Current Genomics - Volume 8, Issue 5, 2007

The phosphoinositide 3-kinase (PI3K) pathway is frequently activated in human cancer and represents an attractive target for therapies based on small molecule inhibitors. PI3K isoforms play an essential role in the signal transduction events activated by cell surface receptors including receptor tyrosine kinases (RTKs) and G-protein-coupled receptors (GPCRs). There are eight known PI3K isoforms in humans, which have been subdivided into three classes (I-III). Therefore PI3Ks show considerable diversity and it remains unclear which kinases in this family should be targeted in cancer. The class IA of PI3K comprises the p110α, p110β and p110δ isoforms, which associate with activated RTKs. In human cancer, recent reports have described activating mutations in the PIK3CA gene encoding p110α, and inactivating mutations in the phosphatase and tensin homologue (PTEN) gene, a tumour suppressor and antagonist of the PI3K pathway. The PIK3CA mutations described in cancer constitutively activate p110α and, when expressed in cells drive oncogenic transformation. Moreover, these mutations cause the constitutive activation of downstream signaling molecules such as Akt/protein kinase B (PKB), mammalian target of rapamycin (mTOR) and ribosomal protein S6 kinase (S6K) that is commonly observed in cancer cells. In addition to p110α, the other isoforms of the PI3K family may also play a role in human cancer, although their individual functions remain to be precisely identified. In this review we will discuss the evidence implicating individual PI3K isoforms in human cancer and their potential as drug targets in this context.

Detecting the patterns of DNA sequence variants across the human genome is a crucial step for unraveling the genetic basis of complex human diseases. The human HapMap constructed by single nucleotide polymorphisms (SNPs) provides efficient sequence variation information that can speed up the discovery of genes related to common diseases. In this article, we present a generalized linear model for identifying specific nucleotide variants that encode complex human diseases. A novel approach is derived to group haplotypes to form composite diplotypes, which largely reduces the model degrees of freedom for an association test and hence increases the power when multiple SNP markers are involved. An efficient two-stage estimation procedure based on the expectation-maximization (EM) algorithm is derived to estimate parameters. Non-genetic environmental or clinical risk factors can also be fitted into the model. Computer simulations show that our model has reasonable power and type I error rate with appropriate sample size. It is also suggested through simulations that a balanced design with approximately equal number of cases and controls should be preferred to maintain small estimation bias and reasonable testing power. To illustrate the utility, we apply the method to a genetic association study of large for gestational age (LGA) neonates. The model provides a powerful tool for elucidating the genetic basis of complex binary diseases.

Access to nuclear genes in eukaryotes is provided by members of the importin (IMP) superfamily of proteins, which are of α- or β-types, the best understood nuclear import pathway being mediated by a heterodimer of an IMP α and IMP β1. IMP α recognises specific targeting signals on cargo proteins, while IMP β1 mediates passage into, and release within, the nucleus by interacting with other components of the transport machinery, including the monomeric guanine nucleotide binding protein Ran. In this manner, hundreds of different proteins can be targeted specifically into the nucleus in a tightly regulated fashion. The IMP α gene family has expanded during evolution, with only a single IMP α (Srp1p) gene in budding yeast, and three (IMP α1, 2/pendulin and 3) and five (IMP α1, -2, -3, -4 and -6) IMP α genes in Drosophila melanogaster and mouse respectively, which fall into three phylogenetically distinct groups. The fact that IMP α3 and IMP α2 are only present in metazoans implies that they emerged during the evolution of multicellular animals to perform specialised roles in particular cells and tissues. This review describes what is known of the IMP α gene family in mouse and in D. melanogaster, including a comparitive examination of their mRNA expression profiles in a highly differentiated tissue, the testis. The clear implication of their highly regulated synthesis during the course of spermatogenesis is that the different IMP αs have distinct expression patterns during cellular differentiation, implying tissue/cell type-specific roles.

Among the gynaecological malignancies, ovarian cancer is one of the neoplastic forms with the poorest prognosis and with the bad overall and disease-free survival rates than other gynaecological cancers; several studies, analyzing clinical data and pathological features on ovarian cancers, have focused on the identification of both diagnostic and prognostic markers for applications in clinical practice. High-throughput technologies have accelerated the process of biomarker discovery, but their validity should be still demonstrated by extensive researches on sensibility and sensitivity of ovarian cancer novel biomarkers, determining whether gene profiling and proteomics could help differentiate between patients with metastatic ovarian cancer and primary ovarian carcinomas, and their potential impact on management. Therefore, considerable interest lies in identifying molecular prognostic biomarkers and protein indicators to guide treatment decisions and clinical follow up; the current state of knowledge about the potential clinical value of gene expression profiling in ovarian cancer is discussed, focusing on three main areas: distinguishing normal ovarian tissue from ovarian tumors, identifying different subtypes of ovarian cancer and identifying cancer likely to be responsive to therapy. In this elaborate we discuss the use of novel molecules, discovered by proteomics and genomics approaches, as potential protein biomarkers in the management of ovarian cancer, to improve the anticancer therapy for malignant ovarian tumors and to monitor the clinical follow up.

Uncertainty about linkage phases of multiple single nucleotide polymorphisms (SNPs) in heterozygous diploids challenges the identification of specific DNA sequence variants that encode a complex trait. A statistical technique implemented with the EM algorithm has been developed to infer the effects of SNP haplotypes from genotypic data by assuming that one haplotype (called the risk haplotype) performs differently from the rest (called the non-risk haplotype). This assumption simplifies the definition and estimation of genotypic values of diplotypes for a complex trait, but will reduce the power to detect the risk haplotype when non-risk haplotypes contain substantial diversity. In this article, we incorporate general quantitative genetic theory to specify the differentiation of different haplotypes in terms of their genetic control of a complex trait. A model selection procedure is deployed to test the best number and combination of risk haplotypes, thus providing a precise and powerful test of genetic determination in association studies. Our method is derived on the maximum likelihood theory and has been shown through simulation studies to be powerful for the characterization of the genetic architecture of complex quantitative traits.