Person:

Luquette, Joe

Background: Recent advances in sequencing technologies have enabled generation of large-scale genome sequencing data. These data can be used to characterize a variety of genomic features, including the DNA copy number profile of a cancer genome. A robust and reliable method for screening chromosomal alterations would allow a detailed characterization of the cancer genome with unprecedented accuracy. Results: We develop a method for identification of copy number alterations in a tumor genome compared to its matched control, based on application of Smith-Waterman algorithm to single-end sequencing data. In a performance test with simulated data, our algorithm shows >90% sensitivity and >90% precision in detecting a single copy number change that contains approximately 500 reads for the normal sample. With 100-bp reads, this corresponds to a ~50 kb region for 1X genome coverage of the human genome. We further refine the algorithm to develop rSW-seq, (recursive Smith-Waterman-seq) to identify alterations in a complex configuration, which are commonly observed in the human cancer genome. To validate our approach, we compare our algorithm with an existing algorithm using simulated and publicly available datasets. We also compare the sequencing-based profiles to microarray-based results. Conclusion: We propose rSW-seq as an efficient method for detecting copy number changes in the tumor genome.

The systematic identification of effective drug combinations has been hindered by the unavailability of methods that can explore the large combinatorial search space of drug interactions. Here we present a multiplex screening method named MuSIC (Multiplex Screening for Interacting Compounds), which expedites the comprehensive assessment of pair-wise compound interactions. We examined ~500,000 drug pairs from 1000 FDA-approved or clinically tested drugs and identified drugs that synergize to inhibit HIV replication. Our analysis reveals an enrichment of anti-inflammatory drugs in drug combinations that synergize against HIV, indicating HIV benefits from inflammation that accompanies its infection. Multiple drug pairs identified in this study, including glucocorticoid and nitazoxanide, synergize by targeting different steps of the HIV life cycle. As inflammation accompanies HIV infection, our findings indicate that inhibiting inflammation could curb HIV propagation. MuSIC can be applied to a wide variety of disease-relevant screens to facilitate efficient identification of compound combinations.