| Description |
As personalized medicine is integrated into clinical practice for the treatment of cancer, patient care will be centered around new methods of tumor diagnosis that are predictive of an individual patient's outcome based on a tumor's biology. Rather than prognosticating a tumor based solely on its observable anatomic features, the clinical and research communities recognize the importance of also considering the molecular features of a tumor that impact a patient's outcome. This paradigm shift toward personalized diagnosis and treatment of tumors requires the identi cation of robust molecular signatures that have high analytic and clinical validity. However, these fundamental patterns of biological variation that characterize a tumor's progression, as well as a patient's outcome, are hidden in large, high-dimensional genomic datasets. Comparative spectral decompositions are a set of universal mathematical frame- works that separate a signal into its underlying sources of variation, the same way a prism separates white light into its component colors. Rather than simplifying the data, as is commonly done, the decompositions leverage the complexity of the datasets in order to tease out the patterns within them. We recently demonstrated the e ectiveness of these frameworks for modeling DNA copy-number pro les from glioblastoma (GBM) brain cancer patients, which revealed a genome-wide pattern of DNA copy-number aberrations (CNAs) that is predictive of patient survival and response to chemotherapy. Recurring DNA CNAs had been observed in GBM tumors' genomes for decades; however, copy-number subtypes that are predictive of a patient's outcome had not been conclusively established, illustrating the ability of comparative spectral decompositions to nd what other methods have missed. In this research, we build on those results by using comparative spectral decom- positions to study lower-grade astrocytoma (LGA) patients' copy-number pro les, enabling prognostication of the LGA tumors and comparison of genomic aberrations that characterize the lower- and high-grade tumors. Additionally, we demonstrate the analytic and clinical validity of the GBM pattern as a platform- and technology- independent prognostic predictor in the combined astrocytoma population, by clas- sifying astrocytoma tumors based on genomic pro les measured by both microrarray and next generation sequencing technologies. The results reported here bring the GBM pattern a step closer to the clinic, where it can be implemented as a laboratory test and used to improve patient care. iv |
