receptive fields of topographic neurons in the brain, and in a normal barn owl the aural and visual subcortical maps are aligned from early experiences. However, instantaneous misalignment of the aural and visual stimuli has been observed to result in adaptive behavior, manifested by functional and anatomical changes of the auditory processing system. Using methods of information theory and statistical mechanics a model of the adaptive dynamics of the aural receptive field is presented and analyzed. The dynamics is determined by maximizing the mutual information between the neural output and the weighted sensory neural inputs, admixed with noise, subject to biophysical constraints. The reduced costs of neural rewiring, as in the case of young barn owls, reveal two qualitatively different types of receptive field adaptation depending on the magnitude of the audiovisual misalignment. By letting the misalignment increase with time, it is shown that the ability to adapt can be increased even when neural rewiring costs are high, in agreement with recent experimental reports of the increased plasticity of the auditory space map in adult barn owls due to incremental learning. Finally, a critical speed of misalignment is identified, demarcating the crossover from adaptive to nonadaptive behavior.

An information-theoretic analysis of genetics, gender and age in cancer patients.

Germline genetics, gender and hormonal-signaling pathways are all well described modifiers of cancer risk and progression. Although an improved understanding of how germline genetic variants interact with other cancer risk factors may allow better prevention and treatment of human cancer, measuring and quantifying these interactions is challenging. In other areas of research, Information Theory has been used to quantitatively describe similar multivariate interactions. We implemented a novel information-theoretic analysis to measure the joint effect of a high frequency germline genetic variant of the p53 tumor suppressor pathway (MDM2 SNP309 T/G) and gender on clinical cancer phenotypes. This analysis quantitatively describes synergistic interactions among gender, the MDM2 SNP309 locus, and the age of onset of tumorigenesis in p53 mutation carriers. These results offer a molecular and genetic basis for the observed sexual dimorphism of cancer risk in p53 mutation carriers and a model is proposed that suggests a novel cancer prevention strategy for p53 mutation carriers.

Haplotype structure and selection of the MDM2 oncogene in humans.

The MDM2 protein is an ubiquitin ligase that plays a critical role in regulating the levels and activity of the p53 protein, which is a central tumor suppressor. A SNP in the human MDM2 gene (SNP309 T/G) occurs at frequencies dependent on demographic history and has been shown to have important differential effects on the activity of the MDM2 and p53 proteins and to associate with altered risk for the development of several cancers. In this report, the haplotype structure of the MDM2 gene is determined by using 14 different SNPs across the gene from three different population samples: Caucasians, African Americans, and the Ashkenazi Jewish ethnic group. The results presented in this report indicate that there is a substantially reduced variability of the deleterious SNP309 G allele haplotype in all three populations studied, whereas multiple common T allele haplotypes were found in all three populations. This observation, coupled with the relatively high frequency of the G allele haplotype in both and Caucasian and Ashkenazi Jewish population data sets, suggests that this haplotype could have undergone a recent positive selection sweep. An entropy-based selection test is presented that explicitly takes into account the correlations between different SNPs, and the analysis of MDM2 reveals a significant departure from the standard assumptions of selective neutrality.

Information-based clustering.

In an age of increasingly large data sets, investigators in many different disciplines have turned to clustering as a tool for data analysis and exploration. Existing clustering methods, however, typically depend on several nontrivial assumptions about the structure of data. Here, we reformulate the clustering problem from an information theoretic perspective that avoids many of these assumptions. In particular, our formulation obviates the need for defining a cluster "prototype," does not require an a priori similarity metric, is invariant to changes in the representation of the data, and naturally captures nonlinear relations. We apply this approach to different domains and find that it consistently produces clusters that are more coherent than those extracted by existing algorithms. Finally, our approach provides a way of clustering based on collective notions of similarity rather than the traditional pairwise measures.