Feasibility of monitoring peripheral blood to detect emerging clones in children with acute lymphoblastic leukemia†

Relapse‐enriched somatic variants drive drug resistance in childhood acute lymphoblastic leukemia. We used digital droplet‐based polymerase chain reaction to establish whether relapse‐enriched mutations in emerging subclones could be detected in peripheral blood samples before frank relapse. Although limitations in sensitivity for some probes hindered detection of certain variants, we successfully detected variants in NT5C2 and PRPS1 at a fractional abundance of 0.005% to 0.3%, 41 to 116 days before relapse. As mutations in both these genes confer resistance to thiopurines, early detection protocols using peripheral blood could be implemented to preemptively alter maintenance therapy to extinguish resistant clones before overt relapse.     

Host–microbiome interactions: the aryl hydrocarbon receptor as a critical node in tryptophan metabolites to brain signaling

ABSTRACT

Tryptophan (Trp) is not only a nutrient enhancer but also has systemic effects. Trp metabolites signaling through the well-known aryl hydrocarbon receptor (AhR) constitute the interface of microbiome-gut-brain axis. However, the pathway through which Trp metabolites affect central nervous system (CNS) function have not been fully elucidated. AhR participates in a broad variety of physiological and pathological processes that also highly relevant to intestinal homeostasis and CNS diseases. Via the AhR-dependent mechanism, Trp metabolites connect bidirectional signaling between the gut microbiome and the brain, mediated via immune, metabolic, and neural (vagal) signaling mechanisms, with downstream effects on behavior and CNS function. These findings shed light on the complex Trp regulation of microbiome-gut-brain axis and add another facet to our understanding that dietary Trp is expected to be a promising noninvasive approach for alleviating systemic diseases.     

Comparison of PC and iPad administrations of the Cogstate Brief Battery in the Mayo Clinic Study of Aging: Assessing cross-modality equivalence of computerized neuropsychological tests

Objective: Computerized neuropsychological assessments are increasingly used in clinical practice, population studies of cognitive aging and clinical trial enrichment. Subtle, but significant, performance differences have been demonstrated across different modes of test administration and require further investigation.

Method: Participants included cognitively unimpaired adults aged 50 and older from the Mayo Clinic Study of Aging who completed the Cogstate Brief Battery and Cogstate’s Groton Maze Learning Test (GMLT) on an iPad or a personal computer (PC) in the clinic. Mode of administration differences and test–retest reliability coefficients were examined across 3 cohorts: a demographically matched test–retest cohort completing PC and iPad administrations the same day (N?=?168); a test naïve cohort comparing baseline PC (n?=?1820) and iPad (n?=605) performance; and a demographically matched longitudinal cohort completing 3 Cogstate visits over 15 months on either the PC (n?=63) or iPad (n?=63).

Results: Results showed a small but statistically significant and consistent finding for faster performance on PC relative to iPad for several Cogstate Brief Battery measures. Measures of accuracy generally did not differ or differences were very small. The GMLT showed faster performance and higher total errors on iPad. Most Cogstate variables showed no difference in the rate of change across PC and iPad administrations.

Conclusions: There are small, but significant, differences in performance when giving the same cognitive tests on a PC or an iPad. Future studies are needed to better understand if these small differences impact the clinical interpretation of results and research outcomes.     

Population Genetics Identifies Challenges in Analyzing Rare Variants

Geneticists have, for years, understood the nature of genome‐wide association studies using common genomic variants. Recently, however, focus has shifted to the analysis of rare variants. This presents potential problems for researchers, as rare variants do not always behave in the same way common variants do, sometimes rendering decades of solid intuition moot. In this paper, we present examples of the differences between common and rare variants. We show why one must be significantly more careful about the origin of rare variants, and how failing to do so can lead to highly inflated type I error. We then explain how to best avoid such concerns with careful understanding and study design. Additionally, we demonstrate that a seemingly low error rate in next‐generation sequencing can dramatically impact the false‐positive rate for rare variants. This is due to the fact that rare variants are, by definition, seen infrequently, making it hard to distinguish between errors and real variants. Compounding this problem is the fact that the proportion of errors is likely to get worse, not better, with increasing sample size. One cannot simply scale their way up in order to solve this problem. Understanding these potential pitfalls is a key step in successfully identifying true associations between rare variants and diseases.