Multi-Tensor Decompositions for Personalized Pediatric Glioma Diagnostics and Prognostics

Eigengene, Inc., requests access to the tumor and normal whole-genome sequencing data from high- and low-grade pediatric glioma patients, as well as the patient-matched clinical data, in order to create personalized diagnostic and prognostic tests for pediatric gliomas. Eigengene develops powerful universal algorithms, and, by applying these algorithms to cancer genomic data, finds signatures that can predict a patient’s survival and response to treatment far more precisely and robustly than any previously existing indicator, and provide information that no other method does. We already have proven success with adult astrocytomas, both glioblastoma (GBM) and lower-grade astrocytoma, where we found a genomic signature that predicts a patient’s survival and responses to chemotherapy and radiation, and is statistically better than the best indicators currently in clinical use.