Precision oncology involves identifying drugs that will effectively treat a tumor and then prescribing an optimal clinical treatment regimen. However, most first-line chemotherapy drugs do not have biomarkers to guide their application. For molecularly targeted drugs, using the genomic status of a drug target as a therapeutic indicator has limitations. In this study, machine learning methods (e.g., deep learning) were used to identify informative features from genome-scale omics data and to train classifiers for predicting the effectiveness of drugs in cancer cell lines. The methodology introduced here can accurately predict the efficacy of drugs, regardless of whether they are molecularly targeted or nonspecific chemotherapy drugs. This approach, on a per-drug basis, can identify sensitive cancer cells with an average sensitivity of 0.82 and specificity of 0.82; on a per-cell line basis, it can identify effective drugs with an average sensitivity of 0.80 and specificity of 0.82. This report describes a data-driven precision medicine approach that is not only generalizable but also optimizes therapeutic efficacy. The framework detailed herein, when successfully translated to clinical environments, could significantly broaden the scope of precision oncology beyond targeted therapies, benefiting an expanded proportion of cancer patients. .

Exogenous interleukin 6 (IL-6), synthesized at the initiation of the acute phase response, is considered responsible for signaling hepatocytes to produce acute phase proteins. It is widely posited that IL-6 is either delivered to the liver in an endocrine fashion from immune cells at the site of injury, or alternatively, in a paracrine manner by hepatic immune cells within the liver. A recent publication showed there was a muted IL-6 response in lipopolysaccharide (LPS)-injured mice when nuclear NFκB was specifically inactivated in the hepatocytes. This indicates hepatocellular signaling is also involved in regulating the acute phase production of IL-6. Herein, we present extensive in vitro and in vivo evidence that normal hepatocytes are directly induced to synthesize IL-6 mRNAs and protein by challenge with LPS, a bacterial hepatotoxin, and by HGF, an important regulator of hepatic homeostasis. As the IL-6 receptor is found on the hepatocyte, these results reveal that induction of the acute phase response can be regulated in an autocrine as well as endocrine/paracrine fashion. Further, herein we provide data indicating that following partial hepatectomy (PHx), HGF differentially regulates IL-6 production in hepatocytes (induces) versus immune cells (suppresses), signifying disparate regulation of the cell sources involved in IL-6 production is a biologically relevant mechanism that has previously been overlooked. These findings have wide ranging ramifications regarding how we currently interpret a variety of in vivo and in vitro biological models involving elements of IL-6 signaling and the hepatic acute phase response.

SummaryDuring aging, decreases in energy expenditure and locomotor activity lead to body weight and fat gain. Aging is also associated with decreases in muscle strength and endurance leading to functional decline. Here, we show that lifelong deletion of ghrelin prevents development of obesity associated with aging by modulating food intake and energy expenditure. Ghrelin deletion also attenuated the decrease in phosphorylated adenosine monophosphate‐activated protein kinase (pAMPK) and downstream mediators in muscle, and increased the number of type IIa (fatigue resistant, oxidative) muscle fibers, preventing the decline in muscle strength and endurance seen with aging. Longevity was not affected by ghrelin deletion. Treatment of old mice with pharmacologic doses of ghrelin increased food intake, body weight, and muscle strength in both ghrelin wild‐type and knockout mice. These findings highlight the relevance of ghrelin during aging and identify a novel AMPK‐dependent mechanism for ghrelin action in muscle.