University of South Florida

H. Lee Moffitt Cancer Center and Research Institute, Inc.

Cvergenx, Inc.

Javier F Torres-Roca

Steven A Eschrich

Jacob G Scott

Jacob Scott

Timothy J Yeatman

Steven Eschrich

Disclosed herein are methods for personalized treatment of individual patient tumors. A computer software configured to integrate with a radiation therapy treatment planning system is presented. The computer software is configured to: assign a radiation sensitivity index (RSI) of a subject's tumor based at least in part on expression levels of one or more signature genes in the tumor; calculate a recommended personalized radiation dosage (RxRSI) for the subject based at least in part on a pre-determined genomic adjusted radiation dose (GARD) value and the RSI; and provide, to the radiation therapy treatment planning system, the recommended RxRSI as a radiation therapy dose for a radiation plan.

Systems and methods for personalized radiation therapy

Disclosed herein are methods for personalized treatment of individual patient tumors. In one embodiment, a method of calculating a personalized radiation therapy dosage for a subject comprises determining expression levels of one or more signature genes from a subject's tumor sample, applying a linear regression model to the gene expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample, calculating a genomic adjusted radiation dose (GARD) value based on RSI, radiation dose and fractionation schedule of the subject, and calculating a personalized radiation dosage (RxRSI) for the subject based on a pre-determined GARD value.

An example method of treating a subject having a tumor is described herein. The method can include determining a radiosensitivity index of the tumor, deriving a subject-specific variable based on the radiosensitivity index, and obtaining a genomic adjusted radiation dose effect value for the tumor. The radiosensitivity index can be assigned from expression levels of signature genes of a cell of the tumor. Additionally, the genomic adjusted radiation dose effect value can be predictive of tumor recurrence in the subject after treatment. The method can also include determining a radiation dose based on the subject-specific variable and the genomic adjusted radiation dose effect value.

Systems and methods for providing personalized radiation therapy

Described are mathematical models and method, e.g., computer-implemented methods, for predicting tumor sensitivity to radiation therapy, which can be used, e.g., for selecting a treatment for a subject who has a tumor.

Gene signature for the prediction of radiation therapy response

This invention is a model that simulates the complexity of biological signaling in a cell in response to radiation therapy. Using gene expression profiles and radiation survival assays in an algorithm, a systems model was generated of the radiosensitivity network. The network consists of ten highly interconnected genetic hubs with significant signal redundancy. The model was validated with in vitro tests perturbing network components, correctly predicting radiation sensitivity &#x2154; times. The model's clinical relevance was shown by linking clinical radiosensitivity targets to the model network. Clinical applications were confirmed by testing model predictions against clinical response to preoperative radiochemotherapy in patients with rectal or esophageal cancer.

Predictive radiosensitivity network model

A classifier to predict cellular radiation sensitivity based on gene expression profiles in thirty-five cell lines from the NCI panel of 60 cancer cell lines (NCI-60), using a novel approach to predictive gene analysis. Three novel genes are provided, retinoblastoma binding protein 4 (RbAp48), G-protein signaling regulator 19 (RGS19) and ribose-5-phosphate isomerase A (R5PIA) whose expression values were correlated with radiation sensitivity.

Growing community of inventors

St. Petersburg, FL, United States of America

Javier F Torres-Roca