ABSTRACT
Multiple myeloma (MM) is a clonal plasma cell malignancy that is initiated by a number of mutations and the process of disease progression is characterized by further acquisition of mutations. The identification and functional characterization of these myelomagenic mutations is necessary to better understand the underlying pathogenic mechanisms in this disease. Recent advancements in next-generation sequencing have made the identification of most of these mutations a reality. However, the functional characterization of these mutations has been hampered by the lack of proper and efficient tools to dissect these mutations. Here we explored the possible utility of transcription activator-like effector nuclease (TALEN) genome engineering technology to tailoring the genome of MM cells. To test this possibility, we targeted the HPRT1 gene and found that TALENs are a very robust and efficient genome-editing tool in MM cells. Using cotransfected green fluorescent protein as an enrichment marker, single-cell subclones with desirable TALEN modifications in the HPRT1 gene were obtained in as little as 3-4 weeks of time. We believe that TALENs will greatly facilitate the functional study of somatic mutations in MM as well as other cancers.
Subject(s)
Deoxyribonucleases/genetics , Multiple Myeloma/enzymology , Multiple Myeloma/genetics , Mutation , Base Sequence , Deoxyribonucleases/metabolism , Female , Gene Knockout Techniques/methods , Gene Targeting , Genetic Engineering , Humans , Hypoxanthine Phosphoribosyltransferase/genetics , Hypoxanthine Phosphoribosyltransferase/metabolism , Male , Molecular Sequence Data , Tumor Cells, CulturedABSTRACT
At an interim point in a clinical trial, trial organisers may wish to use the data on the initial series of patients to judge the likely consequences of further patient accrual. Halperin and colleagues (Controlled Clin Trials 3:311-323, 1982) have suggested calculating the power of a continued trial, conditional on the data observed so far and the null and alternative hypothesis specified at the start of the trial. Here we argue that this idea should be extended to obtain the predictive power of the trial, derived by averaging the conditional power with respect to the current belief about the unknown parameters. Although numerical methods are generally required for evaluating the necessary integrals, the results may be presented graphically and enable the statistician to answer the question: "With the data so far, what is the chance that the trial will end up showing a conclusive result?"
