Incidence of Elevated Aminotransferases With or Without Bilirubin Elevation During Treatment With Immune Checkpoint Inhibitors: A Retrospective Study of Patients From Community Oncology Clinics in the United States.

Introduction The elevation of aminotransferase levels is regarded as an indicator of hepatocellular injury. The objective of this study was to describe real-world incidence of elevated aminotransferase levels with or without bilirubin elevation among patients treated with immune checkpoint inhibitors (ICIs) for solid tumors. Methods This retrospective cohort study used an electronic health record database representing > 1.

In vitro and in vivo efficacy of an anti-CD203c conjugated antibody (AGS-16C3F) in mouse models of advanced systemic mastocytosis.

Antibody-drug conjugates (ADCs) are a new class of therapeutics that use antibodies to deliver potent cytotoxic drugs selectively to cancer cells. CD203c, an ecto-nucleotide pyrophosphatase-phosphodiesterase 3, is overexpressed on neoplastic mast cells (MCs) in systemic mastocytosis (SM), thus representing a promising target for antibody-mediated therapy. In this study, we have found that human neoplastic MC lines (ROSA and ROSA), which express high levels of CD203c, are highly and specifically sensitive to the antiproliferative effects of an ADC against CD203c (AGS-16C3F).

MicroRNA profiling of the pubertal mouse mammary gland identifies miR-184 as a candidate breast tumour suppressor gene.

Introduction: The study of mammalian development has offered many insights into the molecular aetiology of cancer. We previously used analysis of mammary morphogenesis to discover a critical role for GATA-3 in mammary developmental and carcinogenesis. In recent years an important role for microRNAs (miRNAs) in a myriad of cellular processes in development and in oncogenesis has emerged.

Identification of druggable cancer driver genes amplified across TCGA datasets.

The Cancer Genome Atlas (TCGA) projects have advanced our understanding of the driver mutations, genetic backgrounds, and key pathways activated across cancer types. Analysis of TCGA datasets have mostly focused on somatic mutations and translocations, with less emphasis placed on gene amplifications. Here we describe a bioinformatics screening strategy to identify putative cancer driver genes amplified across TCGA datasets.