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SUMMARYDNA 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.

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. Drug combinations consisting of two cell death-targeting drugs are enriched for antagonism and ‘single-agent dominance’, where the faster-acting drug suppresses the slower-acting drug due to inhibitory crosstalk between cell death pathways.

Summary 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)., Graphical Abstract, Highlights • FLICK generates a death-specific measure of drug response not a correlate of viability • FLICK is high-throughput and easy to use like common plate reader-based assays • FLICK can be used to generate any commonly used drug response metric • FLICK can be used 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.

Targeted therapeutics for the treatment of cancer generally exploit “oncogene addiction”, a phenomenon in which a single protein induces a pathway that drives the growth and survival of the tumor cells. However, the efficacy of oncogene-targeted therapies varies substantially. For instance, epidermal growth factor receptor (EGFR) signaling can be effectively targeted in EGFR-mutant non-small cell lung cancer (NSCLC) but not in triple-negative breast cancer (TNBC), despite evidence that EGFR is activated to a similar degree. In this study, we found that EGFR controls a similar signaling-to-transcriptional network in both TNBC and EGFR-mutant NSCLC cells, but that cell death is induced only in the latter. A genome-wide CRISPR-Cas9 genetic knockout screen revealed that the Elongator (ELP) complex is a mediator of insensitivity to EGFR inhibition in TNBC cells. Depleting ELP proteins induced substantial apoptotic cell death in an EGFR inhibition-dependent manner. The tRNA-modifying function of the ELP complex promoted insensitivity to the EGFR inhibitor erlotinib by facilitating the expression of the anti-apoptotic protein Mcl-1. Pharmacological inhibition of Mcl-1 synergized with EGFR inhibition across 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 co-targeting Mcl-1.

SUMMARY 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., In Brief Anti-cancer drugs affect both the growth and survival of cancer cells. Commonly used measures of drug sensitivity do not distinguish between these two different outcomes. Schwartz et al. developed GRADE, a drug analysis method that reveals the proportional contributions of cell death versus growth inhibition for an observed drug response., Graphical Abstract

Therapeutic regimens for cancer generally involve drugs used in combinations. Most prior work has focused on identifying and understanding synergistic drug-drug interactions; however, understanding sources of antagonistic interactions remains an important and understudied issue. To enrich for antagonistic interactions and reveal common features of these drug combinations, we screened all pairwise combinations of drugs characterized as canonical activators of different forms of regulated cell death. We find that this network is strongly enriched for antagonistic interactions, and in particular, enriched for an extreme form of antagonism, which we call “single agent dominance”. Single agent dominance refers to antagonisms in which a two drug combination phenocopies one of the two agents. We find that dominance results from differences in the cell death onset time, with dominant drugs inducing death earlier and at faster rates than their suppressed counterparts. Finally, we explored the mechanisms by which parthanatotic agents dominate apoptotic agents, finding that dominance in this scenario is caused by mutually exclusive and conflicting use of PARP1. Taken together, our study reveals death activation kinetics as a predictive feature of antagonism, due to inhibitory crosstalk between cell death pathways.

Summary 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., Graphical Abstract, Highlights • Variation in cisplatin sensitivity in TNBC unrelated to DNA repair deficiencies • Systems-level analysis of signal transduction in sensitive versus resistant models • Cell-cycle checkpoint G3BP2 and MK2 activation determines cisplatin sensitivity • Time-resolved cisplatin-induced signal transduction predicts cisplatin sensitivity, Triple-negative breast cancers show large variation in sensitivity to the chemotherapeutic agent cisplatin that cannot be explained by defects in DNA repair. Heijink et al. conducted a systems-level analysis of cisplatin-induced signal transduction and reveal that signaling dynamics can be used to predict cisplatin sensitivity of TNBC models.

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.