A tumor-naive ctDNA assay detects minimal residual disease in resected stage II or III colorectal cancer and predicts recurrence: subset analysis from the GALAXY study in CIRCULATE-Japan.

Purpose: Analysis of circulating tumor DNA (ctDNA) may enable early identification of patients likely to relapse, presenting an opportunity for early interventions and improved outcomes. Tumor-naïve plasma-only approaches for molecular residual disease (MRD) assessment accelerate turnaround time, enabling rapid treatment decisions and ongoing surveillance.

Experimental Design: Plasma samples were obtained from 80 study participants with stage II or III colorectal cancer (CRC) selected from CIRCULATE-Japan GALAXY.

How well do you know your mutation? Complex effects of genetic background on expressivity, complementation, and ordering of allelic effects.

For a given gene, different mutations influence organismal phenotypes to varying degrees. However, the expressivity of these variants not only depends on the DNA lesion associated with the mutation, but also on factors including the genetic background and rearing environment. The degree to which these factors influence related alleles, genes, or pathways similarly, and whether similar developmental mechanisms underlie variation in the expressivity of a single allele across conditions and among alleles is poorly understood.

Complex cis-regulatory landscape of the insulin receptor gene underlies the broad expression of a central signaling regulator.

Insulin signaling plays key roles in development, growth and metabolism through dynamic control of glucose uptake, global protein translation and transcriptional regulation. Altered levels of insulin signaling are known to play key roles in development and disease, yet the molecular basis of such differential signaling remains obscure. Expression of the insulin receptor (InR) gene itself appears to play an important role, but the nature of the molecular wiring controlling InR transcription has not been elucidated.

An image database of Drosophila melanogaster wings for phenomic and biometric analysis.

Background: Extracting important descriptors and features from images of biological specimens is an ongoing challenge. Features are often defined using landmarks and semi-landmarks that are determined a priori based on criteria such as homology or some other measure of biological significance. An alternative, widely used strategy uses computational pattern recognition, in which features are acquired from the image de novo.