Opening Pandora's Box--the new biology of driver mutations and clonal evolution in cancer as revealed by next generation sequencing.

Recent advances in next generation sequencing have greatly enhanced the scope and speed of genomic cancer research. Apart from merely listing identified mutations from cancer genomes sequencing, this review will summarize some insights specifically focusing on the biology of allele generating cancer driver mutations and clonal patterns during tumor evolution. Studies using massively parallel sequencing of primary tumor samples and cancer cell lines have identified neomorphic alleles and other recurrent mutations in proteins involved in chromatin modification and in the regulation of transcription and translation. Further studies with deep sequencing of matched primary and metastatic tumors have also started to characterize distinct patterns of tumor clonal evolution. The development of single cell sequencing is expected to help further elucidate tumor clonality and aid the translation of these discoveries into diagnostic and therapeutic applications.

Intensity calibration and automated cell cycle gating for high-throughput image-based siRNA screens of mammalian cells.

High-content microscopic screening systems are powerful tools for extracting quantitative multiparameter measures from large number of cells under numerous conditions. These systems perform well in applications that monitor the presence of objects, but lack in their ability to accurately estimate object intensities and summarize these findings due to variations in background, aberrations in illumination, and variability in staining over the image and/or sample wells. We present effective and automated methods that are applicable to analyzing intensity-based cell cycle assays under high-throughput screening conditions. We characterize the system aberration response from images of calibration beads and then enhance the detection and segmentation accuracy of traditional algorithms by preprocessing images for local background variations. We also provide a rapid, adaptive, cell-cycle partitioning algorithm to characterize each sample well based on the estimated locally and globally corrected cell intensity measures of BrdU and DAPI incorporation. We demonstrated the utility and range of our cell ploidy and probe density measurement methods in a pilot screen using a siRNA library against 779 human protein kinases. With our method, multiple image-based quantitative phenotypes can be realized from a single high-throughput image-based microtiter-plate screen.