Purpose: The pathways underlying basal-like breast cancer are poorly understood, and as yet, there is no approved targeted therapy for this disease. We investigated the role of mitogenactivated protein kinase kinase (MEK) and phosphatidylinositol 3-kinase (PI3K) inhibitors as targeted therapies for basal-like breast cancer. Experimental Design: We used pharmacogenomic analysis of a large panel of breast cancer cell lines with detailed accompanying molecular information to identify molecular predictors of response to a potent and selective inhibitor of MEK and also to define molecular mechanisms underlying combined MEK and PI3K targeting in basal-like breast cancer. Hypotheses were confirmed by testing in multiple tumor xenograft models. Results: We found that basal-like breast cancer models have an activated RAS-like transcriptional program and show greater sensitivity to a selective inhibitor of MEK compared with models representative of other breast cancer subtypes.We also showed that loss of PTEN is a negative predictor of response to MEK inhibition, that treatment with a selective MEK inhibitor caused up-regulation of PI3K pathway signaling, and that dual blockade of both PI3K and MEK/extracellular signal^regulated kinase signaling synergized to potently impair the growth of basal-like breast cancer models in vitro and in vivo. Conclusions: Our studies suggest that single-agent MEK inhibition is a promising therapeutic modality for basal-like breast cancers with intact PTEN, and also provide a basis for rational combination of MEK and PI3K inhibitors in basal-like cancers with both intact and deleted PTEN.

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IL-19, IL-20, IL-22, IL-24, and IL-26 are members of the IL-10 family of cytokines that have been shown to be up-regulated in psoriatic skin. Contrary to IL-10, these cytokines signal using receptor complex R1 subunits that are preferentially expressed on cells of epithelial origin; thus, we henceforth refer to them as the IL-20 subfamily cytokines. In this study, we show that primary human keratinocytes (KCs) express receptors for these cytokines and that IL-19, IL-20, IL-22, and IL-24 induce acanthosis in reconstituted human epidermis (RHE) in a dose-dependent manner. These cytokines also induce expression of the psoriasisassociated protein S100A7 and keratin 16 in RHE and cause persistent activation of Stat3 with nuclear localization. IL-22 had the most pronounced effects on KC proliferation and on the differentiation of KCs in RHE, inducing a decrease in the granular cell layer (hypogranulosis). Furthermore, gene expression analysis performed on cultured RHE treated with these cytokines showed that IL-19, IL-20, IL-22, and IL-24 regulate many of these same genes to variable degrees, inducing a gene expression profile consistent with inflammatory responses, wound healing re-epithelialization, and altered differentiation. Many of these genes have also been found to be up-regulated in psoriatic skin, including several chemokines, ␤-defensins, S100 family proteins, and kallikreins. These results confirm that IL-20 subfamily cytokines are important regulators of epidermal KC biology with potentially pivotal roles in the immunopathology of psoriasis.

BackgroundAccess to palliative care is a key quality metric which most healthcare organizations strive to improve. The primary challenges to increasing palliative care access are a combination of physicians over-estimating patient prognoses, and a shortage of palliative staff in general. This, in combination with treatment inertia can result in a mismatch between patient wishes, and their actual care towards the end of life.MethodsIn this work, we address this problem, with Institutional Review Board approval, using machine learning and Electronic Health Record (EHR) data of patients. We train a Deep Neural Network model on the EHR data of patients from previous years, to predict mortality of patients within the next 3-12 month period. This prediction is used as a proxy decision for identifying patients who could benefit from palliative care.ResultsThe EHR data of all admitted patients are evaluated every night by this algorithm, and the palliative care team is automatically notified of the list of patients with a positive prediction. In addition, we present a novel technique for decision interpretation, using which we provide explanations for the model’s predictions.ConclusionThe automatic screening and notification saves the palliative care team the burden of time consuming chart reviews of all patients, and allows them to take a proactive approach in reaching out to such patients rather then relying on referrals from the treating physicians.

When devising a course of treatment for a patient, doctors often have little quantitative evidence on which to base their decisions, beyond their medical education and published clinical trials. Stanford Health Care alone has millions of electronic medical records that are only just recently being leveraged to inform better treatment recommendations. These data present a unique challenge because they are high dimensional and observational. Our goal is to make personalized treatment recommendations based on the outcomes for past patients similar to a new patient. We propose and analyze 3 methods for estimating heterogeneous treatment effects using observational data. Our methods perform well in simulations using a wide variety of treatment effect functions, and we present results of applying the 2 most promising methods to data from The SPRINT Data Analysis Challenge, from a large randomized trial of a treatment for high blood pressure.