High-throughput genetic screens have become essential tools for studying a wide variety of biological processes. Here we experimentally compare systems based on clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) or its transcriptionally repressive variant, CRISPR-interference (CRISPRi), with a traditional short hairpin RNA (shRNA)-based system for performing lethality screens. We find that the CRISPR technology performed best, with low noise, minimal off-target effects and consistent activity across reagents.

Current understanding of the mechanisms by which cell growth is regulated lags significantly behind our knowledge of the complex processes controlling cell cycle progression. Recent studies suggest that the mammalian target of rapamycin (mTOR) pathway is a key regulator of cell growth via the regulation of protein synthesis. The key mTOR effectors of cell growth are eukaryotic initiation factor 4E-binding protein 1 (4EBP-1) and the ribosomal protein S6 kinase (S6K). Here we will review the current models for mTOR dependent regulation of ribosome function and biogenesis as well as its role in coordinating growth factor and nutrient signaling to facilitate homeostasis of cell growth and proliferation. We will place particular emphasis on the role of S6K1 signaling and will highlight the points of cross talk with other key growth control pathways. Finally, we will discuss the impact of S6K signaling and the consequent feedback regulation of the PI3K/Akt pathway on disease processes including cancer.

Precise regulation of ribosome biogenesis is fundamental to maintain normal cell growth and proliferation, and accelerated ribosome biogenesis is associated with malignant transformation. Here, we show that the kinase AKT regulates ribosome biogenesis at multiple levels to promote ribosomal RNA (rRNA) synthesis. Transcription elongation by RNA polymerase I, which synthesizes rRNA, required continuous AKT-dependent signaling, an effect independent of AKT's role in activating the translation-promoting complex mTORC1 (mammalian target of rapamycin complex 1). Sustained inhibition of AKT and mTORC1 cooperated to reduce rRNA synthesis and ribosome biogenesis by additionally limiting RNA polymerase I loading and pre-rRNA processing. In the absence of growth factors, constitutively active AKT increased synthesis of rRNA, ribosome biogenesis, and cell growth. Furthermore, AKT cooperated with the transcription factor c-MYC to synergistically activate rRNA synthesis and ribosome biogenesis, defining a network involving AKT, mTORC1, and c-MYC as a master controller of cell growth. Maximal activation of c-MYC-dependent rRNA synthesis in lymphoma cells required AKT activity. Moreover, inhibition of AKT-dependent rRNA transcription was associated with increased lymphoma cell death by apoptosis. These data indicate that decreased ribosome biogenesis is likely to be a fundamental component of the therapeutic response to AKT inhibitors in cancer.

RNAi screening using pooled shRNA libraries is a valuable tool for identifying genetic regulators of biological processes. However, for a successful pooled shRNA screen, it is imperative to thoroughly optimize experimental conditions to obtain reproducible data. Here we performed viability screens with a library of ∼10 000 shRNAs at two different fold representations (100- and 500-fold at transduction) and report the reproducibility of shRNA abundance changes between screening replicates determined by microarray and next generation sequencing analyses. We show that the technical reproducibility between PCR replicates from a pooled screen can be drastically improved by ensuring that PCR amplification steps are kept within the exponential phase and by using an amount of genomic DNA input in the reaction that maintains the average template copies per shRNA used during library transduction. Using these optimized PCR conditions, we then show that higher reproducibility of biological replicates is obtained by both microarray and next generation sequencing when screening with higher average shRNA fold representation. shRNAs that change abundance reproducibly in biological replicates (primary hits) are identified from screens performed with both 100- and 500-fold shRNA representation, however a higher percentage of primary hit overlap between screening replicates is obtained from 500-fold shRNA representation screens. While strong hits with larger changes in relative abundance were generally identified in both screens, hits with smaller changes were identified only in the screens performed with the higher shRNA fold representation at transduction.

Cancer cell lines differ greatly in their sensitivity to anticancer drugs as a result of different oncogenic drivers and drug resistance mechanisms operating in each cell line. Although many of these mechanisms have been discovered, it remains a challenge to understand how they interact to render an individual cell line sensitive or resistant to a particular drug. To better understand this variability, we profiled a panel of 30 breast cancer cell lines in the absence of drugs for their mutations, copy number aberrations, mRNA, protein expression and protein phosphorylation, and for response to seven different kinase inhibitors. We then constructed a knowledge-based, Bayesian computational model that integrates these data types and estimates the relative contribution of various drug sensitivity mechanisms. The resulting model of regulatory signaling explained the majority of the variability observed in drug response. The model also identified cell lines with an unexplained response, and for these we searched for novel explanatory factors. Among others, we found that 4E-BP1 protein expression, and not just the extent of phosphorylation, was a determinant of mTOR inhibitor sensitivity. We validated this finding experimentally and found that overexpression of 4E-BP1 in cell lines that normally possess low levels of this protein is sufficient to increase mTOR inhibitor sensitivity. Taken together, our work demonstrates that combining experimental characterization with integrative modeling can be used to systematically test and extend our understanding of the variability in anticancer drug response. By estimating how different oncogenic mutations and drug resistance mechanisms affect the response of cancer cells to kinase inhibitors, we can better understand and ultimately predict response to these anticancer drugs. http://cancerres.aacrjournals.org/content/canres/78/15/4396/F1.large.jpg .