Editor's Note: Some of the articles described below are not yet available at the PNAS site, but they are scheduled to be posted some time this week.
In a paper slated to appear in PNAS this week, researchers from Boston Children's Hospital, Harvard Medical School, and elsewhere explore cancer immunotherapy approaches for tackling some difficult-to-treat forms of breast cancer, including triple-negative tumors lacking hormone or HER2 targets. Using an aptamer-linked, small-interfering RNA chimera (AsiC) approach centered on the tumor-associated antigen EpCAM, together with RNA sequencing and other methods, the team knocked down genes with potential ties to immune function in mouse models of breast cancer, focusing on targets that might boost tumor antigen visibility to the immune system or dial down immune evasion. "Combining EpCAM-AsiCs targeting multiple pathways worked better than single agents and enhanced tumor inhibition by a checkpoint inhibitor," the authors report, noting that EpCAM-AsiCs "have the potential to boost immunity to tumors that are poorly responsive to checkpoint blockade [immunotherapy]."
A Columbia University-led team explores the consequences of oncogenic histone H3K36M mutations that have been linked to lower-than-usual levels of H3K36me2 dimethylation and H3K36me3 trimethylation in some chondroblastomas, sarcomas, and other cancer types. The researchers relied on a CRISPR-Cas9-based gene knockout, quantitative chromatin immunoprecipitation sequencing, RNA sequencing, and other approaches to profile mesenchymal cells missing the NSD1/2 methyltransferase enzymes that mediate H3K36me2 dimethylation or the SETD2 methyltransferase behind H3K36me3 trimethylation marks. From these and other analyses, the authors conclude that "depletion of H3K36me2 represents a key event downstream of the H3K36M mutation and also exposes potential therapeutic vulnerability of H3K36M-mutant tumor cells."
Investigators at Yale University, the University of California, Davis, and Utah State University present findings from a machine-learning-based analysis of landscape connectivity in the invasive Aedes aegypti mosquito species known for transmitting viral diseases such as Chikungunya or yellow fever. "Vector control methods range from traditional (e.g., insecticides) to cutting edge (e.g., genetic modification)," the team writes. "For all these methods, vector control could be improved with genetic connectivity maps and a greater understanding of the factors that affect dispersal." Focusing on the southern US, for example, the authors demonstrate the feasibility of mapping genetic connectivity in Ae. aegypti mosquitoes with an iterative random forest method that includes both genetic and environmental clues.