RNA Pol II inhibition activates cell death independently from the loss of transcription.

RNA Pol II-mediated transcription is essential for eukaryotic life. Although loss of transcription is thought to be universally lethal, the associated mechanisms promoting cell death are not yet known. Here, we show that death following the loss of RNA Pol II activity does not result from dysregulated gene expression.

Genome-wide profiling identifies the genetic dependencies of cell death following EGFR inhibition.

EGFR is a proto-oncogene that is mutationally activated in a variety of cancers. Small molecule inhibitors targeting EGFR can be effective in slowing the progression of disease, and in some settings these drugs even cause dramatic tumor regression. However, responses to EGFR inhibitors are rarely durable, and the mechanisms contributing to response variation remain unclear.

MEDUSA for Identifying Death Regulatory Genes in Chemo-genetic Profiling Data.

Systematic screening of gain- or loss-of-function genetic perturbations can be used to characterize the genetic dependencies and mechanisms of regulation for essentially any cellular process of interest. These experiments typically involve profiling from a pool of single gene perturbations and how each genetic perturbation affects the relative cell fitness. When applied in the context of drug efficacy studies, often called chemo-genetic profiling, these methods should be effective at identifying drug mechanisms of action.

Pol II degradation activates cell death independently from the loss of transcription.

Pol II-mediated transcription is essential for eukaryotic life. While loss of transcription is thought to be universally lethal, the associated mechanisms promoting cell death are not yet known. Here, we show that death following loss of Pol II is not caused by dysregulated gene expression.

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

Background: Integrating molecular-targeted agents into combination chemotherapy is transformative for enhancing treatment outcomes in cancer. However, realizing the full potential of this approach requires a clear comprehension of the genetic dependencies underlying drug synergy. While the interactions between conventional chemotherapeutics are well-explored, the interplay of molecular-targeted agents with conventional chemotherapeutics remains a frontier in cancer treatment.

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.

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.

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.

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).

ELP-dependent expression of 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.

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.

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.

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.

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.