Gene signature for the prediction of radiation therapy response

Abstract

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.

1 . (canceled) 
     
     
         2 . A method of selecting a treatment regimen for a subject having a solid tumor, the method comprising:
 receiving, by a computing device, information regarding expression levels of signature genes comprising Androgen receptor (AR); Jun oncogene (c-Jun); Signal transducer and activator of transcription 1 (STAT1); Protein kinase C, beta (PRKCB or PKC); V-rel reticuloendotheliosis viral oncogene homolog A (avian) (RELA or p65); c-Abl oncogene 1, receptor tyrosine kinase (ABL1 or c-Abl); SMT3 suppressor of mif two 3 homolog 1 ( S. cerevisiae ) (SUMO1); PAK2; Histone deacetylase 1 (HDAC1); and Interferon regulatory factor 1 (IRF1) in a cell from the solid tumor;   calculating, by a computing device, a radiation sensitivity index for the solid tumor based on expression levels of the signature genes; and   selecting, by a computing device, a treatment regimen for the subject based on the radiation sensitivity index, thereby providing information to select a treatment regimen for the subject.   
     
     
         3 . The method of  claim 2 , wherein a radiation sensitivity index below a threshold indicates that radiation therapy is likely to be effective in treating the tumor, and the method comprises selecting a treatment regimen including radiation therapy; and
 wherein a radiation sensitivity index above a threshold indicates that radiation therapy is not likely to be effective in treating the tumor, and the method comprises selecting a treatment regimen excluding radiation therapy, or a treatment regime including a high dose of radiation therapy.   
     
     
         4 . The method of  claim 2 , wherein the radiation sensitivity index is calculated based on a preselected dose of radiation, and the method comprises selecting a dose of radiation that is greater than the preselected dose of radiation for a subject who has a radiation sensitivity index that is above a threshold. 
     
     
         5 . The method of  claim 2 , wherein calculating a radiation sensitivity index comprises applying a linear regression model to the gene expression levels. 
     
     
         6 . The method of  claim 5 , wherein the model is a rank-based linear regression model. 
     
     
         7 . The method of  claim 6 , wherein the linear regression model is represented by the following algorithm:
     RSI=k   1   *AR+k   2   *c - jun+k   3   *STAT 1+ k   4   *PKC+k   5   *RelA+k   6   *cAbl+         k   7   *SUMO 1+ k   8   *PAK 2+ k   9   *HDAC 1+ k   10   *IRF 1.  I
   
     
     
         8 . The method of  claim 2 , wherein two or more signature genes are weighted. 
     
     
         9 . The method of  claim 2 , wherein the solid tumor originates from a carcinoma of the breast, head and neck, lung, prostate, colon, liver, brain, rectum, ovary, oral cavity, esophagus, cervix, or bone. 
     
     
         10 . The method of  claim 2 , wherein the method further comprises administering the selected treatment to the subject. 
     
     
         11 . A non-transitory computer readable medium storing instructions for causing a computing system to:
 calculate a radiation sensitivity index for a subject having a solid tumor based on expression levels of signature genes comprising Androgen receptor (AR); Jun oncogene (c-Jun); Signal transducer and activator of transcription 1 (STAT1); Protein kinase C, beta (PRKCB or PKC); V-rel reticuloendotheliosis viral oncogene homolog A (avian) (RELA or p65); c-Abl oncogene 1, receptor tyrosine kinase (ABL1 or c-Abl); SMT3 suppressor of mif two 3 homolog 1 ( S. cerevisiae ) (SUMO1); PAK2; Histone deacetylase 1 (HDAC1); and Interferon regulatory factor 1 (IRF1) in a cell from the solid tumor; and   select a treatment regimen for the subject based on the radiation sensitivity index.   
     
     
         12 . The non-transitory computer readable medium of  claim 11 , wherein the medium comprises instructions for causing a computing system to select a dose of radiation that is greater than a preselected dose of radiation for a subject who has a radiation sensitivity index that is above a threshold. 
     
     
         13 . The non-transitory computer readable medium of  claim 11 , wherein the radiation sensitivity index is calculated by applying a linear regression model to the gene expression levels. 
     
     
         14 . The non-transitory computer readable medium of  claim 13 , wherein the model is a rank-based linear regression model. 
     
     
         15 . The non-transitory computer readable medium of  claim 14 , wherein the linear regression model is represented by the following algorithm:
     RSI=k   1   *AR+k   2   *c - jun+k   3   *STAT 1+ k   4   *PKC+k   5   *RelA+k   6   *cAbl+         k   7   *SUMO 1+ k   8   *PAK 2+ k   9   *HDAC 1+ k   10   *IRF 1.  I
   
     
     
         16 . The non-transitory computer readable medium of  claim 11 , wherein two or more signature genes are weighted. 
     
     
         17 . The non-transitory computer readable medium of  claim 11 , wherein the solid tumor originates from a carcinoma of the breast, head and neck, lung, prostate, colon, liver, brain, rectum, ovary, oral cavity, esophagus, cervix, or bone.