Predicting acute severe toxicity for head and neck squamous cell carcinomas by combining dosimetry with a radiosensitivity biomarker: a pilot study

Abstract

Objective: Radiotherapy (RT) against head and neck squamous cell carcinomas (HNSCC) may lead to severe toxicity in 30-40% of patients. The normal tissue complication probability (NTCP) models, based on dosimetric data refined the normal tissue dose/volume tolerance guidelines. In parallel, the radiation-induced nucleoshuttling (RIANS) of the Ataxia-Telangiectasia Mutated protein (pATM) is a predictive approach of individual intrinsic radiosensitivity. Here, we combined NTCP with RADIODTECT©, a blood assay derived from the RIANS model, to predict RT toxicity in HNSCC patients. Methods: RADIODTECT© cutoff values (i.e. 57.8 ng/mL for grade⩾2 toxicity and 46 ng/mL for grade⩾3 toxicity) have been previously assessed. Validation was performed on a prospective cohort of 36 HNSCC patients treated with postoperative RT. Toxicity was graded with the Common Terminology Criteria for Adverse Events (CTCAE) scale and two criteria were considered: grade⩾2 oral mucositis (OM2), grade⩾3 mucositis (OM3) and grade⩾2 dysphagia (DY2), grade⩾3 dysphagia (DY3). pATM quantification was assessed in lymphocytes of HNSCC patients. The discrimination power of the pATM assay was evaluated through the Area Under the Receiver Operator Characteristics Curve (AUC-ROC). Two previously described NTCP models were considered, including the dose to the oral cavity and the mean dose to the parotid glands (OM2 and OM3) and the dose to the oral cavity, to the larynx and the volume of pharyngeal constrictor muscles (DY2 and DY3). Results: Combining NTCP models with RADIODTECT© blood test improved the AUC-ROC. Considering the prediction of mucositis, AUC-ROCNTCP+RADIODTECT©=0.80 was for OM2, and AUC-ROCNTCP+RADIODTECT©=0.78 for OM3. Considering the prediction of acute dysphagia, AUC-ROCNTCP+RADIODTECT©=0.71 for DY2 and for DY3. Conclusions: Combining NTCP models with a radiosensitivity biomarker might significantly improve the prediction of toxicities for HNSCC patients.