Functional genomic screens with death rate analyses reveal mechanisms of drug action.

A common approach for understanding how drugs induce their therapeutic effects is to identify the genetic determinants of drug sensitivity. Because 'chemo-genetic profiles' are performed in a pooled format, inference of gene function is subject to several confounding influences related to variation in growth rates between clones. In this study, we developed Method for Evaluating Death Using a Simulation-assisted Approach (MEDUSA), which uses time-resolved measurements, along with model-driven constraints, to reveal the combination of growth and death rates that generated the observed drug response. MEDUSA is uniquely effective at identifying death regulatory genes. We apply MEDUSA to characterize DNA damage-induced lethality in the presence and absence of p53. Loss of p53 switches the mechanism of DNA damage-induced death from apoptosis to a non-apoptotic death that requires high respiration. These findings demonstrate the utility of MEDUSA both for determining the genetic dependencies of lethality and for revealing opportunities to potentiate chemo-efficacy in a cancer-specific manner.

Drug antagonism and single-agent dominance result from differences in death kinetics.

Cancer treatment generally involves drugs used in combinations. Most previous work has focused on identifying and understanding synergistic drug-drug interactions; however, understanding antagonistic interactions remains an important and understudied issue. To enrich for antagonism and reveal common features of these combinations, we screened all pairwise combinations of drugs characterized as activators of regulated cell death. This network is strongly enriched for antagonism, particularly a form of antagonism that we call 'single-agent dominance'. Single-agent dominance refers to antagonisms in which a two-drug combination phenocopies one of the two agents. Dominance results from differences in cell death onset time, with dominant drugs acting earlier than their suppressed counterparts. We explored mechanisms by which parthanatotic agents dominate apoptotic agents, finding that dominance in this scenario is caused by mutually exclusive and conflicting use of Poly(ADP-ribose) polymerase 1 (PARP1). Taken together, our study reveals death kinetics as a predictive feature of antagonism, due to inhibitory crosstalk between cell death pathways.

Tumor-stroma interactions differentially alter drug sensitivity based on the origin of stromal cells.

Due to tumor heterogeneity, most believe that effective treatments should be tailored to the features of an individual tumor or tumor subclass. It is still unclear, however, what information should be considered for optimal disease stratification, and most prior work focuses on tumor genomics. Here, we focus on the tumor microenvironment. Using a large-scale coculture assay optimized to measure drug-induced cell death, we identify tumor-stroma interactions that modulate drug sensitivity. Our data show that the chemo-insensitivity typically associated with aggressive subtypes of breast cancer is not observed if these cells are grown in 2D or 3D monoculture, but is manifested when these cells are cocultured with stromal cells, such as fibroblasts. Furthermore, we find that fibroblasts influence drug responses in two distinct and divergent manners, associated with the tissue from which the fibroblasts were harvested. These divergent phenotypes occur regardless of the drug tested and result from modulation of apoptotic priming within tumor cells. Our study highlights unexpected diversity in tumor-stroma interactions, and we reveal new principles that dictate how fibroblasts alter tumor drug responses.

Functional genomics reveals an off-target dependency of drug synergy in gastric cancer therapy.

The rational combination of anticancer agents is critical to improving patient outcomes in cancer. Nonetheless, most combination regimens in the clinic result from empirical methodologies disregarding insight into the mechanism of action and missing the opportunity to improve therapy outcomes incrementally. Deciphering the genetic dependencies and vulnerabilities responsible for synergistic interactions is crucial for rationally developing effective anticancer drug combinations. Hence, we screened pairwise pharmacological interactions between molecular-targeted agents and conventional chemotherapeutics and examined the genome-scale genetic dependencies in gastric adenocarcinoma cell models. Since this type of cancer is mainly chemoresistant and incurable, clinical situations demand effective combination strategies. Our pairwise combination screen revealed SN38/erlotinib as the drug pair with the most robust synergism. Genome-wide CRISPR screening and a shRNA-based signature assay indicated that the genetic dependency/vulnerability signature of SN38/erlotinib is the same as SN38 alone. Additional investigation revealed that the enhanced cell death with improved death kinetics caused by the SN38/erlotinib combination is surprisingly due to erlotinib's off-target effect that inhibits ABCG2 but not its on-target effect on EGFR. Our results confirm that a genetic dependency signature different from the single-drug application may not be necessary for the synergistic interaction of molecular-targeted agents with conventional chemotherapeutics in gastric adenocarcinoma. The findings also demonstrated the efficacy of functional genomics approaches in unveiling biologically validated mechanisms of pharmacological interactions.

p53 controls choice between apoptotic and non-apoptotic death following DNA damage.

DNA damage can activate apoptotic and non-apoptotic forms of cell death; however, it remains unclear what features dictate which type of cell death is activated. We report that p53 controls the choice between apoptotic and non-apoptotic death following exposure to DNA damage. In contrast to the conventional model, which suggests that p53-deficient cells should be resistant to DNA damage-induced cell death, we find that p53-deficient cells die at high rates following DNA damage, but exclusively using non-apoptotic mechanisms. Our experimental data and computational modeling reveal that non-apoptotic death in p53-deficient cells has not been observed due to use of assays that are either insensitive to cell death, or that specifically score apoptotic cells. Using functional genetic screening - with an analysis that enables computational inference of the drug-induced death rate - we find in p53-deficient cells that DNA damage activates a mitochondrial respiration-dependent form of cell death, called MPT-driven necrosis. Cells deficient for p53 have high basal respiration, which primes MPT-driven necrosis. Finally, using metabolite profiling, we identified mitochondrial activity-dependent metabolic vulnerabilities that can be targeted to potentiate the lethality of DNA damage specifically in p53-deficient cells. Our findings reveal how the dual functions of p53 in regulating mitochondrial activity and the DNA damage response combine to facilitate the choice between apoptotic and non-apoptotic death.

FLICK: an optimized plate reader-based assay to infer cell death kinetics.

Evaluating drug sensitivity is improved by directly quantifying death kinetics, rather than correlates of viability, such as metabolic activity. This is challenging, requiring time-lapse microscopy and genetically encoded labels to distinguish live and dead cells. Here, we describe fluorescence-based and lysis-dependent inference of cell death kinetics (FLICK). This method requires only a standard fluorescence plate reader, retaining the high-throughput nature and broad accessibility of common viability assays. However, FLICK specifically quantifies death, including an accurate inference of death kinetics. For complete details on the use and execution of this protocol, please refer to Richards et al. (2020).

ELP-dependent expression of MCL1 promotes resistance to EGFR inhibition in triple-negative breast cancer cells.

Targeted therapeutics for cancer generally exploit "oncogene addiction," a phenomenon in which the growth and survival of tumor cells depend on the activity of a particular protein. However, the efficacy of oncogene-targeted therapies varies substantially. For instance, targeting epidermal growth factor receptor (EGFR) signaling is effective in some non-small cell lung cancer (NSCLC) but not in triple-negative breast cancer (TNBC), although these cancers show a similar degree of increase in EGFR activity. Using a genome-wide CRISPR-Cas9 genetic knockout screen, we found that the Elongator (ELP) complex mediates insensitivity to the EGFR inhibitor erlotinib in TNBC cells by promoting the synthesis of the antiapoptotic protein Mcl-1. Depleting ELP proteins promoted apoptotic cell death in an EGFR inhibition-dependent manner. Pharmacological inhibition of Mcl-1 synergized with EGFR inhibition in a panel of genetically diverse TNBC cells. The findings indicate that TNBC "addiction" to EGFR signaling is masked by the ELP complex and that resistance to EGFR inhibitors in TNBC might be overcome by cotargeting Mcl-1.

Drug GRADE: An Integrated Analysis of Population Growth and Cell Death Reveals Drug-Specific and Cancer Subtype-Specific Response Profiles.

When evaluating anti-cancer drugs, two different measurements are used: relative viability, which scores an amalgam of proliferative arrest and cell death, and fractional viability, which specifically scores the degree of cell killing. We quantify relationships between drug-induced growth inhibition and cell death by counting live and dead cells using quantitative microscopy. We find that most drugs affect both proliferation and death, but in different proportions and with different relative timing. This causes a non-uniform relationship between relative and fractional response measurements. To unify these measurements, we created a data visualization and analysis platform called drug GRADE, which characterizes the degree to which death contributes to an observed drug response. GRADE captures drug- and genotype-specific responses, which are not captured using traditional pharmacometrics. This study highlights the idiosyncratic nature of drug-induced proliferative arrest and cell death. Furthermore, we provide a metric for quantitatively evaluating the relationship between these behaviors.

Modeling of Cisplatin-Induced Signaling Dynamics in Triple-Negative Breast Cancer Cells Reveals Mediators of Sensitivity.

Triple-negative breast cancers (TNBCs) display great diversity in cisplatin sensitivity that cannot be explained solely by cancer-associated DNA repair defects. Differential activation of the DNA damage response (DDR) to cisplatin has been proposed to underlie the observed differential sensitivity, but it has not been investigated systematically. Systems-level analysis-using quantitative time-resolved signaling data and phenotypic responses, in combination with mathematical modeling-identifies that the activation status of cell-cycle checkpoints determines cisplatin sensitivity in TNBC cell lines. Specifically, inactivation of the cell-cycle checkpoint regulator MK2 or G3BP2 sensitizes cisplatin-resistant TNBC cell lines to cisplatin. Dynamic signaling data of five cell cycle-related signals predicts cisplatin sensitivity of TNBC cell lines. We provide a time-resolved map of cisplatin-induced signaling that uncovers determinants of chemo-sensitivity, underscores the impact of cell-cycle checkpoints on cisplatin sensitivity, and offers starting points to optimize treatment efficacy.