Germline genetic biomarkers to stratify patients for personalized radiation treatment

Abstract

Background Precision medicine incorporating genetic profiling is becoming a standard of care in medical oncology. However, in the field of radiation oncology there is limited use of genetic profiling and the impact of germline genetic biomarkers on radiosensitivity, radioresistance, or patient outcomes after radiation therapy is poorly understood. In HNSCC, the toxicity associated with treatment can cause delays or early cessation which has been associated with worse outcomes. Identifying potential biomarkers which can help predict toxicity, as well as response to treatment, is of significant interest. Methods Patients with HNSCC who received RT and underwent next generation sequencing of somatic tumor samples, transcriptome RNA-seq with matched normal tissue samples were included. Patients were then grouped by propensity towards increased late vs. early toxicity (Group A) and those without (Group B), assessed by CTCAE v5.0. The groups were then analyzed for association of specific germline variants with toxicity and clinical outcomes. Results In this study we analyzed 37 patients for correlation between germline variants and toxicity. We observed that TSC2, HLA-A, TET2, GEN1, NCOR2 and other germline variants were significantly associated with long term toxicities. 34 HNSCC patients treated with curative intent were evaluated for clinical outcomes. Group A had significantly improved overall survival as well as improved rates of locoregional recurrence and metastatic disease. Specific variants associated with improved clinical outcomes included TSC2, FANCD2, and PPP1R15A, while the HLA-A and GEN1 variants were not correlated with survival or recurrence. A group of five HLA-DMA/HLA-DMB variants was only found in Group B and was associated with a higher risk of locoregional recurrence. Conclusions This study indicates that germline genetic biomarkers may have utility in predicting toxicity and outcomes after radiation therapy and deserve further investigation in precision radiation medicine approaches.

Evaluation of in vitro and in vivo personalized cancer treatment assays for oral squamous cell carcinoma

“…Additionally, we used Deichaite et al. [28] and Guan et al. [29] as references to find variants that significantly correlate with the radiotherapy response/resistance.…”

A novel stratification framework based on anoikis-related genes for predicting the prognosis in patients with osteosarcoma

“…The downregulation of HLA genes may reduce antigen presentation and facilitate immune evasion ( 55 ). HLA-DMA variants have been reported to be associated with a higher risk of local recurrence in head and neck squamous cell carcinoma ( 56 ). HLA-DOA is also a key molecule in the antigen processing and presentation pathway and has been implicated in OS progression through the downregulation of HLA-DOA expression ( 57 , 58 ).…”

The Normal, the Radiosensitive, and the Ataxic in the Era of Precision Radiotherapy: A Narrative Review

“…NTCP models can also include dose-modifying factors explicitly describing individual patients’ radiosensitivity, such as polygenic risk scores or results from biomarker assays [ 43 , 44 , 45 , 46 ]. These biologically-extended NTCP models can drive personalized decision-making and personalized optimization of treatments by setting goals on dose distributions that are tuned to the patient’s own genetics/biology [ 45 , 47 ].…”

“…NTCP models can also include dose-modifying factors explicitly describing individual patients’ radiosensitivity, such as polygenic risk scores or results from biomarker assays [ 43 , 44 , 45 , 46 ]. These biologically-extended NTCP models can drive personalized decision-making and personalized optimization of treatments by setting goals on dose distributions that are tuned to the patient’s own genetics/biology [ 45 , 47 ]. Tucker et al first proposed an NTCP model (mixture Lyman-Kutcher-Burman) for the prediction of grade≥3 lung pneumonitis, including the mean dose to the lung (defined as the total lung minus Gross Tumor Volume) and smoking status and 5 SNPs as modifying factors of the effective dose [ 48 ].…”