Hypercholesterolemia is a risk factor for estrogen receptor (ER) positive breast cancers and is associated with a decreased response of tumors to endocrine therapies. Here we show that 27-Hydroxycholesterol (27HC), a primary metabolite of cholesterol and an ER and Liver X receptor (LXR) ligand, increases ER-dependent growth and LXR-dependent metastasis in mouse models of breast cancer. The effects of cholesterol on tumor pathology required its conversion to 27HC by the cytochrome P450 oxidase CYP27A1, and were attenuated by treatment with CYP27A1 inhibitors. In human breast cancer specimens, CYP27A1 expression levels correlated with tumor grade. In high-grade tumors, both tumor cells and tumor-associated macrophages exhibited high expression levels of the enzyme. Thus, lowering circulating cholesterol levels or interfering with its conversion to 27HC may be a useful strategy to prevent and/or treat breast cancer.

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Obesity and elevated circulating cholesterol are risk factors for breast cancer recurrence, while the use of statins, cholesterol biosynthesis inhibitors widely used for treating hypercholesterolemia, is associated with improved disease-free survival. Here, we show that cholesterol mediates the metastatic effects of a high-fat diet via its oxysterol metabolite, 27-hydroxycholesterol. Ablation or inhibition of CYP27A1, the enzyme responsible for the rate-limiting step in 27-hydroxycholesterol biosynthesis, significantly reduces metastasis in relevant animal models of cancer. The robust effects of 27-hydroxycholesterol on metastasis requires myeloid immune cell function, and it was found that this oxysterol increases the number of polymorphonuclear-neutrophils and γδ-T cells at distal metastatic sites. The pro-metastatic actions of 27-hydroxycholesterol requires both polymorphonuclear-neutrophils and γδ-T cells, and 27-hydroxycholesterol treatment results in a decreased number of cytotoxic CD8+T lymphocytes. Therefore, through its actions on γδ-T cells and polymorphonuclear-neutrophils, 27-hydroxycholesterol functions as a biochemical mediator of the metastatic effects of hypercholesterolemia.

Cancer cells can activate diverse signaling pathways to evade the cytotoxic action of drugs. We created and screened a library of barcoded pathway-activating mutant cDNAs to identify those that enhanced the survival of cancer cells in the presence of 13 clinically relevant, targeted therapies. We found that activation of the RAS– MAPK (mitogen-activated protein kinase), Notch1, PI3K (phosphoinositide 3-kinase)–mTOR (mechanistic target of rapamycin), and ER (estrogen receptor) signaling pathways often conferred resistance to this selection of drugs. Activation of the Notch1 pathway promoted acquired resistance to tamoxifen (an ER-targeted therapy) in serially-passaged breast cancer xenografts in mice, and treating mice with a γ-secretase inhibitor to inhibit Notch signaling restored tamoxifen sensitivity. Markers of Notch1 activity in tumor tissue correlated with resistance to tamoxifen in breast cancer patients. Similarly, activation of Notch1 signaling promoted acquired resistance to MAPK inhibitors in BRAFV600E melanoma cells in culture, and the abundance of Notch1 pathway markers were increased in tumors from a subset of melanoma patients. Thus, Notch1 signaling may be a therapeutic target in some drug-resistant breast cancers and melanomas. Additionally, multiple resistance pathways were activated in melanoma cell lines with intrinsic resistance to MAPK inhibitors, and simultaneous inhibition of these pathways synergistically induced drug sensitivity. These data illustrate the potential for systematic identification of the signaling pathways controlling drug resistance that could inform clinical strategies and drug development for multiple types of cancer. This approach may also be used to advance clinical options in other disease contexts.

SUMMARY Targeted cancer therapies that use genetics are successful, but principles for selectively targeting tumor metabolism that is also dependent on the environment remain unknown. We now show that differences in rate-controlling enzymes during the Warburg Effect (WE), the most prominent hallmark of cancer cell metabolism, can be used to predict a response to targeting glucose metabolism. We establish a natural product, koningic acid (KA), to be a selective inhibitor of GAPDH, an enzyme we characterize to have differential control properties over metabolism during the WE. With machine learning and integrated pharmacogenomics and metabolomics, we demonstrate that KA efficacy is not determined by the status of individual genes, but by the quantitative extent of the WE leading to a therapeutic window in vivo. Thus, the basis of targeting of the WE can be encoded by molecular principles that extend beyond the status of individual genes.