Multi-Tensor Decompositions for Personalized Pediatric Glioma Diagnostics and Prognostics

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Sri Priya Ponnapalli

CBTN Data Used

Completed

About this

Project

By getting 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, Eigengene proposes 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.

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What are the goals of this project?

The goal of this project is to use Eigengene, Inc. algorithms to create personalized diagnostic and prognostic tests for pediatric gliomas.

What is the impact of this project?

Eigengene develops powerful universal multi-tensor algorithms, which are capable of finding meaningful patterns in big data that all other approaches miss. By applying these algorithms to cancer genomic data, and finding signatures in the data, we create tests 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.

Specimen Data

The Children's Brain Tumor Network contributed to this project by providing access to the Pediatric Brain Tumor Atlas.

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