Predictive radiosensitivity network model

Abstract

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 2/3 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.

1 . A method of generating a radiation network model for predicting cellular radiation sensitivity comprising the steps of:
 developing a multivariate linear regression model of radiosensitivity and gene expression comprising:
 establishing the radiation sensitivity of at least one cell line; 
 establishing the genomic expression of the at least one cell line; 
 selecting expressed genes based on statistical relevance to radiation induction; and 
 identifying at least one gene of interest, expressed by the at least one cell line, predictive of a radiation response using the regression model; 
   identifying radiosensitivity network components using the multivariate linear regression model, comprising:
 identifying genes reactive to radiation induction using the multivariate linear regression model; and 
 identifying interconnected genes; and 
   incorporating biological interactions of common radiation response elements with the radiosensitivity network components.   
   
   
       2 . The method of  claim 1 , wherein the radiation sensitivity of the cell line is established by the survival fraction of the cell line after exposure to about 2 Gy of radiation. 
   
   
       3 . The method of  claim 1 , wherein genomic expression of the at least one cell line is established from a microarray. 
   
   
       4 . The method of  claim 3 , wherein the microarray data is translated using a blast program to match consensus sequences of gene probesets. 
   
   
       5 . The method of  claim 1 , wherein the radiosensitivity network components are identified by:
 plotting the interconnection of gene reactive to radiation induction; and   selecting genes with at least 5 connections to other genes.   
   
   
       6 . The method of  claim 1 , wherein the common radiation response elements are major hubs of the radiation network model comprising:
 genes with at least 5 connections to other genes; and   no more than 50% of the edges hidden within the network.   
   
   
       7 . The method of  claim 4 , wherein the radiosensitivity network components are selected from the group consisting of: c-jun, HDAC-1, RelA, PKC, SUMO-1, c-Abl, STAT-1, AR, CDK1, and IRF1. 
   
   
       8 . The method of  claim 1 , wherein the selected expressed genes are identified by measuring the correlation between the expression of the gene of interest and the radiation sensitivity of the cell line expressing the gene. 
   
   
       9 . The method of  claim 1 , wherein the biological interactions of common radiation response elements are incorporated by:
 creating a linear model for each gene in the dataset;   analyzing the variability of radiation response in multiple cell lines.   
   
   
       10 . The method of  claim 1 , wherein the common radiation response elements are selected from the group consisting of: tissue origin, ras mutation status, p53 status, tissue origin interaction with gene expression, ras mutation status interaction with gene expression, and p53 status interaction with gene expression. 
   
   
       11 . The method of  claim 7 , wherein the genomic data is normalized by ranking each gene using the radiosensitivity network components. 
   
   
       12 . A method of predicting a clinical response to anticancer therapy of a patient in need thereof comprising:
 obtaining a sample of target cells from the patient;   establishing the genomic expression of the sample; and   applying the genomic expression of the sample to a multivariate linear regression model of treatment sensitivity whereby a high expression value correlates with a treatment sensitive phenotype thereby predicting the clinical response to treatment.   
   
   
       13 . The method of  claim 12 , wherein the anticancer therapy is selected from the group consisting of radiation therapy, radiochemotherapy, and chemotherapy. 
   
   
       14 . The method of  claim 12 , wherein the sample of target cells comprises cancer cells. 
   
   
       15 . The method of  claim 12 , wherein the multivariate linear regression model is created comprising the steps of:
 developing a multivariate linear regression model of radiosensitivity and gene expression comprising:
 establishing the radiation sensitivity of at least one cell line; 
 establishing the genomic expression of the at least one cell line; 
 selecting expressed genes based on statistical relevance to radiation induction; and 
 identifying at least one gene of interest, expressed by the at least one cell line, predictive of a radiation response using a regression model; 
   identifying radiosensitivity network components using the multivariate linear regression model, comprising:
 identifying genes reactive to radiation induction using the multivariate linear regression model; and 
 identifying interconnected genes; and 
   incorporating biological interactions of common radiation response elements with the radiosensitivity network components.   
   
   
       16 . The method of  claim 15 , wherein genomic expression of the cell line is established from a microarray. 
   
   
       17 . The method of  claim 15 , wherein the radiosensitivity network components are identified by:
 plotting the interconnection of gene reactive to radiation induction; and   selecting genes with at least 5 connections to other genes.   
   
   
       18 . The method of  claim 17 , wherein the radiosensitivity network components are selected from the group consisting of: c-jun, HDAC-1, RelA, PKC, SUMO-1, c-Abl, STAT-1, AR, CDK1, and IRF1. 
   
   
       19 . The method of  claim 15 , wherein the selected expressed genes are identified by measuring the correlation between the expression of the gene of interest and the radiation sensitivity of the cell line expressing the gene. 
   
   
       20 . The method of  claim 15 , wherein the common radiation response elements are selected from the group consisting of: tissue origin, ras mutation status, p53 status, tissue origin interaction with gene expression, ras mutation status interaction with gene expression, and p53 status interaction with gene expression.