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

Abstract

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).

Keywords: Intestinal microbiota; Machine learning; Prostate cancer; Radiation toxicity.

Fig. 1

Analysis of the risk factors for the development of early acute GI toxicity. (a) Values of the mean rectal dose across the patient population, stratified by RT intent (n = 215) and by toxicity group. (b) Density plots of the risk score associated with RT-related pro-inflammatory cytokines (polycytokinic risk) across the overall population (grey) and in the toxicity groups (blue, red). (c) Frequency of potential protective and risk factors measured within the toxicity groups. (d) Statistic of the intestinal microbiota bacterial genera detected in the RT-baseline faecal samples of the patient population with a relative abundance of at least 2%; bacterial genera measured with at least 2% relative abundance in at least 10% of the sample population define the core intestinal microbiota. (e) Shannon's index, (f) Simpson's index and (g) number of core genera measured from the microbiota profiles (genus level) and stratified by toxicity group (a small vertical jitter is applied to dots to improve visualisation).

Fig. 2

Association of the intestinal microbiota with RT-induced GI toxicity. (a) Heatmap showing the normalised abundance profiles (standardised clr-transformed relative abundance values) of the core microbiota genera (rows) from patients in the MicroLearner PCa discovery cohort (columns); core genera were defined as those genera found in at least 10% of the cohort with relative abundance ≥2%; hierarchical clustering was used to discover 8 clusters of patients (black vertical lines) with similar core compositions to which we assigned a microbiota class for the risk for developing acute GI toxicity during RT. (b) Bar plot showing the toxicity rate observed in each cluster of patients and the cluster size in unit of population percentage (cluster sequence same as in the heatmap); cluster number three was significantly enriched for toxicity (60% toxicity rate, high microbiota risk) while cluster five was significantly enriched for no toxicity (0% toxicity rate, low microbiota risk); other clusters show toxicity rate in the range 3–25% (moderate microbiota risk). (c) Statistic of normalised indices of toxicity risk factors visualised by microbiota risk class. (d) Heatmap showing the microbiota functional profiles (standardised relative abundance values) of 13 selected KEGG Orthologs (KOs, rows) imputed from the core microbiota abundance profiles of the patients in the MicroLearner PCa discovery cohort (columns); KOs selected have an average within-group absolute z-score abundance value >1 in the high-risk microbiota patients; hierarchical clustering was used to define 3 clusters of patients (yellow vertical lines) and to reveal the functional pattern associated with the microbiota composition at high-risk for toxicity.

Fig. 3

Prediction of acute GI toxicity from RT-baseline faecal samples. (a) Statistics of the relative abundance of the six core genera used as predictors of acute GI toxicity, visualised across the eight clusters of the discovery cohort, including the high- and the low-risk microbiota clusters. (b) Schematic of the clinical decision tree algorithm (MICLIDE) developed to predict the risk of developing RT-induced acute GI toxicity from individual faecal samples at treatment baseline; the prediction rules to assign patients to risk classes based on the proportion of selected genera are shown in blue under each node of the tree; starting from the root node (top), the decision rules are evaluated for any individual patient microbiota profile (if yes, follow left branch otherwise follow right branch); each node reports the percentage of the discovery population classified to that level of the tree hierarchy and it is coloured according to the prevalence of microbiota risk class within. (c) Bar plots showing the performance of the MICLIDE tree predicting acute GI toxicity; the plot on the left shows the rates of acute GI toxicity measured in the populations used to train the model (MicroLearner discovery cohort) and to test the model (MicroLearner validation cohort and MARS cohort); the plot in the centre compares the prevalence of patients at high-risk microbiota found in the MicroLearner discovery cohort with the same prevalence predicted by the model in the MicroLearner validation cohort and in the MARS cohort; the plot on the right shows the rate of acute GI toxicity measured in the subgroup of patients with high-risk microbiota across the discovery and the validation cohorts. (d) Statistic of mean rectal dose received by patients in the MicroLearner validation cohort stratified by microbiota risk class predicted by the MICLIDE tree and by toxicity group.