Characterization of oral and gut microbiome temporal variability in hospitalized cancer patients

Abstract

Background: Understanding longitudinal variability of the microbiome in ill patients is critical to moving microbiome-based measurements and therapeutics into clinical practice. However, the vast majority of data regarding microbiome stability are derived from healthy subjects. Herein, we sought to determine intra-patient temporal microbiota variability, the factors driving such variability, and its clinical impact in an extensive longitudinal cohort of hospitalized cancer patients during chemotherapy.

Methods: The stool (n = 365) and oral (n = 483) samples of 59 patients with acute myeloid leukemia (AML) undergoing induction chemotherapy (IC) were sampled from initiation of chemotherapy until neutrophil recovery. Microbiome characterization was performed via analysis of 16S rRNA gene sequencing. Temporal variability was determined using coefficients of variation (CV) of the Shannon diversity index (SDI) and unweighted and weighted UniFrac distances per patient, per site. Measurements of intra-patient temporal variability and patient stability categories were analyzed for their correlations with genera abundances. Groups of patients were analyzed to determine if patients with adverse outcomes had significantly different levels of microbiome temporal variability. Potential clinical drivers of microbiome temporal instability were determined using multivariable regression analyses.

Results: Our cohort evidenced a high degree of intra-patient temporal instability of stool and oral microbial diversity based on SDI CV. We identified statistically significant differences in the relative abundance of multiple taxa amongst individuals with different levels of microbiota temporal stability. Increased intra-patient temporal variability of the oral SDI was correlated with increased risk of infection during IC (P = 0.02), and higher stool SDI CVs were correlated with increased risk of infection 90 days post-IC (P = 0.04). Total days on antibiotics was significantly associated with increased temporal variability of both oral microbial diversity (P = 0.03) and community structure (P = 0.002).

Conclusions: These data quantify the longitudinal variability of the oral and gut microbiota in AML patients, show that increased variability was correlated with adverse clinical outcomes, and offer the possibility of using stabilizing taxa as a method of focused microbiome repletion. Furthermore, these results support the importance of longitudinal microbiome sampling and analyses, rather than one time measurements, in research and future clinical practice.

Keywords: Antibiotics; Chemotherapy; Leukemia; Microbiome; Temporal variability.

Fig. 1

Intra-patient temporal variability in oral and stool microbiomes of hospitalized AML patients undergoing IC. a The oral and stool microbial α-diversity intra-patient temporal variability. Each point represents the coefficient of variation (CV) of the Shannon diversity index (SDI) for samples derived from each patient. b The correlation between the CV of the SDI values originating from oral and stool samples from the same patient. The Pearson’s correlation (r) value and P value from correlation analyses also are indicated. c The oral and stool microbial β-diversity intra-patient temporal variability using either the CV of the weighted or unweighted UniFrac distances for samples derived from each patient. In panels a and c, the bars represent mean ± standard deviation, and P values comparing the different body sites were calculated using a Mann–Whitney U-test

Fig. 2

Temporal instability of microbiome community structure correlates with increasing abundance of pathogenic-associated genera over time. Heatmap of all samples and untransformed relative abundance values of indicated bacterial taxa colored white to red as denoted in the figure. Samples from each patient are clustered together and arranged by timepoint (i.e., consecutive samples) from left to right. Additionally, clusters of patient samples are organized in accordance with temporal variability as determined by the coefficient of variation of the weighted UniFrac distance (cv_w.unifrac) with increasing variability from left to right. Taxa are organized from top to bottom by highest positive correlation of relative abundance of genera with CV of the weighted UniFrac distance, to negative correlation as determined by Pearson’s correlation (r) values depicted in the colored inlaid figure legend. A P value for the correlation’s significance (Corr.SigLvl) was derived from the test statistic based on Pearson’s product moment correlation coefficient, corrected for multiple comparisons with the Benjamini and Hochberg method, and displayed on the plot

Fig. 3

Temporal instability of microbial α-diversity correlates with increasing abundance of pathogenic-associated genera over time. This figure is arranged in the same way as Fig. 2, except temporal variability is defined using coefficient of variation of the Shannon diversity index (cv_shannon)

Fig. 4

Taxonomic composition differences among different stability categories. The significant differences in relative abundances of genera between different patient stability categories based on either the coefficient of variation (CV) of the Shannon diversity index (SDI; top two panels) or the CV of the weighted UniFrac distances (bottom two panels). For each body habitat the population was divided into quartiles, where the first quartile was defined as low/stable, second and third as average, and fourth as high/variable. Differences in genera abundance across categories were determined using non-parametric Kruskal–Wallis analysis of variance, then corrected for the false discovery rate using the Benjamini and Hochberg method. Asterisks indicate adjusted P values: *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, respectively. a Taxa by patient microbiome diversity. b Taxa by patient microbiome stability

Fig. 5

Temporal instability of microbiome α-diversity is associated with infectious outcomes during and after chemotherapy. Shown are the coefficient of variation (CV) of the Shannon diversity index (SDI) for oral (a) and stool (b) samples stratified by patients who did or did not contract an infection during the induction phase of chemotherapy before neutrophil recovery. c, d The CVs of the SDI for oral and stool samples, respectively, stratified by patients who did or did not contract an infection in the 90 days following neutrophil recovery. In all panels the bars represent mean ± standard deviation, and P values comparing SDI CV values among infectious outcomes were calculated using a two-sample t-test with Welch’s correction. e Summarized genera abundance differences between patients who did or did not contract an infection during IC. Significance was determined by individual Mann–Whitney tests for the three different genera (*P < 0.05, **P < 0.01, ***P < 0.001)

Fig. 6

Prolonged antibiotic exposure is associated with long-term infectious complications. Illustrated are patients who did or did not contract a microbiologically determined infection in the 90 days post-neutrophil recovery and their a days on treatment antibiotics or b days on all antibiotics to include prophylaxis. In both panels the bars represent mean ± standard deviation, and P values comparing antibiotic exposure among infectious outcomes were calculated using a two-sample t-test with Welch’s correction