The PD-1 checkpoint inhibitor Opdivo (nivolumab) has been approved by the FDA as an initial treatment for advanced gastric cancer, adding to its approvals as a treatment for melanoma and a number of other cancers.
The researchers found that mutations in KMT2C and KMT2D make non-small cell lung cancer more sensitive to Poly (ADP-ribose) polymerase (PARP) inhibitors, which are already approved for treating prostate, pancreatic, ovarian, and breast cancer patients.
The researchers identified 25 new rare pathogenic variants associated with lung cancer susceptibility and validated five of those variants. Of those five, two variants involved genes with known connections to lung cancer risk, ATM and MPZL2. Three variants involved novel lung cancer susceptibility genes, POMC, STAU2 and MLNR.
The researchers used mRNA technology to code for Cas13a, which destroys parts of the RNA genetic code that viruses use to replicate in cells in the lungs. Using a guide strand, researchers can provide a map that basically tells the Cas13a protein where to attach to the viruses’ RNA and begin to destroy it.
Researchers at the Baylor College of Medicine report that they have found that microbes in the gut may contribute to certain symptoms associated with complex neurological disorders, which also suggests that microbe-inspired therapies may one day help to treat them.
SARS-CoV-2 affects many parts of the body. Although the primary manifestations of COVID-19 involve the respiratory system, gastrointestinal effects may play a critical role in both disease severity and transmission.
A team of researchers from Centre de Recherche en Cancerologie de Marseille (CRCM), INSERM in France have developed a new method for capturing the metabolic signature of pancreatic ductal adenocarcinoma to predict clinical outcomes.
The new automation friendly reagents allow laboratories testing for COVID-19 have a tool that allows them to skip the RNA extraction step of the many testing workflows and move directly to PCR amplification.
The research team drew from Mount Sinai's BioMe BioBank program, a repository of genomic information for diverse populations, for its study. Using machine learning methodology, they identified 17 distinct ethnic communities from among the 30,000 participants in the BioMe BioBank.
Known as Explainable Multi-Omics Graph Integration (EMOGI), the machine learning method predicted 165 new cancer genes by combining multiomics pan-cancer data—such as mutations, copy number changes, DNA methylation and gene expression—together with protein–protein interaction (PPI) networks.
With the aid of the n-lorem Foundation, doctors and patients now have hope of finding treatments to the most rare of genetic diseases.
A wider reach—in patients and data—promises better outcomes.
Broader, more diverse genomic data sets allow better training of polygenic risk score algorithms, moving them from research models into the clinic.
This year we highlight a range of leaders in precision medicine ranging from infectious disease experts to CRISPR researchers to company founders.