The general conditions for ethical approval, patient selection and informed consent, blood sampling and individual patient dosimetry were described in detail in Moquet et al., (2018) (19). Briefly, blood samples from five breast, four endometrial, five lung, three prostate, two oesophagus and one colon cancer patients, treated with Intensity Modulated Radiotherapy (IMRT) using a linear accelerator (LINAC) were collected at four different time points during the course of the treatment: before the start of the treatment, 24 h after the first fraction, just before the fifth or sixth fraction and the last fraction. The prescribed doses for each patient were as follow: breast cancer patients received 40-40.5 Gy in 15 fractions, endometrial 45 Gy in 25 fractions, lung 55 Gy in 20 fractions, prostate 60 Gy in 20 fractions, oesophagus 36 in 12 fractions/20 Gy in 5 fractions and colon 40 Gy in 15 fractions. Patients did not receive previous radio- and/or chemotherapy treatments except for one of the lung cancer patients who received chemotherapy five weeks before the start of radiotherapy. Blood was collected at the Royal Marsden Hospital and Institute of Cancer Research (Surrey, UK) with written informed consent from all subjects as part of the RTGene study (ClinicalTrials.gov NCT02780375), which was ethically approved by the South Central-Hampshire B Research Ethics Committee (16/SC/0307).
RNA isolation and reverse transcription
Total RNA was extracted with the PAXgene Blood miRNA kit (Qiagen, PreAnalytiX GmbH, Hilden, Germany) using a robotic workstation Qiacube (Qiagen, Manchester, UK). The quantity of isolated RNA was determined by spectrophotometry with a ND-1000 NanoDrop and quality was assessed using a Tapestation 220 (Agilent Technologies, CA, USA). cDNA was prepared from 350 ng of the total RNA using High Capacity cDNA reverse transcription kit (Applied Biosystems, FosterCity, CA, USA) according to the manufacturer’s protocol.
Samples were analysed by the nCounter Analysis System (NanoString Technologies®, Inc., Seattle, WA, USA) according to the manufacturers’ guidelines. The samples were run using 100 ng RNA per sample on the Human Inflammation V2 panel, which consists of 249 genes.
Quantitative Real-time Polymerase Chain Reaction
SYBRGreen qPCR was performed using Rotor-Gene Q (Qiagen, Hilden, Germany). All reactions were run in triplicate using PerfeCTa SYBR® Green SuperMix (Quanta Biosciences, Inc., Gaithersburg, MD, USA) with primer sets for target genes at 500 nM concentration each. Cycling parameters were 2 min at 95°C, then 40 cycles of 10 s at 95°C and 60 s at 60°C. Data were collected and analysed by Rotor-Gene Q Series software. Fold of change values were calculated using the delta–delta Ct method (20). The primer sequences for SYBRGreen analysis were HPRT1 F: 5′ TCAGGCAGTATAATCCAAAGATGGT 3′, R: 5′ AGTCTGGCTTATATCCAACACTTCG 3′; IL7 5’ CTCCCCTGATCCTTGTTCTG 3’, R: 5’ TCATTATTCAGGCAATTGCTACC 3’; CD40LG F: 5’ CACCCCCTGTTAACTGCCTA 3’; R: 3’ CTGGATGTCTGCATCAGTGG 5’.
BRBArrayTools and MANOVA
Statistical analysis was performed with BRBArrayTools, to identify genes for which there were statistically significant changes (up or down regulation; p < 0.05) associated with number of radiotherapy (RT) fractions and time since exposure, with a false discovery rate (FDR) < 0.05. General Linear Model Multivariate Analysis of Variance (GLM ANOVA) and Multivariate Analysis of Variance (MANOVA) was then carried out with Minitab18®, to identify panels of genes significantly associated (p < 0.05) with radiation exposure, taking into account radiotherapy patient group by type of cancer treated (breast, lung, gastrointestinal or genitourinary tumours) and time since exposure (just before exposure, 24 hours post-exposure, and just before the final fraction -range 3-5 weeks for all patients).
Differential expression and pathway analysis
Nanostring nCounter nSolver 4.0 (Nanostring Technologies) with the Advanced analysis plugging (version 2.0.134) was used to perform the differential expression (DE) and pathway analysis. DE analysis includes several multivariate linear regression models to identify significant genes (mixture negative binomial, simplified negative binomial, or log-linear model). FDR p-value adjustment was performed with Benjamini–Yekutieli method (21). Statistically significant, differentially expressed genes were defined as those with expression levels corresponding to a log2 ratio >0.5 or < −0.5 and p-value < 0.05.
Gene set analysis (GSA) is a quantitative summary of DE for gene sets. Gene set’s global significance score for a covariate is calculated as the square root of the mean squared t-statistic of genes. Global and directed significance scores were calculated for each pathway. Pathway scores were used to summarize data from a pathway's genes into a single score. Pathway scores were calculated as the first principal component of the pathway genes' normalized expression and standardized by Z scaling. Pathway scoring helps to see how pathway scores change across samples. Increasing score corresponds to mostly increasing expression.
Immune cell type profiling
The cell type profiling module in Nanostring nCounter nSolver 4.0 advanced analysis was used to quantify cell populations using marker genes. Raw cell type measurements are calculated as the log2 expression of each cell type’s marker genes and show the estimated abundances of each individual cell type between samples.