Intestinal microbiome analyses identify melanoma patients at risk for checkpoint-blockade-induced colitis

Krista Dubin, Margaret K Callahan, Boyu Ren, Raya Khanin, Agnes Viale, Lilan Ling, Daniel No, Asia Gobourne, Eric Littmann, Curtis Huttenhower, Eric G Pamer, Jedd D Wolchok, Krista Dubin, Margaret K Callahan, Boyu Ren, Raya Khanin, Agnes Viale, Lilan Ling, Daniel No, Asia Gobourne, Eric Littmann, Curtis Huttenhower, Eric G Pamer, Jedd D Wolchok

Abstract

The composition of the intestinal microbiota influences the development of inflammatory disorders. However, associating inflammatory diseases with specific microbial members of the microbiota is challenging, because clinically detectable inflammation and its treatment can alter the microbiota's composition. Immunologic checkpoint blockade with ipilimumab, a monoclonal antibody that blocks cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4) signalling, is associated with new-onset, immune-mediated colitis. Here we conduct a prospective study of patients with metastatic melanoma undergoing ipilimumab treatment and correlate the pre-inflammation faecal microbiota and microbiome composition with subsequent colitis development. We demonstrate that increased representation of bacteria belonging to the Bacteroidetes phylum is correlated with resistance to the development of checkpoint-blockade-induced colitis. Furthermore, a paucity of genetic pathways involved in polyamine transport and B vitamin biosynthesis is associated with an increased risk of colitis. Identification of these biomarkers may enable interventions to reduce the risk of inflammatory complications following cancer immunotherapy.

Figures

Figure 1. C-F and PtC patients harbour…
Figure 1. C-F and PtC patients harbour distinct microbial populations.
(a) Faecal sample collection (blue circle) and the onset of colitis (red triangle) are shown at the indicated time points during treatment with ipilimumab, a monoclonal antibody that blocks CTLA-4 signalling. Dates are relative to first dose of treatment. An average abundance >0.01% was used as the threshold for considering an OTU to be present within faecal samples in either patient group (OTUs, n=578); OTUs at an abundance of ≤0.01% were not considered to be present in the patients' microbiota. Using this definition, we calculated (b) the number of OTUs present in C-F patients only (purple), PtC patients only (turquoise) or shared between the patient groups (blue) in a scaled Venn diagram. (c) The mean relative abundance of OTUs, (d) total abundance of OTUs, (e) distribution of OTUs that are present in C-F patients only, PtC patients only or shared between the patient. C-F patients, n=24; PtC patients, n=10. OTUs, operational taxonomic units.
Figure 2. Composition of the intestinal microbiota…
Figure 2. Composition of the intestinal microbiota between C-F and PtC patients.
(a) OTUs with an average abundance >0.01% within either patient group were classified at the family taxonomic level. Families with an average abundance of 2.5% across all samples or an abundance of greater than 5% in a single sample are plotted. Each bar represents the faecal microbial composition of one patient. (b) The relative abundances of the 146 bacterial OTUs in C-F and PtC patients represented in a heat map. OTUs plotted were present at a mean abundance of ≥0.1%. Patients are ordered by CTCAE-based colitis score. C-F patients, n=24; PtC patients, n=10. OTUs, operational taxonomic units; Actino, Actinobacteria; Bact, Bacteroidetes; Firm, Firmicutes; Proteo, Proteobacteria; Verr, Verrucomicrobia.
Figure 3. Increased abundance of the Bacteroidetes…
Figure 3. Increased abundance of the Bacteroidetes phylum and select families correlates with protection from colitis.
OTUs with an average abundance >0.01% within either patient group were binned at different levels of taxonomic classification (phylum, class, order, family and genus). (a) Correlation of bacterial phylotypes to CTCAE-based colitis score by Spearman analysis. Taxa with P-values <0.05 are plotted. (b) Relative abundance of the phylum Bacteriodetes in PtC and C-F patients. (c) The number of OTUs assigned to the Bacteroidetes phylum in each patient group. Relative abundances of the families (d) Bacteroidaceae, (e) Rikenellaceae and (f) Barnesiellaceae within the Bacteroidetes phylum in PtC and C-F patients. P-values were determined by Mann–Whitney test. Height of bar represents the mean, error bars represent s.d. C-F patients, n=24; PtC patients, n=10. r, ρ coefficient; OTUs, operational taxonomic units.
Figure 4. Bacterial modules involved in polyamine…
Figure 4. Bacterial modules involved in polyamine transport and vitamin B synthesis are associated with resistance to colitis.
(a) Relative abundance of 102 microbial KEGG modules in C-F and PtC patients. (b) Association of genetic modules with colitis status by LEfSe analysis. Modules with a linear discriminant analysis (LDA) score >3 are plotted. (c) Correlation of genetic modules to colitis score by Spearman analysis. Modules with P-values <0.05 are plotted. (d) Relative abundances of modules associated with C-F patients. P-values were determined by Mann–Whitney test. Height of bar represents mean, error bars represent s.d. C-F patients, n=12; PtC patients, n=10.
Figure 5. Predictive accuracy of bacterial modules…
Figure 5. Predictive accuracy of bacterial modules to identify patients who develop colitis.
(a) The recursive partitioning algorithm was used to construct a classification tree, based on the abundance of the polyamine transport module. (b) Leave-one-out cross-validation of the probit regression analysis predicts the probability of colitis using four modules associated with colitis resistance: polyamine transport system, thiamine biosynthesis, riboflavin biosynthesis and pantothenate biosynthesis. One patient is represented per column. Specificity and sensitivity calculated based on a probability threshold of 50%. (c) The sensitivity and specificity of each module to predict patients' colitis status by their faecal microbial samples was determined using a probability threshold of 50%, as compared with the four-module model. (d) Receiver operating characteristic (ROC) curve of the four-module model predicting colitis risk. ROC curve was created by calculating the true-positive rate and false-positive rate for 10,000 thresholds of the predicted probability of colitis between 0 and 1. True-positive rate represents the test sensitivity, calculated by: true positives/(true positives + false negatives). False-positive rate, which is given by 1−test specificity, is calculated by: false positives/(false positives + true negatives). Poly, polyamine transport system; Thi, thiamine biosynthesis; Ribo, riboflavin biosynthesis; Panto, pantothenate biosynthesis. C-F patients, n=12; PtC patients, n=10.

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