This study evaluated the efficacy and safety of single-agent bortezomib in indolent B-cell lymphoma that had relapsed from or was refractory to rituximab. Sixty patients enrolled: 59 were treated with bortezomib 1.3 mg/m(2) on days 1, 4, 8, and 11 for up to eight 21-day cycles; responders could receive 4 additional cycles; maintenance was optional.
View Article and Find Full Text PDFCancer Genet Cytogenet
April 2006
Biological processes are often accompanied by occurrences of multiple events, such as activation of certain cell types, change in prevalence of cell subpopulations (such as T cells), changes in concentration of proteins or peptides, or breaks in chromosomes. Some co-occurrences of these events are by chance, but others may have meaningful relations to the underlying biological process. The methodology of linksets is designed to detect the presence of potentially meaningful co-occurrences.
View Article and Find Full Text PDFWe describe several analytical techniques for use in developing genetic models of oncogenesis including: methods for the selection of important genetic events, construction of graph models (including distance-based trees, branching trees, contingency trees and directed acyclic graph models) from these events and methods for interpretation of the resulting models. The models can be used to make predictions about: which genetic events tend to occur early, which events tend to occur together and the likely order of events. Unlike simple path models of oncogenesis, our models allow dependencies to exist between specific genetic changes and allow for multiple, divergent paths in tumor progression.
View Article and Find Full Text PDFWe report the cytogenetic abnormalities from a series of 206 primary malignant melanoma specimens referred to a single institution. A total of 169 out of 206 unique cases had chromosome breakpoints. A previously described statistical method was used to detect nonrandom distribution of chromosome breakpoints at the level of chromosome regions.
View Article and Find Full Text PDFCancer geneticists seek to identify genetic changes in tumor cells and to relate the genetic changes to tumor development. Because single changes can disrupt the cell cycle and promote other genetic changes, it is extremely hard to distinguish cause from effect. In this article we illustrate how 7 techniques from statistics, theoretical computer science, and phylogenetics can be used to infer and test possible models of tumor progression from single genome-wide descriptions of aberrations in a large sample of tumors.
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