Diagnosis, prognosis, and treatment of solid tumors, such as glioblastoma (GBM) and ovarian serous cystadenocarcinoma (OV), has remained largely unchanged for decades, despite the increased availability of patient genomic data. Many tumors develop resistance to platinum-based drugs, the current first-line treatment, yet no tool exists that distinguishes between resistant and sensitive tumors prior to treatment.

This technology provides a computational assessment of cancer genomic profiles for personalized prognostics and drug companion diagnostics. Comparing patient data to proprietary signatures, the algorithm predicts patient response to chemotherapy. Predicting patient response to treatment helps guide clinician decisions and ultimately improves patient outcomes.