High-resolution analyses of associations between medications, microbiome, and mortality in cancer patients

Abstract

Discerning the effect of pharmacological exposures on intestinal bacterial communities in cancer patients is challenging. Here, we deconvoluted the relationship between drug exposures and changes in microbial composition by developing and applying a new computational method, PARADIGM (parameters associated with dynamics of gut microbiota), to a large set of longitudinal fecal microbiome profiles with detailed medication-administration records from patients undergoing allogeneic hematopoietic cell transplantation. We observed that several non-antibiotic drugs, including laxatives, antiemetics, and opioids, are associated with increased Enterococcus relative abundance and decreased alpha diversity. Shotgun metagenomic sequencing further demonstrated subspecies competition, leading to increased dominant-strain genetic convergence during allo-HCT that is significantly associated with antibiotic exposures. We integrated drug-microbiome associations to predict clinical outcomes in two validation cohorts on the basis of drug exposures alone, suggesting that this approach can generate biologically and clinically relevant insights into how pharmacological exposures can perturb or preserve microbiota composition. The application of a computational method called PARADIGM to a large dataset of cancer patients' longitudinal fecal specimens and detailed daily medication records reveals associations between drug exposures and the intestinal microbiota that recapitulate in vitro findings and are also predictive of clinical outcomes.

Keywords: 16S sequencing; computational modeling; hematopoietic cell transplantation; metagenomics; microbiota; pharmacological exposures.

Figure 1.. Patient selection criteria for the discovery and validation cohorts.

The MSKCC discovery cohort was included in the clustering of sequencing data and PARADIGM algorithm training set. The validation cohorts were included in the analysis of clinical outcomes.

Figure 2.. The intestinal microbiota of allo-HCT patients is highly dynamic.

a, b, Compositional space of the intestinal microbiota in the MSKCC discovery cohort visualized by tSNE projection. Each point represents a sample, colored according to the taxon of highest relative abundance based on (a) 16S rRNA (7,454 samples; 778 patients) or (b) shotgun metagenomic sequencing profiles (980 samples; 340 patients) (p: phylum; f: family; o: order; g: genus). Samples were collected between day −30 and 2,205 relative to HCT. c, Ten clusters of intestinal microbiome compositions are assigned by k-means unsupervised clustering. d, e, Relative abundance of the top 20 most observed (d) genera in the 16S rRNA profiles and (e) species in the shotgun metagenomic profiles in the MSKCC discovery cohort. Each column is one sample, each row is one genus or species. Rows are clustered by hierarchical clustering. f, Cluster alpha-diversity (reciprocal Simpson index). The horizontal dashed line represents the median alpha-diversity of the MSKCC discovery cohort. g, Cluster relative frequency over time relative to HCT. h, Network map depicting the transitions among the ten intestinal microbiota clusters over time (5,482 pairs of subsequent samples; 677 patients; collection between day −16 and 1,084 relative to HCT). The thickness of the line is proportional to transition frequency, while the node size is proportional to the number of samples per cluster.

Figure 3.. PARADIGM predicts changes in microbiome features such as genus relative abundance and alpha-diversity following drug exposures.

a, Schematic representation of PARADIGM which takes advantage of daily sampling 16S rRNA-sequenced samples and cluster transitions to infer how drug exposures are associated with microbial dynamics. Bacteria response scores translate drug-cluster associations into drug-genus associations. b, Associations between drug exposures and microbiome cluster dynamics (full results in Figure S3). Self coefficients indicate whether drug exposure increases or decreases the log-odds of cluster stability. Attractor coefficients indicate whether drug exposure increases or decreases the log-odds of transition to a given cluster. TMP-SMX; Sulfamethoxazole/trimethoprim. ATG; Anti-thymocyte globulin. c, Bacteria response scores predict the association between a given drug exposure and changes in genus relative abundance or alpha-diversity (full results in Figure S4). d, Pearson’s correlation between Enterococcus response scores and alpha-diversity response scores. Each point represents an individual drug. e, Pearson’s correlation between bacteria response scores and measurements of in vitro inhibition 3. Each point represents the association between a unique drug-species pair. f, Predicted bacteria response scores by in vitro inhibition. Two-sided Wilcoxon’s rank-sum test.

Figure 4.. Antibiotics are strong predictors of strain genetic convergence during allo-HCT.

a, Strain convergence over time relative to HCT (middle row), or by species relative abundance (bottom row). Each point represents the tree-based phylogenetic distance between the dominant strains of a given species in a pair of subsequently collected samples. Higher phylogenetic distance suggests genetic dissimilarity, while lower phylogenetic distance suggests strain genetic similarity. b, c, Antibiotic exposure (b), but not non-antibiotic exposure (c) is associated with increased E. faecium dominant strain convergence. Each point represents the phylogenetic distance between E. faecium dominant strains in a pair of subsequently collected samples, stratified by drug exposures during the time gap of sample pair collection. Two-sided Wilcoxon’s rank-sum test.

Figure 5.. Drug exposure profiles are predictive of future microbiome trajectories and allo-HCT patient outcomes in two distinct validation cohorts.

a, Schematic of the patient-specific bacteria response score calculation. b, Patients-specific Enterococcus response scores in the validation cohort were derived based solely on drug exposure profiles (between day −14 to 14 relative to HCT) and bacteria response scores presented in Figure 3c. A negative score indicates that the drug exposure profile is associated with an Enterococcus-inhibiting effect, while a positive score indicates that the drug exposure profile is associated with an Enterococcus-promoting effect. c, Pearson’s correlation between patient-specific bacteria response scores and observed genus relative abundance or alpha-diversity in samples collected between day 14 and 45 relative to HCT in the MSKCC validation cohort (423 patients) and Duke cohort (142 patients). Adjusted p-values by Benjamini-Hochberg’s correction. d, Patient-specific bacteria response scores are predictive of overall and cause-specific mortality in the MSKCC and Duke validation cohorts, in each respective multivariate Cox proportional hazard or Fine-Gray model, controlled for age, sex, conditioning intensity, graft source and underlying disease. Adjusted p-values by Benjamini-Hochberg’s correction.