The formation of precise connections between retina and LGN involves the activity-dependent elimination of some synapses, with strengthening and retention of others. Here we show that the MHC Class I (MHCI) molecule H2-Db is necessary and sufficient for synapse elimination in the retinogeniculate system. In mice lacking both H2-Kb and H2-Db (KbDb−/−) despite intact retinal activity and basal synaptic transmission, the developmentally-regulated decrease in functional convergence of retinal ganglion cell synaptic inputs to LGN neurons fails and eye-specific layers do not form. Neuronal expression of just H2-Db in KbDb−/− mice rescues both synapse elimination and eye specific segregation despite a compromised immune system. When patterns of stimulation mimicking endogenous retinal waves are used to probe synaptic learning rules at retinogeniculate synapses, LTP is intact but LTD is impaired in KbDb−/− mice. This change is due to an increase in Ca2+ permeable AMPA receptors. Restoring H2-Db to KbDb−/− neurons renders AMPA receptors Ca2+ impermeable and rescues LTD. These observations reveal an MHCI mediated link between developmental synapse pruning and balanced synaptic learning rules enabling both LTD and LTP, and demonstrate a direct requirement for H2-Db in functional and structural synapse pruning in CNS neurons.

TAC may reflect the airway-related disease changes that accumulate in the "quiet" zone in early/mild COPD, indicating that TAC acquired with commercially available software across various CT platforms may be a biomarker to predict accelerated COPD progression.

We describe a comprehensive modeling approach to combining genomic and clinical data for personalized prediction in disease outcome studies. This integrated clinicogenomic modeling framework is based on statistical classification tree models that evaluate the contributions of multiple forms of data, both clinical and genomic, to define interactions of multiple risk factors that associate with the clinical outcome and derive predictions customized to the individual patient level. Gene expression data from DNA microarrays is represented by multiple, summary measures that we term metagenes; each metagene characterizes the dominant common expression pattern within a cluster of genes. A case study of primary breast cancer recurrence demonstrates that models using multiple metagenes combined with traditional clinical risk factors improve prediction accuracy at the individual patient level, delivering predictions more accurate than those made by using a single genomic predictor or clinical data alone. The analysis also highlights issues of communicating uncertainty in prediction and identifies combinations of clinical and genomic risk factors playing predictive roles. Implicated metagenes identify gene subsets with the potential to aid biological interpretation. This framework will extend to incorporate any form of data, including emerging forms of genomic data, and provides a platform for development of models for personalized prognosis. G enomic information, in the form of gene expression patterns, has an established capacity to define clinically relevant risk factors in disease prognosis. Recent studies have generated such patterns related to lymph node metastasis and disease recurrence in breast cancer (1-8), as well as in other cancers and disease contexts (9-16). The challenge now is the integration of such genomic information into prognostic models that can be applied in a clinical setting to improve the accuracy of treatment decisions.Achievement of this goal requires modeling approaches that focus on the generation of predictions for the individual patient and that can evaluate and combine multiple risk factors to produce informed predictions. Gene expression profiles may indeed prove to be powerful individual indicators of tumor behavior, but analysis should not force a choice of one form of data over the other; rather, analysis should evaluate and combine all forms of potentially relevant information. This integrative view underlies our development of clinicogenomic models and should underlie prognostic systems in support of personalized health planning.Consistent with this view, the example of breast cancer recurrence presented here highlights the predictive value of multiple genomic patterns in models defining accurate predictions at the individual patient level. This analysis uses integrative models that combine clinical and genomic factors, such as multiple gene expression patterns, clinical risk factors, and treatment information, and that predict recurrence for individual patients. The example shows im...