Role of finite populations in determining evolutionary dynamics.

The connection between the finite size of an evolving population and its dynamical behavior is examined through analytical and computational studies of a simple model of evolution. The infinite population limit of the model is shown to be governed by a special case of the quasispecies equations. A flat fitness landscape yields identical results for the dynamics of infinite and finite populations. On the other hand, a monotonically increasing fitness landscape shows "epochs" in the dynamics of finite populations that become more pronounced as the rate of mutation decreases. The details of the dynamics are profoundly different for any two simulation runs in that events arising from the stochastic noise in the pseudorandom number sequence are amplified. As the population size is increased or, equivalently, the mutation rate is increased, these epochs become smaller but do not entirely disappear.

Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning.

Diffuse large B-cell lymphoma (DLBCL), the most common lymphoid malignancy in adults, is curable in less than 50% of patients. Prognostic models based on pre-treatment characteristics, such as the International Prognostic Index (IPI), are currently used to predict outcome in DLBCL. However, clinical outcome models identify neither the molecular basis of clinical heterogeneity, nor specific therapeutic targets. We analyzed the expression of 6,817 genes in diagnostic tumor specimens from DLBCL patients who received cyclophosphamide, adriamycin, vincristine and prednisone (CHOP)-based chemotherapy, and applied a supervised learning prediction method to identify cured versus fatal or refractory disease. The algorithm classified two categories of patients with very different five-year overall survival rates (70% versus 12%). The model also effectively delineated patients within specific IPI risk categories who were likely to be cured or to die of their disease. Genes implicated in DLBCL outcome included some that regulate responses to B-cell-receptor signaling, critical serine/threonine phosphorylation pathways and apoptosis. Our data indicate that supervised learning classification techniques can predict outcome in DLBCL and identify rational targets for intervention.