AMRC

Gastric cancer is among the most common malignancies worldwide, characterized by geographical, epidemiological and histological heterogeneity. Here, we report an extensive, multiancestral landscape of driver events in gastric cancer, involving 1,335 cases. Seventy-seven significantly mutated genes (SMGs) were identified, including ARHGAP5 and TRIM49C. We also identified subtype-specific drivers, including PIGR and SOX9, which were enriched in the diffuse subtype of the disease. SMGs also varied according to Epstein–Barr virus infection status and ancestry.

Background SARS-CoV-2 Omicron variants are highly resistant to vaccine-induced immunity and human monoclonal antibodies. Methods We previously reported that two nanobodies, P17 and P86, potently neutralize SARS-CoV-2 VOCs. In this study, we modified these nanobodies into trimers, called TP17 and TP86 and tested their neutralization activities against Omicron BA.1 and subvariant BA.2 using pseudovirus assays. Next, we used TP17 and TP86 nanobody cocktail to treat ACE2 transgenic mice infected with lethal dose of SARS-CoV-2 strains, original, Delta and Omicron BA.1.

Chromosomal translocation generates the MLL-AF4 fusion gene, which causes acute leukemia of multiple lineages. MLL-AF4 is a strong oncogenic driver that induces leukemia without additional mutations and is the most common cause of pediatric leukemia. However, establishment of a murine disease model via retroviral transduction has been difficult owning to a lack of understanding of its regulatory mechanisms.

We developed a diagnostic method for repeat expansion diseases using a long-read sequencer to improve currently available, low throughput diagnostic methods. We employed the real-time target enrichment system of the nanopore GridION sequencer using the adaptive sampling option, in which software-based target assignment is available without prior sample enrichment, and built an analysis pipeline that prioritized the disease-causing loci.