Mutationally activated kinases define a clinically validated class of targets for cancer drug therapy1. However, the efficacy of kinase inhibitors in patients whose tumours harbour such alleles is invariably limited by innate or acquired drug resistance2,3. The identification of resistance mechanisms has revealed a recurrent theme—the engagement of survival signals redundant to those transduced by the targeted kinase4. Cancer cells typically express multiple receptor tyrosine kinases (RTKs) that mediate signals that converge on common critical downstream cell-survival effectors—most notably, phosphatidylinositol-3-OH kinase (PI(3)K) and mitogen-activated protein kinase (MAPK)5. Consequently, an increase in RTK-ligand levels, through autocrine tumour-cell production, paracrine contribution from tumour stroma6 or systemic production, could confer resistance to inhibitors of an oncogenic kinase with a similar signalling output. Here, using a panel of kinase-‘addicted’ human cancer cell lines, we found that most cells can be rescued from drug sensitivity by simply exposing them to one or more RTK ligands. Among the findings with clinical implications was the observation that hepatocyte growth factor (HGF) confers resistance to the BRAF inhibitor PLX4032 (vemurafenib) in BRAF-mutant melanoma cells. These observations highlight the extensive redundancy of RTK-transduced signalling in cancer cells and the potentially broad role of widely expressed RTK ligands in innate and acquired resistance to drugs targeting oncogenic kinases.

Phenotypic drug discovery (PDD) approaches do not rely on knowledge of the identity of a specific drug target or a hypothesis about its role in disease, in contrast to the target-based strategies that have been widely used in the pharmaceutical industry in the past three decades. However, in recent years, there has been a resurgence in interest in PDD approaches based on their potential to address the incompletely understood complexity of diseases and their promise of delivering first-in-class drugs, as well as major advances in the tools for cell-based phenotypic screening. Nevertheless, PDD approaches also have considerable challenges, such as hit validation and target deconvolution. This article focuses on the lessons learned by researchers engaged in PDD in the pharmaceutical industry and considers the impact of 'omics' knowledge in defining a cellular disease phenotype in the era of precision medicine, introducing the concept of a chain of translatability. We particularly aim to identify features and areas in which PDD can best deliver value to drug discovery portfolios and can contribute to the identification and the development of novel medicines, and to illustrate the challenges and uncertainties that are associated with PDD in order to help set realistic expectations with regard to its benefits and costs.

Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene are the most common cause of familial Parkinson's disease (PD). Although biochemical studies have shown that certain PD mutations confer elevated kinase activity in vitro on LRRK2, there are no methods available to directly monitor LRRK2 kinase activity in vivo. We demonstrate that LRRK2 autophosphorylation on Ser(1292) occurs in vivo and is enhanced by several familial PD mutations including N1437H, R1441G/C, G2019S, and I2020T. Combining two PD mutations together further increases Ser(1292) autophosphorylation. Mutation of Ser(1292) to alanine (S1292A) ameliorates the effects of LRRK2 PD mutations on neurite outgrowth in cultured rat embryonic primary neurons. Using cell-based and pharmacodynamic assays with phosphorylated Ser(1292) as the readout, we developed a brain-penetrating LRRK2 kinase inhibitor that blocks Ser(1292) autophosphorylation in vivo and attenuates the cellular consequences of LRRK2 PD mutations in vitro. These data suggest that Ser(1292) autophosphorylation may be a useful indicator of LRRK2 kinase activity in vivo and may contribute to the cellular effects of certain PD mutations.

There has been a resurgence of interest in the use of phenotypic screens in drug discovery as an alternative to target-focused approaches. Given that oncology is currently the most active therapeutic area, and also one in which target-focused approaches have been particularly prominent in the past two decades, we investigated the contribution of phenotypic assays to oncology drug discovery by analysing the origins of all new small-molecule cancer drugs approved by the US Food and Drug Administration (FDA) over the past 15 years and those currently in clinical development. Although the majority of these drugs originated from target-based discovery, we identified a significant number whose discovery depended on phenotypic screening approaches. We postulate that the contribution of phenotypic screening to cancer drug discovery has been hampered by a reliance on 'classical' nonspecific drug effects such as cytotoxicity and mitotic arrest, exacerbated by a paucity of mechanistically defined cellular models for therapeutically translatable cancer phenotypes. However, technical and biological advances that enable such mechanistically informed phenotypic models have the potential to empower phenotypic drug discovery in oncology.

Although targeting cancer metabolism is a promising therapeutic strategy, clinical success will depend on an accurate diagnostic identification of tumor subtypes with specific metabolic requirements. Through broad metabolite profiling, we successfully identified three highly distinct metabolic subtypes in pancreatic ductal adenocarcinoma (PDAC). One subtype was defined by reduced proliferative capacity, whereas the other two subtypes (glycolytic and lipogenic) showed distinct metabolite levels associated with glycolysis, lipogenesis, and redox pathways, confirmed at the transcriptional level. The glycolytic and lipogenic subtypes showed striking differences in glucose and glutamine utilization, as well as mitochondrial function, and corresponded to differences in cell sensitivity to inhibitors of glycolysis, glutamine metabolism, lipid synthesis, and redox balance. In PDAC clinical samples, the lipogenic subtype associated with the epithelial (classical) subtype, whereas the glycolytic subtype strongly associated with the mesenchymal (QM-PDA) subtype, suggesting functional relevance in disease progression. Pharmacogenomic screening of an additional ∼200 non-PDAC cell lines validated the association between mesenchymal status and metabolic drug response in other tumor indications. Our findings highlight the utility of broad metabolite profiling to predict sensitivity of tumors to a variety of metabolic inhibitors.metabolite profiling | metabolic subtypes in PDAC | glycolysis | lipid synthesis | biomarkers for metabolic inhibitors M etabolic reprogramming during tumorigenesis is an essential process in nearly all cancer cells. Tumors share a common phenotype of uncontrolled cell proliferation and must efficiently generate the energy and macromolecules required for cellular growth. The first example of metabolic reprogramming was discovered more than 80 y ago by Otto Warburg: tumor cells can shift from oxidative to fermentative metabolism in the course of oncogenesis (1). More recently, there has been a resurgence of interest in targeting cancer metabolism (2-4) because it may not only be effective in inhibiting tumor growth, but may also provide a therapeutic window (5, 6). For example, inactivation of lactate dehydrogenase-A (LDHA), an enzyme that catalyzes the final step of aerobic glycolysis, thereby reducing pyruvate to lactate, decreases tumorigenesis and induces regression of established tumors in mouse models of lung cancer driven by oncogenic KRAS or epidermal growth factor receptor (EGFR) while minimally affecting normal cell function (7). The finding that cancers have altered metabolism has prompted substantial investigation, both preclinically and in clinical trials, of several metabolically targeted agents, including those that elevate reactive oxygen species (ROS) or block glycolysis, lipid synthesis, mitochondrial function, and glutamine synthesis pathways (8).The identification of distinct metabolic reprogramming events or metabolic subtypes in cancer may inform patient selection for investigational...