With sequence data for more than 118,000 tumors profiled at Foundation Medicine, investigators tracked PDL1 amplification prevalence and possible treatment implications.
With integrated data for thousands of TCGA tumors, teams explored cancer subtypes, searched for prognostic insights, began mapping immune cell interactions, and more.
DCell, a visible neural network, uses deep learning techniques to model cellular behavior almost as accurately as has been observed in laboratory settings.
New data now suggests that evaluating PD-L2 levels might better predict response to PD-1 targeting immune-oncology drugs than current tests that only measure PD-L1.
Members of the ProfiLER trial identified actionable mutations in more than half of individuals with advanced cancers, though only a fraction received targeted therapy.
Researchers hope to tease out the signature effects that different carcinogens leave on the genome to determine their contributions to disease, Mosaic reports.
