Using a temporal multi-omics approach, a team led by scientists from Heidelberg University has identified a protein involved in tumor metastasis that could serve as a new therapeutic cancer target. Metastasis is the leading cause of cancer-related mortality and the mechanistically least well-understood step of the tumor progression cascade. However, it is known that successful metastasis is tied to the close interaction of tumor cells with the metastatic vascular niche, highlighting the need for a better understanding of the vascular niche as it evolves after surgical resection of cancer. In their study, which appears in this week’s Science Translational Medicine, the investigators used endothelial cell (EC) transcriptomics and serum proteomics over time to study postsurgical mouse models of spontaneous metastasis. They find that LRG1 is produced by ECs in response to tumor-induced systemic inflammation. Increased systemic LRG1 led to greater numbers of perivascular cells in the lungs and increased metastases, while LRG1-neutralizing antibody treatment at the time of surgical resection resulted in decreased metastatic burden and prolonged survival. The findings establish LRG1 as an antimetastatic target that warrants further clinical investigation, the authors write.
The use of a novel DNA methylation sequencing method for cancer detection is reported in Science Advances this week. Recently, a team led by researchers from the University of Oxford developed a mild, bisulfite-free method for base-resolution direct DNA methylation sequencing called TET-assisted pyridine borane sequencing, or TAPS. In the new study, the researchers optimized TAPS for circulating, cell-free DNA (cfDNA) and applied it to 85 cfDNA samples from patients with hepatocellular carcinoma (HCC) or pancreatic ductal adenocarcinoma (PDAC) and noncancer controls. With the modified TAPS, the investigators were able to generate multimodal information about cfDNA characteristics including DNA methylation, tissue of origin, and DNA fragmentation. By analyzing these epigenetic and genetic features, they were able to accurately distinguish cfDNA samples from patients with HCC and PDAC from controls and from patients with precancerous inflammatory conditions.
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