Low-coverage whole genome sequencing of low-grade dysplasia strongly predicts advanced neoplasia risk in ulcerative colitis.

Background: The risk of developing advanced neoplasia (AN; colorectal cancer and/or high-grade dysplasia) in ulcerative colitis (UC) patients with a low-grade dysplasia (LGD) lesion is variable and difficult to predict. This is a major challenge for effective clinical management.

Objective: We aimed to provide accurate AN risk stratification in UC patients with LGD.

Neutral hydrolysis of poly(ethylene terephthalate) catalysed by highly active terephthalate-based ionic liquids at low loadings.

Novel ionic liquid catalysts comprising terephthalate anions are capable of promoting the neutral hydrolysis of relatively large flake sizes of poly(ethylene terephthalate) at 0.5 mol% loading (200 °C, 4 h, 94% yield) without either attendant product inhibition or product contamination by protonated catalyst. Catalysts with large, lipophilic phosphonium cations outperform more polar variants.

Tumour purity assessment with deep learning in colorectal cancer and impact on molecular analysis.

Tumour content plays a pivotal role in directing the bioinformatic analysis of molecular profiles such as copy number variation (CNV). In clinical application, tumour purity estimation (TPE) is achieved either through visual pathological review [conventional pathology (CP)] or the deconvolution of molecular data. While CP provides a direct measurement, it demonstrates modest reproducibility and lacks standardisation.

Targeting the PREX2/RAC1/PI3Kβ Signaling Axis Confers Sensitivity to Clinically Relevant Therapeutic Approaches in Melanoma.

Metastatic melanoma remains a major clinical challenge. Large-scale genomic sequencing of melanoma has identified bona fide activating mutations in RAC1, which are associated with resistance to BRAF-targeting therapies. Targeting the RAC1-GTPase pathway, including the upstream activator PREX2 and the downstream effector PI3Kβ, could be a potential strategy for overcoming therapeutic resistance, limiting melanoma recurrence, and suppressing metastatic progression.

Dual gene set enrichment analysis (dualGSEA); an R function that enables more robust biological discovery and pre-clinical model alignment from transcriptomics data.

Gene set enrichment analysis (GSEA) tools can identify biological insights within gene expression-based studies. Although their statistical performance has been compared, the downstream biological implications that arise when choosing between the range of pairwise or single sample forms of GSEA methods remain understudied. We compare the statistical and biological results obtained from various pre-ranking methods/options for pairwise GSEA, followed by a stand-alone comparison of GSEA, single sample GSEA (ssGSEA) and gene set variation analysis (GSVA).