Post-perturbational transcriptional signatures of cancer cell line vulnerabilities

Abstract

While chemical and genetic viability screens in cancer cell lines have identified many promising cancer vulnerabilities, simple univariate readouts of cell proliferation fail to capture the complex cellular responses to perturbations. Complementarily, gene expression profiling offers an informationrich measure of cell state that can provide a more detailed account of cellular responses to perturbations. Relatively little is known, however, about the relationship between transcriptional responses to perturbations and the long-term cell viability effects of those perturbations. To address this question, we integrated thousands of post-perturbational transcriptional profiles from the Connectivity Map with large-scale screens of cancer cell lines' viability response to genetic and chemical perturbations. This analysis revealed a generalized transcriptional signature associated with reduced viability across perturbations, which was consistent across post-perturbation time-points, perturbation types, and viability datasets. At a more granular level, we lay out the landscape of treatment-specific expression-viability relationships across a broad panel of drugs and genetic reagents, and we demonstrate that these postperturbational expression signatures can be used to infer long-term viability. Together, these results help unmask the transcriptional changes that are associated with perturbation-induced viability loss in cancer cell lines. Co-senior authors

Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action

“…We also used MIX-Seq to show that transcriptional responses measured 6-24 h after drug treatment can be used to predict long-term cell viability remarkably well across selected targeted cancer drugs. These results are in broad agreement with recently published analyses 36,37 comparing drug sensitivity data and transcriptional profiling data from the CMAP project 10,12 , spanning many compounds in a core set of cell lines. By allowing efficient profiling of a given drug's transcriptional effects across many cell lines, MIX-Seq offers unique opportunities to evaluate these relationships in detail for individual compounds, rather than requiring analyses that pool data across compounds, as in previous work.…”

Application of perturbation gene expression profiles in drug discovery—From mechanism of action to quantitative modelling

“…Also, it is important to highlight that similarity between gene expression profiles does not necessarily imply shared MoA, especially in case of anti-cancer drugs. Anti-cancer drugs lead to decreased cell viability, which is represented in their perturbation gene expression signatures (Szalai et al, 2019;Jones et al, 2020;McFarland et al, 2020). Thus two cytotoxic drugs can have similar gene expression signature, despite having distinct mechanisms of action, which effect can be removed by appropriate statistical models (Szalai et al, 2019;McFarland et al, 2020).…”