Big flies, small repeats: the "Thr-Gly" region of the period gene in Diptera

The region of the clock gene period (per) that encodes a repetitive tract of threonine-glycine (Thr-Gly) pairs has been compared between Dipteran species both within and outside the Drosophilidae. All the non- Drosophilidae sequences in this region are short and present a remarkably stable picture compared to the Drosophilidae, in which the region is much larger and extremely variable, both in size and composition. The accelerated evolution in the repetitive region of the Drosophilidae appears to be mainly due to an expansion of two ancestral repeats, one encoding a Thr-Gly dipeptide and the other a pentapeptide rich in serine, glycine, and asparagine or threonine. In some drosophilids the expansion involves a duplication of the pentapeptide sequence, but in Drosophila pseudoobscura both the dipeptide and the pentapeptide repeats are present in larger numbers. In the nondrosophilids, however, the pentapeptide sequence is represented by one copy and the dipeptide by two copies. These observations fulfill some of the predictions of recent theoretical models that have simulated the evolution of repetitive sequences.      

Classification of microarrays to nearest centroids

MOTIVATION: Classification of biological samples by microarrays is a topic of much interest. A number of methods have been proposed and successfully applied to this problem. It has recently been shown that classification by nearest centroids provides an accurate predictor that may outperform much more complicated methods. The 'Prediction Analysis of Microarrays' (PAM) approach is one such example, which the authors strongly motivate by its simplicity and interpretability. In this spirit, I seek to assess the performance of classifiers simpler than even PAM. RESULTS: I surprisingly show that the modified t-statistics and shrunken centroids employed by PAM tend to increase misclassification error when compared with their simpler counterparts. Based on these observations, I propose a classification method called 'Classification to Nearest Centroids' (ClaNC). ClaNC ranks genes by standard t-statistics, does not shrink centroids and uses a class-specific gene-selection procedure. Because of these modifications, ClaNC is arguably simpler and easier to interpret than PAM, and it can be viewed as a traditional nearest centroid classifier that uses specially selected genes. I demonstrate that ClaNC error rates tend to be significantly less than those for PAM, for a given number of active genes. AVAILABILITY: Point-and-click software is freely available at http://students.washington.edu/adabney/clanc.     

Dissection of a metastatic gene expression signature into distinct components

Background

Metastasis, the process whereby cancer cells spread, is in part caused by an incompletely understood interplay between cancer cells and the surrounding stroma. Gene expression studies typically analyze samples containing tumor cells and stroma. Samples with less than 50% tumor cells are generally excluded, thereby reducing the number of patients that can benefit from clinically relevant signatures.

Results

For a head-neck squamous cell carcinoma (HNSCC) primary tumor expression signature that predicts the presence of lymph node metastasis, we first show that reduced proportions of tumor cells results in decreased predictive accuracy. To determine the influence of stroma on the predictive signature and to investigate the interaction between tumor cells and the surrounding microenvironment, we used laser capture microdissection to divide the metastatic signature into six distinct components based on tumor versus stroma expression and on association with the metastatic phenotype. A strikingly skewed distribution of metastasis associated genes is revealed.

Conclusion

Dissection of predictive signatures into different components has implications for design of expression signatures and for our understanding of the metastatic process. Compared to primary tumors that have not formed metastases, primary HNSCC tumors that have metastasized are characterized by predominant down-regulation of tumor cell specific genes and exclusive up-regulation of stromal cell specific genes. The skewed distribution agrees with poor signature performance on samples that contain less than 50% tumor cells. Methods for reducing tumor composition bias that lead to greater predictive accuracy and an increase in the types of samples that can be included are presented.