Type 2 diabetes (T2D) is a heterogeneous complex disease affecting more than 29 million Americans alone with a rising prevalence trending toward steady increases in the coming decades. Thus, there is a pressing clinical need to improve early prevention and clinical management of T2D and its complications. Clinicians have understood that patients who carry the T2D diagnosis have a variety of phenotypes and susceptibilities to diabetes-related complications. We used a precision medicine approach to characterize the complexity of T2D patient populations based on high-dimensional electronic medical records (EMRs) and genotype data from 11,210 individuals. We successfully identified three distinct subgroups of T2D from topology-based patient-patient networks. Subtype 1 was characterized by T2D complications diabetic nephropathy and diabetic retinopathy; subtype 2 was enriched for cancer malignancy and cardiovascular diseases; and subtype 3 was associated most strongly with cardiovascular diseases, neurological diseases, allergies, and HIV infections. We performed a genetic association analysis of the emergent T2D subtypes to identify subtype-specific genetic markers and identified 1279, 1227, and 1338 single-nucleotide polymorphisms (SNPs) that mapped to 425, 322, and 437 unique genes specific to subtypes 1, 2, and 3, respectively. By assessing the human disease–SNP association for each subtype, the enriched phenotypes and biological functions at the gene level for each subtype matched with the disease comorbidities and clinical differences that we identified through EMRs. Our approach demonstrates the utility of applying the precision medicine paradigm in T2D and the promise of extending the approach to the study of other complex, multi-factorial diseases.

Background: Ubiquitous digital technologies such as smartphone sensors promise to fundamentally change biomedical research and treatment monitoring in neurological diseases such as PD, creating a new domain of digital biomarkers. Objectives: The present study assessed the feasibility, reliability, and validity of smartphone‐based digital biomarkers of PD in a clinical trial setting. Methods: During a 6‐month, phase 1b clinical trial with 44 Parkinson participants, and an independent, 45‐day study in 35 age‐matched healthy controls, participants completed six daily motor active tests (sustained phonation, rest tremor, postural tremor, finger‐tapping, balance, and gait), then carried the smartphone during the day (passive monitoring), enabling assessment of, for example, time spent walking and sit‐to‐stand transitions by gyroscopic and accelerometer data. Results: Adherence was acceptable: Patients completed active testing on average 3.5 of 7 times/week. Sensor‐based features showed moderate‐to‐excellent test‐retest reliability (average intraclass correlation coefficient = 0.84). All active and passive features significantly differentiated PD from controls with P < 0.005. All active test features except sustained phonation were significantly related to corresponding International Parkinson and Movement Disorder Society–Sponsored UPRDS clinical severity ratings. On passive monitoring, time spent walking had a significant (P = 0.005) relationship with average postural instability and gait disturbance scores. Of note, for all smartphone active and passive features except postural tremor, the monitoring procedure detected abnormalities even in those Parkinson participants scored as having no signs in the corresponding International Parkinson and Movement Disorder Society–Sponsored UPRDS items at the site visit. Conclusions: These findings demonstrate the feasibility of smartphone‐based digital biomarkers and indicate that smartphone‐sensor technologies provide reliable, valid, clinically meaningful, and highly sensitive phenotypic data in Parkinson's disease. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

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Small cell lung cancer (SCLC) is a recalcitrant, aggressive neuroendocrine-type cancer for which little change to first-line standard-of-care treatment has occurred within the last few decades. Unlike nonsmall cell lung cancer (NSCLC), SCLC harbors few actionable mutations for therapeutic intervention. Lysine-specific histone demethylase 1A (LSD1 also known as KDM1A) inhibitors were previously shown to have selective activity in SCLC models, but the underlying mechanism was elusive. Here, we found that exposure to the selective LSD1 inhibitor ORY-1001 activated the NOTCH pathway, resulting in the suppression of the transcription factor ASCL1 and the repression of SCLC tumorigenesis. Our analyses revealed that LSD1 bound to the NOTCH1 locus, thereby suppressing NOTCH1 expression and downstream signaling. Reactivation of NOTCH signaling with the LSD1 inhibitor reduced the expression of ASCL1 and neuroendocrine cell lineage genes. Knockdown studies confirmed the pharmacological inhibitor-based results. In vivo, sensitivity to LSD1 inhibition in SCLC patient-derived xenograft (PDX) models correlated with the extent of consequential NOTCH pathway activation and repression of a neuroendocrine phenotype. Complete and durable tumor regression occurred with ORY-1001–induced NOTCH activation in a chemoresistant PDX model. Our findings reveal how LSD1 inhibitors function in this tumor and support their potential as a new and targeted therapy for SCLC.

The accuracy with which cancer phenotypes can be predicted by selecting and combining molecular features is compromised by the large number of potential features available. In an effort to design a robust prognostic model to predict breast cancer survival, we hypothesized that signatures consisting of genes that are coexpressed in multiple cancer types should correspond to molecular events that are prognostic in all cancers, including breast cancer. We previously identified several such signatures-called attractor metagenes-in an analysis of multiple tumor types. We then tested our attractor metagene hypothesis as participants in the Sage Bionetworks-DREAM Breast Cancer Prognosis Challenge. Using a rich training data set that included gene expression and clinical features for breast cancer patients, we developed a prognostic model that was independently validated in a newly generated patient data set. We describe our model, which was based on three attractor metagenes associated with mitotic chromosomal instability, mesenchymal transition, or lymphocyte-based immune recruitment. INTRODUCTIONMedical tests that incorporate molecular profiling of tumors for clinical decision-making (predictive tests) or prognosis (prognostic tests) are typically based on models that combine values associated with particular molecular features, such as the expression levels of specific genes. These genes are selected after analyzing rich gene expression data sets (acquired from testing patient tumors) annotated with clinical phenotypes such as drug responses or survival times. The data sets used to define a model are referred to as "training data sets." A computational technique is typically used to identify a number of genes that, when properly combined, are associated with a phenotype of interest in a statistically significant manner. The predictive power of the resulting model is later confirmed in independent "validation data sets."There are, however, vast numbers-tens or hundreds of thousandsof potentially relevant molecular features to choose from when developing a model, making it difficult to precisely identify those at the core of the biological mechanisms responsible for the phenotype of interest. Spurious or suboptimal predictions may occur, and the end result may be a model that only partly reflects physiological reality. Such a model may still be clinically useful, but there is room for improvement.One way to address this problem is by using molecular features preselected on the basis of previous knowledge. In such an approach, a training data set is used mainly for pinpointing the combination of preselected features that is most associated with the phenotype of interest. We used this approach during our participation in the Sage Bionetworks-DREAM Breast Cancer Prognosis Challenge, an open challenge to build computational models that accurately predict breast cancer survival (hereinafter referred to as the Challenge) (1). Specifically, we hypothesized that selected gene coexpression signatures present in multiple cancer...