Intestinal microbiota composition is predictive of radiotherapy-induced acute gastrointestinal toxicity in prostate cancer patients.

BACKGROUND: The search for factors beyond the radiotherapy dose that could identify patients more at risk of developing radio-induced toxicity is essential to establish personalised treatment protocols for improving the quality-of-life of survivors. To investigate the role of the intestinal microbiota in the development of radiotherapy-induced gastrointestinal toxicity, the MicroLearner observational cohort study characterised the intestinal microbiota of 136 (discovery) and 79 (validation) consecutive prostate cancer patients at baseline radiotherapy. METHODS: Gastrointestinal toxicity was assessed weekly during RT using CTCAE. An average grade >1.3 over time points was used to identify patients suffering from persistent acute toxicity (endpoint). The microbiota of patients was quantified from the baseline faecal samples using 16S rRNA gene sequencing technology and the Ion Reporter metagenomic pipeline. Statistical techniques and computational and machine learning tools were used to extract, functionally characterise, and predict core features of the bacterial communities of patients who developed acute gastrointestinal toxicity. FINDINGS: Analysis of the core bacterial composition in the discovery cohort revealed a cluster of patients significantly enriched for toxicity, displaying a toxicity rate of 60%. Based on selected high-risk microbiota compositional features, we developed a clinical decision tree that could effectively predict the risk of toxicity based on the relative abundance of genera Faecalibacterium, Bacteroides, Parabacteroides, Alistipes, Prevotella and Phascolarctobacterium both in internal and external validation cohorts. INTERPRETATION: We provide evidence showing that intestinal bacteria profiling from baseline faecal samples can be effectively used in the clinic to improve the pre-radiotherapy assessment of gastrointestinal toxicity risk in prostate cancer patients. FUNDING: Italian Ministry of Health (Promotion of Institutional Research INT-year 2016, 5 × 1000, Ricerca Corrente funds). Fondazione Regionale per la Ricerca Biomedica (ID 2721017). AIRC (IG 21479).

A Multicentre Evaluation of Dosiomics Features Reproducibility, Stability and Sensitivity.

Dosiomics is a texture analysis method to produce dose features that encode the spatial 3D distribution of radiotherapy dose. Dosiomic studies, in a multicentre setting, require assessing the features' stability to dose calculation settings and the features' capability in distinguishing different dose distributions. Dose distributions were generated by eight Italian centres on a shared image dataset acquired on a dedicated phantom. Treatment planning protocols, in terms of planning target volume coverage and dose-volume constraints to the organs at risk, were shared among the centres to produce comparable dose distributions for measuring reproducibility/stability and sensitivity of dosiomic features. In addition, coefficient of variation (CV) was employed to evaluate the dosiomic features' variation. We extracted 38,160 features from 30 different dose distributions from six regions of interest, grouped by four features' families. A selected group of features (CV < 3 for the reproducibility/stability studies, CV > 1 for the sensitivity studies) were identified to support future multicentre studies, assuring both stable features when dose distributions variation is minimal and sensitive features when dose distribution variations need to be clearly identified. Dosiomic is a promising tool that could support multicentre studies, especially for predictive models, and encode the spatial and statistical characteristics of the 3D dose distribution.