Background: Many functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way. Results: To address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knockout. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community. Conclusions: Our analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used.

KRAS is the most frequently mutated oncogene. The incidence of specifi c KRAS alleles varies between cancers from different sites, but it is unclear whether allelic selection results from biological selection for specifi c mutant KRAS proteins. We used a crossdisciplinary approach to compare KRAS G12D , a common mutant form, and KRAS A146T , a mutant that occurs only in selected cancers. Biochemical and structural studies demonstrated that KRAS A146T exhibits a marked extension of switch 1 away from the protein body and nucleotide binding site, which activates KRAS by promoting a high rate of intrinsic and guanine nucleotide exchange factorinduced nucleotide exchange. Using mice genetically engineered to express either allele, we found that KRAS G12D and KRAS A146T exhibit distinct tissue-specifi c effects on homeostasis that mirror mutational frequencies in human cancers. These tissue-specifi c phenotypes result from allele-specifi c signaling properties, demonstrating that context-dependent variations in signaling downstream of different KRAS mutants drive the KRAS mutational pattern seen in cancer. SIGNIFICANCE: Although epidemiologic and clinical studies have suggested allele-specifi c behaviors for KRAS , experimental evidence for allele-specifi c biological properties is limited. We combined structural biology, mass spectrometry, and mouse modeling to demonstrate that the selection for specifi c KRAS mutants in human cancers from different tissues is due to their distinct signaling properties.

Highlights d ROS varies with the cell cycle in freely cycling cancer cells d ROS levels peak in G2 and mitosis d Oxidation of biomolecules is maximal in mitosis d Mitotic arrest further enhances ROS and oxidative damage to proteins and nucleotides

Highlights d The MTH1 inhibitor TH588 synergizes with Plk1 inhibition to drive cancer cell death d VISAGE implicates the mitotic spindle, not MTH1, as the target of drug synergy d TH588 binds the colchicine binding site of b-tubulin blocking microtubule assembly d The cancer cell spindle is particularly vulnerable to Plk1 + microtubule inhibitors

SUMMARY RNA-binding proteins (RBPs) play critical roles in regulating gene expression by modulating splicing, RNA stability, and protein translation. Stimulus-induced alterations in RBP function contribute to global changes in gene expression, but identifying which RBPs are responsible for the observed changes remains an unmet need. Here, we present Transite, a computational approach that systematically infers RBPs influencing gene expression through changes in RNA stability and degradation. As a proof of principle, we apply Transite to RNA expression data from human patients with non-small-cell lung cancer whose tumors were sampled at diagnosis or after recurrence following treatment with platinum-based chemotherapy. Transite implicates known RBP regulators of the DNA damage response and identifies hnRNPC as a new modulator of chemotherapeutic resistance, which we subsequently validated experimentally. Transite serves as a framework for the identification of RBPs that drive cell-state transitions and adds additional value to the vast collection of publicly available gene expression datasets.