DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity.

An individual malignant tumor is composed of a heterogeneous collection of single cells with distinct molecular and phenotypic features, a phenomenon termed intratumoral heterogeneity. Intratumoral heterogeneity poses challenges for cancer treatment, motivating the need for combination therapies. Single-cell technologies are now available to guide effective drug combinations by accounting for intratumoral heterogeneity through the analysis of the signaling perturbations of an individual tumor sample screened by a drug panel. In particular, Mass Cytometry Time-of-Flight (CyTOF) is a high-throughput single-cell technology that enables the simultaneous measurements of multiple ([Formula: see text]40) intracellular and surface markers at the level of single cells for hundreds of thousands of cells in a sample. We developed a computational framework, entitled Drug Nested Effects Models (DRUG-NEM), to analyze CyTOF single-drug perturbation data for the purpose of individualizing drug combinations. DRUG-NEM optimizes drug combinations by choosing the minimum number of drugs that produce the maximal desired intracellular effects based on nested effects modeling. We demonstrate the performance of DRUG-NEM using single-cell drug perturbation data from tumor cell lines and primary leukemia samples

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Peer reviewe

Androgens regulate prostate cancer cell growth via an AMPK-PGC-1α-mediated metabolic switch

Prostate cancer is the most commonly diagnosed malignancy among men in industrialized countries, accounting for the second leading cause of cancer-related deaths. While we now know that the androgen receptor (AR) is important for progression to the deadly advanced stages of the disease, it is poorly understood what AR-regulated processes drive this pathology. Here, we demonstrate that AR regulates prostate cancer cell growth via the metabolic sensor 5′-AMP-activated protein kinase (AMPK), a kinase that classically regulates cellular energy homeostasis. In patients, activation of AMPK correlated with prostate cancer progression. Using a combination of radiolabeled assays and emerging metabolomic approaches, we also show that prostate cancer cells respond to androgen treatment by increasing not only rates of glycolysis, as is commonly seen in many cancers, but also glucose and fatty acid oxidation. Importantly, this effect was dependent on androgen-mediated AMPK activity. Our results further indicate that the AMPK-mediated metabolic changes increased intracellular ATP levels and peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α)-mediated mitochondrial biogenesis, affording distinct growth advantages to the prostate cancer cells. Correspondingly, we used outlier analysis to determine that PGC-1α is overexpressed in a subpopulation of clinical cancer samples. This was in contrast to what was observed in immortalized benign human prostate cells and a testosterone-induced rat model of benign prostatic hyperplasia. Taken together, our findings converge to demonstrate that androgens can co-opt the AMPK-PGC-1α signaling cascade, a known homeostatic mechanism, to increase prostate cancer cell growth. The current study points to the potential utility of developing metabolic-targeted therapies directed towards the AMPK-PGC-1α signaling axis for the treatment of prostate cancer