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 selecting a treatment regimen for a subject having a solid 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); PAK2; Histone deacetylase 1 (HDAC1); and Interferon regulatory factor 1 (IRF1) in a cell from the solid tumor;
assigning a radiation sensitivity index to the solid tumor based on expression levels of the signature genes, wherein assigning a radiation sensitivity index comprises applying a rank-based linear regression model to the gene expression levels; and
selecting 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 preselected threshold.
2. The method of claim 1 , wherein the signature genes are weighted.
3. The method of claim 1 , 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 *Rel A+k 6 *cAbl+k 7 *SUMO1 +k 8 *PAK2 +k 9 *HDAC1 +k 10 *IRF1. I
4. The method of claim 1 , wherein the method is computer-implemented.
5. The method of claim 1 , wherein the solid tumor originates from a carcinoma of the head and neck, lung, prostate, colon, liver, brain, rectum, ovary, oral cavity, esophagus, cervix, or bone.
6. The method of claim 1 , wherein the method further comprises administering the selected treatment to the subject.
7. The method of claim 1 , wherein the linear regression model is represented by the following algorithm:
RSI=−0.0098009*AR+0.0128283 *c -jun+0.0254552*STAT1−0.0017589*PKC−0.0038171*Rel A+ 0.1070213 *cABL− 0.0002509*SUMO1−0.0092431*PAK2−0.0204469*HDAC1−0.0441683*IRF1. II