Molecular Profiling for Predictors of Radiosensitivity in Patients with Breast or Head-and-Neck Cancer

Abstract

Nearly half of all cancers are treated with radiotherapy alone or in combination with other treatments, where damage to normal tissues is a limiting factor for the treatment. Radiotherapy-induced adverse health effects, mostly of importance for cancer patients with long-term survival, may appear during or long time after finishing radiotherapy and depend on the patient's radiosensitivity. Currently, there is no assay available that can reliably predict the individual's response to radiotherapy. We profiled two study sets from breast (n = 29) and head-and-neck cancer patients (n = 74) that included radiosensitive patients and matched radioresistant controls.. We studied 55 single nucleotide polymorphisms (SNPs) in 33 genes by DNA genotyping and 130 circulating proteins by affinity-based plasma proteomics. In both study sets, we discovered several plasma proteins with the predictive power to find radiosensitive patients (adjusted p < 0.05) and validated the two most predictive proteins (THPO and STIM1) by sandwich immunoassays. By integrating genotypic and proteomic data into an analysis model, it was found that the proteins CHIT1, PDGFB, PNKD, RP2, SERPINC1, SLC4A, STIM1, and THPO, as well as the VEGFA gene variant rs69947, predicted radiosensitivity of our breast cancer (AUC = 0.76) and head-and-neck cancer (AUC = 0.89) patients. In conclusion, circulating proteins and a SNP variant of VEGFA suggest that processes such as vascular growth capacity, immune response, DNA repair and oxidative stress/hypoxia may be involved in an individual's risk of experiencing radiation-induced toxicity.

Keywords: biomarker; breast cancer; head-and-neck cancer; ionizing radiation; mandibular osteoradionecrosis; personalized radiotherapy; plasma proteins; prediction; radiosensitivity; radiotherapy; radiotherapy side effects; skin reaction.

Figure 1

The overall experimental strategy.

Figure 2

Protein candidates of radiosensitivity measured in exploratory bead arrays. Protein profiles of the top candidates (A) STIM1 (B) THPO; (C) Correlation between significant antibodies identified in univariate analysis.

Figure 3

Validation of selected candidate proteins using sandwich immunoassays in the head-and-neck cancer (HNC) data set. (A) Correlation between tested antibodies for STIM1 and THPO; Using the sandwich immunoassays to confirm the trends of (B) STIM1 and (C) THPO in the HNC sample set.

Figure 4

VEGFA protein level for patients with different genotype (AA-wild type, Aa-heterozygous, aa-mutant) grouped by resistance status (RR or RS).

Figure 5

(A) Sorted importance score from multiple random validation-based feature selection. Seventeen antibodies were selected based on the “knee” method. (B) Receiver-operator curve for three logistic regression models with different numbers and types of features.