Background: Extracellular vesicles (EVs) are important mediators of cell‐to‐cell communication in healthy and pathological environments. Because EVs are present in a variety of biological fluids and contain molecular signatures of their cell or tissue of origin, they have great diagnostic and prognostic value. The ability of EVs to deliver biologically active proteins, RNAs and lipids to cells has generated interest in developing novel therapeutics. Despite their potential medical use, many of the mechanisms underlying EV biogenesis and secretion remain unknown.Methods: Here, we characterized vesicle secretion across the NCI‐60 panel of human cancer cells by nanoparticle tracking analysis. Using CellMiner, the quantity of EVs secreted by each cell line was compared to reference transcriptomics data to identify gene products associated with vesicle secretion.Results: Gene products positively associated with the quantity of exosomal‐sized vesicles included vesicular trafficking classes of proteins with Rab GTPase function and sphingolipid metabolism. Positive correlates of larger microvesicle‐sized vesicle secretion included gene products involved in cytoskeletal dynamics and exocytosis, as well as Rab GTPase activation. One of the identified targets, CD63, was further evaluated for its role in vesicle secretion. Clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 knockout of the CD63 gene in HEK293 cells resulted in a decrease in small vesicle secretion, suggesting the importance of CD63 in exosome biogenesis.Conclusion: These observations reveal new insights into genes involved in exosome and microvesicle formation, and may provide a means to distinguish EV sub‐populations. This study offers a foundation for further exploration of targets involved in EV biogenesis and secretion.

Patient‐generated health data (PGHD), or health‐related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self‐reported health and treatment histories, patient‐reported outcomes (PROs), and biometric sensor data. Advances in wireless technology, smartphones, and the Internet of Things have facilitated new ways to collect PGHD during clinic visits and in daily life. The goal of the current review was to provide an overview of the current clinical, regulatory, technological, and analytic landscape as it relates to PGHD in oncology research and care. The review begins with a rationale for PGHD as described by the US Food and Drug Administration, the Institute of Medicine, and other regulatory and scientific organizations. The evidence base for clinic‐based and remote symptom monitoring using PGHD is described, with an emphasis on PROs. An overview is presented of current approaches to digital phenotyping or device‐based, real‐time assessment of biometric, behavioral, self‐report, and performance data. Analytic opportunities regarding PGHD are envisioned in the context of big data and artificial intelligence in medicine. Finally, challenges and solutions for the integration of PGHD into clinical care are presented. The challenges include electronic medical record integration of PROs and biometric data, analysis of large and complex biometric data sets, and potential clinic workflow redesign. In addition, there is currently more limited evidence for the use of biometric data relative to PROs. Despite these challenges, the potential benefits of PGHD make them increasingly likely to be integrated into oncology research and clinical care.

This article resulted from our participation in the session on the “role of expert opinion and judgment in statistical inference” at the October 2017 ASA Symposium on Statistical Inference. We present a strong, unified statement on roles of expert judgment in statistics with processes for obtaining input, whether from a Bayesian or frequentist perspective. Topics include the role of subjectivity in the cycle of scientific inference and decisions, followed by a clinical trial and a greenhouse gas emissions case study that illustrate the role of judgments and the importance of basing them on objective information and a comprehensive uncertainty assessment. We close with a call for increased proactivity and involvement of statisticians in study conceptualization, design, conduct, analysis, and communication.

The course of preclinical pain symptoms sheds light on the etiology and prognosis of chronic pain. We aimed to quantify rates of developing initial and recurrent symptoms of painful temporomandibular disorder (TMD) and to evaluate associations with health behaviors. In the OPPERA prospective cohort study, 2,719 individuals aged 18 to 44years with lifetime absence of TMD when enrolled completed 25,103 quarterly (3-monthly) questionnaires during a median 2.3-year follow-up period. Questionnaires documented TMD symptom episodes, headache, other body pain, health care attendance, and analgesic use, and. Kaplan-Meier methods for clustered data estimated symptom-free survival time. Multivariable models assessed demographic variation in TMD symptom rates and evaluated associations with health care and analgesic use. One-third of the study subjects developed TMD symptoms and for a quarter of symptomatic episodes, pain intensity was severe. Initial TMD symptoms developed at an annual rate of 18.8 episodes per 100 persons. The annual rate more than doubled for first-recurrence and doubled again for second or subsequent recurrence such that, 1year after first recurrence, 71% of study subjects experienced a second recurrence. The overall rate increased with age and was greater in African Americans and lower in Asians relative to those of white race/ethnicity. The probability of TMD symptoms was strongly associated with concurrent episodes of headache and body pain and with past episodes of TMD symptoms. Episodes of TMD symptoms, headache, and body pain were associated with increases of ∼10% in probability of analgesic use and health care attendance. Yet, even when TMD, headache, and body pain occurred concurrently, 27% of study subjects neither attended health care nor used analgesics.