Gene signature for the prediction of radiation therapy response
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. A method of treating a subject having a breast tumor, the method comprising:
determining 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); p21 (CDKN1A)-activated kinase 2 (PAK2); Histone deacetylase 1 (HDAC1); and Interferon regulatory factor 1 (IRF1) in a cell from the tumor;
assigning a radiation sensitivity index to the tumor based on expression levels of the signature genes;
selecting a treatment regimen for the subject based on the radiation sensitivity index; and
administering the selected treatment to the subject.
2. The method of claim 1 , wherein a radiation sensitivity index below a threshold indicates that radiation therapy is likely to be effective in treating the tumor, and the method includes 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 includes selecting a treatment regimen excluding radiation therapy, or a treatment regime including a high dose of radiation therapy.
3. The method of claim 1 , comprising selecting a dose of radiation that is the greater than the preselected dose of radiation, if the radiation sensitivity index is above a threshold.
4. The method of claim 1 , wherein assigning a radiation sensitivity index comprises applying a linear regression model to the gene expression levels.
5. The method of claim 4 , wherein the model is a rank-based linear regression model.
6. The method of claim 1 , wherein the two or more signature genes are weighted.
7. The method of claim 5 , wherein the linear regression model is represented by the following algorithm:
RSI= k 1 *AR+ k 2 *c -jun+ k 3 *STAT1 +k 4 *PKC+ k 5 *RelA+ k 6 *cAbl+ k 7 *SUMO1 +k 8 *PAK2 +k 9 *HDAC+ k 10 *IRF1. I
8. The method of claim 1 , wherein the method is computer-implemented.