Predictive gene lists for breast cancer prognosis: a topographic visualisation study

Background: The controversy surrounding the non-uniqueness of predictive gene lists (PGL) of small selected subsets of genes from very large potential candidates as available in DNA microarray experiments is now widely acknowledged 1. Many of these studies have focused on constructing discriminative semi-parametric models and as such are also subject to the issue of random correlations of sparse model selection in high dimensional spaces. In this work we outline a different approach based around an unsupervised patient-specific nonlinear topographic projection in predictive gene lists. Methods: We construct nonlinear topographic projection maps based on inter-patient gene-list relative dissimilarities. The Neuroscale, the Stochastic Neighbor Embedding(SNE) and the Locally Linear Embedding(LLE) techniques have been used to construct two-dimensional projective visualisation plots of 70 dimensional PGLs per patient, classifiers are also constructed to identify the prognosis indicator of each patient using the resulting projections from those visualisation techniques and investigate whether a-posteriori two prognosis groups are separable on the evidence of the gene lists. A literature-proposed predictive gene list for breast cancer is benchmarked against a separate gene list using the above methods. Generalisation ability is investigated by using the mapping capability of Neuroscale to visualise the follow-up study, but based on the projections derived from the original dataset. Results: The results indicate that small subsets of patient-specific PGLs have insufficient prognostic dissimilarity to permit a distinction between two prognosis patients. Uncertainty and diversity across multiple gene expressions prevents unambiguous or even confident patient grouping. Comparative projections across different PGLs provide similar results. Conclusion: The random correlation effect to an arbitrary outcome induced by small subset selection from very high dimensional interrelated gene expression profiles leads to an outcome with associated uncertainty. This continuum and uncertainty precludes any attempts at constructing discriminative classifiers. However a patient's gene expression profile could possibly be used in treatment planning, based on knowledge of other patients' responses. We conclude that many of the patients involved in such medical studies are intrinsically unclassifiable on the basis of provided PGL evidence. This additional category of 'unclassifiable' should be accommodated within medical decision support systems if serious errors and unnecessary adjuvant therapy are to be avoided.

The effects of mailing design characteristics on direct mail campaign performance

Designing effective direct mail pieces is considered a key success factor in direct marketing. However, related published empirical research is scarce while design recommendations are manifold and often conflicting. Compared with prior work, our study aims to provide more elaborate and empirically validated findings for the effects of direct mail design characteristics by analyzing 677 direct mail campaigns from non-profit organizations and financial service providers. We investigate the effects of (1) various envelope characteristics and observable cues on opening rates, and (2) characteristics of the envelope content on the keeping rates of direct mail campaigns. We show that visual design elements on the outer envelope – rather than sender-related details – are the predominant drivers of opening rates. Factors such as letter length, provision of sender information in the letter, and personalization positively influence the keeping rate. We also observe that opening and keeping rates are uncorrelated at the campaign level, implying that opening direct mail pieces is only a necessary condition for responding to offers, but not per se a driver of direct mail response.