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Cancer. Author manuscript; available in PMC 2017 August 29. Published in final edited form as: Cancer. 2016 July 15; 122(14): 2186–2196. doi:10.1002/cncr.30039.

The Role Of The Gastrointestinal Microbiome in Infectious Complications During Induction Chemotherapy For Acute Myeloid Leukemia

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Jessica Galloway-Peña, PhD1, Daniel P. Smith, PhD7, Pranoti Sahasrabhojane, MS1, Nadim J. Ajami, PhD7, W. Duncan Wadsworth2, Naval G. Daver, MD3, Roy F. Chemaly, MD1, Lisa Marsh, MSN1, Shashank S. Ghantoji, PhD1, Naveen Pemmaraju, MD3, Guillermo GarciaManero, MD3, Katayoun Rezvani, MD, PhD4, Amin M. Alousi, MD4, Jennifer A. Wargo, MD5,6, Elizabeth J. Shpall, MD4, Phillip A. Futreal, PhD6, Michele Guindani, PhD2, Joseph F. Petrosino, PhD7, Dimitrios P. Kontoyiannis, MD, ScD1, and Samuel A. Shelburne, MD, PhD1,6 1Department

of Infectious Disease, Infection Control and Employee Health, MD Anderson Cancer Center, Houston, TX, 77030

2Department

of Biostatistics, MD Anderson Cancer Center, Houston, TX, 77030

3Department

of Leukemia, MD Anderson Cancer Center, Houston, TX, 77030

4Department

of Stem Cell Transplantation and Cellular Therapy, MD Anderson Cancer Center, Houston, TX, 77030

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5Department

of Surgical Oncology, MD Anderson Cancer Center, Houston, TX, 77030

6Department

of Genomic Medicine, MD Anderson Cancer Center, Houston, TX, 77030

Corresponding Author: Samuel A. Shelburne MD, PhD, 1515 Holcombe Blvd, Unit 1460, Houston, TX 77030, Phone: 713-500-0614, [email protected]. Conflicts of Interest: NJA and JFP are the project director and Founder/Chief Science Officer, respectively, of Diversgen. All other authors have no conflicts of interest specific to this work.

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Author Contributions: Jessica Galloway-Peña: Conceptualization, methodology, validation, formal analysis, investigation, data curation, writing – original draft, writing – review and editing, visualization, project administration, and funding acquisition. Daniel P. Smith: Methodology, software, validation, formal analysis, resources, data curation, writing – review and editing, visualization, and project administration. Pranoti Sahasrabhojane: Methodology, investigation, resources, and writing – review and editing. Nadim J. Ajami: Methodology, software, validation, formal analysis, resources, data curation, writing – review and editing, visualization, and project administration. W. Duncan Wadsworth: Methodology, software, validation, formal analysis, data curation, writing – review and editing, and visualization. Naval G. Daver: Investigation, resources, writing – review and editing, and visualization. Roy F. Chemaly: Conceptualization, validation, investigation, writing – review and editing, and visualization. Lisa Marsh: Methodology, investigation, resources, data curation, writing – review and editing, supervision, and project administration. Shashank S. Ghantoji: Methodology, validation, investigation, resources, writing – review and editing, and project administration. Naveen Pemmaraju: Conceptualization, investigation, resources, writing – original draft, and writing – review and editing. Guillermo Garcia-Manero: Project administration. Katayoun Rezvani: Conceptualization, methodology, writing – review and editing, and funding acquisition. Amin M. Alousi: Resources and writing – review and editing. Jennifer A. Wargo: Conceptualization, resources, writing – review and editing, and funding acquisition. Elizabeth J. Shpall: Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing – original draft, writing – review and editing, visualization, supervision, project administration, and funding acquisition. Phillip A. Futreal: Conceptualization, resources, writing – review and editing, and funding acquisition. Michele Guindani: Methodology, software, validation, formal analysis, data curation, writing – original draft, writing – review and editing, and visualization. Joseph F. Petrosino: Software, validation, formal analysis, resources, data curation, writing – review and editing, visualization, and project administration. Dimitrios P. Kontoyiannis: Conceptualization, methodology, resources, writing – original draft, writing – review and editing, and funding acquisition. Samuel A. Shelburne: Conceptualization, methodology, formal analysis, investigation, data curation, writing – review and editing, visualization, supervision, project administration, and funding acquisition.

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7The

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Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, 77030

Abstract Background—Despite increasing data on the impact of the microbiome on cancer, the dynamics and role of the microbiome in infection during acute myelogenous leukemia (AML) therapy are unknown. Thus, we sought to determine relationships between microbiome composition and infectious outcomes in AML patients receiving induction chemotherapy (IC). Methods—Buccal and fecal specimens (478 samples) were collected twice weekly from 34 AML patients undergoing IC. Oral and stool microbiomes were characterized by 16S rRNA V4 sequencing using Illumina MiSeq. Microbial diversity and genera composition were associated with clinical outcomes.

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Results—Baseline stool α-diversity was significantly lower in patients that developed infections during IC compared to those that did not (P = 0.047). Significant decreases in both oral and stool microbial α-diversity were observed over the course of IC, with a linear correlation between αdiversity change at the two sites (P = 0.02). Loss of both oral and stool α-diversity was significantly associated with carbapenem receipt (P < 0.01). Domination events by the majority of genera were transient (median duration = 1 sample), while the number of domination events by pathogenic genera significantly increased over the course of IC (P=0.002). Moreover, patients who lost microbial diversity over the course of induction chemotherapy were significantly more likely to contract a microbiologically documented infection within the 90 days post-IC neutrophil recovery (P=0.04).

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Conclusion—These data present the largest longitudinal analyses of oral and stool microbiomes in AML patients and suggest that microbiome measurements could assist with mitigation of infectious complications of AML therapy. Keywords acute myeloid leukemia; microbiome; induction chemotherapy; gastrointestinal; infectious complications

INTRODUCTION

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With the advent of the Human Microbiome Project (HMP)1, numerous advances have been made in elucidating the critical role commensal microbiota play in human health, immune maintenance, and disease.2, 3 Recent studies in animal models have highlighted the microbiome’s impact in areas such as malignancy development, chemotherapeutic metabolism, and modulation of the immune response and tumor microenvironment.4–6 Given the majority of infections in cancer patients are caused by commensal bacteria7, infection is a cancer care area likely to be profoundly influenced by microbiome investigations.8 Recent examination of patients undergoing hematopoietic stem cell transplantation (HSCT) have shown that intestinal domination by pathogenic bacteria is associated with subsequent infection9 and loss of intestinal microbial diversity post-HSCT is associated with a higher risk of graft vs. host disease and overall mortality.10, 11 However,

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outside of HSCT patients, serial determination of the microbiome of treated cancer patients is limited.12.

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Herein, we present longitudinal analysis of the oral and stool microbiomes of 34 adults undergoing induction chemotherapy (IC) for acute myelogenous leukemia (AML). Patients receiving IC are highly susceptible to infections resulting in routine utilization of antimicrobial prophylaxis.13 However, increasing antimicrobial resistance compromises the efficacy of universal prophylaxis indicating that more personalized medicine approaches are needed to optimize infection prevention and treatment.14 Moreover, there are minimal data regarding the oral microbiome in treated cancer patients.15 Due to the paucity of microbiome data in this clinical scenario, investigations of both the oral and stool microbiome are potentially valuable as both sites serve as portals of infection in immunocompromised patients.16 Thus, we sought to test the hypothesis that microbiome composition could be associated with infectious complications in AML patients during IC.

MATERIALS AND METHODS Patients and Specimen Collection

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Subjects consisted of 34 newly diagnosed AML patients (>18 years of age) undergoing IC at MD Anderson Cancer Center (MDACC) from September 2013 to May 2014. Buccal and fecal specimens were collected from each patient prior to starting chemotherapy, continued every 96 hours, and stopped when polymorphonuclear (PMN) cells returned to >500/µL. The study protocol was approved by the MDACC Institutional Review Board (PA13-0339) and conducted in compliance with the Declaration of Helsinki. Written informed consent was obtained from all subjects prior to enrollment. Despite the limited sample size, our prestudy power calculations showed that a sample size of 34 patients allows to detect a medium Cohen’s effect size f2 of at least 0.3 with 80% power or more, using a one-way repeated measurements ANOVA for the analysis of either the buccal or fecal specimen, and conservatively assuming a within-subject correlation of 0.1 and at least 3 within-patient observations over the study time (5% level of significance).17 Specimen Processing, 16S rRNA Sequencing, and Microbiome Community Analyses

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DNA was extracted from patient samples, and the 16S rRNA V4 region PCR amplified and sequenced on the Illumina MiSeq platform using a 2×250 bp paired-end protocol adapted from the methods developed for the NIH-Human Microbiome Project.1, 18 16S rRNA gene sequences were assigned into operational taxonomic units (OTUs) using the UPARSE pipeline and alignment to the SILVA SSURef_NR99_119 database.19 Analysis and visualization of microbiome communities was conducted in the publically available software R (R Core Team 2015, version 3.2.2)20, utilizing the phyloseq package21 to import sample data, calculate α- and β-diversity metrics, and microbiome community profiles. The Human Microbiome Project sequencing reads from the 16S V3–V4 region were obtained from http://hmpdacc.org/HMQCP, trimmed to match the V4 region amplified by this study’s primers, and processed as above.

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Clinical Definitions

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Febrile neutropenia was defined according to Infectious Diseases Society of America criteria22 and episodes of febrile neutropenia were classified into four groups according to guidelines for neutropenic fever in clinical trials23 : 1) microbiologically defined infection (MDI); 2) clinically defined infection (CDI); 3) fever of unknown origin (FUO); and 4) noninfectious fever (NIF). Subsequent infectious episodes were defined as MDIs that occurred within 90 days of cessation of longitudinal sampling, which typically occurred at the time of neutropenia resolution to >500 cells/µL following IC. Complete remission (CR) and overall response rate were assessed using standard defintions.24 Statistical Analyses

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Microbial α-diversity was determined using the Shannon Diversity Index unless otherwise indicated.25 The assumption of normality both overall and within patients was graphically determined using Q-Q plots and histograms, and confirmed using a Shapiro-Wilk Test for normality. The Shannon Diversity Index was generally assumed to be normally distributed and Student’s t-test was used to assess differences in mean Shannon indices when data were normally distributed and Welch’s correction was employed given that equal standard deviations were not assumed. The Mann-Whitney U test was used to test for differences if the normality assumption was not met. For changes in specific genera, only mapped genera >1% in abundance were analyzed (a total of 29 genera). The Benjamin-Hochberg false discovery rate was applied to account for multiple comparisons when testing changes in individual taxa. Correlations were determined using Pearson’s ρ value, and significance was determined using standard rank correlation tests. Change over time was assessed via a linear mixed-effects model with random slope and intercept terms for each patient, employing an unstructured covariance. The model fit of fitted models versus alternatives (e.g. with respect to a random intercept linear mixed effect model with exponential covariance structure) was assessed using residuals QQ-plots and scatterplots, inspection of the individual patients’ residual autocorrelation functions, and also by fitting a simple linear regression between the fitted and observed values and then calculating that regression's R2.26 Statistical significance in the linear mixed-effects was determined by computing P values using Satterthwaite’s approximation for the degrees of freedom as well as a bootstrap confidence interval for the grand mean slope parameters across patient samples.27 Relationships between two categorical variables were analyzed using χ2 or Fisher’s exact test in the case of 2×2 contingency tables. Statistical analyses were performed using SPSS Statistics Version 22 (IBM, Armonk, NY), the R base package20, and GraphPad Prism 6 for significance testing and plotting. More specifically, the lme4 lmerTest and boot strap packages were used for linear mixed-effects modeling28. All tests of significance were two-sided, and statistical significance was defined at P ≤ 0.05. More detailed descriptions of the patient population, specimen collection, specimen processing, 16S rRNA sequencing, microbiome community analyses, clinical definitions, can be found in the Supporting Information.

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RESULTS Patient Characteristics, Treatment, and Response

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Clinical characteristics, patient classifications, treatment information, and chemotherapeutic response are listed in Table 1. Ninety-one percent of patients were treated on clinical protocols where AML induction regimens differed among younger and older patients. Patients < 60 years often received triplet chemotherapy with a purine analog (clofarabine, fludarabine or cladribine) in combination with idarubicin and cytarabine29, whereas patients ≥60 years frequently received hypomethylator-based combinations (decitabine and azacytidine).30 Four patients had core-binding factor AML and were treated with fludarabine/idarubicin/cytarabine with G-CSF (FLAG-Ida).31 The overall response rate was 82% with 41% of patients achieving morphologic complete remission.24 All patients developed neutropenia (median duration of 28.5 days) and received prophylactic antimicrobials during IC. Seventy-nine percent of patients developed neutropenic fever with febrile episode classification and antibiotic administration shown in Table 1. Specimen Collection and Bacterial Sequences Obtained 276 buccal swabs and 202 fecal samples were obtained for a total of 478 samples. The average number of oral and stool samples per patient were 8.12 and 6.12, respectively. From these specimens, a total number of 16,082,550 high-quality 16S rRNA-encoding sequences were generated for an average of 33,645 sequences per specimen. Characterization of Baseline Oral and Stool Samples from AML Patients

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We first determined the baseline α-diversity, which measures the number of different types of bacteria, of oral and stool samples.25 We observed a wide range of α-diversity among baseline samples with a mean Shannon Index for oral samples of 2.1 (95% CI 1.8, 2.3, Range: 0.5 – 3.9) and a mean Shannon Index of stool samples of 2.0 (95% CI 1.7, 2.4, Range: 0.1 to 3.5) (Fig. 1A). We applied the same algorithm used for our samples to buccal swab and stool sample data from the Human Microbiome Project1 and found a similar distribution of α-diversity in the healthy HMP cohort (Fig. 1A). There was a statistically significant correlation between the α-diversity of the baseline oral and stool samples suggesting that similar factors affecting α-diversity were acting at both two sites (Pearson’s r = 0.44, P = 0.02, Fig. 1B). Similar to the α-diversity analysis, the baseline oral and stool samples demonstrated marked inter-patient β-diversity heterogeneity (Fig. 1C, 1D) analogous to the composition variability previously observed in healthy adults1. Baseline Stool Microbiome α-Diversity Is Associated With Infections During IC

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Given that a diverse microbiome is thought to protect individuals from infection9, we sought to test the hypothesis that patients with a low baseline α-diversity would be at increased risk for infectious complications during IC. When patients were classified into two groups: 1) the infection group which included patients with MDIs and CDIs; and 2) the non-infection group which included patients with FUO, NIF, and no infection/no fever, we found that the α-diversity of baseline stool samples from the infection group was significantly lower than the non-infection group (P = 0.047, Fig. 2A). To ensure our findings were not limited to one

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measurement of α-diversity, we repeated our analysis using four additional diversity measurements and found that the patients in the MDI + CDI group had statistically significantly lower initial stool values compared to the non-infection group for three of the four indices (Supporting Information). As FUO patients could be considered as having an unrecognized infection, we also reclassified the FUO patients to the infection group and found that a significant difference remained between these two groups in terms of baseline α-diversity of the stool samples (P = 0.05, Fig. 2B). Regardless of how patients were classified, no statistically significant relationships were identified between baseline αdiversity of the oral samples and subsequent infectious episodes (data not shown). Interestingly, neutropenia prior to IC initiation was significantly associated with low baseline oral (P=0.002) and stool (P=0.007) α-diversity (Fig. 2C and D). However, neutropenia prior to IC was not an independent risk factor for infection during IC (data not shown). Additionally, morphologic complete remission was not associated with baseline αdiversity at either site (Supporting Information). Thus, baseline α-diversity of the stool, but not oral, samples was associated with the development of infectious complications during IC. A Subset of AML Patients Experience a Decrease in GI Microbial Diversity During IC

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Consistent with the data from HSCT patients9, there was an overall statistically significant decrease in microbial diversity over the course of IC in both the oral and stool samples (P=0.004 and P=0.045, respectively, Fig. 3A). Moreever, similar to the baseline α-diversity data, when individual patients were analyzed, there was a broad distribution of changes in αdiversity over the course of IC (Fig. 3B). There was no statistically significant difference in the change in α-diversity over the course of IC between the two sites (Fig. 3B). However, there was a statistically significant linear correlation between the two sites in terms of change in α-diversity between the baseline and final samples (P = 0.02) (Fig. 3C). When specific genera were analyzed, we observed statistically significant increases for Lactobacillus in both oral and stool samples, and significant decreases were primarily observed for anaerobic genera such as Blautia, Prevotella, and Leptotrichia (Fig. 3D–E).

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Given the known relationship between broad-spectrum antibiotic use and loss of microbial diversity32, we examined the association between loss of α-diversity and antibiotic administration and found that patients who had received a carbapenem antibiotic for over 72 hours were significantly more likely to have a decrease in both oral and stool α-diversity over the course of IC compared to those who did not receive a carbapenem (P < 0.001 by x2 analysis) (Supporting Information). Conversely, no significant differences were seen in the percentage of patients that lost α-diversity at both sites for those who did vs. those that did not recieve either piperacillin-tazobactam or cefepime (P = 0.67 for piperacillin-tazobactam and P = 0.46 for cefepime). Domination by Pathogenic Associated Genera Significantly Increased Over the IC In HSCT patients, intestinal domination (defined as occupation of at least 30% of the microbiota by a single bacterial taxon) was predictive of subsequent infectious episodes9. Thus, we sought to determine the frequency of domination among our cohort and delineate the genera involved. We observed domination in 80% of samples with no significant

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difference (P = 0.82) in rates of domination among stool (79.2%) or oral samples (80.2%). The duration of domination by any particular genera was generally short-lived, as 73% of samples did not maintain domination in the subsequent sample. Nineteen genera accounted for 90% of the domination events (Fig. 4A) and were divided into common causes of bacteremia in neutropenic patients and typical commensal microbiota (Supporting Information).1, 33 We removed Streptococcus spp. from this analysis because of its common role as a commensal in healthy hosts and as an invasive pathogen in neutropenic patient.33 We discovered that the rates of domination shifted such that by the completion of IC nearly 50% of the domination events were caused by common causes of bacteremia (e.g. Staphylococcus, Enterobacter, Escherichia, etc.) as compared to ~20% prior to or in the first week of IC (P=0.002) (Fig. 4B). Use of the Microbiome Measurements to Predict Subsequent Infections

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As low baseline stool α-diversity was associated with development of infections during IC, we sought to determine whether α-diversity at the completion of IC or change in α-diversity over the course of IC could predict subsequent MDI episodes during consolidation chemotherapy. Nine out of 28 fully evaluable patients developed a MDI (primarily bacteremias) in the 90 days post longitudinal sampling (longitudinal sampling typically ceased upon IC neutrophil recovery) (Supporting Information). Although the Shannon Index was lower in both the final stool and oral samples in patients who subsequently developed an MDI, the difference was not statistically significant (P = 0.50 and 0.12, respectively, Table 2). Similarly, we observed a greater decrease in the Shannon Index between the initial and final stool and oral samples in the patients who went on to develop an MDI, but the difference was not statistically significant (P = 0.24 and 0.45, respectively, Table 2). Notably, patients who had a decrease in the Shannon diversity index of both oral and stool samples had a significantly higher rate of MDI in the follow-up period compared to patients who did not experience a decrease in diversity (P = 0.04) (Table 2). Given the known relationship between antibiotic administration and loss of microbial diversity, we analyzed and determined an association between the number of antibiotics administered during IC, and the occurrence of infections in the 90 days post longitudinal sampling (P=0.02) (Supporting Information).

DISCUSSION

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Herein, we report the largest longitudinal microbiome dual-site sampling (stool and oral) of hematologic malignancy patients during intensive treatment. One of our key findings was that a low baseline α-diversity in stool samples is associated with development of infection during IC (Fig. 2). Similar to healthy adults, patients entering into leukemia IC have a wide range of α-diversity, although the factors driving this variability are not fully understood and may include previous antibiotic exposure, diet, and genetics, among others. We did observe a significant relationship between pre-IC neutropenia and low baseline stool α-diversity, although neutropenia itself was not a risk factor for subsequent infectious episodes in our cohort. Given that we do not have pre-IC antibiotic administration data on these patients, as they were often referred to our center, it is possible that neutropenic patients were more likely to have received antimicrobials which could have lowered their α-diversity or the

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neutropenia itself may have affected the microbiome given the close relationship between the human immune system and the GI flora. If corroborated with larger studies, baseline αdiversity could help delineate patients into high- and low-risk infection groups therebyfacilitating targeting of prophylactic and empiric antimicrobials.

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In concordance with other stool microbiome studies in treated cancer patients9–12, 34, our cohort experienced a progressive decrease in stool bacterial diversity over the course of chemotherapy. We additionally found that the overall oral microbiome α-diversity also decreases during leukemia treatment (Fig. 3A). However, the loss in diversity was not universal, and some patients actually gained diversity during IC (Fig. 3B; upper right quadrant). A statistically significant proportion of the patients that decreased in α-diversity in both the oral and stool samples over the course of IC contracted MDIs in the 90 days post longitudinal sampling. Additionally, we found that the greater number of antibiotics received during IC was significantly associated with increased risk of infection post-IC. Thus, an increased understanding of the factors that allow patients to maintain microbial diversity during IC, such as diet or antibiotic exposure, could assist in designing interventions to mitigate post-IC infections. Similarly, comparisons of baseline and final α-diversity measurements may prove useful in deciding the intensity of post-IC infection monitoring strategies.

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A recent study in HSCT patients found that increased microbial diversity and abundance of Blautia was associated reduced GVHD lethality and that loss of Blautia was associated with anti-anaerobic antimicrobial administration.34 Interestingly, the one genera that had a statistically significant decrease in stool samples over the course of IC was Blautia (Fig. 3D). The mechanism by which Blautia might protect HSCT patients from GVHD is not currently known, but Blautia species are known to induce regulatory T-cell proliferation which in turn may assist with limiting GI epithelial inflammation.35, 36 Nearly 60% of our cohort received a carbapenem, and receipt of a carbapenem was associated with a significant percentage of patients losing α-diversity in both stool and oral samples which was not statistically significant for pipericillin-tazobactam and cefepime. As we did not isolate a single infection causing bacteria for which a cabapenem may offer increased activity compared in the two other major treatment antibiotics, these data provide increasing impetus to better understand the trade-off between broad-spectrum antimicrobial administration and the collateral damage on the microbiome caused by such agents, especially in hematologic malignancy patients who are at prolonged risk for infection due to multiple rounds of intensive chemotherapy or HSCT.

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It is important to note the limitations of this study. First, the majority of our patients were treated on clinical protocols and thus the generalizability of our findings to other AML treatment centers is unknown. Additionally, low numbers of bacteremia/other MDIs limited our ability perform further statistical analyses of infections. However, our study is strengthened by a large number of longitudinal samples (averaging 14 samples per patient), sampling at two sites, and the depth of sequencing (average of 33,645 sequence reads per specimen) which provided an improved ability to fully characterize the microbiome compared to previous longitudinal studies in treated cancer patients. Given that 16S rRNA sequencing is limited only to genera based conclusions, species-level or functional analyses,

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such as RNAseq or metabolic profiling, may be needed to fully elucidate the complex relationships between microbiota and the health of the cancer patient. In summary, these data markedly extend understanding of the clinical impact of the microbiome on infections during cancer treatment. Expansion of this type of investigation to larger cohorts and other cancer types are needed to realize the potential impact that delineating, monitoring, and manipulating the microbiome may have on the care of the cancer patient.

Supplementary Material Refer to Web version on PubMed Central for supplementary material.

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Acknowledgments Funding: Financial support for this study was provided by the UTMDACC AML MoonShot Knowledge Gap Project (to DPK) and the Odyssey Program and CFP Foundation at UTMDACC (to JGP).

References

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The role of the gastrointestinal microbiome in infectious complications during induction chemotherapy for acute myeloid leukemia.

Despite increasing data on the impact of the microbiome on cancer, the dynamics and role of the microbiome in infection during therapy for acute myelo...
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