Cancer treatment generally involves drugs used in combinations. Most prior 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 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 death onset time, with dominant drugs acting earlier than their suppressed counterparts. We explored mechanisms by which parthanatotic agents dominate apoptotic agents, finding dominance in this scenario caused by mutually exclusive and conflicting use of PARP1. Taken together, our study reveals death kinetics as a predictive feature of antagonism, due to inhibitory crosstalk between death pathways.

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.

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.

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 the pre-clinical evaluation of anti-cancer drugs, two different measurement approaches are used: relative viability, which scores an amalgam of growth arrest and cell death, and fractional viability, which more specifically scores the degree of cell killing. In this study, we directly quantify relationships between drug-induced growth inhibition and drug-induced cell death by counting live and dead cells over time using quantitative microscopy. We find that most drugs affect both growth and death, but with different proportions and with different relative timing.These features lead to a non-uniform and unpredictable relationship between the canonical relative and fractional drug response measurements. To unify these disparate measurements, we create a new data visualization and data analysis platform, called drug GRADE, which characterizes the degree to which cell death contributes to an observed reduction in population size for any given drug. Our new method reveals both drug-and genotype-specific drug responses, which are not captured using traditional pharmaco-metrics. Taken together, this study highlights the extremely idiosyncratic nature of drug-induced growth and cell death and provides a new analysis tool for quantitatively evaluating these behaviors.